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A Robust Interacting Multi-Model Multi-Bernoulli Mixture Filter for Maneuvering Multitarget Tracking under Glint

Benru Yu¹, Hong Gu¹, Weimin Su¹

¹School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.

Sensors (Basel, Switzerland)

|May 11, 2024

Summary

This study introduces a robust filter for tracking multiple maneuvering targets in radar systems, addressing challenges posed by heavy-tailed glint noise. The proposed method adaptively learns unknown noise statistics for improved tracking accuracy.

Keywords:

glint noise interacting multi-model algorithm maneuvering target tracking multi-Bernoulli mixture filter multivariate Laplace distribution variational Bayesian

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Area of Science:

Radar Systems Engineering
Signal Processing
Statistical Inference

Background:

Radar systems face glint noise, a non-Gaussian disturbance caused by target aspect changes, impacting tracking accuracy.
Conventional tracking algorithms often fail due to their reliance on Gaussian noise models, which are inadequate for glint noise.
Tracking a time-varying number of maneuvering targets under these conditions presents a significant challenge.

Purpose of the Study:

To develop a robust filtering approach for tracking maneuvering targets in the presence of glint noise with unknown statistics.
To address the limitations of traditional Gaussian-based tracking methods in radar applications.
To enhance the accuracy and reliability of multi-target tracking in complex environments.

Main Methods:

Proposed a robust interacting multi-model multi-Bernoulli mixture filter.
Utilized the variational Bayesian method for adaptive learning of unknown noise statistics.
Employed the multivariate Laplace distribution to model the heavy-tailed characteristics of glint noise.

Main Results:

The proposed filter adaptively learns unknown noise statistics during the tracking process.
The variational lower bound was used for approximate calculation of the predictive likelihood.
Computer simulations demonstrated the effectiveness and superiority of the proposed filter over conventional methods.

Conclusions:

The developed filter provides a robust solution for multi-target tracking in radar systems affected by glint noise.
Adaptive learning of noise statistics significantly improves tracking performance.
The approach offers a promising advancement for practical radar tracking applications.