Target Tracking Using the Interactive Multiple Model Method

Aim To develop a practical target tracking algorithm for different motion modes. Methods After creation of the new model, it was implemented by computer simulation to prove its performance and compared with the often-used current statistical model. Results The simulation results show that the new IMM (interactive multiple model) have low tracking error in both maneuVering segment and non^Inaneuwi segment while the current statistical model bas muCh higher tracking error in non-maneuvering segment. Conclusion In the point of trackintaccuracy, the new IMM method is much better than the current acceleration method. It can develop into a practical target hacking method.

Multiple Model Filtering in the Presence of Gaussian Mixture Measurement Noises

A simplified multiple model filter is developed for discrete-time systems inthe presence of Gaussian mixture measurement noises. Theoretical analysis proves that the proposedfilter has the same estimation performance as the interacting multiple model filter at the price ofless computational cost. Numerically robust implementation of the filter is presented to meetpractical applications. An example on bearings-only guidance demonstrates the effect of the proposedalgorithm.

ADAPTIVE MULTIPLE MODEL FILTER USING IMM AND STF

In fault identification, the Strong Tracking Filter (STF) has strong ability to track the change of some parameters by whitening filtering innovation. In this paper, the authors give out a modified STF by searching the fading factor based on the Least Squared Estimation. In hybrid estimation, the well known Interacting Multiple Model (IMM) Technique can model the change of the system modes. So one can design a new adaptive filter — SIMM. In this filter, our modified STF is a parameter adaptive part and IMM is a mode adaptive part. The benefit of the new filter is that the number of models can be reduced considerably. The simulations show that SIMM greatly improves accuracy of velocity and acceleration compared with the standard IMM to track the maneuvering target when 2 model conditional estimators are used in both filters. And the computation burden of SIMM increases only 6% compared with IMM.

An interacting multiple model-based two-stage Kalman filter for vehicle positioning

To address the problem that a general augmented state Kalman filter or a two-stage Kalman filter cannot achieve satisfactory positioning performance when facing uncertain noise of the micro-electro-mechanical system（MEMS） inertial sensors, a novel interacting multiple model-based two-stage Kalman filter（IMM-TSKF） is proposed to adapt to the uncertain inertial sensor noise. Three bias filters are developed based on different noise characteristics to cover a wide range of noise levels. Then, an accurate estimation of biases is calculated by the interacting multiple model algorithm to correct the bias-free filter. Thus, the vehicle positioning system can achieve good performance when suffering from uncertain inertial sensor noise. The experimental results indicate that the average position error of the proposed IMMTSKF is 25% lower than that of the general TSKF.

Maneuvering target tracking using threshold interacting multiple model algorithm

To avoid missing track caused by the target maneuvers in automatic target tracking system, a new maneuvering target tracking technique called threshold interacting multiple model （TIMM） is proposed. This algorithm is based on the interacting multiple model （IMM） method and applies a threshold controller to improve tracking accuracy. It is also applicable to other advanced algorithms of IMM. In this research, we also compare the position and velocity root mean square （RMS） errors of TIMM and IMM algorithms with two different examples. Simulation results show that the TIMM algorithm is superior to the traditional IMM alzorithm in estimation accuracy.

一种基于模型概率单调性变化的自适应IMM-UKF改进算法

针对现有交互式多模型(IMM)算法模型间切换迟滞和转换速率慢的缺点,提出一种基于模型概率单调性变化的自适应交互式多模型无迹卡尔曼滤波改进算法(mIMM-UKF)。该算法利用后验信息模型概率的单调性,对马尔可夫转移概率矩阵及模型估计概率进行二次修正,加快了匹配模型的切换速度及转换速率。仿真结果表明,与现有算法相比,该算法通过快速切换匹配模型,有效提高了水下目标跟踪精度。

基于改进自适应IMM算法的高速列车组合定位

针对列车高精度定位问题,该文提出基于改进自适应交互多模型(IMM)的高速列车高精度组合定位方法。首先,根据列车定位需求和各传感器特点,设计了卫星接收器、轮轴测速传感器、测速雷达以及单轴陀螺仪4种传感器的组合定位方案。然后,针对IMM融合滤波算法因先验信息不准导致固定参数设置不当的问题,引入Sage-Husa自适应滤波和转移概率矩阵(TPM)自适应更新集成为自适应IMM算法。针对多模型切换的滞后问题,利用子模型似然函数值能快速反映模型变化趋势的特点,将似然函数值设为判定标志,并引入判定窗对TPM矩阵元素进行修正,有效提升了模型的切换速度。最后,基于改进自适应IMM算法对4种传感器定位信息进行融合滤波,实现高速列车的高精度组合定位。仿真结果表明:改进后的算法相比其他自适应IMM算法提升定位精度1.6%~14.7%,并且能通过提高模型间切换速度来有效降低位置误差峰值,同时具备较好的抗噪性能。

Using interacting multiple model particle filter to track airborne targets hidden in blind Doppler

In airborne tracking,the blind Doppler makes the target undetectable,resulting in tracking difficulties. In this paper,we studied most possible blind-Doppler cases and summed them up into two types:targets' intentional tangential flying to radar and unintentional flying with large tangential speed. We proposed an interacting multiple model(IMM) particle filter which combines a constant velocity model and an acceleration model to handle maneuvering motions. We compared the IMM particle filter with a previous particle filter solution. Simulation results showed that the IMM particle filter outperforms the method in previous works in terms of tracking accuracy and continuity.

Modeling of UAV path planning based on IMM under POMDP framework

In order to enhance the capability of tracking targets autonomously of unmanned aerial vehicle (UAV), the partially observable Markov decision process (POMDP) model for UAV path planning is established based on the POMDP framework. The elements of the POMDP model are analyzed and described. The state transfer law in the model can be described by the method of interactive multiple model (IMM) due to the diversity of the target motion law, which is used to switch the motion model to accommodate target maneuvers, and hence improving the tracking accuracy. The simulation results show that the model can achieve efficient planning for the UAV route, and effective tracking for the target. Furthermore, the path planned by this model is more reasonable and efficient than that by using the single state transition law.

An Algorithm of the Adaptive Grid and Fuzzy Interacting Multiple Model

This paper studies the algorithm of the adaptive grid and fuzzy interacting multiple model （AGFIMM） for maneuvering target tracking, while focusing on the problems of the fixed structure multiple model （FSMM） algorithm＇s cost-efficiency ratio being not high and the Markov transition probability of the interacting multiple model （IMM） algorithm being difficult to determine exactly. This algorithm realizes the adaptive model set by adaptive grid adjustment, and obtains each model matching degree in the model set by fuzzy logic inference. The simulation results show that the AGFIMM algorithm can effectively improve the accuracy and cost-efficiency ratio of the multiple model algorithm, and as a result is suitable for enineering apolications.

Expectation-maximization (EM) Algorithm Based on IMM Filtering with Adaptive Noise Covariance

A novel method under the interactive multiple model (IMM) filtering framework is presented in this paper, in which the expectation-maximization (EM) algorithm is used to identify the process noise covariance Q online. For the existing IMM filtering theory, the matrix Q is determined by means of design experience, but Q is actually changed with the state of the maneuvering target. Meanwhile it is severely influenced by the environment around the target, i.e., it is a variable of time. Therefore, the experiential covariance Q can not represent the influence of state noise in the maneuvering process exactly. Firstly, it is assumed that the evolved state and the initial conditions of the system can be modeled by using Gaussian distribution, although the dynamic system is of a nonlinear measurement equation, and furthermore the EM algorithm based on IMM filtering with the Q identification online is proposed. Secondly, the truncated error analysis is performed. Finally, the Monte Carlo simulation results are given to show that the proposed algorithm outperforms the existing algorithms and the tracking precision for the maneuvering targets is improved efficiently.

基于IMM-JPDA-ISTUKF的车载毫米波雷达多目标跟踪算法

为提高车载毫米波雷达多目标跟踪精度指标,提升道路车辆行驶安全性,文中在交互多模型无迹卡尔曼滤波(IMM-UKF)和联合概率数据关联(JPDA)融合的算法基础上,针对车辆运动状态突变处UKF鲁棒性差、滤波精度低的问题,提出了一种基于改进强跟踪UKF(ISTUKF)的IMM-JPDA-ISTUKF算法。通过模拟道路场景搭建的仿真环境对算法性能进行了验证,且为证明该算法在实际道路工况下跟踪精度的提升,还进行了雷达道路测试,通过雷达在道路上获取的车辆数据进一步验证了该算法的有效性。结果表明,该算法在目标车辆运动状态发生变化时的距离跟踪精度和速度跟踪精度方面均得到了提高。

A novel maneuvering multi-target tracking algorithm based on multiple model particle filter in clutters

To solve the problem of strong nonlinear and motion model switching of maneuvering target tracking system in clutter environment, a novel maneuvering multi-target tracking algorithm based on multiple model particle filter is presented in this paper. The algorithm realizes dynamic combination of multiple model particle filter and joint probabilistic data association algorithm. The rapid expan- sion of computational complexity, caused by the simple combination of the interacting multiple model algorithm and particle filter is solved by introducing model information into the sampling process of particle state, and the effective validation and utilization of echo is accomplished by the joint proba- bilistic data association algorithm. The concrete steps of the algorithm are given, and the theory analysis and simulation results show the validity of the method.

Aircraft Trajectory Prediction Based on Modified Interacting Multiple Model Algorithm

In order to realize the aircraft trajectory prediction,a modified interacting multiple model(M-IMM) algorithm is proposed,which is based on the performance analysis of the standard interacting multiple model(IMM) algorithm.In the proposed M-IMM algorithm,a new likelihood function is defined for the sake of updating flight mode probabilities,in which the influences of interacting to residual's mean error are taken into account and the assumption of likelihood function being a zero mean Gaussian function is discarded.Finally,the proposed M-IMM algorithm is applied to the simulation of the aircraft trajectory prediction,and the comparative studies are conducted to existing algorithms.The simulation results indicate the proposed M-IMM algorithm can predict aircraft trajectory more quickly and accurately.

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.

Application of interacting multiple model in integrated positioning system of vehicle

To solve low precision and poor stability of the extended Kalman filter （EKF） in the vehicle integrated positioning system owing to acceleration, deceleration and turning （hereinafter referred to as maneuvering） , the paper presents an adaptive filter algorithm that combines interacting multiple model （IMM） and non linear Kalman filter. The algorithm describes the motion mode of vehicle by using three state spacemode]s. At first, the parallel filter of each model is realized by using multiple nonlinear filters. Then the weight integration of filtering result is carried out by using the model matching likelihood function so as to get the system positioning information. The method has advantages of nonlinear system filter and overcomes disadvantages of single model of filtering algorithm that has poor effects on positioning the maneuvering target. At last, the paper uses IMM and EKF methods to simulate the global positioning system （OPS）/inertial navigation system （INS）/dead reckoning （DR） integrated positioning system, respectively. The results indicate that the IMM algorithm is obviously superior to EKF filter used in the integrated positioning system at present. Moreover, it can greatly enhance the stability and positioning precision of integrated positioning system.

基于改进ATPM-IMM算法的外辐射源雷达机动目标跟踪

针对外辐射源雷达进行机动目标跟踪时,现有的自适应交互式多模型(AIMM)算法难以达到高精确度跟踪的问题,提出一种基于改进的自适应转移概率交互式多模型(ATPM-IMM)的机动目标跟踪算法。该算法在ATPM-IMM算法的基础上增加了自适应控制窗,对转移概率矩阵进行再次修正,从而可根据目标的机动情况自适应切换机动模型,提高真实模型的匹配概率。仿真和实测数据结果表明,所提算法可有效提高外辐射源雷达进行机动目标跟踪的精确度。

基于IMM-KF算法改进的欺骗式干扰检测算法

针对基于接收机基线长度的欺骗干扰检测方法在短基线和低定位精度下检测性能差的问题,提出了一种交互多模型卡尔曼滤波(Interactive Multi-model Kalman Filtering,IMM-KF)算法改进的欺骗式干扰检测算法。该方法通过IMM-KF算法对两个接收机天线的位置信息进行最优估计,提高基线解算精度,从而提升基于基线长度的欺骗干扰检测方法的检测性能。首先,详细分析了基于基线长度的欺骗干扰检测方法的数学原理;其次,建立欺骗检测算法模型,并对其性能进行仿真分析;然后,根据算法应用场景引入IMM-KF算法优化基线长度估计量;最后,针对不同的基线长度和定位精度进行仿真实验,对算法进行性能评估。仿真结果表明,该算法可以在接收机伪距测量精度为0.1 m、基线长度为0.5 m的情况下达到86%的检测成功率,而传统算法在此情况下检测成功率仅为10%。

基于IMM-UIF的多无人机纯角度机动目标跟踪

针对单无人机不能及时捕捉到目标的运动状态信息,很容易跟丢目标的问题,结合无迹信息滤波(UIF)算法和交互多模型(IMM)算法,提出了基于IMM-UIF的多无人机分布式融合估计算法。将各个无人机上的观测信息传输至中心节点,并统一优化各无人机的控制输入。仿真结果表明,基于IMM-UIF的多无人机分布式融合估计算法比基于IMM-UIF的单无人机跟踪精度提高了约30%,有效融合多无人机平台的量测信息,实现对目标稳定的高精度跟踪。

基于GPDA-IMM和时间管理的相控阵雷达多目标跟踪算法

多功能相控阵雷达具有灵活性强、跟踪能力强的优势。为了提高相控阵雷达目标跟踪器精确度,进行相控阵雷达能量调节和任务执行的科学管理,通过合理调整机动目标和非机动目标的回访率,进而实现搜索、跟踪时间资源管理。设计了广义概率数据关联-交互式多模型(Generalized Probability Data Association-Interacting Multiple Model, GPDA-IMM)算法,GPDA运算量小,IMM综合了无迹和容积卡尔曼滤波和粒子滤波多模型滤波的特点,且优化权重因子,达到了较好跟踪性能。最后,通过仿真平台模拟8个运动目标的现实场景,结合时间管理和目标跟踪调整回访率,进行大量的仿真实验,证明了算法对不同目标类型和机动状态的有效性和实用性。