一种用于时差提取的卡尔曼-最优阶互相关算法

在定位系统的时差提取方法中,传统的互相关算法抗噪能力较弱、时差提取精度较低,针对以上缺点,提出卡尔曼-最优阶互相关算法,并且通过仿真对比分析相同条件下该算法和基本互相关算法、广义加权互相关算法的时差提取精度,验证了其更优的性能.最后,通过FPGA设计,将所提算法运用到实际工程中.仿真和试验结果表明,相对于传统算法,卡尔曼-最优阶互相关算法在较低信噪比的情况下具有更强的抗噪能力和更高的时差提取精度.

A kalman filter and optimal fourier transform rank combined cross correlation algorithm for Time-difference Extraction

Considering time difference extraction methods used in the positioning system, traditional cross-correlation algorithms suffer poor noise-resistant ability and low time difference extraction accuracy. In order to overcome these shortcomings, this paper proposes a Kalman-optimal order cross-correlation algorithm and validates it through simulation and experiments. Under the same conditions, the accuracy of the proposed algorithm is significantly improved compared to basic cross-correlation algorithm and generalized weighted cross-correlation algorithm. Finally, through FPGA implementation, the proposed algorithm is applied to an actual project. Simulative and experimental results show that compared with the traditional algorithms, the Kalman-optimal cross-correlation algorithm has stronger noise immunity and higher time difference extraction accuracy in lower signal-to-noise ratio environment.