UWB 3D near-field imaging with a sparse MIMO antenna array for concealed weapon detection

Efficient physics-based learned reconstruction methods for real-time 3D near-field MIMO radar imaging

“…In this section, we demonstrate the effectiveness of the developed methods using both simulated data and experimental measurements. We first analyze the performance of the developed methods under various observation scenarios using simulated data, and compare their performance with backprojection [7,15] and sparsity-based CS [32] reconstructions.…”

“…Considering the forward model in Eq. (3) with constant p(k), the three-dimensional reflectivity distribution of the scene can be reconstructed with the frequency-domain BP algorithm [7,15] using…”

“…We now present the performance of the developed methods, namely Deep2S and DeepDI, by considering different compression levels in the observations. To compare their performance, the results are also obtained using the adjoint operation, backprojection (BP) [7,15] and total-variation (TV) based CS reconstruction [32]. For optimum parameter selection in TV regularization, we choose the regularization parameter λ in Eqn.…”

“…Near-field radar imaging systems are of interest in diverse fields such as airport security, surveillance, concealed weapon detection, through-wall imaging, and medicine [1][2][3][4][5][6][7][8]. Earlier near-field radar imaging systems have operated in monostatic mode using collocated transmitter and receiver antennas [3,6,9].…”

“…multistatic arrays) that contain spatially distributed transmit and receive antennas have gained more attention since they enable high resolution with reduced cost, acquisition time, and hardware complexity. The number of antennas in MIMO systems can be significantly reduced compared to the monostatic case while maintaining high image quality [2,4,7,[9][10][11][12][13].…”