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In this paper, we investigate a novel framework on the waveform optimization for multiple-input multiple-output (MIMO) radar systems under assumptions of statistical information of scattering matrices and colored noise. Different from most of traditional designs under total power constraint, in our framework, a general power constraint named as multiple weighted power constraints is provided, which includes total power constraint, per-antenna power constraints as its special cases. In the framework, the transmit and receive correlations of parameter matrix to estimate and the spatial and time correlations of noise matrix are all taken into account. Moreover, the signal-dependent interference is also taken into account, which can be considered as a self-interference noise. Based on the available statistics of scattering matrices and colored noise, two kinds of estimation schemes are proposed in this paper, which can realize different trade-offs between complexity and performance. Finally, some numerical results are given to demonstrate the performance advantages of the proposed radar waveform designs.

This paper investigates the problem of the spectrally compatible waveform design for multiple-input multiple-output (MIMO) radar in the presence of multiple targets and signal-dependent interference. Unlike the existing approaches to designing the waveform for a single target via output signal-to-interference-plus-noise ratio (SINR) maximization, a new method is proposed to deal with a more general problem, i.e., designing a spectrally compatible waveform for multiple targets, by minimizing the waveform energy of the overlayed space-frequency bands under constraints of waveform similarity and individual SINR requirements. Due to the existence of signal dependence, the formulated optimization problem is NP-hard. Toward this end, an iterative algorithm is developed. Concretely, at each iteration, the receive filter is first obtained with the aid of the minimum variance distortionless response method, then the transmit waveform is optimized by employing the dual ascent method. Both the convergence and asymptotic optimality of this algorithm are discussed. Numerical simulations are provided to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

We propose a novel robust design method to jointly optimize the radar transmit code and receive filter, exploiting the Signal-to-Interference plus Noise Ratio (SINR) at the receiver end as design figure of merit. We confer robustness to our method against uncertainties on the target impulse response (TIR) using a worst-case optimization approach based on two different uncertainty sets. The former is composed of a finite collection of TIRs, obtained by sampling the actual TIR at some aspect angles; the latter is a spherical uncertainty set. We further enforce a peak-to-average power ratio (PAR) constraint to the transmit code, which is very important for radar applications where the transmitter operates close to saturation. The design problem is tackled using a sequential optimization procedure alternating between two semi-definite programming (SDP) problems, followed by randomization steps. Our numerical results highlight the robustness and applicability of the proposed method in different scenarios. [ABSTRACT FROM PUBLISHER]

We propose to design a peak-to-average power ratio (PAPR) constrained transmit waveform that achieves the optimal performance (following the Neyman-Pearson lemma) in detecting a target in the presence of signal-dependent interference. The direct time-domain approach allows straightforward characterizations of the correlation and PAPR properties of the designed signals, which are critically important to analyze the system performance in the presence of multiple targets and to assess the transmitter power-utilization, respectively. Therefore, instead of designing a transmit signal only for the optimal detection performance, we solve a biobjective Pareto-optimization problem, subjecting to the PAPR and total energy constraints, in order to simultaneously optimize the detection and cross-correlation performances. With extensive numerical examples, we demonstrate that PAPR-constrained signals produce nearly optimum detection performance even with a strict PAPR requirement, and also highlight the conflicting behavior of the detection and correlation performances. [ABSTRACT FROM PUBLISHER]