低分辨机载雷达空地运动目标的分类识别算法

分类识别技术是雷达当今和未来发展的重要需求，也是雷达的关键技术之一。目前研究较多的是基于宽带信号的目标识别，对雷达系统和目标信噪比具有较高的要求，且对角度非常敏感。针对低分辨机载雷达工作在下视模式下，慢速飞行目标和地面运动目标由于具有相似的多普勒速度和雷达散射截面(RCS)，使得其对机载雷达慢速飞行目标检测、跟踪和识别形成干扰，该文提出了一种基于窄带分形和相位调制特征的机载雷达空地运动目标分类识别算法。文中以实测试飞数据进行分析验证，以支持向量机(SVM)为分类器，试验结果表明，该方法能对机载雷达直升机、汽车运动目标进行有效分类识别，当SNR 15 dB 时，平均分类识别率在89%以上。 

Low-resolution Airborne Radar Air/ground Moving Target Classification and Recognition

Radar Target Recognition (RTR) is one of the most important needs of modern and future airborne surveillance radars, and it is still one of the key technologies of radar. The majority of present algorithms are based on wide-band radar signal, which not only needs high performance radar system and high target Signal-to-Noise Ratio (SNR), but also is sensitive to angle between radar and target. Low-Resolution Airborne Surveillance Radar (LRASR) in downward-looking mode, slow flying aircraft and ground moving truck have similar Doppler velocity and Radar Cross Section (RCS), leading to the problem that LRASR air/ground moving targets can not be distinguished, which also disturbs detection, tracking, and classification of low altitude slow flying aircraft to solve these issues, an algorithm based on narrowband fractal feature and phase modulation feature is presented for LRASR air/ground moving targets classification. Real measured data is applied to verify the algorithm, the classification results validate the proposed method, helicopters and truck can be well classified, the average discrimination rate is more than 89% when SNR 15 dB.