本文關(guān)鍵詞： 信息融合 多維分配 空時(shí)偏差 濾波 目標(biāo)跟蹤　出處：《哈爾濱工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：在多傳感器信息融合系統(tǒng)中,傳感器觀測(cè)數(shù)據(jù)來(lái)源存在不確定性及觀測(cè)數(shù)據(jù)可能存在的未知系統(tǒng)偏差是影響信息融合性能的兩個(gè)關(guān)鍵問(wèn)題。解決觀測(cè)源不確定問(wèn)題,需要研究通用、有效的多傳感器數(shù)據(jù)關(guān)聯(lián)方法。在對(duì)未知系統(tǒng)偏差配準(zhǔn)的研究中,目前多數(shù)算法研究的是空間偏差配準(zhǔn)和時(shí)間對(duì)齊問(wèn)題,未對(duì)時(shí)間偏差及同時(shí)存在空時(shí)偏差的問(wèn)題進(jìn)行研究;而實(shí)際應(yīng)用中觀測(cè)數(shù)據(jù)時(shí)戳存在時(shí)延,傳感器之間會(huì)存在時(shí)間偏差,需配準(zhǔn)傳感器間的時(shí)間和空間偏差來(lái)保證融合效果。本文針對(duì)多傳感器信息融合系統(tǒng)中的數(shù)據(jù)關(guān)聯(lián)和空時(shí)偏差估計(jì)問(wèn)題,重點(diǎn)進(jìn)行以下三方面研究。第一,基于多維分配的數(shù)據(jù)關(guān)聯(lián)方法。以多被動(dòng)傳感器多目標(biāo)跟蹤為例,研究基于多維分配的數(shù)據(jù)關(guān)聯(lián)方法。該算法首先利用航跡的先驗(yàn)信息建立預(yù)選波門,只對(duì)各傳感器落入航跡波門內(nèi)的量測(cè)數(shù)據(jù)產(chǎn)生關(guān)聯(lián)假設(shè)并對(duì)其分配代價(jià)函數(shù),利用各傳感器量測(cè)數(shù)據(jù)和航跡之間一一對(duì)應(yīng)的關(guān)系構(gòu)建約束條件,利用拉格朗日松弛算法對(duì)約束條件依次松弛得到二維分配問(wèn)題,利用拍賣算法求得二維分配問(wèn)題的分配組合,基于分配結(jié)果對(duì)約束條件依次實(shí)施,最終得到各傳感器量測(cè)數(shù)據(jù)的關(guān)聯(lián)結(jié)果。仿真實(shí)驗(yàn)表明該算法能兼顧時(shí)間復(fù)雜度和關(guān)聯(lián)正確率。此外該算法可應(yīng)用于其它類型多傳感器多目標(biāo)跟蹤系統(tǒng)。第二,數(shù)據(jù)率已知多傳感器系統(tǒng)空時(shí)偏差配準(zhǔn)方法。以兩個(gè)傳感器為例,分析了傳感器觀測(cè)與目標(biāo)狀態(tài)及空時(shí)偏差的關(guān)系,提出了傳感器空時(shí)偏差與目標(biāo)狀態(tài)聯(lián)合估計(jì)模型。對(duì)傳感器數(shù)據(jù)率相同和不同兩種情況均進(jìn)行了研究,提出了批處理和序貫處理兩種傳感器空時(shí)偏差與目標(biāo)狀態(tài)聯(lián)合估計(jì)方案。仿真結(jié)果驗(yàn)證了所提傳感器空時(shí)偏差與目標(biāo)狀態(tài)聯(lián)合估計(jì)模型和方法的有效性,可以同時(shí)實(shí)現(xiàn)空時(shí)偏差配準(zhǔn)和目標(biāo)狀態(tài)的融合估計(jì);其中,序貫處理方案能取得更優(yōu)效果。另外,針對(duì)傳感空時(shí)偏差估計(jì)值收斂較慢問(wèn)題,在均方誤差最小準(zhǔn)則下,對(duì)多個(gè)目標(biāo)同一時(shí)刻的空時(shí)偏差估計(jì)進(jìn)行加權(quán)融合,仿真結(jié)果表明,多目標(biāo)加權(quán)融合可以獲得更精確的空時(shí)偏差估計(jì),收斂速度更快。第三,數(shù)據(jù)率未知多傳感器系統(tǒng)空時(shí)偏差配準(zhǔn)方法。以兩個(gè)傳感器為例,在僅知傳感器觀測(cè)數(shù)據(jù)時(shí)戳且時(shí)戳存在固定延時(shí)的情況下,建立了未知數(shù)據(jù)率傳感器空時(shí)偏差與目標(biāo)狀態(tài)聯(lián)合估計(jì)模型,提出了序貫處理傳感器空時(shí)偏差與目標(biāo)狀態(tài)聯(lián)合估計(jì)方法。仿真結(jié)果驗(yàn)證所提未知數(shù)據(jù)率目標(biāo)狀態(tài)與傳感器空時(shí)偏差聯(lián)合估計(jì)模型與方法能有效的實(shí)現(xiàn)空時(shí)偏差的配準(zhǔn)和目標(biāo)狀態(tài)的融合估計(jì)。同時(shí)在均方根誤差最小準(zhǔn)則下,對(duì)多個(gè)目標(biāo)同一時(shí)刻的空時(shí)偏差估計(jì)進(jìn)行加權(quán)融合以解決空時(shí)偏差估計(jì)值收斂慢的問(wèn)題。仿真結(jié)果表明多目標(biāo)加權(quán)融合能顯著的提高空時(shí)偏差的估計(jì)精度和收斂速度。
[Abstract]:In the multi-sensor information fusion system, the uncertainty of the source of the sensor observation data and the unknown system deviation that may exist in the observation data are two key problems affecting the performance of the information fusion. It is necessary to study general and effective multi-sensor data association methods. In the research of unknown system bias registration, most of the current algorithms focus on spatial bias registration and time alignment. The problem of time deviation and space-time deviation is not studied, while in practical application, there is time delay in the time stamp of observation data, and there will be time deviation between sensors. It is necessary to register the time and space deviation between sensors to ensure the fusion effect. In this paper, the data association and space-time deviation estimation in multi-sensor information fusion system are studied in the following three aspects. Data association method based on multi-dimension assignment. Taking multi-passive sensors and multi-target tracking as an example, the data association method based on multi-dimension assignment is studied. Only the correlation hypothesis is generated and the cost function is assigned to the measured data of each sensor falling into the track gate, and the constraint condition is constructed by using the one-to-one correspondence between the measured data and the track of each sensor. The Lagrangian relaxation algorithm is used to relax the constraint conditions in turn to obtain the two-dimensional assignment problem, and the auction algorithm is used to obtain the allocation combination of the two-dimensional assignment problem. Based on the allocation results, the constraint conditions are implemented sequentially. Finally, the correlation results of the measured data of each sensor are obtained. The simulation results show that the algorithm can take into account both the time complexity and the correlation accuracy. In addition, the algorithm can be applied to other types of multi-sensor and multi-target tracking systems. Secondly, The relationship between sensor observation and target state and space-time deviation is analyzed with two sensors as an example. A joint estimation model of sensor space-time deviation and target state is proposed. Two joint estimation schemes of sensor space-time deviation and target state are proposed for batch processing and sequential processing. The simulation results verify the validity of the proposed model and method for the joint estimation of sensor space-time deviation and target state. The fusion estimation of space-time deviation registration and target state can be realized at the same time, in which the sequential processing scheme can obtain better results. In addition, for the problem of slow convergence of sensor space-time deviation estimation, under the minimum mean square error criterion, The weighted fusion of space-time deviation estimation of multiple targets at the same time shows that the multi-objective weighted fusion can obtain more accurate estimation of space-time deviation, and the convergence speed is faster. Third, Space-time bias registration method for multi-sensor systems with unknown data rate. Taking two sensors as an example, when only the time stamp of sensor observation data is known and the time stamp has a fixed time delay, The joint estimation model of space-time deviation and target state of unknown data rate sensor is established. A joint estimation method of sensor space-time deviation and target state is proposed in this paper, and the simulation results show that the proposed joint estimation model and method of target state and sensor space-time deviation of unknown data rate can effectively realize space-time deviation. At the same time, under the RMS error minimization criterion, In order to solve the problem of slow convergence of space-time deviation estimates, the weighted fusion of multiple targets at the same time is carried out. The simulation results show that the multi-objective weighted fusion can significantly improve the estimation accuracy and convergence speed of space-time deviations.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP212;TP202

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本文編號(hào)：1520699