本文關鍵詞：基于隨機有限集的多目標跟蹤及航跡維持算法研究　出處：《江南大學》2017年博士論文　論文類型：學位論文

  更多相關文章： 多目標跟蹤 隨機有限集 概率假設密度 高斯混合 航跡維持

【摘要】：多目標跟蹤技術作為信息融合理論與先進濾波方法中最活躍的研究領域之一,被廣泛應用于以航空、航天為代表的軍事與民用領域。由于無需傳統(tǒng)跟蹤方法中所需的復雜的數據關聯(lián)技術,基于隨機有限集理論的多目標跟蹤方法備受國、內外相關研究領域學者及工程技術人員的廣泛關注。本文以隨機有限集理論為支撐,采用概率假設密度濾波器為主要工具,重點圍繞復雜跟蹤場景中多目標跟蹤及航跡維持問題開展了較為深入、系統(tǒng)的研究工作,主要包括以下幾個方面:1.針對緊鄰目標跟蹤場景中目標狀態(tài)及數目估計問題,線性高斯假設下提出一種緊鄰多目標GM-PHD跟蹤算法。在標準GM-PHD濾波器各離散時刻目標預測強度量測更新結束后,采用目標權值再分配方法檢測并重新分配目標后驗強度中目標分量不合理的權值;在目標后驗強度的分量刪減階段,提出一種融合了分量標記法和分量權值度量法的目標分量剪枝與融合方法,一定程度上能夠避免重要目標分量的融合錯誤問題。與現(xiàn)有相關緊鄰多目標PHD濾波器相比,提出算法具有較高的目標狀態(tài)估計精度和準確的目標數目估計。2.針對多目標跟蹤場景中新生目標先驗強度未知時,標準PHD濾波器難以正確估計場景中目標狀態(tài)及數目問題,提出一種基于新生目標強度自適應估計的多目標GM-PHD濾波算法。新生目標強度自適應估計方法分別利用PHD預濾波技術和目標速度特征方案,從各離散時刻量測集中獲取最大可能的源于真實新生目標的量測集,然后利用這些量測建模未知的新生目標先驗強度。此外,在標準GM-PHD濾波器的量測更新步引入量測驅動更新方案,將各離散時刻量測集劃分存活目標量測集、新生目標量測集和雜波集。更新步中不同類型的目標預測強度采用其對應的量測集分別更新,且禁止雜波集更新目標預測強度。仿真實驗結果表明,提出算法不僅具有較高的目標狀態(tài)估計精度和較低的目標數目估計誤差,而且具有穩(wěn)定且較低的計算代價。3.為解決不精確檢測概率環(huán)境下的多目標跟蹤問題,提出一種融合指數衰減函數的目標權值更新策略和多幀目標狀態(tài)抽取策略的多目標GM-PHD濾波算法。目標權值更新策略利用指數衰減函數及目標前一時刻權值,對由目標量測在狀態(tài)空間中不合理分布導致的偽漏檢目標的權值進行慣性衰減,以確保目標后驗強度中各目標分量具有一個合理、有效的權值;多幀目標狀態(tài)抽取策略利用各個目標若干個歷史權值為參考,從各離散時刻目標后驗強度中抽取由較低檢測概率環(huán)境中因目標量測丟失導致的真漏檢目標的狀態(tài)估計。仿真實驗表明,不精確檢測概率環(huán)境下的多目標跟蹤場景中提出算法具有較高的目標狀態(tài)和數目估計精度,且濾波性能相對穩(wěn)定。4.為了實現(xiàn)緊鄰目標跟蹤場景中多目標航跡維持,提出一種基于高斯混合概率假設密度濾波的多目標航跡維持算法。與經典GM-PHD跟蹤器相比,所提多目標航跡維持算法濾波迭代中融合了目標狀態(tài)關聯(lián)與更新策略和不規(guī)則窗口多目標航跡管理方案�；谀繕祟A測強度中目標分量的多個歷史狀態(tài)估計和當前量測集,目標狀態(tài)關聯(lián)與更新策略構建一個用于目標預測強度更新的關聯(lián)更新因子矩陣。量測更新步中利用該關聯(lián)更新因子矩陣實現(xiàn)了目標強度更新及目標與量測最優(yōu)關聯(lián)的同步。不規(guī)則窗口多目標航跡管理方法通過充分利用一段時刻內目標航跡的狀態(tài)估計,不僅有效地維持了真實目標航跡的連續(xù)性,而且有效地解決了濾波過程中由虛警或雜波產生的虛假目標航跡。多目標交叉與平行跟蹤場景仿真實驗表明,提出的多目標GM-PHD航跡維持算法不僅能夠改善緊鄰目標狀態(tài)及數目的估計精度,且其計算代價相對較小及具有優(yōu)良的目標航跡維持性能。
[Abstract]:As one of the multiple target tracking information fusion theory and advanced filtering method in the most active research fields of technology, is widely used in aviation, military and civilian fields of space represented. Because of the complicated data association technique without the traditional tracking method, random finite set theory for multi target tracking method by country based on the extensive attention of scholars and related research in the field of engineering and technical personnel. Based on the random finite set theory, using probability hypothesis density filter as the main tool, focusing on complex scene tracking multiple targets tracking and track maintenance issues carried out in-depth, systematic research work, mainly including the following aspects: 1. for close to the target tracking in the scene and the number of target state estimation problem, a multi object tracking algorithm is proposed to GM-PHD under the assumption of quasi linear Gauss GM-PH in the standard. The discrete time D filter target prediction strength measurement update after the detection and re distribution of the target by using the target weight redistribution method posterior weights unreasonable target component strength; strength test component in the target after the deletion phase, this paper proposes a new mixed component labeling method and component weight measurement of target component pruning and fusion method, to a certain extent can avoid fusion component important targets. Errors associated with the existing close to the multi-objective PHD filter algorithm has higher than the target state estimation.2. for multi target tracking unknown new targets in the scene prior strength estimation accuracy and accurate target number, the standard PHD filter is difficult to correctly estimate the target state and the number of in the scene, put forward a new target strength estimation based on multi-objective adaptive GM-PHD filtering algorithm. The new target strength Adaptive estimation method using PHD pre filtering technique and target speed characteristics, concentrated to get the highest possible new target based on real measurement set from the discrete time measurement, and then use the new object prior strength measurement modeling unknown. In addition, the update step into the measurement test driver update scheme in the standard GM-PHD filter the amount of each discrete time measurement set division survival target measurement set new target measurement set and clutter update step set. Different types of target strength prediction using its corresponding measurement set were updated and forbidden clutter set to update the target strength prediction. Simulation results show that the proposed algorithm not only with higher target state estimation error estimation accuracy and lower number of targets, but also has.3. stable and low computational cost to solve the inaccurate multi target detection probability environment. The problem, put forward a target weight fusion index attenuation function and updating strategy of multi frame target state extraction strategy of multi-objective GM-PHD algorithm. The target weights update strategy using exponential attenuation function and the target weight for a moment ago, by the target measurement in the state space of unreasonable distribution leads to the pseudo target weight of inertia detection in order to ensure the target posterior attenuation, the target component strength has a reasonable and effective weight; multi frame target state extraction strategy using a plurality of weights for each objective historical reference, drawn from the lower strength test environment because the detection probability of target measurement caused by the loss of the target from the discrete time really missed the target state estimation. Simulation results show that the proposed estimation accuracy of target state and the number of algorithm has high detection probability is not accurate under the environment of multi target tracking in the scene, and The filtering performance is relatively stable.4. in order to achieve close to the target tracking problem of multi target track maintenance, proposes a maintenance algorithm of multi target track Gauss mixture probability hypothesis density based filtering. Compared with the classical GM-PHD tracker, the target tracking algorithm to maintain Titus filter iterative integration track management scheme associated with the target state update strategy and irregular window multi target. Target prediction of multiple target state estimation history component in the intensity and the current measurement set based on target state associated with the update strategy to build a target for updating the predicted strength of the updating factor matrices. Update factor matrix to achieve the target strength and target and measurement update using the optimal relevance correlation measurement update step in the irregular window. Multi target track management method by making full use of the target track within a period of time the state estimation, Not only effectively maintain the continuity of the real target track, and effectively solve the false target track caused by false alarm or clutter filtering. In the process of multi-objective cross and parallel tracking show scene simulation, multi objective GM-PHD algorithm is proposed for track maintenance can not only improve the estimation accuracy and the number of targets close to the state, and the computational cost is relatively small and has excellent performance to maintain the target track.

【學位授予單位】：江南大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：TN713

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