本文選題：卡爾曼濾波 + 非線性濾波�。� 參考：《大連海事大學》2015年博士論文

【摘要】：容積卡爾曼濾波算法是近年提出的一種獲得廣泛關(guān)注的非線性濾波算法。為滿足近似線性最小方差條件,CKF仍須假設過程與量測噪聲均為已知獨立的零均值高斯白噪聲。然而實際噪聲環(huán)境往往不滿足上述嚴格假設,這可能導致標準CKF的性能退化甚至發(fā)散。為克服這一局限性,進而增強標準CKF算法的魯棒性。本研究基于標準CKF算法,針對不同噪聲條件,提出幾種改進自適應CKF算法并應用于雷達目標跟蹤仿真實驗。主要研究工作如下：1.為克服統(tǒng)計特征未知的過程噪聲在濾波過程中對SCKF算法性能的不利影響,提出基于Sage-Husa噪聲估計器的自適應SCKF算法。機動目標跟蹤的仿真實驗結(jié)果表明該算法在未知恒定和時變過程噪聲兩種背景中都比同等條件下的SCKF算法具有更好的濾波精度和穩(wěn)定性。2.有色量測噪聲不符合CKF算法的零均值高斯白噪聲假設,因此基于一階馬爾科夫有色噪聲模型、高斯濾波器和三度容積規(guī)則提出同時適用于有色和白色量測噪聲條件的自適應CKF算法及其平方根版本。隨后的目標跟蹤仿真實驗結(jié)果表明兩種改進算法的有效性。3.針對高度容積卡爾曼濾波算法運行過程中存在的不定噪聲對濾波性能造成的影響,基于非線性H無窮濾波框架和五度容積規(guī)則提出高度容積H無窮濾波算法,仿真實驗結(jié)果證明了該改進算法的有效性。4.基于兩種解相關(guān)原則和五度容積規(guī)則,提出兩種互相關(guān)噪聲背景下的改進高度容積濾波器,用于解決同時互相關(guān)噪聲背景下高度容積卡爾曼濾波算法的性能退化的問題。目標跟蹤仿真實驗結(jié)果表明兩種改進濾波算法能有效克服高度容積卡爾曼濾波算法的上述局限性。5.通過穩(wěn)定性分析和仿真實驗比較分析基于極大后驗噪聲估計的自適應CKF、強跟蹤CKF、以及容積H無窮濾波算法這三種自適應CKF算法的性能。穩(wěn)定分析結(jié)果表明三者之中,容積H無窮濾波算法的穩(wěn)定性主要取決于標量β；而另兩個濾波器的穩(wěn)定性都受到過程噪聲初值的影響。仿真實驗結(jié)果表明三種濾波器在過程噪聲未知恒定和時變的條件下均能有效改進CKF算法,但性能各有優(yōu)劣。
[Abstract]:Volumetric Kalman filter (VKF) is a nonlinear filtering algorithm which has received wide attention in recent years. In order to satisfy the approximate linear minimum variance condition (CKF), it is necessary to assume that both the process and the measurement noise are known to be independent of Gao Si white noise with zero mean. However, the actual noise environment often does not satisfy these strict assumptions, which may lead to the degradation or even divergence of the performance of the standard CKF. In order to overcome this limitation, the robustness of the standard CKF algorithm is enhanced. Based on the standard CKF algorithm, this paper proposes several improved adaptive CKF algorithms for different noise conditions and applies them to radar target tracking simulation experiments. The main research work is as follows: 1. In order to overcome the adverse effect of process noise with unknown statistical characteristics on the performance of SCKF algorithm in the filtering process, an adaptive SCKF algorithm based on Sage-Husa noise estimator is proposed. The simulation results of maneuvering target tracking show that the proposed algorithm has better filtering accuracy and stability than the SCKF algorithm under the same conditions in both unknown constant and time-varying process noise background. The colored measurement noise does not accord with the null mean Gao Si white noise assumption of the CKF algorithm, so it is based on the first-order Markov colored noise model. An adaptive CKF algorithm and its square root version for both colored and white measurement noise conditions are proposed by Gao Si filter and cubic volume rule. The simulation results of target tracking show the effectiveness of the two improved algorithms. Aiming at the influence of the uncertain noise in the operation of the high volume Kalman filter algorithm, a high volume H infinite filter algorithm is proposed based on the nonlinear H-infinity filter framework and the five-degree volumetric rule. Simulation results show the effectiveness of the improved algorithm. 4. 4. Based on two kinds of decorrelation principles and five degree volume rule, an improved height volume filter with cross-correlation noise is proposed to solve the degradation of the performance of the high volume Kalman filter algorithm in the background of simultaneous cross-correlation noise. The simulation results of target tracking show that the two improved filtering algorithms can effectively overcome the limitations of the high volume Kalman filtering algorithm. The performance of three adaptive CKF algorithms based on maximum posteriori noise estimation, strongly tracked CKF, and volume H-infinity filtering algorithm are compared and analyzed by stability analysis and simulation experiments. The results of stability analysis show that the stability of the volumetric H-infinity filter is mainly dependent on the scalar 尾, while the stability of the other two filters is affected by the initial value of the process noise. The simulation results show that the three filters can effectively improve the CKF algorithm under the condition that the process noise is unknown constant and time-varying, but the performance has its own advantages and disadvantages.
【學位授予單位】：大連海事大學
【學位級別】：博士
【學位授予年份】：2015
【分類號】：TN713;TN953

【參考文獻】

相關(guān)期刊論文 前10條

1 王思思;齊國清;;有色量測噪聲下的改進求容積卡爾曼濾波器[J];控制理論與應用;2015年01期

2 田國會;張慶賓;丁娜娜;;基于WT-UKF的PDR/GPS組合定位算法[J];控制與決策;2015年01期

3 劉濟;高麗君;;基于UKF和神經(jīng)網(wǎng)絡的一類非線性系統(tǒng)狀態(tài)估計[J];控制與決策;2014年11期

4 喬俊飛;袁喜春;韓紅桂;;基于EKF的自組織T-S模糊Elman網(wǎng)絡[J];控制與決策;2014年05期

5 丁家琳;肖建;;基于極大后驗估計的自適應容積卡爾曼濾波器[J];控制與決策;2014年02期

6 王宏健;傅桂霞;李娟;李村;;基于強跟蹤CKF的無人水下航行器SLAM[J];儀器儀表學報;2013年11期

7 劉穎;蘇俊峰;朱明強;;基于迭代容積粒子濾波的蒙特卡洛定位算法[J];信息與控制;2013年05期

8 王思思;齊國清;;自適應SCKF在機動目標跟蹤中的應用[J];電光與控制;2013年08期

9 徐樹生;林孝工;趙大威;謝業(yè)海;;強跟蹤SRCKF及其在船舶動力定位中的應用[J];儀器儀表學報;2013年06期

10 趙利強;羅達燦;王建林;于濤;;自適應強跟蹤容積卡爾曼濾波算法[J];北京化工大學學報(自然科學版);2013年03期

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本文編號：1806973