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Adaptive Estimation of Spatial Clutter Measurement Density Using Clutter Measurement Probability for Enhanced
Seung Hyo Park1, Sa Yong Chong1, Hyung June Kim1
1Department of Electronic Systems Engineering, Hanyang University, Ansan 15588, Korea.
Sensors (Basel, Switzerland)
|December 28, 2019
Summary
This study introduces a new method to improve multi-target tracking by addressing biases in clutter density estimation. The proposed MTT-SCMDE enhances accuracy in radar and sonar surveillance systems.
Area of Science:
- Signal Processing
- Surveillance Systems
- Data Association
Background:
- Radar and sonar systems generate point detections including both target and clutter measurements.
- Accurate target tracking relies on data association to differentiate targets from clutter.
- Existing clutter density estimation methods, like SCMDE, exhibit biases in multi-target scenarios, degrading tracking performance.
Purpose of the Study:
- To analyze the source of performance degradation in existing SCMDE for multi-target tracking.
- To propose an improved clutter measurement density estimation method for enhanced multi-target tracking.
- To validate the effectiveness of the proposed method through simulations and real-world data.
Main Methods:
- Analysis of spatial clutter measurement density estimator (SCMDE) biases in multi-target tracking.
- Introduction of clutter measurement probability as a corrective measure.
- Development of a novel adaptive clutter measurement density estimation method for multi-target tracking (MTT-SCMDE).
- Expansion of hyper-sphere volume for sparsity orders to reduce estimation bias.
Main Results:
- Identified a key source of tracking performance degradation in SCMDE for multi-target tracking.
- Demonstrated that MTT-SCMDE significantly improves multi-target tracking performance.
- Validated performance improvements through Monte Carlo simulations and real radar data analysis.
- Showcased enhanced clutter measurement density estimation and target tracking performance across various sparsity orders.
Conclusions:
- The proposed MTT-SCMDE effectively mitigates biases in clutter density estimation for multi-target tracking.
- The new method leads to more robust and accurate target tracking in surveillance environments.
- MTT-SCMDE offers a significant advancement for applications relying on radar and sonar data association.
