Abstract:

Prediction of target detection performance is the basis for radar adaptive resource scheduling and refined information processing design. However， conventional statistical models are difficult to accurately describe the statistical distribution of the radar scattering cross section of stealth targets， resulting in inaccurate detection performance prediction. For this reason， this paper proposes a radar detection performance evaluation method based on a hybrid chi-square distribution model， which models and evaluates the detection probability under constant false alarm conditions. Based on the hybrid model， the target detection probability is modeled under the condition of constant false alarm using the folded line approximation and Kummer transform method； the accuracy of the model is examined by Monte Carlo simulation； the hybrid model is compared with the traditional statistical model from the perspective of detection probability by fixing the false alarm rate. The simulation results show that compared with the traditional Swerling model， the single-pulse detection performance of the hybrid cardinality model has a large difference in different azimuth ranges， which better reflects the real detection scenarios and provides theoretical support for the overall optimization design of the stealth target detection system.

Key words: weak target detection, radar cross sections （RCS）, hybrid chi-square distribution, detection probability, statistical modeling, single pulse detection

摘要：

对目标检测性能进行预估是雷达自适应资源调度和精细化信息处理设计的基础。然而，常规统计模型难以准确描述隐身目标雷达散射截面统计分布，导致检测性能预估不准。为此，提出了一种基于混合卡方分布模型的雷达检测性能评估方法，在恒虚警条件下对检测概率进行建模与评估。基于混合模型，采用折线近似、库默尔变换方法在恒虚警条件下对目标检测概率进行建模；采用蒙特卡罗仿真对模型的精确程度进行检验；最后固定虚警率从检测概率角度将混合模型与传统统计模型进行了对比分析。仿真结果表明，混合卡方模型和传统的Swerling模型相比，在不同方位角范围内，单脉冲检测性能具有较大差异，从而更能够反映真实探测场景，并为隐身目标探测系统总体优化设计提供了理论支持。

关键词: 弱目标检测, 雷达散射截面积, 混合卡方分布, 检测概率, 统计建模, 单脉冲检测