【摘要】：為了避免被動跟蹤中非線性帶來的計算復雜化及跟蹤精度的下降,提出將平方根無跡卡爾曼濾波平滑算法(SR-UKFS)應用到水下純方位目標跟蹤。SR-UKFS利用Rauch-Tung-Striebel(RTS)平滑算法將平方根無跡卡爾曼濾波(SR-UKF)作為前向濾波算法得到的目標狀態(tài)估計向后平滑,得到前一時刻目標狀態(tài)估計,再利用該狀態(tài)估計值進行再次濾波得到當前時刻目標狀態(tài)估計。該算法得到的前一時刻的目標狀態(tài)估計更加精確,從而進一步提高了目標跟蹤的精度。最后,通過對SR-UKFS算法和SR-UKF算法的跟蹤性能進行了對比分析和驗證,仿真結果表明在相同條件下,SR-UKFS算法能減少59%的位置誤差和54%的速度誤差,SR-UKFS算法應用于水下純方位目標跟蹤系統(tǒng)是有效的,為水下純方位目標跟蹤系統(tǒng)的工程實現提供了非常有價值的參考。
[Abstract]:In order to avoid the computational complexity and the decrease of tracking accuracy caused by inlinearity in passive tracking, In this paper, the square root unscented Kalman filter smoothing algorithm (SR-UKFS) is applied to underwater azimuth target tracking. Sr-UKFS uses the Rauch-Tung-Striebel (RTS) smoothing algorithm to use the square root unscented Kalman filter (SR-UKF) as the square root unscented Kalman filter (SR-UKF). The target state estimated by the forward filtering algorithm is smoothed backward. The target state estimation at the previous time is obtained, and then the target state estimation at the current time is obtained by using the state estimation value to filter again. The target state estimation obtained by the algorithm is more accurate at the previous time, which further improves the accuracy of target tracking. Finally, the tracking performance of SR-UKFS algorithm and SR-UKF algorithm is compared and verified. The simulation results show that the SR-UKFS algorithm can reduce the position error by 59% and the speed error by 54% under the same conditions. The application of SR-UKFS algorithm to underwater azimuth-only target tracking system is effective, which provides a very valuable reference for the engineering implementation of underwater azimuth-only target tracking system.
【作者單位】： 中國船舶重工集團第七一六研究所;南京理工大學自動化學院;
【基金】：國家自然科學基金(61473153,61301217)
【分類號】：TB56

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1 孫開敏;李德仁;眭海剛;;基于多尺度分割的對象級影像平滑算法[J];武漢大學學報(信息科學版);2009年04期

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