Multiple-Signal-Classification for Superior Target Detection in Subarray-MIMO Radar Systems

One of the keys to ensuring optimal radar performance in various applications, from security to navigation, is a high target detection probability. This paper uses a multiple-signal-classification (MUSIC) algorithm to increase the detection probability which is implemented on a MIMO radar with subarrays in the transmit (Tx) and receiver (Rx) antenna array elements called the SMIMO radar. The subarray method on this radar is known to increase the angular resolution of detection, expand the detection range, add virtual arrays, and minimize the influence of interference compared to conventional radars such as phased-array and MIMO radars. The evaluation and effectiveness of the detection probability performance of this radar are compared to previous radars by considering the number of Tx-Rx antenna elements, the number of subarrays in Tx-Rx, false alarm probability, and SNR variations. SMIMO radar with the MUSIC algorithm and Tx-Rx subarrays demonstrates superior detection performance at low SNR, achieving a Pd of 0.9 at SNR -24.4 dB and outperforming PhA, MISO, and MIMO radars. Increasing Tx subarrays (W) significantly enhances detection capabilities, with ROC analysis showing Pd above 0.92 at Pfa around 10?² and SNR above -15 dB, making it highly effective for weak signal detection.