【摘要】 水下方位频率机动目标运动分析常需已知中心频率先验信息且存在机动检测错误等,因此提出了一种基于方位与多线谱信息融合的自适应无迹卡尔曼滤波算法。该算法将方位与多线谱频率信息融合处理,重新构建了方位-多线谱目标运动分析模型。将中心频率信息引入状态向量中构建实时迭代估计,同时引入了自适应时变渐消因子,实时调整过程噪声协方差矩阵使其适应机动目标。仿真与海试数据处理结果表明,所提方法的机动目标运动分析性能最优,其目标运动分析结果能以最快的速度逼近克拉美罗下界。更多还原

【Abstract】 This paper is concerned with the underwater bearing and frequency maneuvering target motion analysis problem. To address the existing problems of requiring known prior center frequency information and the existence of maneuvering detection errors, an adaptive unscented Kalman filter algorithm based on multiple frequencies and bearing(MFB-AUKF) algorithm is presented. The MFB-AUKF algorithm constructs a new target motion analysis model by fusing bearing and multiple frequency information. The center frequencies are introduced into the state vector and the real-time estimation of the center frequencies can be obtained by iteration. The algorithm introduces a time-varying fading factor to adjust the process noise covariance matrix, which makes the MFB-AUKF algorithm capable of processing maneuvering targets. Simulation and sea trial analysis results show that the proposed method achieves better tracking performance and the target motion analysis result can approach the Cramer-Rao lower bound with the fastest speed.更多还原