【摘要】 通感一体化（ISAC）将通信能力与感知能力进行进一步的深度融合，是6G典型场景之一。在ISAC系统评估中，既要考虑传统通信的性能，也要考虑新增感知的性能需求。目标检测与定位技术是ISAC关键技术之一，在基于OFDM的通感融合信号目标检测中，首先采用循环互相关算法获取目标回波信号的距离-多普勒图谱（RDM），然后借助恒虚警率（CFAR）检测算法消除RDM中噪声的影响。单元平均CFAR(CA-CFAR)检测算法由于其较低的复杂度已受到广泛的研究和关注。进一步考虑了在噪声背景下准确检测出目标，在CA-CFAR检测基础上引入了有序统计和异常值剔除机制，提出了有序统计平均-恒虚警率（OSCA-CFAR）检测方案，其检测窗口呈十字形，利用有序统计和剔除窗口内异常单元值后进行噪声估计，并通过仿真验证了所提OSCA-CFAR检测方案可实现检测效率和检测概率的双提升。更多还原

【Abstract】 Integrated sensing and communication(ISAC) further enables in-depth integration of communication capabilities and sensing capabilities, making it one of the typical scenarios for 6G. In the evaluation of ISAC systems, it is necessary to consider both the performance of traditional communication and the performance requirements of the newly added sensing function. Target detection and localization technology is one of the key technologies for ISAC. In the target detection based on OFDM ISAC signals, the cyclic cross correlation algorithm is first used to obtain the range doppler matrix(RDM) of the target echo signal, and then the constant false alarm rate(CFAR) detection algorithm is used to eliminate the influence of noise in the RDM. The cell average-CFAR(CA-CFAR) has attracted widespread attention due to its low complexity. To accurately detect targets in a noisy environment, this paper further introduces ordered statistics and outlier elimination mechanism on the basis of the CA-CFAR detection, and proposes an ordered statistical CA-CFAR(OSCA-CFAR) detection scheme. The detection window of this scheme is cross-shaped, leveraging ordered statistics and eliminating abnormal cell values within the window for noise estimation. Simulation results verify that the proposed OSCA-CFAR detection scheme can achieve a dual improvement in detection efficiency and detection probability.更多还原