A Sparse Method for Joint Range and Angle Estimation in OFDM SonarCom Systems With Phase Errors

Joint sonar-communication (SonarCom) systems offer interesting and promising perspectives for target detection in military or civilian applications. Compared to the traditional sonar systems, orthogonal frequency division multiplexing (OFDM) SonarCom system suffers from frequency offset. The fluctuation due to array uncertainty and acoustic propagation characteristics lead to random phase errors, which heavily impair the estimation accuracy. To deal with this drawback, we present a compressed sensing-based range-angle estimation algorithm when phase errors are present for OFDM SonarCom systems. In order to overcome multitarget interference, cyclic prefix (CP)-based OFDM technique is applied. Exploiting the structure of the CP-OFDM signal model, the redundant dictionary and minimization problem are defined. The coupling information of sparse data are considered as a global metric to minimize the effect of phase errors and recover super-resolution range-angle estimation. Regardless the presence of random phase errors and strong noise, the approach returns high-quality estimates of range and angle. Moreover, a low complexity solution algorithm is developed to improve the computational complexity and memory requirements. The performance assessment underlines that the proposed approach can be a viable means to solve the joint range and angle estimation problem.