本文選題：協同跟蹤 + 數據融合��； 參考：《西南交通大學》2017年碩士論文

【摘要】：在軍事、交通、工業(yè)等諸多領域,多傳感器協同跟蹤技術的應用十分廣泛。多傳感器協同跟蹤的目標是最優(yōu)化多傳感器系統的整體跟蹤性能。其技術基礎為傳感器管理技術,通過建立某種傳感器管理模型,在各觀測時刻實時地為各目標分配最優(yōu)的傳感器組合,實現對監(jiān)視范圍內各目標的跟蹤。與同質傳感器相比,異質傳感器在應用場景和特性上能優(yōu)勢互補,可以提高跟蹤的性能。本文的主要工作如下:首先,查閱了大量相關文獻,從三個方面綜述了目前異質多傳感器協同跟蹤問題的研究現狀。其次,非線性濾波問題和機動目標協同跟蹤的精度和穩(wěn)定性緊密相關。在其相關研究中,DMCKF算法采用協方差矩陣的對角化變換,取代標準CKF中的Cholesky分解,獲得算術平方根矩陣,提高了計算的準確度。但是DMCKF和標準CKF在濾波過程中其協方差矩陣有時會失去正定性,導致濾波中斷�；谇蠼飧饔^測時刻協方差矩陣的最鄰近半正定矩陣,提出了一種改進的DMCKF算法,確保了濾波過程中觀測值容積點的傳播不被中斷,提升了 DMCKF算法的穩(wěn)定性。同時,基于改進DMCKF算法,仿真分析了在集中式量測融合和分布式狀態(tài)兩種融合架構下的異質多傳感器數據融合算法的性能和適用的情形。然后,針對異質多傳感器管理的關鍵問題:異質多傳感器-多目標協同分配問題,提出了一種基于Renyi信息增量的異質多傳感器管理算法。該算法通過改進的DMCKF的濾波協方差計算Renyi信息增量,基于求得的Renyi信息增量構造異質多傳感器管理模型,在各觀測時刻對各機動目標進行異質傳感器組合的實時分配。接著,結合改進的DMCKF算法、基于改進DMCKF算法的異質多傳感器數據融合算法和異質多傳感器管理算法,提出了一種異質多傳感器多機動目標的協同跟蹤方法。根據異質多傳感器的資源分配結果,采用基于改進的DMCKF的異質多傳感器數據融合算法獲得融合觀測值,并在交互式多模型算法(IMM)框架下采用改進的DMCKF對多機動目標進行跟蹤。對標準CKF和UKF算法樣做了對協方差矩陣求最鄰近半正定矩陣處理的改進,仿真驗證了改進的DMCKF相比前兩者具有更高的協同跟蹤精度。同時,改進的DMCKF、CKF和UKF算法相比改進前穩(wěn)定性顯著提升。最后,總結了本文所做的工作,指出了當前研究的不足和下一步研究的方向。
[Abstract]:In military, traffic, industry and many other fields, multi-sensor cooperative tracking technology is widely used. The goal of multi-sensor cooperative tracking is to optimize the overall tracking performance of multi-sensor systems. Its technical foundation is sensor management technology. By establishing a kind of sensor management model, the optimal sensor combination is allocated to each target in real time at each observation time, and the tracking of each target in the surveillance range can be realized. Compared with homogeneous sensors, heterogeneous sensors can complement each other in application scenarios and characteristics, and can improve tracking performance. The main work of this paper is as follows: firstly, a large number of related literatures are reviewed, and the current research status of heterogeneous multi-sensor cooperative tracking is reviewed from three aspects. Secondly, nonlinear filtering is closely related to the accuracy and stability of maneuvering target tracking. In this paper, the diagonalization transformation of covariance matrix is used to replace the Cholesky decomposition in standard CKF, and the arithmetic square root matrix is obtained, which improves the accuracy of calculation. However, the covariance matrix of DMCKF and standard CKF sometimes lose the positive definiteness in the filtering process, which leads to the interruption of filtering. Based on the nearest positive semidefinite matrix of covariance matrix at each observation time, an improved DMCKF algorithm is proposed, which ensures that the propagation of the volume point of the observed value is not interrupted during the filtering process, and improves the stability of the DMCKF algorithm. At the same time, based on the improved DMCKF algorithm, the performance and application of heterogeneous multi-sensor data fusion algorithm based on centralized measurement fusion and distributed state fusion are analyzed. Then, a heterogeneous multi-sensor management algorithm based on Renyi information increment is proposed to solve the key problem of heterogeneous multi-sensor management: heterogeneous multi-sensor multi-objective co-assignment. The algorithm calculates the increment of Renyi information through the filter covariance of improved DMCKF, and constructs a heterogeneous multi-sensor management model based on the obtained increment of Renyi information, and realtime allocates the heterogeneous sensor combinations to each maneuvering target at each observation time. Then, based on the improved DMCKF algorithm, the heterogeneous multi-sensor data fusion algorithm and the heterogeneous multi-sensor management algorithm based on the improved DMCKF algorithm, a heterogeneous multi-sensor multi-maneuvering target cooperative tracking method is proposed. According to the resource allocation results of heterogeneous multi-sensor, the heterogeneous multi-sensor data fusion algorithm based on improved DMCKF is used to obtain the fusion observations, and the improved DMCKF is used to track multiple maneuvering targets under the framework of interactive multi-model algorithm (IMM). The standard CKF and UKF algorithms are improved to deal with the covariance matrix to find the nearest positive semidefinite matrix. The simulation results show that the improved DMCKF has higher tracking accuracy than the former two algorithms. At the same time, the stability of the improved DMCKF / CKF algorithm is improved significantly compared with that of the UKF algorithm. Finally, this paper summarizes the work done in this paper, points out the shortcomings of the current research and the direction of the next research.
【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP212

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