【摘要】：隨著計算機(jī)、傳感器、通訊技術(shù)的飛速發(fā)展,各種面向復(fù)雜應(yīng)用的多傳感器系統(tǒng)大量涌現(xiàn),將多個傳感器信息進(jìn)行有效的融合是提高目標(biāo)探測性能的有效途徑。有限的傳感器能力和不確定目標(biāo)環(huán)境使得傳感網(wǎng)無法利用所有傳感器跟蹤同一個目標(biāo)。如何選擇有限的傳感器,通過不同傳感器之間的通信和協(xié)同完成跟蹤任務(wù)是傳感器組網(wǎng)協(xié)同跟蹤的核心。隨著傳感器數(shù)目和目標(biāo)數(shù)目的增多,協(xié)同跟蹤的復(fù)雜程度隨之增加。在大規(guī)模傳感網(wǎng)絡(luò)中,受容量和處理能力限制,采用分布式處理可以避免單傳感器故障對整個網(wǎng)絡(luò)性能的影響,同時降低通信要求。本文在前人工作的基礎(chǔ)上,從不同的分布式融合框架以及傳感器資源管理優(yōu)化方法的角度研究了分布式多傳感器組網(wǎng)協(xié)同跟蹤問題,主要研究的內(nèi)容如下:1.針對大規(guī)模傳感網(wǎng)融合中心處理能力限制,考慮傳感網(wǎng)內(nèi)各傳感器采樣異步情況下,提出了一種分布式異步傳感網(wǎng)協(xié)同跟蹤算法。首先,對監(jiān)視區(qū)內(nèi)布署的傳感器進(jìn)行聚類,構(gòu)建有限個子網(wǎng);其次,基于最大信息增量的原則進(jìn)行傳感器選擇,確定下一時刻各子網(wǎng)內(nèi)參與協(xié)同跟蹤的傳感器集,并對參與協(xié)同跟蹤的傳感器進(jìn)行異步順序融合跟蹤;隨后采用多跳方式進(jìn)行傳感器之間信息傳遞,確定最優(yōu)通信傳輸路徑,并進(jìn)行全局?jǐn)?shù)據(jù)融合;最后,仿真驗證了算法的可行性。2.在監(jiān)視區(qū)域存在多個目標(biāo)情況下,考慮坐標(biāo)變換造成傳感器量測誤差時變影響,提出了一種時變量測方差下基于PCRLB的多傳感器多目標(biāo)協(xié)同跟蹤算法。首先,給出了時變量測誤差的描述,分析了時變量測誤差和跟蹤性能的影響,建立了時變量測誤差下PCRLB指標(biāo)。隨后基于PCRLB優(yōu)化指標(biāo)進(jìn)行多傳感器多目標(biāo)分配,確定下一時刻用于跟蹤目標(biāo)的傳感器集。同時在濾波跟蹤過程中,考慮坐標(biāo)轉(zhuǎn)化帶來的傳感器量測方差變化,通過轉(zhuǎn)換卡爾曼濾波實現(xiàn)時變量測方差下的多傳感器協(xié)同跟蹤,最后,仿真驗證了算法的可行性。3.針對大規(guī)模傳感網(wǎng)中傳感器之間通信傳輸?shù)南拗?提出了一種PCRLB的分散式傳感器網(wǎng)協(xié)同跟蹤算法。采用樹拓?fù)浣Y(jié)構(gòu)的分散式傳感器網(wǎng)絡(luò)結(jié)構(gòu),設(shè)計分散PCRLB指標(biāo)�；诜稚CRLB指標(biāo)進(jìn)行傳感器選擇,通過并行濾波的方式進(jìn)行局部融合中心的估計。仿真通過與集中式融合算法比較,分散式多傳感器協(xié)同跟蹤算法具有一定的優(yōu)勢。
[Abstract]:With the rapid development of computer, sensor and communication technology, a large number of multi-sensor systems for complex applications have emerged. The effective fusion of multi-sensor information is an effective way to improve the performance of target detection. The limited sensor capacity and uncertain target environment make it impossible for the sensor network to track the same target using all sensors. How to select limited sensors, communicate among different sensors and cooperate to complete tracking task is the core of cooperative tracking in sensor network. As the number of sensors and targets increases, the complexity of cooperative tracking increases. In large scale sensor networks, due to the limitation of capacity and processing capacity, distributed processing can avoid the effect of single sensor failure on the performance of the whole network and reduce the communication requirements. Based on the previous work, this paper studies the cooperative tracking problem of distributed multi-sensor network from different distributed fusion frameworks and sensor resource management optimization methods. The main research contents are as follows: 1. In view of the limited processing capacity of the fusion center of large-scale sensor networks, a distributed cooperative tracking algorithm for asynchronous sensor networks is proposed, considering the asynchronous sampling of each sensor in the sensor network. Firstly, the sensors deployed in the surveillance area are clustered and the finite subnet is constructed. Secondly, based on the principle of maximum information increment, sensor selection is carried out to determine the set of sensors involved in cooperative tracking in each subnet at the next moment, and the sensors involved in cooperative tracking are tracked by asynchronous sequential fusion. Then the multi-hop method is used to transfer information between sensors, to determine the optimal communication transmission path, and to perform global data fusion. Finally, the simulation verifies the feasibility of the algorithm. 2. A multi-sensor multi-target cooperative tracking algorithm based on PCRLB is proposed under the condition of multiple targets in the monitoring region and considering the time-varying influence of coordinate transformation on the measurement error of the sensor. Firstly, the description of time-varying measurement error is given, the influence of time-varying measurement error and tracking performance is analyzed, and the PCRLB index under time-varying measurement error is established. Then the multi-sensor multi-target assignment based on the PCRLB optimization index is carried out to determine the sensor set for tracking the target at the next time. At the same time, in the process of filtering and tracking, considering the change of sensor measurement variance brought by coordinate transformation, the multi-sensor cooperative tracking under time-varying measurement variance is realized by transforming Kalman filter. Finally, the feasibility of the algorithm is verified by simulation. 3. Aiming at the limitation of communication and transmission between sensors in large scale sensor networks, a distributed tracking algorithm for sensor networks based on PCRLB is proposed. The decentralized PCRLB index is designed by using the distributed sensor network structure based on tree topology. The sensor is selected based on decentralized PCRLB index, and the local fusion center is estimated by parallel filtering. The simulation results show that the decentralized multi-sensor cooperative tracking algorithm has some advantages compared with the centralized fusion algorithm.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP212

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