Abstract
In this paper, the problem of cognitive radar (CR) waveform optimization design for target detection and estimation in multiple extended targets situations is investigated. This problem is analyzed in signal-dependent interference, as well as additive channel noise for extended targets with unknown target impulse response (TIR). To address this problem, an improved algorithm is employed for target detection by maximizing the detection probability of the received echo on the promise of ensuring the TIR estimation precision. In this algorithm, an additional weight vector is introduced to achieve a trade-off among different targets. Both the estimate of TIR and transmit waveform can be updated at each step based on the previous step. Under the same constraint on waveform energy and bandwidth, the information theoretical approach is also considered. In addition, the relationship between the waveforms that are designed based on the two criteria is discussed. Unlike most existing works that only consider single target with temporally correlated characteristics, waveform design for multiple extended targets is considered in this method. Simulation results demonstrate that compared with linear frequency modulated (LFM) signal, waveforms designed based on maximum detection probability and maximum mutual information (MI) criteria can make radar echoes contain more multiple-target information and improve radar performance as a result.
Highlights
Cognitive radar is a recently proposed system concept, one of whose most important characteristics is its closed-loop operation [1]
A weight vector is adopted to control the importance of different targets
Radar with adaptive transmitted waveforms is defined as cognitive radar (CR), and radar with fixed linear frequency modulated (LFM) waveform is defined as traditional radar
Summary
Cognitive radar is a recently proposed system concept, one of whose most important characteristics is its closed-loop operation [1]. Current design criteria include the maximum signal to interference plus noise ratio (SINR) criterion, the maximum detection probability criterion, the minimum mean square error (MMSE) criterion, the maximum MI criterion, and so on The selection of these criteria usually depends on the task of the radar system. A novel iterative algorithm is proposed by the authors [15] to joint optimization of waveforms and receiving filters in the multiple-input multiple-output (MIMO) radar such that the detection performance can be maximized. In CR, target detection and estimation employ feedback from the environment to tune the radar’s transmit waveform or the radar’s receive processing in such a way as to improve the performance in complex radar environments. The whole optimization process in a multiple-target environment is established to design the transmit waveform by maximizing the detection probability of the received echo at each KF iteration. Notations used in this paper are defined as follows: symbols for vectors (lower case) and matrices (upper case) are in bold face. (·) H , (·) T , (·)∗ , diag{·}, Tr {·}, I, CN (0, R), k · k2 , F, ∗ denote the complex conjugate transpose, the transpose operation, the complex conjugate operation, the diagonal matrix, the trace of a matrix, the identity matrix, the complex Gaussian distribution with zero mean and covariance being R, the l2 norm, the Fourier transform, convolution, respectively
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