基于混合粒子群算法的信息物理融合系統(tǒng)多目標(biāo)跟蹤優(yōu)化
發(fā)布時間：2019-03-09 17:38
【摘要】：信息物理融合系統(tǒng)(CPS)是融合計算、通信與控制于一體的復(fù)雜信息系統(tǒng)。多目標(biāo)跟蹤是CPS的重要應(yīng)用領(lǐng)域,涉及多目標(biāo)的信息獲取、實時定位和跟蹤預(yù)測等。為進一步提高多目標(biāo)跟蹤在定位的精度和路徑預(yù)測方面的準(zhǔn)確性,本學(xué)位論文在CPS多目標(biāo)跟蹤中引入混合粒子群算法,設(shè)計與實現(xiàn)多目標(biāo)定位與路徑預(yù)測的優(yōu)化方案。本文首先設(shè)計CPS多目標(biāo)定位模型,再提出基于混合粒子群算法的CPS多目標(biāo)定位方法,接著設(shè)計CPS多目標(biāo)跟蹤模型,最后提出基于混合粒子群算法的CPS多目標(biāo)路徑預(yù)測方法。針對上述CPS多目標(biāo)定位和路徑預(yù)測的優(yōu)化方案,本文還分別進行仿真實驗與性能分析。本文的工作創(chuàng)新主要體現(xiàn)在以下兩個方面:(1)針對CPS當(dāng)前目標(biāo)定位算法在處理約束優(yōu)化問題時存在收斂速度慢和易陷入局部最優(yōu)等缺點,在應(yīng)用粒子群算法時,引入了交叉和變異策略,避免CPS目標(biāo)在迭代過程中陷入局部最優(yōu)。此外,將空間距離約束與幾何拓?fù)浼s束作為CPS多目標(biāo)定位模型中的目標(biāo)函數(shù),采用混合粒子群算法求得CPS多目標(biāo)跟蹤中的最優(yōu)解,與標(biāo)準(zhǔn)粒子群定位算法相比較,上述方案減少了CPS多目標(biāo)定位誤差,并縮短了目標(biāo)到達(dá)極值點的時間。(2)針對CPS當(dāng)前多目標(biāo)跟蹤方法在對目標(biāo)進行路徑預(yù)測方面精度不足等缺點,在應(yīng)用粒子群算法時,引入采樣和重采樣策略,將預(yù)測更新過后的粒子權(quán)值進行歸一化處理,挑選和復(fù)制權(quán)值較大的種群粒子再次進行迭代,避免了粒子的退化現(xiàn)象。運用優(yōu)化后的公式獲取種群中的粒子在不同時刻的觀察值,使得CPS中的目標(biāo)粒子不停地朝實際狀態(tài)慢慢逼近,解決了粒子在不斷迭代時因粒子稀少而降低預(yù)估精度等問題。
[Abstract]:Information physical fusion system (CPS) is a complex information system which integrates computing, communication and control. Multi-target tracking is an important application field of CPS, which involves multi-target information acquisition, real-time location and tracking prediction. In order to further improve the accuracy of multi-target tracking in location and path prediction, this thesis introduces hybrid particle swarm optimization (PSO) algorithm into CPS multi-target tracking to design and implement the optimization scheme of multi-target location and path prediction. In this paper, we first design the CPS multi-target location model, then propose the CPS multi-target location method based on hybrid particle swarm optimization (HPSO), then design the CPS multi-target tracking model, and finally propose the CPS multi-target path prediction method based on the hybrid particle swarm optimization (HPSO) algorithm. Aiming at the optimization scheme of CPS multi-objective location and path prediction, the simulation experiment and performance analysis are also carried out in this paper. The innovation of this paper is mainly reflected in the following two aspects: (1) in view of the shortcomings of CPS's current target location algorithm in dealing with constrained optimization problems, such as slow convergence rate and easy to fall into local optimization, particle swarm optimization (PSO) is applied. The crossover and mutation strategies are introduced to avoid the local optimization of the CPS target in the iterative process. In addition, the spatial distance constraints and geometric topological constraints are regarded as the objective functions of CPS multi-target location model. The hybrid particle swarm optimization algorithm is used to obtain the optimal solution in CPS multi-target tracking, which is compared with the standard particle swarm localization algorithm. The above scheme reduces the error of CPS multi-target location and shortens the time of target reaching the extreme point. (2) in view of the shortcomings of the current CPS multi-target tracking method in path prediction, the particle swarm optimization algorithm is applied. The strategy of sampling and resampling is introduced to normalize the predicted and updated particle weights, and the population particles with larger weights are selected and replicated again, thus avoiding the degradation of the particles. The optimized formula is used to obtain the observed values of the particles in the population at different times, which makes the target particles in the CPS approach to the actual state ceaselessly, and solves the problem that the prediction accuracy of the particles in the iterative process is reduced due to the scarcity of the particles.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP29
本文編號：2437706
[Abstract]:Information physical fusion system (CPS) is a complex information system which integrates computing, communication and control. Multi-target tracking is an important application field of CPS, which involves multi-target information acquisition, real-time location and tracking prediction. In order to further improve the accuracy of multi-target tracking in location and path prediction, this thesis introduces hybrid particle swarm optimization (PSO) algorithm into CPS multi-target tracking to design and implement the optimization scheme of multi-target location and path prediction. In this paper, we first design the CPS multi-target location model, then propose the CPS multi-target location method based on hybrid particle swarm optimization (HPSO), then design the CPS multi-target tracking model, and finally propose the CPS multi-target path prediction method based on the hybrid particle swarm optimization (HPSO) algorithm. Aiming at the optimization scheme of CPS multi-objective location and path prediction, the simulation experiment and performance analysis are also carried out in this paper. The innovation of this paper is mainly reflected in the following two aspects: (1) in view of the shortcomings of CPS's current target location algorithm in dealing with constrained optimization problems, such as slow convergence rate and easy to fall into local optimization, particle swarm optimization (PSO) is applied. The crossover and mutation strategies are introduced to avoid the local optimization of the CPS target in the iterative process. In addition, the spatial distance constraints and geometric topological constraints are regarded as the objective functions of CPS multi-target location model. The hybrid particle swarm optimization algorithm is used to obtain the optimal solution in CPS multi-target tracking, which is compared with the standard particle swarm localization algorithm. The above scheme reduces the error of CPS multi-target location and shortens the time of target reaching the extreme point. (2) in view of the shortcomings of the current CPS multi-target tracking method in path prediction, the particle swarm optimization algorithm is applied. The strategy of sampling and resampling is introduced to normalize the predicted and updated particle weights, and the population particles with larger weights are selected and replicated again, thus avoiding the degradation of the particles. The optimized formula is used to obtain the observed values of the particles in the population at different times, which makes the target particles in the CPS approach to the actual state ceaselessly, and solves the problem that the prediction accuracy of the particles in the iterative process is reduced due to the scarcity of the particles.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP29
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