Tracking Algorithm Based on Improved Interacting Multiple Model Particle Filter

Measurements are always interfered with glint noise in a radar target tracking system, which makes the performance of traditional filtering fall sharply and even divergent.Against this problem, a new Interactive Multiple Model Particle Filter (IMMPF) algorithm is proposed for target tracking by introducing PF into Interactive Multiple Model (IMM).Different from the general method to select importance density function from PF, the particles are extracted from observation likelihood function within depending on observation noises.Observation noise is modelled, and the latest observation is fused, then the target can be effectively tracked.Finally, the optimized method is simulated with respect to bearings-only tracking of maneuvering target in a glint noise environment.Compared with the existing filtering algorithms, it turns out that the developed filtering algorithm is more efficient and closer to the real-time tracking requirement of high maneuvering targets.

Multiple model efficient particle filter based track-before-detect for maneuvering weak targets

It is a tough problem to jointly detect and track a weak target, and it becomes even more challenging when the target is maneuvering. The above problem is formulated by using the Bayesian theory and a multiple model(MM) based filter is proposed. The filter presented uses the MM method to accommodate the multiple motions that a maneuvering target may travel under by adding a random variable representing the motion model to the target state. To strengthen the efficiency performance of the filter,the target existence variable is separated from the target state and the existence probability is calculated in a more efficient way. To examine the performance of the MM based approach, a typical track-before-detect(TBD) scenario with a maneuvering target is used for simulations. The simulation results indicate that the MM based filter proposed has a good performance in joint detecting and tracking of a weak and maneuvering target, and it is more efficient than the general MM method.

An unscented particle filter for ground maneuvering target tracking

In this study, an unscented particle filtering method based on an interacting multiple model (IMM) frame for a Markovian switching system is presented. The method integrates the multiple model (MM) filter with an unscented particle filter (UPF) by an interaction step at the beginning. The framework (interaction/mixing, filtering, and combination) is similar to that in a standard IMM filter, but an UPF is adopted in each model. Therefore, the filtering performance and degeneracy phenomenon of particles are improved. The filtering method addresses nonlinear and/or non-Gaussian tracking problems. Simulation results show that the method has better tracking performance compared with the standard IMM-type filter and IMM particle filter.

TRACKING DEFORMABLE AND OCCLUDED OBJECTS USING PARTICLE FILTERING AND GVF-SNAKE

An adaptive object tracking algorithm based on particle filtering and a modified Gradient Vector Flow (GVF) Snake is proposed for tracking moving and deforming objects. The original contours of objects are obtained by using the background differencing method,and the true contours of objects can be converged by means of the powerful searching ability of a modified GVF-Snake. Finally,an Energetic Particle Filtering (EPF) algorithm is obtained by combining particle filtering and a modified GVF-Snake,and by using K-means and the EPF algorithm,multiple objects can be tracked. The proposed tracking tactic for partially occluded objects can effectively improve its anti-occlusion ability. Experiments show that this algorithm can obtain better tracking effect even though the tracked object is occluded.

Image-Aided Analysis of Ballast Particle Movement Along a High-Speed Railway

As a core infrastructure of high-speed railways,ballast layers constituted by graded crushed stones feature noteworthy particle movement compared with normal railways,which may cause excessive settlement and have detrimental effects on train operation.However,the movement behavior remains ambiguous due to a lack of effective measurement approaches and analytical methods.In this study,an image-aided technique was developed in a full-scale model test using digital cameras and a colorbased identification approach.A total of 1274 surface ballast particles were manually dyed by discernible colors to serve as tracers in the test.The movements of the surface ballast particles were tracked using the varied pixels displaying tracers in the photos that were intermittently taken during the test in the perpendicular direction.The movement behavior of ballast particles under different combinations of train speeds and axle loads was quantitatively evaluated.The obtained results indicated that the surface ballast particle movements were slight,mainly concentrated near sleepers under low-speed train loads and greatly amplified and extended to the whole surface when the train speed reached 360 km.h-1.Additionally,the development of ballast particle displacement statistically resembled its rotation.Track vibration contributed to the movements of ballast particles,which specifically were driven by vertical acceleration near the track center and horizontal acceleration at the track edge.Furthermore,the development trends of ballast particle movements and track settlement under long-term train loading were similar,and both stabilized at nearly the same time.The track performance,including the vibration characteristics,accumulated settlement,and sleeper support stiffness,was determined to be closely related to the direction and distribution of ballast particle flow,which partly deteriorated under high-speed train loads.

EFFECTIVE APPEARANCE MODEL FOR PROBABILISTIC OBJECT TRACKING

This paper presents a robust object tracking approach via a spatially constrained colour model. Local image patches of the object and spatial relation between these patches are informative and stable during object tracking. So, we propose to partition an object into patches and develop a Spatially Constrained Colour Model (SCCM) by combining the colour distributions and spatial configuration of these patches. The likelihood of the candidate object is given by estimating the confidences of the pixels in the candidate object region. The appearance model is learnt from the first frame and the tracking is carried out by particle filter. The experimental results show that the proposed tracking approach can accurately track the object with scale changes, pose variance and partial occlusion.

VIDEO MULTI-TARGET TRACKING BASED ON PROBABILISTIC GRAPHICAL MODEL

In the technique of video multi-target tracking,the common particle filter can not deal well with uncertain relations among multiple targets.To solve this problem,many researchers use data association method to reduce the multi-target uncertainty.However,the traditional data association method is difficult to track accurately when the target is occluded.To remove the occlusion in the video,combined with the theory of data association,this paper adopts the probabilistic graphical model for multi-target modeling and analysis of the targets relationship in the particle filter framework.Ex-perimental results show that the proposed algorithm can solve the occlusion problem better compared with the traditional algorithm.

Passive Target Tracking Based on Current Statistical Model

Bearing-only passive tracking is regarded as a nonlinear hard tracking problem. There are still no completely good solutions to this problem until now. Based on current statistical model, the novel solution to this problem utilizing particle filter (PF) and the unscented Kalman filter (UKF) is proposed. The new solution adopts data fusion from two observers to increase the observability of passive tracking. It applies the residual resampling step to reduce the degeneracy of PF and it introduces the Markov Chain Monte Carlo methods (MCMC) to reduce the effect of the “sample impoverish”. Based on current statistical model, the EKF, the UKF and particle filter with various proposal distributions are compared in the passive tracking experiments with two observers. The simulation results demonstrate the good performance of the proposed new filtering methods with the novel techniques.

Double-plume Lagrangian particle tracking model and its application in deep water oil spill

Due to the density stratification of sea water,the dispersed oil droplets and gas bubbles with small diameters,as well as the dissolved components,may remain in some specific depths.The double-plume Lagrangian particle tracking model for bubbly plumes in vertical density stratified environments is improved and applied to predict the underwater pollutants in a blowout.This model considers the different properties and dissolution processes of components in crude oil and focuses on their behavior and stratification differences in the plume.The crude oil components are divided into several groups and the dissolution of oil and gas is also considered.The model is applied to simulate the“Deepwater Horizon”oil spill accident in the Gulf of Mexico in 2010.The results show several enrichment layers of oil and gas at different depth and the differences in concentration between components,which corresponds to the distribution of petroleum pollutants in the in-situ observation.

Novel sensor selection strategy for LPI based on an improved IMMPF tracking method

Sensor platforms with active sensing equipment such as radar may betray their existence, by emitting energy that will be intercepted by enemy surveillance sensors. The radar with less emission has more excellent performance of the low probability of intercept(LPI). In order to reduce the emission times of the radar, a novel sensor selection strategy based on an improved interacting multiple model particle filter(IMMPF) tracking method is presented. Firstly the IMMPF tracking method is improved by increasing the weight of the particle which is close to the system state and updating the model probability of every particle. Then a sensor selection approach for LPI takes use of both the target's maneuverability and the state's uncertainty to decide the radar's radiation time. The radar will work only when the target's maneuverability and the state's uncertainty exceed the control capability of the passive sensors. Tracking accuracy and LPI performance are demonstrated in the Monte Carlo simulations.

Method of target tracking with Doppler blind zone constraint

Doppler blind zone (DBZ) has a bad influence on the airborne early warning radar,although it has good detection performance for low altitude targets with pulse Doppler (PD) technology.In target tracking,the blind zone can cause target tracking breakage easily.In order to solve this problem,a parallel particle filter (PF) algorithm based on multi-hypothesis motion models (MHMMs)is proposed.The algorithm produces multiple possible target motion models according to the DBZ constraint.Particles are updated with the constraint in each motion model.Once the first measurement from the target which reappears from DBZ falls into the particle cloud formed by any model,the measurementtrack association succeeds and track breakage is avoided.The simulation results show that on the condition of different DBZ ranges,a high association ratio can be got for targets with different maneuverability levels,which accordingly improves the tracking quality.

Robust Object Tracking under Appearance Change Conditions

We propose a robust visual tracking framework based on particle filter to deal with the object appearance changes due to varying illumination, pose variantions, and occlusions. We mainly improve the observation model and re-sampling process in a particle filter. We use on-line updating appearance model, affine transformation, and M-estimation to construct an adaptive observation model. On-line updating appearance model can adapt to the changes of illumination partially. Affine transformation-based similarity measurement is introduced to tackle pose variantions, and M-estimation is used to handle the occluded object in computing observation likelihood. To take advantage of the most recent observation and produce a suboptimal Gaussian proposal distribution, we incorporate Kalman filter into a particle filter to enhance the performance of the resampling process. To estimate the posterior probability density properly with lower computational complexity, we only employ a single Kalman filter to propagate Gaussian distribution. Experimental results have demonstrated the effectiveness and robustness of the proposed algorithm by tracking visual objects in the recorded video sequences.

面向机动目标的分布式弹群在线协同定位方法

机动目标的高精度协同定位是协同打击的关键,通过分布式弹群来实现协同定位是目前研究的热点。提出了一种分布式弹群在线协同定位的策略,解决弹群通信受限条件下协同定位的实时性问题;针对目标状态估计中模型非线性、噪声非高斯分布等特点,提出了容积卡尔曼粒子滤波算法;设计了自适应转弯速率的匀速转弯模型,并将现有的二维匀速转弯模型扩展至三维,解决了现有匀速转弯模型先验转弯速率固定,导致定位精度不高的问题;设计了自适应模型转移概率的交互多模型方法,实时修正马尔可夫转移概率,解决了单模型滤波定位精度不高的问题。通过仿真验证了所提策略和方法的有效性和准确性。

粒子群优化算法在智能车辆轨迹跟踪的应用

针对智能车辆的局部规划与路径跟踪的协同控制问题,提出了一种基于改进粒子群优化(IPSO)的模型预测控制(MPC)方法。首先将模型预测控制与人工势场(APF)相结合,将时变安全约束作为排斥力的范围和非对称的车道势场函数,通过将时变安全约束视为排斥力的范围和非对称车道势场函数来获得无碰撞路径,在此基础上,将APF与IPSO-MPC相结合,采用伪速度规划算法来处理交通灯和运动障碍的约束,从而有效地解决了路径优化问题。仿真结果验证了该算法的有效性,与一般算法相比具有明显的优越性。

区间量测下自适应交互多模型箱粒子滤波机动目标跟踪

针对现有交互多模型箱粒子滤波(Interacting Multiple Model Box Particle Filter,IMMBPF)算法在区间量测目标跟踪过程中模型切换和跟踪精度方面的不足,结合自适应交互多模型算法,提出了一种自适应交互多模型箱粒子滤波(Adaptive IMMBPF,AIMMBPF)算法。该算法利用模型似然后验信息构建修正因子,并结合阈值对马尔可夫转移概率矩阵进行自适应修正,使得匹配模型的概率快速增大,并且可以减小非匹配模型的影响,即使在目标运动模型先验信息不足或者不准确情况下,也能对模型转移概率进行自适应更新。对于量测常受到未知分布和偏差的区间误差所影响而呈现区间形式的问题,将箱粒子代替普通粒子,拟合后验概率密度从而进行滤波。仿真结果表明,相比于原有算法,该算法在区间量测机动目标跟踪的应用中,拥有更优的模型匹配度和目标跟踪精度。

面向非广域目标的无人机对峙跟踪方法

针对无人机对峙跟踪非广域目标问题,开展目标状态估计与无人机制导方法研究。首先建立非广域地理环境模型,将非广域地理约束作为伪观测方程引入粒子滤波器的观测方程。其次,鉴于目标在运动过程中可能受到多个模型的约束,采用交互多模型滤波算法进行状态估计,即每个模型对应的受约束粒子滤波器并行工作,并对多个滤波器估计结果进行加权,得到更精确的目标运动状态估计值。然后,提出时间最优导航向量场,通过计算期望航向角,引导无人机快速收敛至目标极限环。最后,仿真实验表明,受约束粒子滤波-多交互模型算法相比于传统的滤波算法,估计精度提高了20%,时间最优导航向量场方法相比于传统的导航向量场方法,引导效率提高了15%,所提方法可更有效地用于解决非广域目标对峙跟踪问题。

Ground Moving Target Tracking with VS-IMM Using Mean Shift Unscented Particle Filter

为了追踪地面移动，指向，交往的可变结构用吝啬的移动 unscented 粒子过滤器(MS-UPF ) 的多重模型(VS-IMM ) 在这份报纸被建议。在调节模型的过滤，从 unscented 粒子过滤器获得的样品粒子通过吝啬的移动向目标状态的最大的以后的密度评价被移动。根据在 VS-IMM 的站模型，兽皮模型被建议。一旦目标被地面遮住，在优先的时间的预言在以后的时间被使用而不是测量；另外，使用的道路模型集合没被改变。一架扎根的动人的目标指示(GMTI ) 雷达在地面的情形指向的三普通模拟被采用：进入或离开一条道路，穿过一个连接和没有测量。二个评估索引，根均方差(RMSE ) 和一般水准使摆平的评价错误(ANEES ) 正常化，被使用。当时，结果显示那移动的目标在上变化的道路，追踪的精确性有效地在建议算法被改进。而且，如果目标太慢慢地正在移动或由地面掩盖，轨道打断能被避免。

MULTITARGET STATE AND TRACK ESTIMATION FOR THE PROBABILITY HYPOTHESES DENSITY FILTER

The particle Probability Hypotheses Density (particle-PHD) filter is a tractable approach for Random Finite Set (RFS) Bayes estimation, but the particle-PHD filter can not directly derive the target track. Most existing approaches combine the data association step to solve this problem. This paper proposes an algorithm which does not need the association step. Our basic ideal is based on the clustering algorithm of Finite Mixture Models (FMM). The intensity distribution is first derived by the particle-PHD filter, and then the clustering algorithm is applied to estimate the multitarget states and tracks jointly. The clustering process includes two steps: the prediction and update. The key to the proposed algorithm is to use the prediction as the initial points and the convergent points as the es- timates. Besides, Expectation-Maximization (EM) and Markov Chain Monte Carlo (MCMC) ap- proaches are used for the FMM parameter estimation.