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| Comparison of detection performances of different methods on a test set of radar images processed using the proposed methods |
Marine Radar Image Sequence Target Detection Based on Space–Time Adaptive Filtering and Hough Transform
B. Wen, Z. Lu, Y. Mao and B. Zhou, "Marine Radar Image Sequence Target Detection Based on Space–Time Adaptive Filtering and Hough Transform," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 13506-13522, 2024, doi: 10.1109/JSTARS.2024.3434358.
Abstract: The performance of marine radar target detection is largely affected by the intricate and dynamic space–time variations of sea clutter signals, which cause substantial numbers of false and missed alarms.
To improve the target detection performance of rotating scanning marine radar, this study proposes a marine radar image sequence target detection algorithm based on space–time adaptive filtering and the Hough transform algorithm. The algorithm adopts a two-stage approach of coarse detection followed by precise detection. During the coarse detection stage, the sea clutter energy in the 3-D frequency–wavenumber spectrum of the marine radar image sequence is suppressed by a sea clutter suppression algorithm in the space–time domain, space–time clutter suppression (STCS). Subsequently, moving targets are extracted from the image sequence using a target energy extraction method based on the Hough transform algorithm in the 3-D frequency–wavenumber domain. The result is a processed image sequence with sea clutter signal reduction and target signal extraction.
The precise detection stage detects the target point in this processed image sequence using a constant false alarm rate method based on a real clutter background distribution model. During verification tests on real X-band marine radar data, the detection probability of the proposed method reaches 99.89% under low sea state, 95.34% under medium sea state, and 94.44% under high sea state. Compared with the WHOS-CFAR and GMOS-CFAR, the average improvement is 10.1% and 16.6%, respectively. Furthermore, compared to the STCS, there is a maximum improvement of 3.7%. The enhancement in detection performance is significant.
keywords: {Clutter;Radar;Frequency-domain analysis;Radar imaging;Image sequences;Radar clutter;Object detection;Constant false alarm rate;marine radar;radar image sequence;space–time adaptive filtering (STAF);target detection},
Summary
The focus of this paper is on processing the radar image sequences rather than on the radar waveform itself. The method is specifically designed to work with an IMO 192 79 compliant non-coherent magnetron type radar data as described in marine radar 101, using space-time processing techniques to overcome the limitations of not having coherent or Doppler information available. Authors are associated with Harbin Engineering University, College of Intelligent Systems Science and Engineering, Harbin, China
This paper presents a new method for detecting marine targets in radar image sequences using space-time adaptive filtering (STAF) and the Hough transform.
The key points are:
1. The method uses a two-stage approach:- Coarse detection: Suppresses sea clutter using space-time clutter suppression (STCS) and extracts target energy using space-time target extraction (STTE).
- Precise detection: Uses a real clutter background distribution constant false alarm rate (RCBD-CFAR) method.
2. Key innovations:
- Establishes a model for target energy in the frequency-wavenumber domain
- Proposes an effective strategy for extracting target energy
- Uses real-time background clutter analysis for CFAR detection
3. Performance:
- Outperforms other methods, especially in complex sea clutter conditions
- Improves signal-to-clutter ratio (SCR) by average of 6.76 dB over STCS alone
- Achieves detection probabilities of 94-99% across different sea conditions
- Improves detection probability by 11-23% over other CFAR methods
4. Limitations/future work:
- Slight loss of target energy during filtering
- Requires ~80 seconds to accumulate image sequences
- Performance reduced for very small targets
- Further optimization needed for targets with highly variable motion
5. The method shows promise for improving marine radar target detection in challenging sea clutter environments, achieving both high detection rates and low false alarms.
Hough Transform
The Hough transform is a feature extraction technique used in image analysis, computer vision, and digital image processing. In the context of this paper, it's used as part of the Space-Time Target Extraction (STTE) stage. Here's a more detailed explanation:
1. Purpose: The Hough transform is primarily used to detect simple shapes, particularly lines, in an image. It can be extended to identify other shapes like circles or ellipses.
2. How it works:
- It transforms points in the image space to a parameter space.
- For line detection, it uses the parametric representation of a line: ρ = x cos(θ) + y sin(θ)
- Each point (x,y) in the image space is transformed into a sinusoidal curve in the (ρ,θ) space.
- Points that lie on the same line in the image space will intersect at a single point in the Hough space.
3. In this paper:
- It's used to detect linear features in the frequency-wavenumber domain.
- These linear features correspond to the spectra of moving targets.
- The paper mentions: "The target spectrum was detected using the Hough detection method based on the duality between points and lines."
4. Advantages:
- It's robust against noise and partial occlusion of the shape.
- It can detect multiple instances of a shape in a single pass.
5. Application in target detection:
- By identifying these linear features, the method can distinguish between the spectra of moving targets and sea clutter.
- This allows for more effective extraction of target signals from the complex radar image.
The use of the Hough transform in this context is an innovative application, adapting a common image processing technique to the specific challenges of marine radar target detection in the frequency-wavenumber domain.
1. Purpose: The Hough transform is primarily used to detect simple shapes, particularly lines, in an image. It can be extended to identify other shapes like circles or ellipses.
2. How it works:
- It transforms points in the image space to a parameter space.
- For line detection, it uses the parametric representation of a line: ρ = x cos(θ) + y sin(θ)
- Each point (x,y) in the image space is transformed into a sinusoidal curve in the (ρ,θ) space.
- Points that lie on the same line in the image space will intersect at a single point in the Hough space.
3. In this paper:
- It's used to detect linear features in the frequency-wavenumber domain.
- These linear features correspond to the spectra of moving targets.
- The paper mentions: "The target spectrum was detected using the Hough detection method based on the duality between points and lines."
4. Advantages:
- It's robust against noise and partial occlusion of the shape.
- It can detect multiple instances of a shape in a single pass.
5. Application in target detection:
- By identifying these linear features, the method can distinguish between the spectra of moving targets and sea clutter.
- This allows for more effective extraction of target signals from the complex radar image.
The use of the Hough transform in this context is an innovative application, adapting a common image processing technique to the specific challenges of marine radar target detection in the frequency-wavenumber domain.
Polar to Cartesian Coordinate Image Transformation
In prior work, when I designed Extended Kalman Filters for tracking using radar, we formed contacts in polar coordinates using the known radar range and azimuth resolution and clustering for large targets, and tracked target state in Cartesian coordinates. Observation covariance was formed from the radar accuracy characteristics. Using Polar to Rectangular prior to processing introduces distortions and limits, and reduces information. This is the transformation showing reduction in information from 1.2 Mpixels to 0.72 Mpixels:
| Coordinate system | Azimuth | Range bins | Pixels |
| Polar | 2,048 | 600 | 1,228,800 |
| East | North | ||
| Cartesian | 848 | 848 | 719,104 |
Here are some important observations about potential limitations in the study's methodology. Let's address each point:
1. Loss of radar information in polar to Cartesian transformation: This transformation can lead to a loss of resolution, especially at longer ranges. In radar systems, the angular resolution is constant, which means that the spatial resolution degrades with increasing range in polar coordinates. When converting to rectangular coordinates, this non-uniform resolution can lead to:
The authors don't explicitly address this issue, which could indeed affect the quality of their results, especially for targets at longer ranges.
2. Singularity at zero range: The polar to rectangular transformation has a singularity at the origin (zero range), which can cause significant distortion and data artifacts near the radar location. Common approaches to address this include:
The fact that the authors don't discuss how they handled this singularity is a significant oversight. It could potentially affect their results, especially for targets or clutter near the radar.
These points highlight important considerations in radar image processing that weren't adequately addressed in the paper. They could impact the method's performance and the interpretation of results, especially in scenarios involving targets at very short or long ranges. This kind of critical analysis is crucial for fully understanding the strengths and limitations of proposed methods in radar signal processing.
1. Loss of radar information in polar to Cartesian transformation: This transformation can lead to a loss of resolution, especially at longer ranges. In radar systems, the angular resolution is constant, which means that the spatial resolution degrades with increasing range in polar coordinates. When converting to rectangular coordinates, this non-uniform resolution can lead to:
- - Oversampling at short ranges
- - Undersampling at long ranges
- - Potential loss of fine detail, particularly at longer ranges
- - Interpolation artifacts
The authors don't explicitly address this issue, which could indeed affect the quality of their results, especially for targets at longer ranges.
2. Singularity at zero range: The polar to rectangular transformation has a singularity at the origin (zero range), which can cause significant distortion and data artifacts near the radar location. Common approaches to address this include:
- - Excluding a small circular area around the radar from processing
- - Using specialized interpolation techniques near the origin
- - Applying correction factors to compensate for the distortion
The fact that the authors don't discuss how they handled this singularity is a significant oversight. It could potentially affect their results, especially for targets or clutter near the radar.
These points highlight important considerations in radar image processing that weren't adequately addressed in the paper. They could impact the method's performance and the interpretation of results, especially in scenarios involving targets at very short or long ranges. This kind of critical analysis is crucial for fully understanding the strengths and limitations of proposed methods in radar signal processing.
Time Integration Lag
A significant limitation of the method described in the paper is the integration lag time. Integrating radar information over 32 scans before processing could indeed introduce large lags for maneuvering targets. This approach has several implications:
1. Time delay: the method introduces a substantial time lag. With 32 scans at 26 r/min (from Table I), this integration period is about 74 seconds. This is a significant delay for real-time tracking and decision-making.
2. Assumption of linear motion: The method appears to assume that targets move with constant velocity (speed and direction) during this integration period. This assumption is often invalid for real-world scenarios, especially in maritime environments where vessels can change course and speed frequently.
3. Maneuvering target issues: For targets that change course or speed within the 32-scan period, the method could:
- Blur the target's signature in the frequency-wavenumber domain
- Potentially split a single maneuvering target into multiple apparent targets
- Significantly degrade detection and tracking performance for non-linear trajectories
4. Loss of maneuver information: Quick maneuvers or speed changes within the integration period might be averaged out or lost entirely, reducing the system's ability to detect and characterize target behavior accurately.
5. Reduced effectiveness for agile targets: The method might perform poorly against small, fast, or highly maneuverable targets like small boats or certain types of military vessels.
6. Operational limitations: This long integration time could limit the system's usefulness in scenarios requiring rapid detection and response, such as collision avoidance or security applications.
This is a critical trade-off in the paper's approach: while longer integration times can improve signal-to-noise ratio and clutter suppression, they come at the cost of responsiveness and accuracy for non-linear target motions. This limitation could significantly impact the method's practical applicability in dynamic maritime environments and should have been more thoroughly addressed by the authors.
1. Time delay: the method introduces a substantial time lag. With 32 scans at 26 r/min (from Table I), this integration period is about 74 seconds. This is a significant delay for real-time tracking and decision-making.
2. Assumption of linear motion: The method appears to assume that targets move with constant velocity (speed and direction) during this integration period. This assumption is often invalid for real-world scenarios, especially in maritime environments where vessels can change course and speed frequently.
3. Maneuvering target issues: For targets that change course or speed within the 32-scan period, the method could:
- Blur the target's signature in the frequency-wavenumber domain
- Potentially split a single maneuvering target into multiple apparent targets
- Significantly degrade detection and tracking performance for non-linear trajectories
4. Loss of maneuver information: Quick maneuvers or speed changes within the integration period might be averaged out or lost entirely, reducing the system's ability to detect and characterize target behavior accurately.
5. Reduced effectiveness for agile targets: The method might perform poorly against small, fast, or highly maneuverable targets like small boats or certain types of military vessels.
6. Operational limitations: This long integration time could limit the system's usefulness in scenarios requiring rapid detection and response, such as collision avoidance or security applications.
This is a critical trade-off in the paper's approach: while longer integration times can improve signal-to-noise ratio and clutter suppression, they come at the cost of responsiveness and accuracy for non-linear target motions. This limitation could significantly impact the method's practical applicability in dynamic maritime environments and should have been more thoroughly addressed by the authors.
Space-Time Adaptive Filtering (STAF)
Space-Time Adaptive Filtering (STAF) is a signal processing technique used in radar systems to improve target detection and tracking in the presence of clutter and interference. In the context of this paper, STAF is a key component of the proposed target detection method. Here's a breakdown of STAF:
1. Purpose:
- To suppress unwanted signals (clutter and interference) while preserving desired target signals.
- To improve the Signal-to-Clutter Ratio (SCR) in radar returns.
2. Concept:
- It processes radar data in both the spatial and temporal domains simultaneously.
- "Adaptive" means the filter adjusts its parameters based on the characteristics of the received signals.
3. How it works:
- It exploits differences in the space-time characteristics of target and clutter signals.
- Clutter typically has different spatial and temporal properties compared to moving targets.
4. In this paper:
- STAF is implemented through the Space-Time Clutter Suppression (STCS) and Space-Time Target Extraction (STTE) stages.
- STCS uses a dispersion relation filter in the frequency-wavenumber domain to suppress sea clutter.
- STTE uses the Hough transform to extract target signals based on their unique space-time characteristics.
5. Advantages:
- More effective than processing in either space or time domain alone.
- Can handle non-stationary clutter environments, which is crucial for marine radar.
- Improves detection of slow-moving targets that might be masked by clutter.
6. Application in marine radar:
- Particularly useful for dealing with sea clutter, which has complex space-time characteristics.
- Helps in detecting small or slow-moving targets that might otherwise be lost in the clutter.
The paper's innovation lies in combining STAF with other techniques like the Hough transform and a novel CFAR method to achieve better target detection in challenging marine environments.
1. Purpose:
- To suppress unwanted signals (clutter and interference) while preserving desired target signals.
- To improve the Signal-to-Clutter Ratio (SCR) in radar returns.
2. Concept:
- It processes radar data in both the spatial and temporal domains simultaneously.
- "Adaptive" means the filter adjusts its parameters based on the characteristics of the received signals.
3. How it works:
- It exploits differences in the space-time characteristics of target and clutter signals.
- Clutter typically has different spatial and temporal properties compared to moving targets.
4. In this paper:
- STAF is implemented through the Space-Time Clutter Suppression (STCS) and Space-Time Target Extraction (STTE) stages.
- STCS uses a dispersion relation filter in the frequency-wavenumber domain to suppress sea clutter.
- STTE uses the Hough transform to extract target signals based on their unique space-time characteristics.
5. Advantages:
- More effective than processing in either space or time domain alone.
- Can handle non-stationary clutter environments, which is crucial for marine radar.
- Improves detection of slow-moving targets that might be masked by clutter.
6. Application in marine radar:
- Particularly useful for dealing with sea clutter, which has complex space-time characteristics.
- Helps in detecting small or slow-moving targets that might otherwise be lost in the clutter.
The paper's innovation lies in combining STAF with other techniques like the Hough transform and a novel CFAR method to achieve better target detection in challenging marine environments.
Authors:
Baotian Wen; Zhizhong Lu; Yongfeng Mao; Bowen Zhou
All authors are affiliated with Harbin Engineering University, specifically the College of Intelligent Systems Science and Engineering, in Harbin, China. The authors appear to have a focus on marine radar signal processing, particularly in areas of sea clutter suppression, target detection, and environmental parameter retrieval from radar data.
All authors are affiliated with Harbin Engineering University, specifically the College of Intelligent Systems Science and Engineering, in Harbin, China. The authors appear to have a focus on marine radar signal processing, particularly in areas of sea clutter suppression, target detection, and environmental parameter retrieval from radar data.
Figures and Tables
Here's a list of the key figures and tables in the paper, along with descriptions of their contents and purposes:
Figures:
1. Figure 1: Integrated image along the θ-direction in the 3-D frequency–wavenumber spectral domain.
Purpose: Shows the components of the image spectrum in the frequency-wavenumber domain.
2. Figure 2: Integration images showing pure sea clutter and moving targets.
Purpose: Illustrates how moving targets appear differently from sea clutter in the frequency-wavenumber domain.
3. Figure 3: Original radar images and 2-D cross sections of the 3-D spectral domain with multiple targets.
Purpose: Demonstrates how target motion is represented in the frequency-wavenumber domain.
4. Figure 4: Distribution curves of background clutter under different sea conditions.
Purpose: Compares the proposed RCBD model with traditional distribution models.
5. Figure 5: Architecture of the proposed target detection method.
Purpose: Provides an overview of the entire detection process.
6. Figures 6-7: Results of the Hough transform filtering process.
Purpose: Shows how the Hough transform extracts target spectra.
7. Figure 8: Detailed structure of the STCS, STTE, and RCBD-CFAR segments.
Purpose: Illustrates the specific steps in each stage of the detection process.
9. Figures 9-12: Radar images under different conditions and processing stages.
Purpose: Shows the radar data used and how it's processed.
10. Figures 13-15: Results of sea clutter suppression and SCR improvement.
Purpose: Demonstrates the effectiveness of the proposed method in suppressing sea clutter.
11. Figures 16-19: ROC curves and detection performance comparisons.
Purpose: Compares the detection performance of the proposed method with other methods.
Tables:
1. Table I: X-Band Marine Radar Parameters
Purpose: Lists the technical specifications of the radar used in the experiments.
2. Table II: SCR Improvement Results
Purpose: Compares the signal-to-clutter ratio improvement of different methods under various sea conditions.
3. Table III: Detection Probabilities of Different Methods
Purpose: Shows the detection probabilities achieved by different methods under various sea conditions.
These figures and tables collectively demonstrate the theoretical basis of the proposed method, illustrate its implementation, and provide evidence of its superior performance compared to existing methods across various sea conditions and target scenarios.
Figures:
1. Figure 1: Integrated image along the θ-direction in the 3-D frequency–wavenumber spectral domain.
Purpose: Shows the components of the image spectrum in the frequency-wavenumber domain.
2. Figure 2: Integration images showing pure sea clutter and moving targets.
Purpose: Illustrates how moving targets appear differently from sea clutter in the frequency-wavenumber domain.
3. Figure 3: Original radar images and 2-D cross sections of the 3-D spectral domain with multiple targets.
Purpose: Demonstrates how target motion is represented in the frequency-wavenumber domain.
4. Figure 4: Distribution curves of background clutter under different sea conditions.
Purpose: Compares the proposed RCBD model with traditional distribution models.
5. Figure 5: Architecture of the proposed target detection method.
Purpose: Provides an overview of the entire detection process.
6. Figures 6-7: Results of the Hough transform filtering process.
Purpose: Shows how the Hough transform extracts target spectra.
7. Figure 8: Detailed structure of the STCS, STTE, and RCBD-CFAR segments.
Purpose: Illustrates the specific steps in each stage of the detection process.
9. Figures 9-12: Radar images under different conditions and processing stages.
Purpose: Shows the radar data used and how it's processed.
10. Figures 13-15: Results of sea clutter suppression and SCR improvement.
Purpose: Demonstrates the effectiveness of the proposed method in suppressing sea clutter.
11. Figures 16-19: ROC curves and detection performance comparisons.
Purpose: Compares the detection performance of the proposed method with other methods.
Tables:
1. Table I: X-Band Marine Radar Parameters
Purpose: Lists the technical specifications of the radar used in the experiments.
2. Table II: SCR Improvement Results
Purpose: Compares the signal-to-clutter ratio improvement of different methods under various sea conditions.
3. Table III: Detection Probabilities of Different Methods
Purpose: Shows the detection probabilities achieved by different methods under various sea conditions.
These figures and tables collectively demonstrate the theoretical basis of the proposed method, illustrate its implementation, and provide evidence of its superior performance compared to existing methods across various sea conditions and target scenarios.
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