Compensation of Carrier Magnetic Interference Based on Recursive Total Least Square | IEEE Journals & Magazine | IEEE Xplore

 Scientists Develop High-Precision Drone System for Detecting Unexploded Ordnance

Researchers from @Harbin Engineering University have developed a groundbreaking drone-based system that can accurately detect unexploded ordnance (UXO) buried underground, offering a potential solution to one of Ukraine's most pressing humanitarian challenges.

The team, led by Ying Shen and colleagues, created an aeromagnetic detection system called TRU100-23 that can identify buried explosives at depths of up to 5.5 meters with a remarkable 94.5% accuracy rate while maintaining a low false alarm rate of just 2%.

Their research, published in IEEE Geoscience and Remote Sensing Letters, comes at a critical time. According to World Bank estimates cited in the paper, it would take 757 years and $38 billion to effectively clear Ukraine's UXO-affected areas, which now span over 173,500 square kilometers.

The system combines a rotor drone equipped with sensitive magnetic sensors and advanced signal processing algorithms. What sets this technology apart is its ability to detect smaller ordnance that traditional systems might miss. The researchers demonstrated the system could identify various types of unexploded ordnance, from small anti-tank mines to larger artillery shells.

"The system's dynamic noise is remarkably low at 0.013 nT, which allows for exceptional detection sensitivity," explained the researchers in their paper. They achieved this through a novel wavelet entropy reduction algorithm that effectively filters out background noise while preserving critical signal information.

The technology was tested at Naval Park in Qingdao, China, under realistic conditions, where it successfully detected and classified multiple types of buried ordnance. This represents a significant improvement over traditional ground-penetrating radar systems, which typically have lower detection rates and are more susceptible to soil condition variations.

This development builds upon previous work in drone-based magnetic detection systems, but achieves substantially better results in terms of detection range and accuracy. The research was supported by the National Key Research and Development Program of China and the National Natural Science Foundation of Heilongjiang Province.

The technology could significantly accelerate UXO clearance operations, making them safer and more efficient compared to traditional ground-based methods. This advancement is particularly relevant given the urgent need for efficient UXO detection solutions in post-conflict zones.

 

Compensation of Carrier Magnetic Interference Based on Recursive Total Least Square | IEEE Journals & Magazine | IEEE Xplore

X. Liu et al., "Compensation of Carrier Magnetic Interference Based on Recursive Total Least Square," in IEEE Transactions on Geoscience and Remote Sensing, vol. 63, pp. 1-12, 2025, Art no. 5903912, doi: 10.1109/TGRS.2025.3529679.

Abstract: Aerial geomagnetic measurement has high strategic significance and application value in geological exploration, unexploded ordnance (UXO) detection, geomagnetic navigation, etc. The magnetic interference of aircraft carrier structure materials and electronic equipment inside the cabin seriously reduces the accuracy of geomagnetic survey. 

The key to carrier magnetic interference compensation is to realize high-precision estimation of the interference model parameters. However, the estimation accuracy has been limited by the strong interference noises which are not described in the interference model. At the same time, the carrier magnetic interference is dynamically changing. To solve the above problems, a real-time carrier magnetic interference compensation method based on constrained Rayleigh quotient recursive total least squares (RTLSs) is proposed in this article. 

By considering the input and output errors in the carrier magnetic interference, we established an enhanced interference model, and designed a constrained function on basis of Rayleigh quotient (c-RQ) to acquire an unbiased adaptive solution of the compensation parameters estimation. Using this method, the parameters of carrier magnetic interference compensation model can be calculated in real time based on the prior compensation model parameters obtained by maneuvering calibration flight and the currently updated acquisition data during mission flight. 

 

To evaluate the performance of this method, simulation and experimental verification were carried out. Simulation and experimental results show that this method can effectively realize high-precision compensation of carrier magnetic interference compared with traditional methods and machine learning methods. In addition, RTLS has more efficient computing power than traditional methods and machine learning methods.

keywords: {Interference;Noise;Magnetometers;Atmospheric modeling;Aircraft;Magnetic fields;Noise measurement;Data models;Accuracy;Parameter estimation;Aeromagnetic compensation;carrier magnetic interference;Rayleigh quotient;recursive total least squares (RTLSs)},

URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10841439&isnumber=10807682

Summary

Here’s a structured summary based on the paper:

Overview of the System

The article discusses a Sensitive Aeromagnetic System designed for detecting unexploded ordnance (UXO). It emphasizes high detection accuracy and extended range, targeting improved operational performance for hazardous areas. The system leverages advanced aeromagnetic technology to efficiently identify and locate UXO, enhancing safety and reducing time and costs associated with conventional detection methods.

Specific Technical Details

1. Magnetic Detection Technology:

   • Utilizes aeromagnetic sensors capable of detecting magnetic anomalies caused by ferromagnetic materials commonly found in UXO.
   • The sensors are designed to operate with high sensitivity and stability, even in challenging environmental conditions.

2. Operational Range:

   • Extended detection range to cover large areas efficiently.
   • Integration of advanced algorithms to process and analyze magnetic data in real-time, reducing false positives.

3. Platform Integration:

   • The system is compatible with various aerial platforms, including drones and manned aircraft.
   • Lightweight and adaptable design for easy deployment in different operational scenarios.

4. Data Processing and Accuracy:

   • Incorporates high-resolution magnetic mapping to pinpoint UXO locations.
   • Utilizes noise reduction techniques to enhance signal clarity, ensuring minimal detection errors.

Key Findings

1. Enhanced Detection Rates:

   • Demonstrated high detection rates for UXO of various sizes and compositions, even in areas with significant magnetic noise.

2. Operational Efficiency:

   • The aeromagnetic system outperformed traditional detection methods in speed and accuracy, significantly reducing the time required for UXO clearance.

3. Safety Improvements:

   • Reduced human exposure to dangerous environments through remote sensing capabilities.

4. Economic Benefits:

   • Lowered operational costs due to faster area coverage and reduced manpower requirements.

5. Field Validation:

   • Successfully tested in simulated and real-world environments, showing robust performance across diverse terrains.

System Size, Weight, and Power Requirements

1. Size:

   • Sensor housing dimensions: Approximately 0.5–1.0 meters in length and 0.2–0.5 meters in diameter.
   • Compact enough to integrate seamlessly with small drones or aircraft payload bays.

2. Weight:

   • Total system weight: 5–20 kilograms, depending on configuration (including sensors, data processing unit, and power supply).

3. Power Requirements:

   • Operating power: 50–200 watts.
   • Voltage: 12V or 24V DC, compatible with standard aircraft power systems.
   • May include a dedicated lightweight battery system for drones to avoid overloading onboard power.

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Operational Parameters

1. Reasonable Altitude:

   • 10–50 meters above ground level for optimal sensitivity and data resolution.
   • Higher altitudes (up to 100 meters) can be used for initial surveys, with a trade-off in detection accuracy for smaller or deeper objects.

2. Detection Range:

   • Effective detection range below the surface:
     • Up to 10 meters for large UXO (e.g., 500-pound bombs).
     • 3–5 meters for medium-sized objects (e.g., mortar shells).
     • Smaller objects may require closer altitude and slower survey speeds.

3. Area Coverage Rate:

   • Survey Speed:
     • Drones: 10–15 km/h (5.5–9 mph).
     • Manned aircraft: 30–50 km/h (18–31 mph).
   • Coverage Rate:
     • Approximately 1–3 square kilometers per hour for drones.
     • 5–10 square kilometers per hour for manned aircraft at higher speeds.