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Target Tracking Using SePDAF under Ambiguous Angles for Distributed Array Radar.

Teng Long¹, Honggang Zhang², Tao Zeng³

¹Beijing Key Laboratory of Embedded Real-time Information Processing Technology, Radar Research Laboratory, School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China. longteng@bit.edu.cn.

Sensors (Basel, Switzerland)

|September 13, 2016

Summary

This summary is machine-generated.

This study introduces a new Second Probability Data Association Filter (SePDAF) to improve distributed array radar tracking accuracy. The method effectively handles angle ambiguities for more stable and precise target trajectory estimation.

Keywords:

ambiguous angles direction-of-arrival (DOA) estimation distributed array radar probability data association filter (PDAF)tracking

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Area of Science:

Radar Systems Engineering
Signal Processing
Estimation Theory

Background:

Distributed array radar offers enhanced detection and measurement accuracy.
Undersampling in large sparse arrays leads to angle estimation ambiguity, degrading tracking performance.
Existing methods struggle with the accuracy and validity of target tracking under angle ambiguity.

Purpose of the Study:

To propose a novel Second Probability Data Association Filter (SePDAF)-based tracking method for distributed array radar.
To address and mitigate the performance degradation caused by angle estimation ambiguities.
To achieve high accuracy and stable target trajectory estimation with reduced computational load.

Main Methods:

Development of target motion and radar measurement models.
Initial filtering using an Extended Kalman Filter (EKF) on fused radar estimations.
Second filtering stage employing SePDAF with prior knowledge and array-processed ambiguous angles.

Main Results:

Significant improvement in azimuth filtering accuracy.
Enhanced accuracy in position filtering.
Demonstrated high accuracy and stability in target trajectory estimation.
Relatively low computational complexity compared to alternative methods.

Conclusions:

The proposed SePDAF-based method effectively resolves angle ambiguities in distributed array radar.
The approach significantly enhances both azimuth and position estimation accuracy for target tracking.
This method provides a robust and computationally efficient solution for improving radar tracking performance.