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In co-located multiple-input multiple-output (MIMO) radar, slow-time code division multiple access (ST-CDMA) is an essential option of orthogonal waveforms, because the transmit power and bandwidth are well exploited with a relatively low level of hardware complexity. However, the code produces high sidelobes in the Doppler spectrum, which poses a negative impact on weak target detection. To address this issue, we propose a novel sidelobe suppression method that leverages the CLEAN framework in conjunction with a quasi-neural network (Quasi-NN). The novelty lies in the application of Quasi-NN for signal modeling in the target parameter estimation step of CLEAN. Specifically, Quasi-NN is employed to represent the signal after range compression. Its inputs, outputs, and internal weights are determined by the characteristics of antennas, waveforms, and targets. In this way, the estimation of target parameters is transformed into the optimization of weights, which thereby can be solved using a back-propagation (BP) algorithm. Simulation results demonstrate the superior performance of the proposed method under various scenarios. Real-data results using a 77-GHz radar also show that the proposed method achieves lower sidelobes and thus improves the detection of weak targets.

Colocated multiple-input multiple-output (MIMO) technology has been widely used in automotive radars as it provides accurate angular estimation of the objects with a relatively small number of transmitting and receiving antennas. Since the Direction Of Departure (DOD) and the Direction Of Arrival (DOA) of line-of-sight targets coincide, MIMO signal processing allows for the formation of a larger virtual array for angle finding. However, multiple paths impinging the receiver is a major limiting factor, in that radar signals may bounce off obstacles, creating echoes for which the DOD does not equal the DOA. Thus, in complex scenarios with multiple scatterers, the direct paths of the intended targets may be corrupted by indirect paths from other objects, which leads to inaccurate angle estimation or ghost targets. In this paper, we focus on detecting the presence of ghosts due to multipath by regarding it as the problem of deciding between a composite hypothesis, ${\cal H}_{0}$ say, that the observations only contain an unknown number of direct paths sharing the same (unknown) DOD's and DOA's, and a composite alternative, ${\cal H}_{1}$ say, that the observations also contain an unknown number of indirect paths, for which DOD's and DOA's do not coincide. We exploit the Generalized Likelihood Ratio Test (GLRT) philosophy to determine the detector structure, offering closed-form expressions for theoretical detection performance, and a convex waveform optimization approach to improve detection performance. In practical scenarios, the unknown parameters of GLRT philosophy are replaced by carefully designed estimators. The angles of both the active direct paths and of the multi-paths are indeed estimated through a sparsity-enforced Compressed Sensing (CS) approach with Levenberg-Marquardt (LM) optimization to estimate the angular parameters in the continuous domain. Simulation and experimental results are finally offered in order to validate the proposed solution.