This paper aims to design multiple-input multiple-output (MIMO) radar receiving weights and transmitting waveforms, in order to obtain better spatial filtering performance and enhance the robustness in the case of signal-dependent interference and jointly inaccurate estimated angles of target and interference. Generally, an alternate iterative optimization algorithm is proposed for the joint design problem. Specifically, the receiving weights are designed by the generalized eigenvalue decomposition of the matrix which contains the estimated information of the target and interference. As the cost function of the transmitting waveform design is fractional, the fractional optimization problem is first converted into a secondary optimization problem. Based on the proposed algorithm, a closed-form solution of the waveform is given using the alternating projection. At the analysis stage, in the presence of estimated errors under the environment of signal-dependent interference, a robust signal-to-interference and noise ratio (SINR) performance is obtained using a small amount of calculation with an iterative procedure. Numerical examples verify the effectiveness of the performances of the designed waveform in terms of the SINR, beampattern and pulse compression.

The issue of robust multi-input multi-output (MIMO) radar waveform design is investigated in the presence of imperfect clutter prior knowledge to improve the worst-case detection performance of space-time adaptive processing (STAP). Robust design is needed because waveform design is often sensitive to uncertainties in the initial parameter estimates. Following the min-max approach, a robust waveform covariance matrix (WCM) design is formulated in this work with the criterion of maximization of the worst-case output signal-interference-noise-ratio (SINR) under the constraint of the initial parameter estimation errors to ease this sensitivity systematically and thus improve the robustness of the detection performance to the uncertainties in the initial parameter estimates. To tackle the resultant complicated and nonlinear robust waveform optimization issue, a new diagonal loading (DL) based iterative approach is developed, in which the inner and outer optimization problems can be relaxed to convex problems by using DL method, and hence both of them can be solved very effectively. As compared to the non-robust method and uncorrelated waveforms, numerical simulations show that the proposed method can improve the robustness of the detection performance of STAP.

The problem of radar constant-modulus (CM) waveform design for the detection of multiple targets is considered in this paper. The CM constraint is imposed from the perspective of hardware realization and full utilization of the transmitter's power. Two types of CM waveforms — the arbitrary-phase waveform and the quadrature phase shift keying waveform — are obtained by maximizing the minimum of the signal-to-clutter-plus-noise ratios of the various targets. Numerical results show that the designed CM waveforms perform satisfactorily, even when compared with their counterparts without constraints on the peak-to-average ratio.

There is a trend towards flexible radios which are able to cope with a range of wireless communication standards. For the integrated processing of widely different signals -- including single-carrier, multi-carrier, and spread-spectrum signals -- monolithic baseband receivers need universal formats for the signal representation and channel description. We consider a reconfigurable receiver architecture building on concepts from time-frequency (TF) signal analysis. The core elements are TF signal representations in form of a Gabor expansion along with a compatible parameterization of time-variant channels. While applicable to arbitrary signal types, the TF channel parameterization offers similar advantages as the frequency domain channel description employed by orthogonal frequency-division multiplexing receivers. The freedom in the choice of the underlying analysis window function and the scalability in time and frequency facilitate the handling of diverse signal types as well as the adaptation to radio channels with different delay and Doppler spreads. Optimized window shapes limit the inherent model error, as demonstrated using the example of direct-sequence spread-spectrum signaling.