A novel adaptive sampling interval algorithm for multitarget tracking is presented. This algorithm which is based on interacting multiple models incorporates the grey relational grade （GRG） into the particle swarm o...A novel adaptive sampling interval algorithm for multitarget tracking is presented. This algorithm which is based on interacting multiple models incorporates the grey relational grade （GRG） into the particle swarm optimization （PSO）. Firstly, the desired tracking accuracy is set for each target. Secondly, sampling intervals are selected as particles, and then the advantage of the GRG is taken as the measurement function for resource management. Meanwhile, the fitness value of the PSO is used to measure the difference between desired tracking accuracy and estimated tracking accuracy. Finally, it is suggested that the radar should track the target whose prediction value of the next sampling interval is the smallest. Simulations show that the proposed method improves both the tracking accuracy and tracking efficiency of the phased-array radar.展开更多
As to the fact that it is difficult to obtain analytical form of optimal sampling density and tracking performance of standard particle probability hypothesis density(P-PHD) filter would decline when clustering algori...As to the fact that it is difficult to obtain analytical form of optimal sampling density and tracking performance of standard particle probability hypothesis density(P-PHD) filter would decline when clustering algorithm is used to extract target states,a free clustering optimal P-PHD(FCO-P-PHD) filter is proposed.This method can lead to obtainment of analytical form of optimal sampling density of P-PHD filter and realization of optimal P-PHD filter without use of clustering algorithms in extraction target states.Besides,as sate extraction method in FCO-P-PHD filter is coupled with the process of obtaining analytical form for optimal sampling density,through decoupling process,a new single-sensor free clustering state extraction method is proposed.By combining this method with standard P-PHD filter,FC-P-PHD filter can be obtained,which significantly improves the tracking performance of P-PHD filter.In the end,the effectiveness of proposed algorithms and their advantages over other algorithms are validated through several simulation experiments.展开更多
针对传统粒子滤波跟踪算法重采样时存在粒子退化、目标与背景颜色相似和尺度变化导致的目标定位不准确问题,本研究提出了一种基于特征融合的粒子群优化粒子滤波跟踪算法,算法利用粒子群优化进行粒子权值更新,用当前状态估计值与各粒子...针对传统粒子滤波跟踪算法重采样时存在粒子退化、目标与背景颜色相似和尺度变化导致的目标定位不准确问题,本研究提出了一种基于特征融合的粒子群优化粒子滤波跟踪算法,算法利用粒子群优化进行粒子权值更新,用当前状态估计值与各粒子状态的差值大小作为评价标准,促使粒子采样向真实状态区域移动,减缓粒子退化,提高了粒子滤波跟踪算法的跟踪性能。针对跟踪目标尺度变化导致的定位不准确情况,引入了归一化转动惯量(Normalized moment of inertia,NMI)特征,并将它与颜色特征采用乘性融合策略进行融合来描述目标特征,提高目标复杂场景下的定位精度。通过在多个标准测试视频上进行试验,实验结果表明,本研究提出的方法对动态背景场景中尺度变化目标和背景颜色相似目标的跟踪具有较好的准确性和鲁棒性。展开更多
基金supported by the Pre-research Fund (N0901-041)the Funding of Jiangsu Innovation Program for Graduate Education(CX09B 081Z CX10B 110Z)
文摘A novel adaptive sampling interval algorithm for multitarget tracking is presented. This algorithm which is based on interacting multiple models incorporates the grey relational grade （GRG） into the particle swarm optimization （PSO）. Firstly, the desired tracking accuracy is set for each target. Secondly, sampling intervals are selected as particles, and then the advantage of the GRG is taken as the measurement function for resource management. Meanwhile, the fitness value of the PSO is used to measure the difference between desired tracking accuracy and estimated tracking accuracy. Finally, it is suggested that the radar should track the target whose prediction value of the next sampling interval is the smallest. Simulations show that the proposed method improves both the tracking accuracy and tracking efficiency of the phased-array radar.
文摘As to the fact that it is difficult to obtain analytical form of optimal sampling density and tracking performance of standard particle probability hypothesis density(P-PHD) filter would decline when clustering algorithm is used to extract target states,a free clustering optimal P-PHD(FCO-P-PHD) filter is proposed.This method can lead to obtainment of analytical form of optimal sampling density of P-PHD filter and realization of optimal P-PHD filter without use of clustering algorithms in extraction target states.Besides,as sate extraction method in FCO-P-PHD filter is coupled with the process of obtaining analytical form for optimal sampling density,through decoupling process,a new single-sensor free clustering state extraction method is proposed.By combining this method with standard P-PHD filter,FC-P-PHD filter can be obtained,which significantly improves the tracking performance of P-PHD filter.In the end,the effectiveness of proposed algorithms and their advantages over other algorithms are validated through several simulation experiments.
文摘针对传统粒子滤波跟踪算法重采样时存在粒子退化、目标与背景颜色相似和尺度变化导致的目标定位不准确问题,本研究提出了一种基于特征融合的粒子群优化粒子滤波跟踪算法,算法利用粒子群优化进行粒子权值更新,用当前状态估计值与各粒子状态的差值大小作为评价标准,促使粒子采样向真实状态区域移动,减缓粒子退化,提高了粒子滤波跟踪算法的跟踪性能。针对跟踪目标尺度变化导致的定位不准确情况,引入了归一化转动惯量(Normalized moment of inertia,NMI)特征,并将它与颜色特征采用乘性融合策略进行融合来描述目标特征,提高目标复杂场景下的定位精度。通过在多个标准测试视频上进行试验,实验结果表明,本研究提出的方法对动态背景场景中尺度变化目标和背景颜色相似目标的跟踪具有较好的准确性和鲁棒性。
