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为提高车载激光多普勒测速低信噪比时的测速精度,同时保持较好的实时性,基于自相关检测算法在车载激光多普勒测速中的应用进行了理论与实验研究。从速度估计的克拉美罗下限(CRLB)出发,将自相关后信噪比与采样点数的关系和频域内信噪比估计相结合;不同原始信号的信噪比,可以选取最优重数和采样点数以便对信号进行自相关处理,从而减小计算量,实现系统信噪比和探测概率的提高。仿真和实验结果表明,在信噪比不低于-10dB时,二重自相关可以使系统速度估计精度接近CRLB。
In order to improve the accuracy of laser Doppler velocimetry at low signal-to-noise ratio (SNR) while maintaining good real-time performance, theoretical and experimental studies on the application of autocorrelation detection algorithm in Doppler velocimetry are carried out. Based on the CRLB of velocity estimation, the relationship between the signal-to-noise ratio and the number of sampling points after autocorrelation is combined with the signal-to-noise ratio estimation in the frequency domain. For the SNR of different original signals, the optimal weight and Sampling points in order to signal autocorrelation processing, thereby reducing the amount of computation, to achieve the system SNR and detection probability increase. Simulation and experimental results show that the dual autocorrelation can approach the system speed estimation accuracy close to CRLB when SNR is no less than -10dB.