A Decoupled Hybrid Correlation Algorithm for High-Squint Spaceborne SAR Data Imaging

Fig. 1. Geometry model of the high-squint spaceborne SAR.

A Decoupled Hybrid Correlation Algorithm for High-Squint Spaceborne SAR Data Imaging

Y. Guo, P. Wang, Z. Men, T. He, T. Qiu and J. Chen, "A Decoupled Hybrid Correlation Algorithm for High-Squint Spaceborne SAR Data Imaging," in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-18, 2024, Art no. 5218018, doi: 10.1109/TGRS.2024.3430294.

Abstract: High-resolution and high-squint spaceborne synthetic aperture radar (SAR) system has excellent earth observation performance. However, high-squint spaceborne SAR data is more challenging to process than the general broadside counterpart because of the severe range-azimuth coupling (RAC). Whereas the classic linear range walk correction (LRWC) method can handle this problem, it is performed in azimuth time-domain and seriously constrains azimuth swath width. 

In this article, a decoupled hybrid correlation Algorithm (DHCA), combining the range-azimuth decoupling (RAD) in 2-D frequency domain with the modified hybrid correlation (MHC), is proposed to handle sliding spotlight spaceborne SAR data of high-squint angle case. The decoupled hybrid correlation (DHC) is the main body of the proposed algorithm, and it starts with RAD filtering in 2-D frequency domain, which is designed to eliminate the majority of the range cell migration (RCM) and RAC caused by high-squint angles. A nonlinear chirp scaling (NLCS) in range direction is subsequently performed to equalize the variant range chirp rate caused by residual RCM and RAC. Coarse range compression can then be realized uniformly by the range-matched filtering. The NLCS operation and range coarse compression ensure that the range-Doppler (RD) signals of all targets within the whole scene occupy very narrow range cell scopes. 

By making full use of the principle of stationary point (POSP) to signals, the range compression position and range frequency mapping relationship after NLCS can be derived. The MHC can therefore be fulfilled by extracting the RD signals along the residual RCM and constructing the reference correlation function after the range NLCS. Thus, both the efficiency and the accuracy of focusing processing are guaranteed. Simulations are carried out to validate the proposed algorithm.

keywords: {Azimuth;Synthetic aperture radar;Focusing;Correlation;Spaceborne radar;Doppler effect;Frequency-domain analysis;Decoupled hybrid correlation (DHC);high-squint;synthetic aperture radar (SAR) imaging},

URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10602522&isnumber=10354519

Paper Summary

Here is a summary of the key points from the paper:

- The paper proposes a new algorithm called Decoupled Hybrid Correlation Algorithm (DHCA) for processing high-squint spaceborne synthetic aperture radar (SAR) data in sliding spotlight mode.

- Key components of DHCA:
1) Sub-aperture processing to handle Doppler spectrum back-folding
2) Decoupled Hybrid Correlation (DHC) focusing method, including:
   - Range-Azimuth Decoupling (RAD) in 2D frequency domain
   - Nonlinear Chirp Scaling (NLCS) in range direction  
   - Consistent coarse range compression
   - Modified Hybrid Correlation (MHC)
3) Azimuth scaling

- Advantages of DHCA:
- Can process high-squint (45°) SAR data with 0.5m resolution in range and azimuth
- Maintains "azimuth-invariant" property, allowing batch processing
- More accurate focusing than existing algorithms, especially for targets away from scene center

- Simulation results show DHCA outperforms existing methods like Linear Range Walk Correction (LRWC) based algorithms in terms of focusing quality, especially for high-squint angles and targets away from scene center.

- DHCA has higher computational complexity than some existing algorithms, but is still relatively efficient.

- The paper validates DHCA through point target and distributed target simulations.

In summary, DHCA enables accurate and efficient processing of high-resolution, high-squint spaceborne SAR data over large scenes, overcoming limitations of existing algorithms.

Authors

Based on the information provided in the paper, here's what I can tell you about the authors, their associated institutions, and some related prior work:

Authors and Institution:

All six authors are affiliated with the School of Electronics and Information Engineering at Beihang University (also known as Beijing University of Aeronautics and Astronautics, BUAA) in Beijing, China. This suggests a strong research group in SAR technology at this institution.

1. Yanan Guo: Graduate Student Member of IEEE, pursuing a Ph.D. at Beihang University.

2. Pengbo Wang: Member of IEEE, an Associate Professor at Beihang University. He has prior experience as a visiting researcher at the University of Sheffield, UK.

3. Zhirong Men: Member of IEEE, an Assistant Professor at Beihang University. He also has experience as a visiting researcher at the University of Sheffield, UK.

4. Tao He: Graduate Student Member of IEEE, pursuing a Ph.D. at Beihang University.

5. Tian Qiu: Graduate Student Member of IEEE, pursuing a Ph.D. at Beihang University.

6. Jie Chen: Senior Member of IEEE, a Professor at Beihang University. He also has experience as a visiting researcher at the University of Sheffield, UK.

Prior Related Work:

While the paper doesn't extensively discuss the authors' prior work, it does reference several related algorithms and techniques that form the background for this research:

1. Linear Range Walk Correction (LRWC) method: Mentioned as a classic approach to handling high-squint SAR data processing.

2. Range Nonlinear Chirp Scaling (RNLCS) method: Another approach for processing high-squint SAR data, which this paper aims to improve upon.

3. Extended Nonlinear Chirp Scaling Algorithm (ENLCSA): A previous algorithm that used LRWC processing.

4. Extended Two-Step Focusing Approach (ETSFA): An earlier method for resolving 2-D spectrum distortion in high-squint SAR data.

The authors seem to have built upon these existing techniques to develop their new Decoupled Hybrid Correlation Algorithm (DHCA). The paper mentions that some of the authors have previously worked on topics such as high-resolution spaceborne SAR image formation, novel techniques for spaceborne SAR systems, multimodal remote sensing data fusion, and ionospheric effects on low-frequency space radars.

The repeated mentions of visiting research positions at the University of Sheffield suggest an ongoing collaboration or knowledge exchange between Beihang University and the University of Sheffield in the field of SAR technology.

Figures and Tables

Here's a list of the figures and tables in the paper, along with explanations of what each aims to show:

Figures:

1. Figure 1: Geometry model of the high-squint spaceborne SAR.
   - Illustrates the geometric configuration of the SAR system, defining key parameters and angles.

2. Figure 2: Block diagram of the proposed DHCA.
   - Provides an overview of the algorithm's structure and processing steps.

3. Figure 3: Echo signal in 2-D time domain for central range cells before and after LRWC processing.
   - Demonstrates the effect of LRWC on the echo signal.

4. Figure 4: Echo signal in 2-D time domain after RAD processing for central and non-central range cells.
   - Shows the impact of RAD processing on different range cells.

5. Figure 5: Range compression results for different range cells in the RD domain before NLCS.
   - Illustrates the need for NLCS by showing compression results before its application.

6. Figure 6: Range compression results for different range cells in the RD domain after NLCS.
   - Demonstrates the improvement in range compression after applying NLCS.

7. Figure 7: Construction block diagram of the reference function.
   - Outlines the process for constructing the reference function used in the algorithm.

8. Figure 8: Echo data extraction results along the residual RCM in the RD domain.
   - Shows the effectiveness of the data extraction method along the residual range cell migration.

9. Figure 9: Layout of the point targets utilized in the simulation.
   - Displays the arrangement of point targets used for algorithm validation.

10-14. Figures 10-14: Various simulation results and comparisons.
   - Present the outcomes of point target simulations, comparing DHCA with other algorithms.

15-18. Figures 15-18: Distributed target simulation results.
   - Show the performance of DHCA and comparative algorithms on distributed targets.

Tables:

1. Table I: Simulation Parameters
   - Lists the key parameters used in the simulations.

2. Table II: Parameter Values
   - Provides specific values for parameters used in computational complexity analysis.

3. Table III: Evaluation Results of the Proposed DHCA
   - Presents quantitative performance metrics for DHCA on different point targets.

4. Table IV: Evaluation Results of the LRWC-Based Algorithm
   - Shows performance metrics for the LRWC-based algorithm for comparison.

5. Table V: Evaluation Results of the RNLCS-Based Algorithm
   - Provides performance metrics for the RNLCS-based algorithm for further comparison.

These figures and tables collectively aim to illustrate the theory behind DHCA, demonstrate its implementation, and provide quantitative and qualitative comparisons with existing algorithms to prove its superiority in processing high-squint SAR data. 

DHCA Algorithm Functional Block Diagram

I'll explain each phase of the algorithm as shown in Figure 2, which illustrates the block diagram of the proposed Decoupled Hybrid Correlation Algorithm (DHCA). The algorithm consists of three main components:

1. Sub-aperture Processing:
   - Purpose: To handle the back-folding problem of the Doppler spectrum in the azimuth frequency domain.
   - Steps:
     a. Sub-aperture partition: Divides the full aperture into smaller sub-apertures.
     b. Nonlinear shift: Applies a frequency shift in the range frequency domain.
     c. Azimuth FFT: Performs Fast Fourier Transform in the azimuth direction for each sub-aperture.
     d. Time delay compensation: Compensates for the time delay caused by sub-aperture partitioning.
     e. Sub-aperture recombination: Combines the processed sub-apertures to form a dealiased 2-D spectrum.

2. Decoupled Hybrid Correlation (DHC):
   This is the main body of the algorithm, consisting of several steps:

   a. Range-Azimuth Decoupling (RAD):
      - Performed in 2-D frequency domain.
      - Eliminates the majority of range cell migration (RCM) and range-azimuth coupling (RAC) caused by high-squint angles.
      - Doesn't introduce extra range cell shifts, preserving the "azimuth-invariant" property.

   b. Nonlinear Chirp Scaling (NLCS) in Range Direction:
      - Equalizes the variant range chirp rate caused by residual RCM and RAC.
      - Prepares the signal for consistent coarse range compression.

   c. Coarse Range Compression:
      - Performs initial range compression uniformly for all targets.
      - Reduces the length of the subsequent Modified Hybrid Correlation (MHC) window.

   d. Modified Hybrid Correlation (MHC):
      - Extracts signals along the residual RCM trajectory.
      - Constructs a reference function in 2-D frequency domain.
      - Performs refined focusing, correcting residual RCM, compensating for residual Doppler phase modulation and RAC.
      - Achieves precise range compression for all range cells.

3. Azimuth Scaling:
   - Purpose: To eliminate the possible back-folded phenomenon in focused SAR images.
   - Steps:
     a. Applies an azimuth scaling function with a quadratic phase form.
     b. Performs azimuth FFT.
     c. Compensates for the consistent quadratic phase term.
     d. Performs azimuth IFFT to obtain the final focused image.

How the algorithm functions:

1. The input SAR data first undergoes sub-aperture processing to handle Doppler spectrum back-folding.
2. The DHC process then takes over:
   - RAD removes most of the RCM and RAC.
   - NLCS equalizes the remaining variant range chirp rate.
   - Coarse range compression is performed.
   - MHC refines the focusing process, correcting residual errors and achieving precise compression.
3. Finally, azimuth scaling is applied to produce a dealiased focused SAR image.

This algorithm allows for efficient and accurate processing of high-squint, high-resolution SAR data over large scenes by addressing the challenges of severe RCM and RAC while maintaining the ability to process data in batches in the azimuth frequency domain. 

Squint Angle Achieved

The DHCA algorithm was successfully demonstrated to process SAR data with a very high squint angle of 45°. This is a key achievement of the paper and represents a significant improvement over previous methods.

Specifically:

1. The point target simulations for the DHCA algorithm were conducted using a squint angle of 45°.

2. The algorithm achieved good focusing performance for all targets within the scene at this 45° squint angle, including targets at the edge of the imaging swath.

3. This 45° squint angle processing capability was maintained while achieving 0.5-meter resolution in both range and azimuth directions.

4. The paper states that under the simulation parameters used (listed in Table I), the DHCA can realize good focusing performance within an azimuth swath width of 10 km at this 45° squint angle.

5. For comparison, the LRWC-based and RNLCS-based algorithms used for comparison were only tested at a 15° squint angle, and showed degraded performance even at this lower angle.

The paper doesn't explicitly state an upper limit for the squint angle that can be processed using DHCA. However, the 45° angle demonstrated is already considered a very high squint angle for spaceborne SAR systems, representing a significant advance in processing capabilities for highly squinted SAR data.

This high squint angle capability allows for more flexible earth observation, faster revisit times, and the ability to image areas that might be challenging to observe with more traditional, less-squinted SAR geometries.

In-Swath and Out-of-Swath Radio Frequency Interference Mitigation for Elevation Multichannel SAR Data

In-Swath and Out-of-Swath Radio Frequency Interference Mitigation for Elevation Multichannel SAR Data

Z. Lv, Z. Zhang, H. Fan, Z. Chen, J. Bi and W. Wang, "In-Swath and Out-of-Swath Radio Frequency Interference Mitigation for Elevation Multichannel SAR Data," in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-19, 2024, Art no. 5217119, doi: 10.1109/TGRS.2024.3428418.

Abstract: The electromagnetic environment is becoming complex as the usable spectrum will be allocated for more services. As a result of this situation synthetic aperture radar (SAR) missions are frequently perturbed by radio frequency interference (RFI) that jeopardizes their scientific observations all over the world. 

The state-of-the-art multichannel SAR has anti-RFI capability since it’s capable of digitally modulating the antenna pattern (AP) in postprocessing, thereby steering the null toward the angle of arrival (AOA) of the RFI in the spatial domain. However, the AOA of RFI is space-variant, meaning that the mitigation performance of beamformers sensitive to AOA will greatly deteriorate. In addition, the AOA of in-swath RFI and the target echo arrive simultaneously, thus the traditional beamformer will generate a distorted AP, deteriorating the SAR imagery. 

In light of these considerations, this article studies the in-swath and out-of-swath RFIs in elevation multichannel SAR and develops their countermeasures. Thereinto, a least  $\ell _{1}$ -norm model is developed to estimate the AOA of the RFI, followed by two schemes developed to separate the RFI. The former develops a beamformer that joint sidelobe control and null expanding to mitigate the space-variant out-of-swaths RFI, whereas the latter develops a blind source separation (BSS)-based technology to mitigate in-swath RFI, avoiding AP distortion and restoring SAR imagery. The effectiveness of the proposed approaches is supported by experiments based on the measured X-band airborne DBF-SAR data as well as the simulated SAR data.

keywords: {Prevention and mitigation;Synthetic aperture radar;Interference;Signal to noise ratio;Remote sensing;Radar antennas;Azimuth;Digital beamforming (DBF);multichannel synthetic aperture radar (SAR);radio frequency interference (RFI)},
URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10598185&isnumber=10354519

Summary

This article discusses radio frequency interference (RFI) mitigation techniques for multichannel synthetic aperture radar (SAR) systems. The key points are:

1. RFI is a growing problem for SAR systems due to increasing use of the radio spectrum. It can severely degrade SAR image quality.

2. Multichannel SAR systems with digital beamforming (DBF) capabilities provide new opportunities for RFI mitigation compared to single-channel systems.

3. The paper distinguishes between in-swath RFI (coming from within the imaging swath) and out-of-swath RFI (from outside the swath). These require different mitigation approaches.

4. For out-of-swath RFI, the paper proposes an optimized beamformer that can steer nulls toward the RFI direction while maintaining desired antenna pattern characteristics.

5. For in-swath RFI, a blind source separation (BSS) technique is proposed to separate the RFI from the desired signal.

6. The paper develops a complete processing flow including RFI angle-of-arrival estimation and the mitigation techniques.

7. Experiments are conducted using both simulated data and real airborne X-band SAR data to demonstrate the effectiveness of the proposed methods.

8. Results show the proposed techniques outperform traditional methods like notch filtering and conventional digital beamforming in terms of RFI suppression and preservation of SAR image quality.

9. The methods can handle space-variant RFI and are not limited by signal-to-interference ratio, which are limitations of some existing techniques.

In summary, the paper presents novel RFI mitigation approaches tailored for multichannel SAR systems that show promising results for both in-swath and out-of-swath interference scenarios. 

Authors, Associations, and Prior Work

Based on the information provided in the paper, here are details about the authors, their associated institutions, and some context about related work:

Authors and Institutions:

1. Zongsen Lv: Graduate Student Member of IEEE, affiliated with:
   - Department of Space Microwave Remote Sensing System, Aerospace Information Research Institute, Chinese Academy of Sciences (CAS), Beijing, China
   - University of CAS, Beijing

2. Zhimin Zhang: Affiliated with Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing

3. Huaitao Fan: Member of IEEE, affiliated with Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing

4. Zhen Chen: Member of IEEE, affiliated with Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing

5. Jianzhong Bi: Affiliated with Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing

6. Wei Wang: Member of IEEE, affiliated with Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing

All authors are associated with the Chinese Academy of Sciences, specifically the Aerospace Information Research Institute in Beijing. This suggests a strong focus on aerospace and remote sensing research at this institution.

Prior Related Work:

The paper cites several prior works related to RFI mitigation in SAR systems. Some key related works mentioned include:

1. Early work on notch filtering for RFI suppression in E-SAR and ALOS PALSAR systems (Buckreuss and Horn, 1998; Rosen et al., 2008).

2. Time-domain and time-frequency domain notch filtering improvements (Li et al., 2022; Han and Zhou, 2022).

3. Eigensubspace-based filtering for narrow-band interference suppression (Zhou et al., 2007).

4. Independent component analysis for RFI suppression (Zhou et al., 2013).

5. Subband spectral cancellation for narrow-band interference suppression (Feng et al., 2012).

6. Sparse recovery methods for RFI extraction (Nguyen et al., 2014, 2016).

7. Digital beamforming-based RFI mitigation (Bollian et al., 2018, 2022).

8. Work on multichannel cancellation for barrage jamming suppression (Cheng et al., 2022, 2023).

The authors' own prior work is not explicitly highlighted in the paper, but given their affiliations, it's likely they have been involved in SAR and RFI mitigation research at the Chinese Academy of Sciences.

The paper builds upon this prior work by addressing limitations in existing methods, particularly for multichannel SAR systems, and proposing novel approaches for both in-swath and out-of-swath RFI mitigation. This processing may be of interest to anyone intending to jam or interfere with such Chinese Satellite SAR.

Artifacts and Data

Based on the information provided in the paper, the following databases and artifacts were used for experimental verification:

1. X-band DBF-SAR Data:
   - Acquired by an X-band DBF-SAR system developed by the Institute of Electronics, Chinese Academy of Sciences (IECAS)
   - Collected during an outfield experiment in Guangdong Province, China, in November 2019
   - The system has 16 receiving channels in elevation
   - This data was used to verify in-swath RFI mitigation performance

2. Sentinel-1 SAR Data:
   - Acquired from the European Space Agency (ESA) Sentinel-1A satellite
   - Operating in strip map mode
   - Data acquisition area: Utrecht, The Netherlands, September 2016
   - This data was used to simulate 16-channel elevation multichannel SAR data for verifying out-of-swath RFI mitigation performance

3. Simulated Data:
   - Point target simulations and distributed target simulations were conducted
   - Parameters for these simulations are provided in Tables II and III of the paper

Regarding code artifacts or radar design data for independent validation:

The paper does not explicitly mention any publicly available code repositories or detailed radar design data. The authors do not state that they have made their implementation code or detailed system specifications publicly accessible.

However, the paper provides:

1. Detailed descriptions of the algorithms and processing steps
2. System parameters for the X-band DBF-SAR system (Table I)
3. Simulation parameters (Tables II and III)
4. Flowcharts and block diagrams of the proposed methods

While these details allow for a general understanding and potential reimplementation of the concepts, they may not be sufficient for exact reproduction of the results without additional information.

For independent validation, researchers would likely need to:

1. Implement the described algorithms based on the provided information
2. Use their own SAR systems or simulations with similar parameters
3. Apply the methods to publicly available SAR datasets (e.g., Sentinel-1 data)

It's worth noting that the lack of publicly available code or exact system specifications is not uncommon in radar research due to potential proprietary or sensitive nature of some technologies. Researchers interested in validating or building upon this work might need to contact the authors directly for more detailed information or collaborate with institutions having similar multichannel SAR capabilities.

Figures and Tables

Here's a list of the figures and tables in the article, along with explanations of what each is intended to show:

Figures:

1. Figure 1: Schematic of the working principle and geometric relationship for an elevation multichannel SAR system and two interferers.
   - Shows the basic geometry of the SAR system, swath, and interferers.

2. Figure 2: Schematic of the receive chain for elevation multichannel SAR in SCORE mode.
   - Illustrates how signals are processed in the SAR system with Scan-On-Receive (SCORE) mode.

3. Figure 3: RFI mitigation performance for elevation multichannel SAR in SCORE mode.
   - Demonstrates how SCORE mode performance degrades with increasing signal-to-interference-plus-noise ratio (SINR).

4. Figure 4: Schematic for the anti-RFI capability of SCORE mode.
   - Shows how SCORE beam scans the swath and amplifies both SAR signal and RFI.

5. Figure 5: Space-variation of AOA of RFI.
   - Illustrates how the angle of arrival (AOA) of RFI changes over time due to platform motion.

6. Figure 6: Beam patterns for spatial filtering RFI.
   - Compares beam patterns of SCORE and Minimum Variance Distortionless Response (MVDR) beamformers.

7. Figure 7: Flowchart of the RFI mitigation scheme for elevation multichannel SAR data -illustrates the flowchart of the proposed RFI mitigation scheme for elevation multichannel SAR data. Let me break down the processing steps in detail:

1. Input: The process starts with raw multichannel SAR data contaminated by RFI.

2. RFI AOA Estimation:
   - A least ℓ1-norm model is used to estimate the angle of arrival (AOA) of the RFI.
   - This step helps determine whether the RFI is in-swath or out-of-swath.

3. Decision: Is the RFI in-swath or out-of-swath?
   - Based on the estimated AOA, the process branches into two paths.

4. Out-of-Swath RFI Mitigation:
   - If the RFI is out-of-swath, the following steps are taken:
     a. Range Pulse Compression: This step compresses the signal in the range direction to reduce pulse extension loss.
     b. Optimized Beamformer: A beamformer is designed that jointly controls sidelobes and expands nulls.
     c. Spatial Filtering: The optimized beamformer is applied to filter out the RFI spatially.

5. In-Swath RFI Mitigation:
   - If the RFI is in-swath, a Blind Source Separation (BSS) approach is used:
     a. Data Preprocessing: This includes data rearrangement, centralization, and whitening.
     b. BSS Processing: The JADE (Joint Approximation Diagonalization of Eigen-matrices) algorithm is applied to separate the RFI from the SAR signal.
     c. Data Postprocessing: This step includes signal screening, data rearrangement, and amplitude compensation to address ambiguities in the BSS results.

6. Output: The final output is the RFI-mitigated SAR data.

How the processing works:

1. The AOA estimation helps identify whether the RFI is coming from within the imaging swath or outside it. This is crucial because different mitigation strategies are needed for each case.

2. For out-of-swath RFI:
   - Range pulse compression is performed first to mitigate pulse extension loss.
   - The optimized beamformer is designed to create deep, wide nulls in the direction of the RFI while maintaining desired characteristics of the antenna pattern.
   - Spatial filtering then effectively suppresses the out-of-swath RFI.

3. For in-swath RFI:
   - BSS is used because spatial filtering alone would distort the desired signal (as the RFI comes from the same direction as the SAR echo).
   - The preprocessing steps prepare the data for BSS by removing correlations and centering the data.
   - The JADE algorithm separates the mixed signals into independent components.
   - Postprocessing then identifies which component is the desired SAR signal and performs necessary corrections.

This approach allows for effective mitigation of both in-swath and out-of-swath RFI, addressing the limitations of traditional methods and taking advantage of the multichannel SAR system's capabilities.

8. Figure 8: Beam pattern of SCORE, LCMV with null expanding, and proposed beamformer.
   - Compares the beam patterns of different beamforming methods.

9. Figure 9: Statistical histograms of the I channel, Q channel, amplitude, and phase of SAR and RFI signals.
   - Shows the different statistical characteristics of SAR and RFI signals.
Figures 10 through 17 demonstrate the results and performance analysis of the proposed RFI mitigation techniques compared to existing methods. I'll describe the key differences shown in these figures:

Figure 10:
- Shows experimental results for X-band DBF-SAR data.
- Compares single channel imaging results (b) with mitigation results from FNF method (c), SCORE beam (d), and the proposed approach (e).
- The proposed approach (e) shows the cleanest image with the least visible RFI artifacts.

Figure 11:
- Provides enlarged areas from Figure 10 for detailed comparison.
- The proposed approach shows better preservation of strong scatterers and better suppression of RFI in water areas compared to FNF and SCORE methods.

Figure 12:
- Analyzes signal power after RFI mitigation in nearshore waters.
- The proposed method shows the lowest overall signal power, indicating better RFI suppression.

Figure 13:
- Illustrates the geometric relationship and target distribution in the simulation setup for point target analysis.

Figure 14:
- Compares mitigation results for simulated point targets.
- The proposed approach shows the highest output SINR and best preservation of target impulse response function (IRF).

Figure 15:
- Shows mitigation results for distributed target simulation using Sentinel-1 data.
- The proposed approach (d) demonstrates the best RFI suppression while preserving image details, compared to FNF (a), SCORE (b), and LCMV (c) methods.

Figure 16:
- Presents the zebra map and RFI angle variation in the simulation.
- Illustrates how the RFI angle changes over the scene, which impacts mitigation performance.

Figure 17:
- Displays coherence coefficients between RFI mitigation results and ground truth.
- The proposed approach (d) shows the highest overall coherence, indicating best preservation of the original signal while suppressing RFI.

Key differences:
1. RFI Suppression: The proposed method consistently shows better RFI suppression across different scenarios (airborne, simulated point targets, and distributed targets).

2. Image Quality: The proposed approach better preserves image details and target responses compared to other methods.

3. Coherence: Higher coherence with ground truth for the proposed method indicates better overall performance in maintaining signal integrity while removing RFI.

4. Robustness: The proposed method handles both in-swath and out-of-swath RFI effectively, showing consistent performance across different RFI scenarios.

5. Spatial Variability: The proposed approach demonstrates better handling of spatially variant RFI, as shown in the distributed target simulations.

Overall, these figures collectively demonstrate the superior performance of the proposed RFI mitigation technique compared to traditional methods across various performance metrics and scenarios.

Tables:

1. Table I: Main parameters of X-band DBF-SAR system
   - Lists the key specifications of the airborne SAR system used in experiments.

2. Table II: Main parameters of point target simulation
   - Provides details of the simulation setup for point target analysis.

3. Table III: Main parameters of distributed target simulation
   - Gives parameters for the distributed target simulation using Sentinel-1 data.

These figures and tables collectively illustrate the problem of RFI in SAR systems, explain the proposed mitigation techniques, and demonstrate their effectiveness through various experiments and simulations.


 

When AI makes a fatal mistake, who's to blame? Air Force Secretary weighs morality and reality - Breaking Defense

When AI makes a fatal mistake, who's to blame? Air Force Secretary weighs morality and reality - Breaking Defense

breakingdefense.com

Michael Marrow

My Summary

This article discusses Air Force Secretary Frank Kendall's views on the ethical and accountability challenges associated with autonomous weapon systems, particularly in the context of the Air Force's Collaborative Combat Aircraft (CCA) program. Key points include:

1. Kendall supports the Air Force's drone wingman efforts but believes more work is needed to establish clear liability standards for autonomous systems that might violate laws of armed conflict.

2. The main challenge is determining who should be held accountable if autonomous weapons make mistakes - the user, designer, tester, or someone in the chain of command.

3. The Air Force is working with vendors like General Atomics and Anduril on CCA platforms, with classified autonomy vendors also involved.

4. Kendall emphasizes the policy of maintaining "meaningful human control" over the use of force, but acknowledges there's a "gray space" in determining threats and avoiding collateral damage.

5. The article notes that DoD policy has been revised to streamline approval for highly automated weapons, and that some systems already have fully automated modes.

6. Kendall has previously stated that having a human in the decision loop could be a disadvantage against fully AI-controlled threats.

7. There are concerns that adversaries like China may not adhere to the same ethical constraints in developing autonomous weapons.

8. The Biden Administration is focusing on building international norms for "responsible" military AI use with allies rather than pursuing binding arms control agreements with adversaries.

---

Secretary of the Air Force Frank Kendall, center, listens as Scott Meredith, technical director of the Arnold Engineering Development Complex 716th Test Squadron, left, discusses the aerodynamic test capabilities of the 716TS. (U.S. Air Force photo by Keith Thornburgh)

FARNBOROUGH 2024 — Air Force Secretary Frank Kendall is a strong proponent of the Air Force’s rapidly accelerating drone wingman effort, but the service’s top civilian also believes that more work is needed to establish a clear standard for liability if unmanned systems violate the laws of armed conflict. 

“It’s obviously something we’re very concerned about,” Kendall remarked about fusing autonomy and lethal weapon systems in a wide-ranging interview with Breaking Defense over the weekend, which was frequently interrupted by the roar of jet engines at the Royal International Air Tattoo (RIAT).

Kendall, who has previously worked as a human rights lawyer, is acutely aware of the ethical conundrums wrapped up in the Air Force’s Collaborative Combat Aircraft (CCA) program. And as the head of the Air Force, Kendall is arguably at the forefront of the Pentagon’s efforts to confront, and ultimately incorporate, more aspects of autonomy and artificial intelligence in weapons as CCA rapidly progress.  

“Whatever weapon systems we employ have to be consistent with the laws of armed conflict. The problem isn’t that. We know what those rules are and I think we know how to impose them on our systems,” he said.

The more vexing issue, Kendall said, is how to seek accountability when things go awry.

“It’s who do you hold accountable,” he continued. “And I think we’ve got to think through. Is it the person who used the weapon? Is it the designer? Is it the tester? Is it somebody in the chain of command? I think there needs to be a discussion about the mechanism by which people are held responsible for whatever weapons do when they do something that’s not allowed.”

The Air Force currently has two vendors under contract — General Atomics and Anduril — to provide the physical platform for the CCA program’s first round of drone production, or “increment.” The service is also working with several autonomy vendors that will plug into the drones, though Air Force spokesperson Ann Stefanek told Breaking Defense that the autonomy vendor pool is classified. CCA are expected to be operational before the end of the decade. 

“Our policy is to have meaningful human control of the application of force, and we’re gonna keep that. But that leaves a lot of gray space in terms of how certain are you, what’s the degree of certainty you have that that’s a threat before you commit a weapon, and what degree of competency you want to have that you’re not going to impose collateral damage and kill civilians unnecessarily,” he observed.

That “gray space” is wider than commentators often assume. The Pentagon’s official policy on autonomous weapons, DoD Directive 3000.09 [PDF], was revised in 2023 to streamline the approval process to field more highly automated weapons. Even before this change, anti-aircraft and missile defense systems like Patriot and Aegis have long had fully automated modes for threats too fast or numerous for humans to respond in time. Despite what is often assumed, DoD policy has never actually required a “human in the loop” for the decision to use lethal force.

Kendall himself has worried aloud for years that a human operator might make decisions too slowly to survive against a fully computer-controlled threat, and has personally witnessed the capabilities of an AI-controlled jet. At the Reagan Forum last December, he declared: “If the human being is in the loop, you will lose. You can have human supervision, you can watch over what the AI is doing. If you try to intervene, you’re going to lose.”

In this latest interview with Breaking Defense, Kendall noted, “I think there are a lot of details to be worked out, but I think the principles are there, and I think we’re going to be compliant.”

Beyond the accountability problem, Kendall also raised concerns he’s voiced repeatedly: that America’s adversaries, namely China, won’t abide by the same ethical constraints.

The Biden Administration has recently gotten Beijing to agree to broad, non-binding discussions of “AI risk.” But its focus has been on building international norms for “responsible” military AI with its allies, while putting little faith or effort into binding AI arms control with adversaries.

“The risk we’re running is that our adversaries won’t be bothered by this at all,” Kendall said. “They will field systems which are clearly about their operational effectiveness, without regard to collateral damage or inappropriate engagements. And the more stressing the operational situation is, the more inclined they’ll be to relax their constraints.” 

Sydney J. Freedberg in Washington and Valerie Insinna in London contributed to this report.

 

Safety Analysis Methods for Complex Systems in Aviation

Current perspective of unmanned aerial vehicle traffic management (UTM) system

Safety Analysis Methods for Complex Systems in Aviation

Electrical Engineering and Systems Science > Systems and Control

[Submitted on 3 Aug 2022 (v1), last revised 23 Jul 2024 (this version, v2)]

Ítalo Romani de Oliveira, José Alexandre T. Guerreiro Fregnani, Gláucia Costa Balvedi, Michael L. Ulrey, Jeffery D. Musiak

Each new concept of operation and equipment generation in aviation becomes more automated, integrated and interconnected. In the case of Unmanned Aircraft Systems (UAS), this evolution allows drastically decreasing aircraft weight and operational cost, but these benefits are also realized in highly automated manned aircraft and ground Air Traffic Control (ATC) systems. The downside of these advances is overwhelmingly more complex software and hardware, making it harder to identify potential failure paths.

Although there are mandatory certification processes based on broadly accepted standards, such as ARP4754 and its family, ESARR 4 and others, these standards do not allow proof or disproof of safety of disruptive technology changes, such as GBAS Precision Approaches, Autonomous UAS, aircraft self-separation and others. In order to leverage the introduction of such concepts, it is necessary to develop solid knowledge on the foundations of safety in complex systems and use this knowledge to elaborate sound demonstrations of either safety or unsafety of new system designs. These demonstrations at early design stages will help reducing costs both on development of new technology as well as reducing the risk of such technology causing accidents when in use.

This paper presents some safety analysis methods which are not in the industry standards but which we identify as having benefits for analyzing safety of advanced technological concepts in aviation.

Subjects:	Systems and Control (eess.SY)
Cite as:	arXiv:2208.02018 [eess.SY]
	(or arXiv:2208.02018v2 [eess.SY] for this version)
	https://doi.org/10.48550/arXiv.2208.02018

Journal reference:	XV SITRAER (Air Transportation Symposium), Sao Luis do Maranhao, MA, Brazil, November 24-26, 2016

Submission history

From: Italo Romani de Oliveira [view email]
[v1] Wed, 3 Aug 2022 12:39:12 UTC (540 KB)
[v2] Tue, 23 Jul 2024 00:30:20 UTC (311 KB)

 Summary Notes

Here is a summary of the key points from the document:

• The paper discusses safety analysis methods for complex systems in aviation, focusing on newer approaches beyond traditional industry standards.
• Traditional methods like safety cases, bow-tie models, and prescriptive approaches have limitations in analyzing increasingly complex and automated aviation systems.
• The paper highlights drawbacks of current approaches, including susceptibility to narrative fallacies and difficulties handling emergent behaviors in complex systems.
• It introduces some non-standard safety analysis methods for complex systems:
• 1) Multi-Agent Dynamic Risk Models (MA-DRM)
• 2) Systems-Theoretic Accident Model and Processes (STAMP)
• The paper discusses applications demanding more advanced safety analyses:
• 1) GBAS GNSS precision landings
• 2) Unmanned Aircraft Systems (UAS)
• 3) Aircraft self-separation concepts
• It concludes that new methodologies are needed to handle the complexity of next-generation aviation systems, requiring multidisciplinary research across engineering, social sciences, and cognitive psychology.
• The goal is to develop safety analysis methods that can evaluate innovative technologies early in the design process and help define appropriate constraints.

The paper argues for expanding beyond traditional safety assessment approaches to better address the challenges of increasingly complex and autonomous aviation systems.

Conclusions and Recommendations

The document offers several important conclusions and recommendations for action regarding safety analysis in complex aviation systems:

1. Need for new methodologies:   - The paper strongly emphasizes the need for safety analysis methodologies that go beyond current industry standards to handle increasingly complex and autonomous aviation technologies.
2. Multidisciplinary approach:   - It recommends a multidisciplinary research approach involving engineering, social sciences, and cognitive psychology to capture the complexity of contemporary systems from a systemic view.
3. Early design stage focus:   - The authors suggest that new safety analysis methods should help define constraints for the design, development, and operation of systems with innovative technologies at early stages, preventing later corrective actions.
4. Complementary use of methods:   - While introducing new methods, the paper doesn't advocate abandoning current approaches entirely. Instead, it suggests using new methods to complement and enhance existing ones.
5. Regulatory adaptation:   - There's an implicit recommendation for regulatory authorities to be open to accepting new methods for demonstration towards certification or conditional approvals.
6. Simulation and prototyping:   - The paper recommends using the outcomes of safety analyses in simulations where next-generation aviation concepts can be represented in virtual environments, followed by physical prototypes.
7. Continuous development:   - There's a call for researchers to look outside their traditional areas to understand the multi-dimensional characteristics of safety in complex systems.
8. Industry-academia collaboration:   - The paper itself is a result of collaboration between Boeing Research & Technology and the University of São Paulo, implying a recommendation for such partnerships in addressing these challenges.
9. Specific focus areas:   - The paper identifies GBAS GNSS precision landings, Unmanned Aircraft Systems (UAS), and aircraft self-separation as key areas requiring advanced safety analysis methods.
10. Balancing innovation and safety:    - Particularly for UAS, there's a recommendation to find ways to balance rapid technological innovation with the need for robust safety assurance.

In conclusion, the paper calls for a proactive and innovative approach to safety analysis that can keep pace with and even guide the development of increasingly complex aviation systems. It emphasizes the need for new tools, multidisciplinary collaboration, and a shift in thinking about system safety to address the challenges posed by next-generation aviation technologies. 

Authors

The authors and institutional associations for this paper are:

From Boeing Research & Technology:

1. Ítalo Romani de Oliveira
2. José Alexandre T. Guerreiro Fregnani
3. Gláucia Costa Balvedi
4. Michael L. Ulrey
5. Jeffery D. Musiak

From Universidade de São Paulo – Escola Politécnica da USP, Grupo de Análise de Segurança (GAS):

1. Ricardo Alexandre Veiga Gimenes
2. João Batista Camargo Jr.
3. Jorge Rady de Almeida Junior

This collaboration between Boeing Research & Technology and the University of São Paulo demonstrates a partnership between industry and academia in addressing complex safety challenges in aviation.

Traditional Methods and Limitations

The traditional approaches to safety analysis in aviation and their limitations, as described in the document, include:

1. Safety Case approach:
   - Uses structured arguments supported by evidence to justify system safety
   - Often employs Goal Structuring Notation (GSN)
   - Limitations:
     - Susceptible to narrative fallacies
     - Difficulty in ensuring full consistency among all elements
     - Challenges in handling complex systems and emergent behaviors

2. Prescriptive approaches:
   - Based on standardized means of compliance for similar systems
   - Rely on official examiners and performance parameterization
   - Limitations:
     - May not be suitable for disruptive or innovative technologies
     - Testing of complex systems can never be exhaustive

3. Bow-tie model:
   - Links causes of hazards (using Fault Tree Analysis) with consequences (using Event Tree Analysis)
   - Widely used in various safety-critical industries
   - Limitations:
     - Assumes completeness of hazard list, which cannot be guaranteed
     - Difficulty in handling dependent events and common causes
     - Challenges in quantifying probabilities for complex interrelated events

4. Auxiliary methods (e.g., Markov Analysis, FMEA, Formal Methods):
   - Used to complement or substitute parts of the main approaches
   - Each has specific uses and limitations
   - For example, FMEA is limited to single-failure analysis and formal methods may have modeling power limitations

General limitations of traditional approaches:

1. Difficulty in handling increasing system complexity
2. Challenges in analyzing emergent behaviors and unforeseen interactions
3. Potential for overlooking critical hazards or failure modes
4. Limited ability to assess innovative or disruptive technologies
5. Reliance on historical data and known risks, which may not apply to new systems
6. Time-consuming and resource-intensive processes, especially for complex systems
7. Potential for cognitive biases and human errors in analysis

The document argues that these limitations become more pronounced as aviation systems become more automated, integrated, and interconnected, necessitating the development and adoption of new safety analysis methods.

New Advanced Safety Methods

The document introduces two main advanced safety methods for complex systems:

1. Multi-Agent Dynamic Risk Models (MA-DRM):

Advantages:
- Combines distributed artificial intelligence with stochastic estimation methods
- Effective for analyzing complex socio-technical systems
- Can identify hazardous event sequences missed by traditional methods
- Better handles dependencies between events and common causes
- More accurate in probability estimations for complex scenarios

Limitations:
- Requires sophisticated mathematical tools and analysis
- May be more computationally intensive

2. Systems-Theoretic Accident Model and Processes (STAMP):

Advantages:
- Focuses on constraints, control loops, and process models rather than events
- Considers interactions among human, hardware, and software components
- Helps identify non-functional interactions and incorrect models/processes
- Useful for recognizing scenarios that may lead to accidents
- Better suited for analyzing emergent properties in complex systems

Limitations:
- More qualitative and argumentative in nature
- May require a shift in thinking from traditional event-based models

General advantages of these advanced methods:

1. Better suited for analyzing complex, interconnected systems
2. Can handle emergent behaviors and unforeseen interactions
3. More effective in early design stages of innovative technologies
4. Provide a more holistic view of system safety
5. Can potentially identify risks missed by traditional methods

Potential limitations or challenges:

1. May require additional expertise or training to implement effectively
2. Could be more time-consuming or resource-intensive initially
3. May face resistance from regulatory bodies accustomed to traditional methods
4. Validation and standardization of these methods may take time
5. Integration with existing safety processes and regulations could be challenging

The document suggests that these advanced methods offer complementary views of safety and can be used alongside traditional approaches to provide a more comprehensive safety analysis. It emphasizes the need for multidisciplinary research to further develop and refine these methods to meet the challenges posed by next-generation aviation systems.

Unmanned Systems

The document discusses safety analysis for Unmanned Aircraft Systems (UAS) as one of the key applications demanding advanced safety methods. Here's a summary of the key points:

1. Regulatory Framework:
   - ICAO principles state that UAS should operate in accordance with standards for manned aircraft, plus additional standards addressing operational, legal, and safety differences.
   - EASA has proposed an operational regulatory framework for UAS, recognizing the diverse and innovative nature of the industry.
   - JARUS (Joint Authorities for Rulemaking on Unmanned Systems) is working on harmonized regulations covering all aspects of UAS operations.

2. Safety Challenges:
   - Integration of UAS into non-segregated airspace is a long-term activity requiring robust regulatory frameworks.
   - Many UAS businesses have short design-to-production cycles and may lack aviation safety experience.
   - Balancing innovation with safety requirements is a key challenge.

3. Specific Safety Initiatives:
   - EASA has created task forces to investigate issues related to small UAS operations, including:
     a) Geo-limitation to address risks of conflict with other airspace users
     b) Assessment of UAS-Aircraft collision consequences
   - JARUS Working Group 6 (Safety & Risk Assessment) has developed guidance material for system safety assessment requirements.

4. Safety Assessment Approach:
   - The document mentions that JARUS WG-6 aims to maintain the same base of manned aircraft safety assessment for RPAS (Remotely Piloted Aircraft Systems).
   - They propose additional means for showing compliance with availability and integrity requirements for RPAS systems.
   - The methodology is based on the objective that RPAS operations must be as safe as manned aircraft.

5. Challenges in Safety Analysis:
   - Lack of solid international regulations for UAS integration into non-segregated airspace.
   - Need for very high safety standards due to the responsibility over airspace.
   - Difficulty in guaranteeing that UAS will maintain the current aviation safety level.

6. Future Outlook:
   - The document suggests that more work needs to be done in safety methodology to ensure UAS safety.
   - It implies that advanced safety analysis methods, such as those discussed earlier (MA-DRM and STAMP), may be beneficial in addressing the complex challenges posed by UAS integration.

The overall message is that safety analysis for UAS is a complex and evolving field. While efforts are being made to adapt existing safety frameworks, the unique characteristics of UAS and their intended integration into shared airspace present challenges that may require new approaches to safety analysis and risk assessment.