Efficient Low-PAR Waveform Design Method for Extended Target Estimation Based on Information Theory in Cognitive Radar

Abstract

This paper addresses the waveform design problem of cognitive radar for extended target estimation in the presence of signal-dependent clutter, subject to a peak-to-average power ratio (PAR) constraint. Owing to this kind of constraint and the convolution operation of the waveform in the time domain, the formulated optimization problem for maximizing the mutual information (MI) between the target and the received signal is a complex non-convex problem. To this end, an efficient waveform design method based on minimization–maximization (MM) technique is proposed. First, by using the MM approach, the original non-convex problem is converted to a convex problem concerning the matrix variable. Then a trick is used for replacing the matrix variable with the vector variable by utilizing the properties of the Toeplitz matrix. Based on this, the optimization problem can be solved efficiently combined with the nearest neighbor method. Finally, an acceleration scheme is used to improve the convergence speed of the proposed method. The simulation results illustrate that the proposed method is superior to the existing methods in terms of estimation performance when designing the constrained waveform.

An Adaptive Multi-Target Radar Waveform Design Based on PWS Algorithm

“…In this case, we can gain higher MI and SINR in the presence of clutter. According to the theoretical derivation of the previous formulas in Equations (9) and (21), it can be concluded that the mutual information in the presence of clutter is larger than the multi-target function without considering the clutter, which is consistent with the conclusion of Goodman [5]. Figure 12 is as expected the SINR-based, clutter-compensated waveform outperforms the noise-only and the presence of clutter MI-based waveform, even the wideband impulse waveform.…”

“…In this subsection, we applied the MI-based and SINR-based algorithms in target class discrimination. The energy spectrum variance of the target and the power spectral density of the clutter are known, that is, the actual target realization is an unknown sample function generated by the PSD of the real target, as shown in Equations (2), (5) and (9); denote that the sampling frequency is 2, the transmit signal length and target length are both 31, the frequency of the signal is f ∈ [0, 1], and the total energy is 1. The noise power spectral density P n ( f ) is 1 and the number of sampling points is 128.…”

“…An important constraint to the improvement of traditional radar target recognition performance is that the fixed transmitter waveform of the existing radar system is mismatched with the current state of the target and the environment. If it can adaptively, intelligently and dynamically optimize and reconfigure the transmit waveform based on the knowledge of the current target which has not been identified, the dynamic optimization and reconfiguration of the transmit waveform will greatly improve the radar system recognition performance and effectively reduce the complexity of traditional recognition algorithms [9]. The design concept of the cognitive radar system is very consistent with the above considerations.…”

Information Theory Applications in Signal Processing

“…The remaining articles of this special issue [ 27 , 28 , 29 , 30 , 31 , 32 , 33 ] have topics within area of communications. On the one hand, the characterization of the achievable rate region of a given dual-hop multiple-access relay network, under linear beamforming, is analyzed in [ 27 ].…”

“…Two papers address the problem of designing the waveforms transmitted by the radar: in [ 31 ], Wang et al propose a technique to maximize the signal-to-interference-plus-noise ratio (SINR) for known- and random-target models, and the mutual information (MI) between the radar echos and the random-target spectrum responses. In [ 32 ], Hao et al present a cognitive waveform design method subject to a peak-to-average power ratio constraint. It is noteworthy that these authors develop a minorization–maximization technique to reduce the computation burden of the overall procedure.…”