LPI Optimization Framework for Target Tracking in Radar Network Architectures Using Information-Theoretic Criteria

Chenguang Shi,Jianjiang Zhou,Fei Wang,Mathini Sellathurai

doi:10.1155/2014/654561

Abstract

Widely distributed radar network architectures can provide significant performance improvement for target detection and localization. For a fixed radar network, the achievable target detection performance may go beyond a predetermined threshold with full transmitted power allocation, which is extremely vulnerable in modern electronic warfare. In this paper, we study the problem of low probability of intercept (LPI) design for radar network and propose two novel LPI optimization schemes based on information-theoretic criteria. For a predefined threshold of target detection, Schleher intercept factor is minimized by optimizing transmission power allocation among netted radars in the network. Due to the lack of analytical closed-form expression for receiver operation characteristics (ROC), we employ two information-theoretic criteria, namely, Bhattacharyya distance and J-divergence as the metrics for target detection performance. The resulting nonconvex and nonlinear LPI optimization problems associated with different information-theoretic criteria are cast under a unified framework, and the nonlinear programming based genetic algorithm (NPGA) is used to tackle the optimization problems in the framework. Numerical simulations demonstrate that our proposed LPI strategies are effective in enhancing the LPI performance for radar network.

Highlights

Radar network architecture, which is often called as distributed multiple-input multiple-output (MIMO) radar, has been recently put forward and is becoming an inevitable trend for future radar system design [1,2,3]
We focus on the low probability of intercept (LPI) optimization problem for radar network architecture, where Schleher intercept factor is minimized by optimizing transmission power allocation among netted radars in the network for a predetermined
It can be observed that exploiting our proposed algorithms can effectively improve the LPI performance of radar network to defend against intercept receiver, and Bhattacharyya distance based LPI optimization algorithm is asymptotically equivalent to the J-divergence based case under the same system constraints and fundamental quantity

Summary

Introduction

Radar network architecture, which is often called as distributed multiple-input multiple-output (MIMO) radar, has been recently put forward and is becoming an inevitable trend for future radar system design [1,2,3]. The performance of radar network heavily depends on optimal power allocation and transmission waveform design, so enhanced improvements on target detection and information extraction would be realized by spatial and signal diversities. System design for target detection and information extraction performance improvement has been a long-term research topic in the distributed radar network literature. Yang and Blum in [5] study the target identification and classification for MIMO radar employing mutual information (MI) and the minimum mean-square error (MMSE) criteria. The authors in [6]

Objectives

Findings

Discussion

Conclusion