This article proposes a compressed-domain signal processing (CSP) multiple-input multiple-output (MIMO) radar, a MIMO radar approach that achieves substantial sample complexity reduction by exploiting the idea of CSP. CSP MIMO radar involves two levels of data compression followed by target detection at the compressed domain. First, compressive sensing is applied at the receive antennas, followed by a Capon beamformer, which is designed to suppress clutter. Exploiting the sparse nature of the beamformer output, a second compression is applied to the filtered data. Target detection is subsequently conducted by formulating and solving a hypothesis testing problem at each grid point of the discretized angle space. The proposed approach enables an eightfold reduction of the sample complexity in some settings as compared to a conventional compressed sensing (CS) MIMO radar, thus enabling faster target detection. Receiver operating characteristic curves of the proposed detector are provided. Simulation results show that the proposed approach outperforms recovery-based CS algorithms.

Multiple-input multiple-output (MIMO) radar is known for its superiority over conventional radar due to its antenna and waveform diversity. Although higher angular resolution, improved parameter identifiability, and better target detection are achieved, the hardware costs (due to multiple transmitters and multiple receivers) and high-energy consumption (multiple pulses) limit the usage of MIMO radars in large scale networks. On one hand, higher angle and velocity estimation accuracy is required, but on the other hand, a lower number of antennas/pulses is desirable. To achieve such a compromise, in this paper, the Cramér-Rao lower bound (CRLB) for the angle and velocity estimator is employed as a performance metric to design the antenna and the pulse placement. It is shown that the CRLB derived for two targets is a more appropriate criterion in comparison with the single-target CRLB since the two-target CRLB takes into account both the mainlobe width and the sidelobe level of the ambiguity function. In this paper, several algorithms for antenna and pulse selection based on convex and submodular optimization are proposed. Numerical experiments are provided to illustrate the developed theory.

Multiple input multiple output (MIMO) radar is known for its superiority over conventional radar due to its antenna and waveform diversity. However, the increased hardware cost (due to multiple transmitters and receivers), power consumption (due to multiple transmitters and pulses), and computational complexity (due to numerous pulses) form the drawbacks of MIMO radar. On one hand, a higher estimation accuracy is required, but on the other hand, a lower number of active antennas/pulses is desirable. Therefore, in this paper, by proposing a convex optimization approach for the general case of transmitter-receiver-pulse selection, we will minimize the total number of active antennas/pulses in order to guarantee a prescribed performance accuracy. The performance measure we will optimize is the Cramer-Rao lower bound (CRLB) for the angle and velocity estimation accuracy of two targets, which provides a trade-off between the main beamwidth and the sidelobe level (SLL) of the ambiguity function.