Problem Of Maneuvering Target Tracking Research Articles

Multiple maneuvering target tracking based on hierarchical Dirichlet process and hidden Markov model

This paper is concerned with multiple maneuvering target tracking problems. Although the interacting multiple model (IMM) algorithm can be used for maneuvering targets tracking, the performance is limited as the motion mode set of the target is fixed in advance. In this paper, we address the problem of multiple maneuvering targets with unknown maneuvering models in cluttered environments. A finite hidden Markov model (HMM) is developed with truncated Hierarchical Dirichlet process (HDP) to recognize the unknown and changing motion modes. Further, the HDP-HMM is introduced into multiple hypothesis tracking (MHT) and probabilistic multiple hypothesis tracking (PMHT) approaches for multiple maneuvering targets tracking with data association uncertainty. To joint estimate the HDP-HMM parameters and target states, the particle learning and variational inference approaches are used, and the solutions of HDP-HMM-MHT and HDP-HMM-PMHT algorithms are obtained based on Rao-Blackwellized particle filter and variational Bayesian methods, respectively. As the changes of maneuver models can be learned using HDP, the multi-target tracking accuracy of the proposed algorithm is improved. Simulation results show that the proposed algorithms outperform the traditional IMM-MHT method with fixed model sets.

Adaptive Kernel Learning Kalman Filtering With Application to Model-Free Maneuvering Target Tracking

Kernel method is a non-parametric linearization method for system modeling, which uses nonlinear projection from input data space to high-dimensional Hilbert feature space and employs kernel function for hiding the projection operator in a linear learner to replace inner product calculation in Hilbert space and to avoid the curse of dimensionality. Kernel method is data-driven and learnable to nonlinear model. For independency to a priori model, it provides a novel way of state estimation for nonlinear dynamic systems with model uncertainties. In this paper, an adaptive kernel learning Kalman filtering method is proposed and applied into the problem of maneuvering target tracking. Without use of a priori system model, the method performs Kalman filtering in a reproducing kernel Hilbert space (RKHS) by estimating conditional embedding operator (CEO) as system state transfer function from training data. For the stochastic uncertainty in system model, the maximum correntropy criterion (MCC) is introduced to obtain kernel parameter optimization and balance of estimation performance, while a sliding window is designed for online updating estimation of state transfer function to get adaptability to unknown system dynamics. Such the construction for kernel Kalman filtering (KKF) is helpful to extend application from time-series signal processing to state estimation of uncertain dynamical systems. Simulation scenarios include average sunspot prediction, hovering target tracking and hypersonic maneuvering target tracking, corresponding to verification in low-dynamic, periodic-dynamic and high-dynamic systems. Numerical results have illustrated that the proposed adaptive KKF can realize model-free tracking for target that has nonlinear dynamical motion, shown adaptability to non-cooperative target maneuver and better precision and convergence speed than typical model-based algorithms.

Maneuvering target tracking of UAV based on MN-DDPG and transfer learning

Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles (UAVs). In this paper, aiming to address the control problem of maneuvering target tracking and obstacle avoidance, an online path planning approach for UAV is developed based on deep reinforcement learning. Through end-to-end learning powered by neural networks, the proposed approach can achieve the perception of the environment and continuous motion output control. This proposed approach includes: (1) A deep deterministic policy gradient (DDPG)-based control framework to provide learning and autonomous decision-making capability for UAVs; (2) An improved method named MN-DDPG for introducing a type of mixed noises to assist UAV with exploring stochastic strategies for online optimal planning; and (3) An algorithm of task-decomposition and pre-training for efficient transfer learning to improve the generalization capability of UAV’s control model built based on MN-DDPG. The experimental simulation results have verified that the proposed approach can achieve good self-adaptive adjustment of UAV’s flight attitude in the tasks of maneuvering target tracking with a significant improvement in generalization capability and training efficiency of UAV tracking controller in uncertain environments.

A consensus-based square-root cubature Kalman filter for manoeuvring target tracking in sensor networks

In this paper, a novel distributed tracking method is proposed for the problem of manoeuvring target tracking in sensor networks. Firstly, an adaptive adjustment tracking model is established by extended state observer (ESO) theory. Then, the consensus-based square-root cubature Kalman filter (SCKF) algorithm is proposed in order to improve the global accuracy and stability. In addition, the integrated model could reduce the influence of measurement noise. Finally, simulation is performed to verify the effectiveness of the scheme, whereby comparison results show that the estimation accuracy of the method proposed is higher than that of the traditional ESO and SCKF.

Generalized pseudo Bayesian algorithms for tracking of multiple model underwater maneuvering target

The strength of Generalized Pseudo Bayesian (GPB) algorithms is exploited in the presented study to enhance the target tracking precision, effective model approximation and rapid convergence of multimodel maneuvering object tracking. The GPB methods are considered to be suitable for approximating systems whose dynamics follow discrete-time and fixed state Markov process. Underwater maneuvering target tracking problems are usually solved with nonlinear Bayesian algorithms, in which kinetics of object are associated with passive bearings using state-space modeling. Here accuracy and convergence of GPB methods based on Interacting Multiple Model Extended Kalman Filter (IMMEKF), Interacting Multiple Model Extended Kalman Smoother (IMMEKS), Interacting Multiple Model Unscented Kalman Filter (IMMUKF) and Interacting Multiple Model Unscented Kalman Smoother (IMMUKS) are efficiently analyzed for tracking of multimodel maneuvering target in complex ocean environment. Application of these algorithms is systematically presented for estimating the real-time state of a maneuvering object that follows a coordinated turn trajectory. Performance analysis of IMM Kalman filters and smoothers is done with variations in the standard deviation of white Gaussian measurement noise by following Bearings Only Tracking (BOT) phenomena. Least Mean Square Error (MSE) between approximated and the real position of maneuvering target in rectangular coordinates is calculated for analyzing the performance of filtering and smoothing techniques. Simulation results of the Monte Carlo runs validate the effectiveness of IMMEKS and IMMUKS over IMMEKF and IMMUKF for scenario of given framework.

Adaptive passive sensor selection for maneuvering target localization and tracking using a multisensor surveillance system

This paper investigates maneuvering-target tracking problem based on a multisensor system and interacting multiple model (IMM). The estimation is performed by a novel particle filter (PF) with a ca...

Consensus variable structure multiple model filtering for distributed maneuvering tracking

This paper considers the problem of maneuvering target tracking for distributed sensor networks. Consensus-based multiple model (MM) filters are effective methods to deal with this problem. However, the model set of these filters is fixed. When the scene is complicated and the model set for MM methods is large, these methods may become inefficient and performance may decrease while the variable structure multiple model (VSMM) method can handle it well. The VSMM version of consensus filter based on the expected mode augmentation (EMA) is presented in this paper to track maneuvering target for distributed sensor networks. The distributed consensus filter for VSMM is presented first and it runs parallel consensus on both the mode probability and the mode-matched probability density function (PDF). Then the EMA algorithm is used to determine the model set utilized at each time step. Simulation results demonstrate the superiority of the proposed method.

A novel recursive T-S fuzzy semantic modeling approach for discrete state-space systems

In this paper, we propose a novel recursive Takagi-Sugeno (T-S) fuzzy semantic modeling approach for discrete state-space system. According to the information learning theoretic (ILT), the correntropy can capture the higher moments of the error probability distribution to deal with non-Gaussian noise. Considering the advantages of fuzzy theory and correntropy, fuzzy correntropy is constructed and a novel kernel fuzzy C-regression model clustering based on fuzzy correntropy is proposed to solve the premise parameter identification problem of the T-S fuzzy model. To the identification of the consequent part parameters of the T-S fuzzy model, a modified extended forgetting factor recursive least squares (MEFRLS) estimator is presented. Moreover, to evaluate the performance of the proposed fuzzy model, the proposed T-S fuzzy model is applied to solve the problem of maneuvering target tracking by incorporating the target feature semantic information. Finally, the experiment results show the proposed algorithm can effectively track a maneuvering target, and its performance is better than the exist algorithms, such as interacting multiple model Kalman filter (IMMKF), interacting multiple model unscented Kalman filter (IMMUKF), the interacting multiple model particle filter (IMMPF) and interacting multiple model Rao-Blackwellized particle filter the (IMMRBPF).

Adaptive Interacting Multiple Model Algorithm Based on Information-Weighted Consensus for Maneuvering Target Tracking.

Networked multiple sensors are used to solve the problem of maneuvering target tracking. To avoid the linearization of nonlinear dynamic functions, and to obtain more accurate estimates for maneuvering targets, a novel adaptive information-weighted consensus filter for maneuvering target tracking is proposed. The pseudo measurement matrix is computed with unscented transform to utilize the information form of measurements, which is necessary for consensus iterations. To improve the maneuvering target tracking accuracy and get a unified estimation in each sensor node across the entire network, the adaptive current statistical model is exploited to update the estimate, and the information-weighted consensus protocol is applied among neighboring nodes for each dynamic model. Based on posterior probabilities of multiple models, the final estimate of each sensor is acquired with weighted combination of model-conditioned estimates. Experimental results illustrate the superior performance of the proposed algorithm with respect tracking accuracy and agreement of estimates in the whole network.

Tracking maneuver target using interacting multiple model-square root cubature Kalman filter based on range rate measurement

The problem of maneuvering target tracking is a hot issue in the field of target tracking. Due to the range rate measurement containing the maneuvering information of target, it has the important p...

Highly maneuvering target tracking using multi-parameter fusion singer model

An algorithm of highly maneuvering target tracking is proposed to solve the problem of large tracking error caused by strong maneuver. In this algorithm, a new estimator, named as multi-parameter fusion Singer (MF-Singer) model is derived based on the Singer model and the fuzzy reasoning method by using radial acceleration and velocity of the target, and applied to the problem of maneuvering target tracking in strong maneuvering environment and operating environment. The tracking performance of the MF-Singer model is evaluated and compared with other manuevering tracking models. It is shown that the MF-Singer model outperforms these algorithms in several examples.

Iterative Diffusion-Based Distributed Cubature Gaussian Mixture Filter for Multisensor Estimation.

In this paper, a distributed cubature Gaussian mixture filter (DCGMF) based on an iterative diffusion strategy (DCGMF-ID) is proposed for multisensor estimation and information fusion. The uncertainties are represented as Gaussian mixtures at each sensor node. A high-degree cubature Kalman filter provides accurate estimation of each Gaussian mixture component. An iterative diffusion scheme is utilized to fuse the mean and covariance of each Gaussian component obtained from each sensor node. The DCGMF-ID extends the conventional diffusion-based fusion strategy by using multiple iterative information exchanges among neighboring sensor nodes. The convergence property of the iterative diffusion is analyzed. In addition, it is shown that the convergence of the iterative diffusion can be interpreted from the information-theoretic perspective as minimization of the Kullback–Leibler divergence. The performance of the DCGMF-ID is compared with the DCGMF based on the average consensus (DCGMF-AC) and the DCGMF based on the iterative covariance intersection (DCGMF-ICI) via a maneuvering target-tracking problem using multiple sensors. The simulation results show that the DCGMF-ID has better performance than the DCGMF based on noniterative diffusion, which validates the benefit of iterative information exchanges. In addition, the DCGMF-ID outperforms the DCGMF-ICI and DCGMF-AC when the number of iterations is limited.

Fuzzy Quadrature Particle Filter for Maneuvering Target Tracking

In this paper, a novel fuzzy quadrature particle filter (FQPF) based on maximum entropy fuzzy clustering for maneuvering target tracking is proposed. The novelties of the fuzzy quadrature particle filter are in the update step in which the predicted and posterior probability density functions are approximated by introducing a set of quadrature point probability densities based on the Gauss–Hermite quadrature rule as a Gaussian. The particle and quadrature point weights can be adaptively estimated based on the weighting exponent and fuzzy membership degrees provided by a modified version of maximum entropy fuzzy clustering algorithm. Unlike the Gaussian particle filter (GPF) using the prior distribution as the proposal distribution, the new FQPF uses a set of modified quadrature point probability densities as the proposal distribution that can effectively enhance the diversity of samples and improve the approximate performance. Finally, simulation results are presented to demonstrate the versatility and improved performance of the fuzzy quadrature particle filter over other nonlinear filtering approaches, namely the unscented Kalman filter, quadrature Kalman filter, particle filter, and GPF, to solve maneuvering target tracking problems.

A modified variable rate particle filter for maneuvering target tracking

To address the problem of maneuvering target tracking, where the target trajectory has prolonged smooth regions and abrupt maneuvering regions, a modified variable rate particle filter (MVRPF) is proposed. First, a Cartesian-coordinate based variable rate model is presented. Compared with conventional variable rate models, the proposed model does not need any prior knowledge of target mass or external forces. Consequently, it is more convenient in practical tracking applications. Second, a maneuvering detection strategy is adopted to adaptively adjust the parameters in MVRPF, which helps allocate more state points at high maneuver regions and fewer at smooth regions. Third, in the presence of small measurement errors, the unscented particle filter, which is embedded in MVRPF, can move more particles into regions of high likelihood and hence can improve the tracking performance. Simulation results illustrate the effectiveness of the proposed method.

Maneuvering Target Tracking Algorithm Based on Interacting Multiple Models

Aiming at improving the accuracy and quick response of the filter in nonlinear maneuvering target tracking problems, the Interacting Multiple Models Cubature Information Filter (IMMCIF) is proposed. In IMMCIF, the Cubature Information Filter (CIF) is brought into Interacting Multiple Model (IMM), which can not only improve the accuracy but also enhance the quick response of the filter. CIF is a multisensor nonlinear filtering algorithm; it evaluates the information vector and information matrix rather than state vector and covariance, which can reduce the error of nonlinear filtering algorithm. IMM disposes all the models simultaneously through Markov Chain, which can enhance the quick response of the filter. Finally, the simulation results show that the proposed filter exhibits fast and smooth switching when disposing different maneuver models; it performs better than the IMMCKF and IMMUKF on tracking accuracy.

Maneuvering Target Tracking Algorithm Based on Particle PHD Filtering

As for the problem of maneuvering target tracking in the clutter environment, this paper combines IMM with PHD and realizes it through approach of particle filter. This algorithm avoids the troublesome problem of data association, and takes advantage of probability hypothesis density (PHD) filter in tracking maneuvering targets and interacting multi-model (IMM) algorithm in the field of model switching effectively, in the clutter environment, the status of the targets can be estimated precisely and steadily. This paper compares the proposed filtering algorithm with the classical IMM algorithm in performance, and the simulation results show that, the improved filtering algorithm has good tracking performance and tracking accuracy.

Particle filter for manoeuvring target tracking via passive radar measurements with glint noise

This study investigates the problem of manoeuvring target tracking with passive coherent location (PCL) radar. The main challenge stems from the non-linearities of the dynamic state space and the non-Gaussian measurement noise. The contribution of the study is twofold. First, to deal with the problem of manoeuvring target tracking in the presence of glint noise, an interacting multiple model (IMM) particle filtering (PF) method using multiple time-of-arrival (TOA) measurements from several transmitter–receiver pairs are proposed and evaluated. Second, because tracking precision is sensitive to the configuration of transmitters and receiver stations, the authors provide insight into the influence of the configuration of transmitters and receiver stations on the tracking methods of PCL radar. Simulations illustrate the effectiveness of the proposed method. Compared with conventional IMM algorithm and PF algorithm in different glint noise environments and various configurations of transmitters and receiver stations, the proposed method obtains better estimates of position, velocity and acceleration.

Multiple Maneuvering Target Tracking by Improved Particle Filter Based on Multiscan JPDA

The multiple maneuvering target tracking algorithm based on a particle filter is addressed. The equivalent‐noise approach is adopted, which uses a simple dynamic model consisting of target state and equivalent noise which accounts for the combined effects of the process noise and maneuvers. The equivalent‐noise approach converts the problem of maneuvering target tracking to that of state estimation in the presence of nonstationary process noise with unknown statistics. A novel method for identifying the nonstationary process noise is proposed in the particle filter framework. Furthermore, a particle filter based multiscan Joint Probability Data Association (JPDA) filter is proposed to deal with the data association problem in a multiple maneuvering target tracking. In the proposed multiscan JPDA algorithm, the distributions of interest are the marginal filtering distributions for each of the targets, and these distributions are approximated with particles. The multiscan JPDA algorithm examines the joint association events in a multiscan sliding window and calculates the marginal posterior probability based on the multiscan joint association events. The proposed algorithm is illustrated via an example involving the tracking of two highly maneuvering, at times closely spaced and crossed, targets, based on resolved measurements.

Process noise identification based particle filter: an efficient method to track highly manoeuvring targets

In this study, a novel method, process noise identification-based particle filter is proposed for tracking highly manoeuvring target. In the proposed method, the equivalent-noise approach is adopted, which converts the problem of manoeuvring target tracking to that of state estimation in the presence of non-stationary process noise with unknown statistics. A novel method for identifying the non-stationary process noise is proposed in the particle filter framework. Compared with the multiple model approaches for manoeuvring target tracking, the proposed method needs to know neither the possible multiple models nor the transition probability matrices. One simple dynamic model is adopted during the whole tracking process. The proposed method is especially suitable for tracking highly manoeuvring target because of its capability of dealing with sample impoverishment, which is a common problem in particle filter and becomes serious when tracking large uncertain dynamics.

Tracking application about singer model based on marginalized particle filter

This article deals with the problem of maneuvering target tracking which results in a mixed linear/non-linear model estimation problem. For maneuvering tracking system, extended Kalman filter (EKF) or particle filter (PF) is traditionally used to estimate the states. In this article, marginalized particle filter (MPF) is presented for application in a mixed linear/non-linear model estimation problem. MPF is a combination of Kalman filter (KF) and PF. So it holds both advantage of them and can be used for mixed linear/non-linear substructure, where the conditionally linear states are estimated using KF and the nonlinear states are estimated using PF. Simulation results show that MPF guarantees the estimation accuracy and alleviates the potential computational burden problem compared with PF and EKF in maneuvering target tracking application.