Radar Network Waveform Design for Target Tracking

TL;DR

This paper proposes a novel waveform design method for radar networks that optimizes target tracking accuracy by minimizing the PCRLB, using an approximation algorithm with proven convergence, leading to improved tracking performance under interference.

Contribution

It introduces a new waveform synthesis approach based on PCRLB minimization, employing a block-MM algorithm for tractable optimization in radar networks.

Findings

Enhanced target state estimation accuracy

Robust tracking under uncertain conditions

Convergence of the proposed optimization algorithm

Abstract

This paper addresses the synthesis of slow-time coded waveforms for single target tracking in a radar network operating under colored Gaussian interference. Based on the Posterior Cram\'er Rao Lower Bound (PCRLB), which characterizes the theoretically optimal accuracy of target state estimation, the problem at each tracking frame is formulated as the minimization of the trace of the PCRLB, together with power budget requirements and a similarity constraint to account for transmitter limitations and appropriate waveform features. To tackle this challenging optimization problem, an approximation solution technique is proposed, aimed at better tracking accuracy than the reference code. The resulting approximated problems, endowed with more tractable objective functions through Taylor-series expansion, are solved using a customized block Majorization-Minimization (block-MM) algorithm. The convergence properties of the developed procedure are thoroughly analyzed. Numerical results illustrate the accuracy improvements in the target state estimation process, and robust tracking performance under uncertain target state conditions achieved by the proposed technique.