This work prioritizes the suppression of mainlobe deceptive jamming within coarray Frequency Diverse Nested Array (FDNA) Multiple-Input Multiple-Output (MIMO) radar architectures. To overcome the range resolution limitation in the conventional FDA, a novel FDNA structure is proposed. Leveraging differential processing for virtual aperture extension, the design enables precise discrimination of mainlobe deceptive jamming and target. Subsequently, a Spatial Smoothing-based Minimum Variance Distortionless Response (SS-MVDR) beamformer is introduced to eliminate the contamination of training samples by target data during jamming suppression. Furthermore, a frequency offset selection strategy is developed to simultaneously suppress both rapid and delayed repeated jamming. The efficacy of the proposed scheme in suppressing mainlobe deceptive jamming is confirmed by simulation results.

This paper addresses the detection and estimation problems of a distributed multiple-input and multiple-output (MIMO) radar system with synchronization errors. In such cases, the outputs of all waveform-specific matched filters contain errors in the resulting time delay estimates, which will cause biases in the corresponding estimation of the active range cells. To overcome the impact resulting from the presence of such errors, a joint robust detection and estimation framework is introduced. We first propose an extended generalized likelihood ratio test (GLRT) detector exhibiting the constant false alarm rate property to robustly detect multi-channel misaligned data by extending the matching range cells among multiple channels, on which a Radon-Fourier transform is employed to coherently accumulate the response of potentially moving targets. Then, a clustering algorithm is employed to obtain the unique time delays and Doppler shifts for each channel from the redundant results generated by the detector. Finally, we estimate the target locations and their velocities using the estimated multi-channel time delays and Doppler shifts using a weighted least squares formulation, which is reformulated as a convex optimization problem in order to allow for an efficient solution. Both Numerical and experimental results demonstrate the performance and the robustness of the proposed framework as compared to other recent approaches.

In this paper, we propose a novel solution to detect multiple targets using a distributed multiple-input multiple-output (MIMO) radar under the so-called “defocused transmit-defocused receive” operating mode. The proposed method employs a grid-based data matching algorithm, aiming to associate the target responses to potential target locations, solving the resulting data puzzle that evaluates the various cells under test (CUTs) in the surveillance area resulting from the intertwined range cells across in all transmit-receive channels. Sketchily, the approach divides the surveillance area into identically interlocking and analytically expressible grid cells, and then selects the grid cells with the best fitting multi-channel data to be equivalently regarded as the CUTs. Next, the generalized likelihood ratio test (GLRT) detector is derived to test for target presence in each of the selected grid cells. A separate procedure is introduced to eliminate the spurious “shadow targets”, false alarms occurring in the grid cells without target while sharing range cells with the targets. The essence of this procedure is to find the source of the observed contributions to the grid cells whose test statistics exceed their thresholds, and simultaneously to obtain the positions of the targets. The proposed method is evaluated using both numerical simulations and experimental data recorded by five small radars, demonstrating the effectiveness of the proposed technique.