【作者】 李灯熬； 邓豆豆； 赵菊敏； 赵晓芳； 韩冲；

【Author】 LI Deng-ao;DENG Dou-dou;ZHAO Ju-min;ZHAO Xiaofang;HAN Chong;Taiyuan university of Technology;

【机构】 太原理工大学信息工程学院；

【摘要】 卫星信号的捕获过程需要一定的硬件资源,且消耗时间,为了节约硬件资源,减少成本,同时为了提高捕获性能,去除量测噪声,文章提出一种基于卡尔曼滤波的压缩感知卫星信号捕获算法。首先,分析北斗卫星信号的稀疏性,构造稀疏变换矩阵,选择测量矩阵进行观测;其次,在信号重构阶段引入Kalman滤波,用Kalman滤波代替最小二乘法,每次迭代都获得最佳信号估计,同时采用弱匹配的方式筛选有效信息,剔除冗余信息从而重构信号。最后得到码相位的准确估计值。实验结果表明,与传统的捕获算法相比,该算法重构性能好,且大大地降低了硬件接收机的成本,计算量明显减少。更多还原

【Abstract】 The satellite signal acquisition process needs certain hardware resources, and time consuming, in order to save hardware resources, reduce costs, improve the capture performance, and the removal of measurement noise, a new algorithm of signal acquisition for compressed sensing based on Kalman filtering is proposed. Firstly, the sparsity of Beidou satellite signals is analyzed, the sparse transformation matrix is constructed and the measurement matrix is selected for observation. Secondly, the introduction of Kalman filtering in the signal reconstruction phase, using Kalman filtering instead of least squares, each iteration to get the best signal estimation, at the same time with the method of weak match filtering information effectively, eliminate redundant information to reconstruct the signal. Finally, the accurate estimation of the code phase is obtained. The experimental results show that compared with the traditional capture algorithm, the reconstruction performance is good, and the cost of the hardware receiver is greatly reduced, the calculation quantity is obviously reduced, and the anti-noise performance is optimal.更多还原