Adaptive Waveform Design and Sequential Hypothesis Testing for Target Recognition With Active Sensors

Wong

International Journal of Distributed Sensor Networks

et al. 2015

The problems of multiple-input multiple-output (MIMO) radar adaptive waveform design in additive white Gaussian noise channels and multitarget recognition based on sequential likelihood ratio test are jointly addressed in this paper. Two informationtheoretic waveform design strategies, namely, the optimal waveform for maximizing the mutual information (MI) between the extended target impulse response and the target echoes and the optimal waveform for maximizing the Kullback-Leibler (KL) divergence (or relative entropy), are applied in the multitarget recognition application. For multitarget case, two adaptive waveform design methods for all possible targets based on the current knowledge of each hypothesis are proposed. Method 1 is the probability weighted waveform method. Method 2 is the probability weighted target signature method. The optimal waveform is transmitted and adaptively changed such that a decision is made based on the likelihood ratio after several illuminations. Numerical results demonstrate that the best waveform is the KL divergence-based optimal waveform using Method 1 as it has the lowest average illumination number and the highest correct decision rate for target recognition. By optimally designing and adaptively changing the transmitted waveform, the average number of illuminations required for multitarget recognition can be much reduced.

Section: Multitarget Recognition and Sequential Likelihood Ratio Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MIMO Radar Adaptive Waveform Design for Extended Target Recognition

Wong

International Journal of Distributed Sensor Networks

et al. 2015

2014 48th Asilomar Conference on Signals, Systems and Computers

“…When classifying a target from multiple candidate targets, [7] proposed an integration of adaptive waveform design with a sequential hypothesis testing (SHT) to form a closed-loop radar (i.e. cognitive radar).…”

Section: B Targets Identification With Map-pwementioning

confidence: 99%

Adaptive waveform for integrated detection and identification of moving extended target

Nieh

Romero

2014

Abstract-We propose an improvement to the maximum a posteriori probability weighted eigenwaveform (MAP-PWE) adaptive waveform design used in target recognition with a cognitive radar platform for which we call match-filtered PWE (MF-PWE). Our interest however is to include moving targets in the identification problem. Combining range-Doppler map (RDM) technique with the the PWE-based adaptive waveform techniques, we propose an integrated detection and identification scheme for moving extended targets. Target detection performance comparison between wideband, MAP-PWE, and MF-PWE techniques are shown. It is noted the MF-PWE performs better than the wideband and MAP-PWE.

“…Goodman et al derived a classification waveform algorithm for enhanced target classification by defining mutual information based on energy spectral variance (MIESV) across the transfer functions of the various target hypotheses in [1,2,3]. Energy spectral variance (ESV) quantifies the statistical variance over a set of finite-duration target transfer functions.…”

Section: Introductionmentioning

confidence: 99%

Classification waveform optimization for MIMO radar

et al. 2017

Self Cite

Abstract:The waveform algorithm for active target classification or identification based on spectral variance has been studied in various pieces of literature. The algorithm was extended for widely-separated MIMO Radar system to take advantage of spatial diversity gain by Bae et al. However, the MIMO waveform can be improved further by considering multiple objective functions from the multiple target paths. In this letter, we optimize the MIMO waveform for target identification system by maximizing the multiple objective functions. We show simulation results to compare the proposed algorithm to other MIMO waveform design methods. Keywords: radar waveform, MIMO, target classification, mutual information, extended target Classification: Sensing References