A radar system to produce a radar image of a region of interest, ROI, wherein the ROI includes targets, comprising: sensors at different positions to transmit source signals to the ROI and to measure echoes reflected back from the targets corresponding to the transmitted source signals, such that each sensor includes received signal data including echoes of target measurements specific to the sensor; a hardware processor is configured to calculate an estimate of a noisy and a partial Euclidean Distance Matrix, EDM, of the sensors and the targets, based on the received signal data, wherein Euclidean distances between the sensors are computed using expected sensor locations, and the Euclidean distances between the sensors and the targets are estimated by correlating the received signal data for each sensor, wherein the noisy and the partial EDM includes distance errors; decompose the noisy and the partial EDM into a low rank EDM that corresponds to locations to actual sensors and target locations, and a sparse matrix of distance errors, using a constrained optimization process which is solved using an alternating direction method of multipliers, ADMM, wherein the low rank EDM is mapped into the sensors and the targets locations, to obtain estimated actual sensor locations; and implement an inverse imaging process using the estimated actual sensor locations and the received signal data, to produce the radar image; and an output interface to output the radar image to a communication channel. sensors at different positions to transmit source signals to the ROI and to measure echoes reflected back from the targets corresponding to the transmitted source signals, such that each sensor includes received signal data including echoes of target measurements specific to the sensor; a hardware processor is configured to calculate an estimate of a noisy and a partial Euclidean Distance Matrix, EDM, of the sensors and the targets, based on the received signal data, wherein Euclidean distances between the sensors are computed using expected sensor locations, and the Euclidean distances between the sensors and the targets are estimated by correlating the received signal data for each sensor, wherein the noisy and the partial EDM includes distance errors; decompose the noisy and the partial EDM into a low rank EDM that corresponds to locations to actual sensors and target locations, and a sparse matrix of distance errors, using a constrained optimization process which is solved using an alternating direction method of multipliers, ADMM, wherein the low rank EDM is mapped into the sensors and the targets locations, to obtain estimated actual sensor locations; and implement an inverse imaging process using the estimated actual sensor locations and the received signal data, to produce the radar image; and calculate an estimate of a noisy and a partial Euclidean Distance Matrix, EDM, of the sensors and the targets, based on the received signal data, wherein Euclidean distances between the sensors are computed using expected sensor locations, and the Euclidean distances between the sensors and the targets are estimated by correlating the received signal data for each sensor, wherein the noisy and the partial EDM includes distance errors; decompose the noisy and the partial EDM into a low rank EDM that corresponds to locations to actual sensors and target locations, and a sparse matrix of distance errors, using a constrained optimization process which is solved using an alternating direction method of multipliers, ADMM, wherein the low rank EDM is mapped into the sensors and the targets locations, to obtain estimated actual sensor locations; and implement an inverse imaging process using the estimated actual sensor locations and the received signal data, to produce the radar image; and an output interface to output the radar image to a communication channel.