本文關(guān)鍵詞： 機動目標(biāo)跟蹤 非線性濾波 交互多模型 協(xié)方差矩陣交互多模型　出處：《大連海事大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著現(xiàn)代航運業(yè)的快速發(fā)展,水上交通形勢日趨嚴(yán)峻,船舶安全航行問題日益突出,從而對船舶跟蹤性能提出了更高的要求,特別是機動目標(biāo)的穩(wěn)定跟蹤更為關(guān)鍵。為改善目標(biāo)跟蹤可靠性,提高機動目標(biāo)跟蹤精度。本文在傳統(tǒng)交互多模型算法(IMM)的基礎(chǔ)上,展開對機動目標(biāo)跟蹤算法的研究。首先,本文概括了機動目標(biāo)跟蹤的基本原理,介紹了常用的目標(biāo)運動模型。然后在卡爾曼濾波的基礎(chǔ)上重點研究了擴展卡爾曼濾波(EKF)和無跡卡爾曼濾波(UKF)這兩種主要的非線性濾波算法。并設(shè)計出目標(biāo)機動運動場景,仿真對比分析了 EKF算法和UKF算法的機動目標(biāo)跟蹤性能,為后文的改進(jìn)算法提供了理論依據(jù)。其次,為了適應(yīng)目標(biāo)的機動變化,在IMM算法原理及優(yōu)缺點進(jìn)行分析的基礎(chǔ)上,將IMM算法分別與EKF和UKF這兩種非線性濾波算法結(jié)合,研究并設(shè)計了基于擴展卡爾曼濾波的交互多模型算法(IMM-EKF)和基于無跡卡爾曼的交互多模型算法(IMM-UKF)。針對目標(biāo)勻速和轉(zhuǎn)向運動的運動場景,分別對EKF、UKF、IMM-EKF和IMM-UKF四種目標(biāo)跟蹤算法進(jìn)行仿真分析。與傳統(tǒng)算法相比,基于非線性濾波的IMM算法提高了機動目標(biāo)的跟蹤精度。最后,為提高模型概率的準(zhǔn)確率,研究了已有的基于模型概率修正的交互多模型算法(SIMMP),分析了該算法模型概率的修正過程,并根據(jù)基于將當(dāng)前協(xié)方差信息引入模型概率的思想,本文提出了一種基于協(xié)方差矩陣修正概率的交互多模型算法(MIMMP),并對此算法與IMM算法和SIMMP算法進(jìn)行仿真對比分析,證明了本文算法的有效性和優(yōu)越性。
[Abstract]:With the rapid development of modern shipping industry, the situation of water transportation is becoming more and more serious, and the problem of ship safe navigation is becoming more and more prominent, which puts forward higher requirements for the performance of ship tracking. In order to improve the reliability of target tracking and improve the precision of maneuvering target tracking, based on the traditional interactive multi-model algorithm (IMM), the maneuvering target tracking algorithm is studied in this paper. In this paper, the basic principle of maneuvering target tracking is summarized. Based on the Kalman filter, two main nonlinear filtering algorithms, the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), are introduced, and the maneuvering moving scene of the target is designed. The simulation results show that the maneuvering target tracking performance of EKF algorithm and UKF algorithm is compared and analyzed, which provides a theoretical basis for the improved algorithm. Secondly, in order to adapt to the maneuvering change of target, the principle of IMM algorithm and its advantages and disadvantages are analyzed. The IMM algorithm is combined with two nonlinear filtering algorithms, EKF and UKF, respectively. This paper studies and designs an interactive multi-model algorithm based on extended Kalman filter (IMM-EKF) and an interactive multi-model algorithm based on unscented Kalman filter (IMM-UKF). Four target tracking algorithms are simulated and analyzed. Compared with the traditional algorithm, IMM algorithm based on nonlinear filtering improves the tracking accuracy of maneuvering target. Finally, in order to improve the accuracy of model probability, the tracking accuracy of maneuvering target is improved. In this paper, the existing interactive multi-model algorithms based on model probability correction are studied, and the process of model probability correction is analyzed. Based on the idea of introducing the current covariance information into the model probability, In this paper, an interactive multi-model algorithm based on the modified probability of covariance matrix is proposed. The simulation results of this algorithm are compared with that of IMM algorithm and SIMMP algorithm. The results show that the proposed algorithm is effective and superior.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN953

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本文編號：1520057