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.
Highlights
Unlike the traditional phased-array radar that transmits a scaled version of a single waveform, multiple-input multipleoutput (MIMO) radar can transmit different waveforms through its antennas and provide more degrees of freedom in the radar system which lead to many advantages such as improved spatial resolution, better parametric identifiability, and greater flexibility to achieve the desired transmit beam pattern [1,2,3,4,5,6]
Numerical results are presented to illustrate the performance of the two optimal waveform design methods applied to multitarget recognition problem
The MIMO radar system parameters are set to P = 2, Q = 1, M = 10, and L = 20
Summary
Unlike the traditional phased-array radar that transmits a scaled version of a single waveform, multiple-input multipleoutput (MIMO) radar can transmit different waveforms through its antennas and provide more degrees of freedom in the radar system which lead to many advantages such as improved spatial resolution, better parametric identifiability, and greater flexibility to achieve the desired transmit beam pattern [1,2,3,4,5,6]. Optimal waveform design is of paramount importance for many kinds of active sensing systems such as radar, sonar, and communication. MIMO radar waveform design has attracted much attention for several years [6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]. According to whether the waveform is designed directly or not, the MIMO radar waveform design problem can be divided into two categories. One is the optimal waveform design based on the ambiguity function [14,15,16] In this case, the waveform is optimized directly to have a good autocorrelation or cross-correlation property which sharpens the ambiguity function. In [26,27,28], KL divergence was employed in sequential MIMO
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