Integrated Sensing and Communication Target Detection Framework and Waveform Design Method Based on Information Theory - Qilong Miao; Xiaofeng Shen; Chenfei Xie; Yong Gao; Lu Chen (Sensors)
Target detection is a core function of integrated sensing and communication (ISAC) systems. The traditional likelihood ratio test (LRT) target detection algorithm performs inadequately under low signal-to-noise ratio (SNR) conditions, and the performance of mainstream orthogonal frequency division multiplexing (OFDM) waveforms declines sharply in high-speed scenarios. To address these issues, an information-theory-based orthogonal time frequency space (OTFS)-ISAC target detection processing framework is proposed. This framework adopts the OTFS waveform as its fundamental signal. The target detection is implemented through a relative entropy test (RET) comparing echo signals against target presence/absence hypotheses. Furthermore, to enhance the system’s target detection capability, the iterative OTFS-ISAC waveform design (I-OTFS-WD) method which maximizes the relative entropy is proposed. This method utilizes the minorization-maximization (MM) algorithm framework and semidefinite relaxation (SDR) technique to transform the non-convex optimization problem into an iterative convex optimization problem for resolution. The simulation results demonstrate that, under sufficient sample conditions, the RET algorithm achieves a 9.12-fold performance improvement over LRT in low-SNR scenarios; additionally, the optimized waveform reduces the sample requirements of the RET algorithm by 40%, further enhancing the target detection capability of the OTFS-ISAC system.