MIMO Radar Adaptive Waveform Design for Extended Target Recognition

Lulu Wang, Kai-Kit Wong, Hongqiang Wang and Yuliang Qin

International Journal of Distributed Sensor Networks, 2015, vol. 11, issue 6, 154931

Abstract: 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 information-theoretic 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.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:11:y:2015:i:6:p:154931

DOI: 10.1155/2015/154931

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