【摘要】：為提高車載激光多普勒測速低信噪比時的測速精度,同時保持較好的實時性,基于自相關(guān)檢測算法在車載激光多普勒測速中的應(yīng)用進行了理論與實驗研究。從速度估計的克拉美羅下限(CRLB)出發(fā),將自相關(guān)后信噪比與采樣點數(shù)的關(guān)系和頻域內(nèi)信噪比估計相結(jié)合;不同原始信號的信噪比,可以選取最優(yōu)重數(shù)和采樣點數(shù)以便對信號進行自相關(guān)處理,從而減小計算量,實現(xiàn)系統(tǒng)信噪比和探測概率的提高。仿真和實驗結(jié)果表明,在信噪比不低于-10dB時,二重自相關(guān)可以使系統(tǒng)速度估計精度接近CRLB。
[Abstract]:In order to improve the accuracy of vehicle laser Doppler velocimetry with low signal-to-noise ratio (SNR) and to maintain good real-time performance, the application of auto-correlation detection algorithm in vehicle laser Doppler velocimetry is studied theoretically and experimentally. Based on the Clemero lower bound (CRLB) of velocity estimation, the relationship between autocorrelation post-SNR and sampling points and the estimation of SNR in frequency domain are combined. The optimal number of duplicates and sampling points can be selected for autocorrelation processing of the signal, thus reducing the computational complexity and improving the signal-to-noise ratio and detection probability of the system. The simulation and experimental results show that when SNR is not lower than-10dB, dual autocorrelation can make the system speed estimation accuracy close to CRLB..
【作者單位】： 北京航空航天大學(xué)儀器科學(xué)與光電工程學(xué)院;
【分類號】：TN957.51

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