本文選題：“北斗”衛(wèi)星導航系統(tǒng)　切入點：頻率估計　出處：《電訊技術》2017年08期 　論文類型：期刊論文

【摘要】：高動態(tài)環(huán)境下的"北斗"導航信號含有較大的多普勒頻率及其變化率,傳統(tǒng)鎖相環(huán)(PLL)在跟蹤時難以保證較高的跟蹤精度。在分析高動態(tài)環(huán)境下"北斗"信號模型的基礎上,提出了一種基于交互式多模型-擴展卡爾曼濾波(IMM-EKF)的自適應濾波算法,對載波相位及其高階分量進行估計。IMM-EKF采用多個跟蹤模型來解決濾波過程中單個模型不準確的問題,并結合改進的SageHusa自適應算法,在線估計和修正過程噪聲及測量噪聲的統(tǒng)計特性,增強了濾波的穩(wěn)定性。仿真結果表明,IMM-EKF相比于PLL和EKF,估計精度更高,算法穩(wěn)定性更強。
[Abstract]:The "Beidou" navigation signal in high dynamic environment contains a large Doppler frequency and its changing rate, so it is difficult for the traditional PLL to ensure a high tracking accuracy when tracking. Based on the analysis of the "Beidou" signal model in the high dynamic environment, An adaptive filtering algorithm based on interactive multi-model-extended Kalman filter (IMM-EKF) is proposed, in which the carrier phase and its high-order components are estimated by using multiple tracking models to solve the problem of inaccuracy of single model in the filtering process. Combined with the improved SageHusa adaptive algorithm, the statistical characteristics of process noise and measurement noise are estimated and corrected on line, and the stability of the filter is enhanced. The simulation results show that the estimation accuracy of IMM-EKF is higher than that of PLL and EKF, and the stability of the algorithm is stronger than that of PLL and EKF.
【作者單位】： 裝備學院光電裝備系;裝備學院科研部;
【分類號】：TN967.1

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