積分粒子濾波方法及其應(yīng)用研究
發(fā)布時間：2018-09-19 17:56
【摘要】：目前,大規(guī)模被動傳感器系統(tǒng)及其相關(guān)關(guān)鍵技術(shù)的研究日益受到國內(nèi)外學(xué)者的重視。針對這種觀測信息有限、數(shù)據(jù)丟失率高,具有非線性、非高斯特征的被動傳感器觀測數(shù)據(jù)的濾波處理是大規(guī)模被動傳感器系統(tǒng)數(shù)據(jù)處理首先要解決的關(guān)鍵和難點問題。結(jié)合自已有非線性非高斯濾波算法,本文提出了Gauss-Hermite積分的高斯和積分粒子濾波算法以及考慮目標特性的輔助高斯和積分粒子濾波算法,在此基礎(chǔ)上,給出了適合并行計算的并行高斯和積分粒子濾波算法。針對大規(guī)模被動傳感器系統(tǒng)中非線性非高斯觀測數(shù)據(jù)的濾波問題,提出了基于Gauss-Hermite積分的高斯和積分粒子濾波器(GSQPF)。GSQPF利用積分點概率密度度函數(shù)作為重要性密度函數(shù),同時利用高斯混合更新后驗概率,有效的提高了采樣粒子的多樣性與準確性。仿真結(jié)果表明,提出的高斯和積分粒子濾波估計性能明顯要好于高斯和粒子濾波(GSPF)、積分粒子濾波(QPF),可以對非線性非高斯系統(tǒng)進行精確的狀態(tài)估計。為增強對非周期稀疏采樣觀測數(shù)據(jù)的濾波處理能力,在GSQPF的基礎(chǔ)上,將時間間隔、目標觀測和目標速度等目標特性融入到重要性密度函數(shù)的構(gòu)建中,提出了一種輔助的高斯和積分粒子濾波(AGSQPF),有效的增強了采樣粒子的多樣性和準確性。實驗結(jié)果表明,提出的AGSQPF估計精度要優(yōu)于GSQPF方法,能夠?qū)Ρ粍幽繕诉M行準確跟蹤。針對大規(guī)模被動傳感器系統(tǒng)觀測數(shù)據(jù)處理量大、通訊需求高的問題,給出一種并行高斯和積分粒子濾波算法。在算法的并行處理上,高斯和積分粒子濾波器的粒子和權(quán)值都是在子系統(tǒng)進行更新,各子系統(tǒng)都考慮了邊界狀態(tài)信息,但各子系統(tǒng)的濾波過程是相互獨立的,大大提高了傳感器系統(tǒng)數(shù)據(jù)處理的效率。由于實驗結(jié)果表明,提出的算法能夠滿足大規(guī)模被動傳感器系統(tǒng)應(yīng)用的需要,能夠?qū)δ繕诉M行準確跟蹤。
[Abstract]:At present, the research of large-scale passive sensor system and its related key technologies has been paid more and more attention by scholars at home and abroad. In view of the limited observation information, high data loss rate, nonlinear, non-Gao Si characteristics of passive sensor observation data filtering processing is the first to solve the key and difficult problem of large-scale passive sensor system data processing. Combined with their own nonlinear non-Gao Si filtering algorithm, this paper proposes the algorithm of Gauss-Hermite integral, which is Gao Si and integral particle filter, as well as the auxiliary Gao Si and integral particle filter algorithm, which takes into account the characteristics of the target. On the basis of this, The parallel Gao Si and integrated particle filter algorithms suitable for parallel computation are presented. Aiming at the filtering problem of nonlinear non-Gao Si observation data in large-scale passive sensor systems, a new method based on Gauss-Hermite integral is proposed, which uses the integral point probability density function as the importance density function, and the integrated particle filter (GSQPF). GSQPF uses the integral point probability density function as the importance density function. At the same time, the diversity and accuracy of sampling particles are improved effectively by using Gao Si mixed update posterior probability. The simulation results show that the performance of Gao Si and integrated particle filter is better than that of Gao Si and (GSPF), integrated particle filter (QPF),. In order to enhance the filtering and processing ability of aperiodic sparse sampling data, based on GSQPF, target characteristics such as time interval, target observation and target velocity are incorporated into the construction of importance density function. An auxiliary Gao Si and integrated particle filter (AGSQPF),) are proposed to effectively enhance the diversity and accuracy of sampling particles. The experimental results show that the proposed AGSQPF estimation method is more accurate than the GSQPF method and can track the passive target accurately. A parallel Gao Si and integrated particle filter algorithm is proposed to solve the problem of large data processing and high communication demand in large-scale passive sensor systems. In parallel processing of the algorithm, the particles and weights of Gao Si and integral particle filter are updated in the subsystem, each subsystem considers the boundary state information, but the filtering process of each subsystem is independent of each other. The efficiency of data processing in sensor system is greatly improved. The experimental results show that the proposed algorithm can meet the needs of large-scale passive sensor system applications and can accurately track the target.
【學(xué)位授予單位】：深圳大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TN713;TP212.9
本文編號：2250893
[Abstract]:At present, the research of large-scale passive sensor system and its related key technologies has been paid more and more attention by scholars at home and abroad. In view of the limited observation information, high data loss rate, nonlinear, non-Gao Si characteristics of passive sensor observation data filtering processing is the first to solve the key and difficult problem of large-scale passive sensor system data processing. Combined with their own nonlinear non-Gao Si filtering algorithm, this paper proposes the algorithm of Gauss-Hermite integral, which is Gao Si and integral particle filter, as well as the auxiliary Gao Si and integral particle filter algorithm, which takes into account the characteristics of the target. On the basis of this, The parallel Gao Si and integrated particle filter algorithms suitable for parallel computation are presented. Aiming at the filtering problem of nonlinear non-Gao Si observation data in large-scale passive sensor systems, a new method based on Gauss-Hermite integral is proposed, which uses the integral point probability density function as the importance density function, and the integrated particle filter (GSQPF). GSQPF uses the integral point probability density function as the importance density function. At the same time, the diversity and accuracy of sampling particles are improved effectively by using Gao Si mixed update posterior probability. The simulation results show that the performance of Gao Si and integrated particle filter is better than that of Gao Si and (GSPF), integrated particle filter (QPF),. In order to enhance the filtering and processing ability of aperiodic sparse sampling data, based on GSQPF, target characteristics such as time interval, target observation and target velocity are incorporated into the construction of importance density function. An auxiliary Gao Si and integrated particle filter (AGSQPF),) are proposed to effectively enhance the diversity and accuracy of sampling particles. The experimental results show that the proposed AGSQPF estimation method is more accurate than the GSQPF method and can track the passive target accurately. A parallel Gao Si and integrated particle filter algorithm is proposed to solve the problem of large data processing and high communication demand in large-scale passive sensor systems. In parallel processing of the algorithm, the particles and weights of Gao Si and integral particle filter are updated in the subsystem, each subsystem considers the boundary state information, but the filtering process of each subsystem is independent of each other. The efficiency of data processing in sensor system is greatly improved. The experimental results show that the proposed algorithm can meet the needs of large-scale passive sensor system applications and can accurately track the target.
【學(xué)位授予單位】：深圳大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TN713;TP212.9
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