Variable Structure Multiple Model Research Articles

Typical motion-based modelling and tracking for vehicle targets in linear road segment

In this paper, a typical motion-based modelling and tracking issue is investigated for vehicle targets in linear road segment by a variable structure multiple model (VSMM) method. Vehicle target motions in a road with multiple lanes are described by typical lane keeping and lane changing manoeuvres. To describe trajectories of typical manoeuvres, an Ornstein–Uhlenbeck process and a sine half-cycle are used to model lane keeping and lane changing motions in lateral direction, respectively. Note that starting point and length of lane changing motion are unknown in target tracking. Time-varying model sets are designed based on the current motion model with different motion parameters. A VSMM tracking frame is constructed to obtain target state estimates with time-varying model set. The effectiveness of the proposed typical motion-based tracking scheme is displayed by simulation results on road vehicle tracking.

Domain-knowledge-aided airborne ground moving targets tracking

This paper investigates the problem of traffic surveillance using an unmanned aerial vehicle (UAV) and proposes a domain-knowledge-aided airborne ground moving target tracking algorithm. To improve the accuracy of multiple target tracking, the proposed algorithm incorporates domain knowledge into the joint probabilistic data association (JPDA) filter as state constraints. The domain knowledge considered in this paper includes both road information extracted from a given map and local traffic regulations. Conventional track update method is modified to enhance the capability of recognition of temporarily track loss. A variable structure multiple model (VS-MM) method is developed to assign the road segment to a given target. The main contribution of this paper is that we establish a comprehensive framework for target tracking with the aid of domain knowledge and analyze the effect of diverse constraints on target tracking performance, while the limitations involve only 2-D target behaviors are considered, and the sophisticated interactions between targets are neglected. The effectiveness of proposed algorithm is demonstrated through extensive numerical simulations.

A Class of Distributed Variable Structure Multiple Model Algorithm Based on Posterior Information of Information Matrix

A Class of Distributed Variable Structure Multiple Model Algorithm Based on Posterior Information of Information Matrix

Variable structure multiple model fixed-interval smoothing

This paper focuses on fixed-interval smoothing for stochastic hybrid systems. When the truth-mode mismatch is encountered, existing smoothing methods based on fixed structure of model-set have significant performance degradation and are inapplicable. We develop a fixed-interval smoothing method based on forward- and backward-filtering in the Variable Structure Multiple Model (VSMM) framework in this paper. We propose to use the Simplified Equivalent model Interacting Multiple Model (SEIMM) in the forward and the backward filters to handle the difficulty of different mode-sets used in both filters, and design a re-filtering procedure in the model-switching stage to enhance the estimation performance. To improve the computational efficiency, we make the basic model-set adaptive by the Likely-Model Set (LMS) algorithm. It turns out that the smoothing performance is further improved by the LMS due to less competition among models. Simulation results are provided to demonstrate the better performance and the computational efficiency of our proposed smoothing algorithms.

Tracking of Maneuvering Extended Target Using Modified Variable Structure Multiple-Model Based on Adaptive Grid Best Model Augmentation

Maneuvering extended target tracking, an important but challenging research field, has attracted increasing attention in the field of radar signal processing. Variable structure multiple-model (VSMM) estimation is the current mainstream tracking algorithm, it possesses high tracking performance but depends largely on the model set used. However, the existing model set design methods still cannot offer higher tracking performance due to the complex maneuverability of the target. The current best model augmentation (BMA) algorithm is an efficient and universal model set design method, but it cannot adapt well to complex maneuvering situations because of its fixed basic and candidate model set. Hence, this paper proposes a modified BMA algorithm for VSMM based on adaptive grid, it can make full use of the previous information to realize an adaptation and time variation of the model set. Overall, two different scenarios were considered, wherein the digital simulation shows that the proposed method can provide a more reliable estimation of the kinematic state and shape of the extended target.

An adaptive nonlinear filter with missing measurements compensation for manoeuvring target tracking

To solve the problem of missing measurements in highly manoeuvring target tracking, an expected-mode-augmentation-based unscented Kalman filter with missing measurements compensation (EMA-MMCUKF) is designed based on the variable-structure multiple model method. In the proposed EMA-MMCUKF, the random missing measurements are described by the Bernoulli distribution, and the one-step prediction is used as the compensation. Based on the proposed EMA-MMCUKF, a Bayesian estimation-based expected-mode-augmentation unscented Kalman filter with missing measurements compensation (BE-EMA-MMCUKF) is proposed for adaptive estimation of the sensor measurement reception rate, where the unknown measurement reception rate can be estimated by fully utilising prior information. Simulation results demonstrate that the proposed EMA-MMCUKF can effectively track the manoeuvring target at different measurement reception rates. Moreover, when the sensor prior information differs significantly from the true measurement reception rate, the proposed BE-EMA-MMCUKF can effectively estimate the unknown sensor measurement reception rate and improve the accuracy of manoeuvring target tracking compared with non-estimation of the sensor measurement reception rate.

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.

Fast Variable-Structure Multiple-Model Estimation Using Modified Likely-Model Set Tracker

A new variable-structure multiple-model (VSMM) estimator which is generally applicable to most hybrid estimation problems is fully developed in this paper. Modified likely-model set (MLMS) algorithm is proposed by introducing the concept of central models in model set adoption process. A set of models that are more probable match the true system mode at any given time plus a set of central models are implemented as active model set. Small decision delay and therefore fast tracking is the major priority of the proposed algorithm. The generality and simplicity in the design and use are other merits of the proposed method. A well-known example of tracking a maneuvering target is given to illustrate the effectiveness of the proposed method in comparison with other VSMM estimators.

Multiple target tracking in occlusion area with interacting object models in urban environments

Multiple target tracking in crowded urban environments is a daunting task. High crowdedness complicates motion modeling, and occlusion makes tracking difficult as well. Based on the variable-structure multiple-model (VSMM) estimation framework, this paper extends an interacting object tracking (IOT) scheme with occlusion detection and a virtual measurement model for occluded areas. IOT is composed of a scene interaction model and a neighboring object interaction model. The scene interaction model considers the long-term interactions of a moving object and surroundings, and the neighboring object interaction model considers three short-term interactions. With these interacting object models, the motion feature of a moving object can be represented with the weight of each model. A virtual measurement model is proposed to exploit the motion feature with the IOT scheme under occlusion. The proposed approach was validated using a stationary 2D LIDAR. To verify in occlusion, a 3D LIDAR based benchmark system was developed to extract occluded moving segments. The ample experimental results show that the proposed IOT scheme tracks over 57% of occluded moving objects in an urban intersection.

Maneuvering multi-target tracking based on variable structure multiple model GMCPHD filter

The traditional multiple model cardinalized probability hypothesis density (MMCPHD) filter uses a fixed model set for all targets. It is clearly inefficient and may cause the decrease of performance in the situations where the size of the model set is large and the scene is complicated. This paper presents a variable structure multiple model (VSMM) version of the Gaussian mixture cardinalized probability hypothesis density (GMCPHD) filter based on the expected mode augmentation (EMA) for the multiple maneuvering target tracking. The GMCPHD filter which is suitable for the VSMM is derived, thus different model sets can be used for different targets. Then EMA algorithm which is an efficient model set adaptation method is introduced and applied to the VSMM version of the GMCPHD (VSMM-GMCPHD) filter. This method costs less computational time and has better estimate precision than that of the traditional multiple model version of GMCPHD (MM-GMCPHD) filter. The gating technique is utilized to further improve the computational efficiency. Simulation results verify the performance of the proposed methods. The estimate precision of VSMM-GMCPHD filters with and without the gate are almost the same, however the former one is more cost-effective. Both of the two VSMM-GMCPHD filters outperform the MM-GMCPHD filter.

An improved multiple model particle filtering approach for manoeuvring target tracking using airborne GMTI with geographic information

This paper proposes a ground vehicle tracking method using an airborne ground moving target indicator radar where the surrounding geographic information is considered to determine vehicle's movement type as well as constrain its positions. Multiple state models corresponding to different movement modes are applied to represent the vehicle's behaviour in different terrain conditions. Based on geographic conditions and multiple state models, a constrained variable structure multiple model particle filter algorithm is proposed. Compared with the traditional multiple model particle filtering schemes, the proposed algorithm utilises a particle swarm optimisation technique which generates more effective particles and generated particles are constrained into the feasible geographic region. Numerical simulation results in a realistic environment show that the proposed method achieves better tracking performance compared with current state-of-the-art ones for manoeuvring vehicle tracking.

Spacecraft autonomous navigation using multiple model adaptive estimator

Purpose The purpose of this paper is to present a variable structure multiple model adaptive estimator (VSMMAE) for liaison navigation system. Liaison navigation is an autonomous navigation method where inter-satellite range measurements are used to estimate the orbits of all participating spacecrafts simultaneously.

Geometrical entropy approach for variable structure multiple-model estimation

The variable structure multiple-model (VSMM) estimation approach, one of the multiple-model (MM) estimation approaches, is popular in handling state estimation problems with mode uncertainties. In the VSMM algorithms, the model sequence set adaptation (MSA) plays a key role. The MSA methods are challenged in both theory and practice for the target modes and the real observation error distributions are usually uncertain in practice. In this paper, a geometrical entropy (GE) measure is proposed so that the MSA is achieved on the minimum geometrical entropy (MGE) principle. Consequently, the minimum geometrical entropy multiple-model (MGEMM) framework is proposed, and two suboptimal algorithms, the particle filter k-means minimum geometrical entropy multiple-model algorithm (PF-KMGEMM) as well as the particle filter adaptive minimum geometrical entropy multiple-model algorithm (PF-AMGEMM), are established for practical applications. The proposed algorithms are tested in three groups of maneuvering target tracking scenarios with mode and observation error distribution uncertainties. Numerical simulations have demonstrated that compared to several existing algorithms, the MGE-based algorithms can achieve more robust and accurate estimation results when the real observation error is inconsistent with a priori.

Tracking Algorithm Design and Comparison of High Speed High Maneuvering Target

Aiming at the problem that the traditional tracking method cannot track high speed high maneuvering target effectively, one modified fixed structure multiple model algorithm (M-FSMM) and one modified variable structure multiple model (M-VSMM) algorithm were proposed. The Constant Velocity (CV) model, Current Statistical (CS) and Modified Coordinate Turn (MCT) model were adopted in the M-FSMM algorithm, by means of Connected Graph (CG) thinking, the model connected graph was made up by models that can describe possible motion, the connected relation was set up and model self-adapting was designed to carry out the variable structure tracking that can quickly jump between models. Monte Carlo simulation results show that the two methods can track high speed high maneuvering target effectively, the computational quantum of M-FVMM algorithm is larger but the tracking accuracy and stability are better than the M-FSMM algorithm. They can be used to track near space hypersonic targets.

Equivalent-Model Augmentation for Variable-Structure Multiple-Model Estimation

A variable-structure multiple-model (VSMM) approach, named equivalent-model augmentation (EqMA), is proposed. Here the model set is augmented by a variable model intended to best match the unknown true mode. To fully utilize the information provided by model sequences (model histories), this variable model depends on the true mode at the previous time. Thus different previous models correspond to different augmenting models. To make the estimation process computationally feasible, the unknown variable model at the previous time is approximated by an equivalent model (EqM) which provides the closest estimation results in the sense of minimum Kullback-Leibler (KL) divergence. EqM also contains the online information provided by the measurements. Performance of the proposed EqMA approach is evaluated via two scenarios of maneuvering target tracking. Simulation results demonstrate the effectiveness of EqMA compared with the interacting multiple-model (IMM) algorithm and the expected-mode augmentation (EMA) algorithm.

Fuzzy-logic adaptive variable structure multiple-model algorithm for tracking a high maneuvering target

The conventional interacting multiple model (IMM) algorithm will increase the computational load when applying a large number of models, meanwhile, it cannot yield accurate estimation results with a small number of models. Furthermore, the unknown target acceleration is regarded as an additional process noise to the target model, and its time-varying variance is hard to be approximated. The paper proposes a fuzzy-logic adaptive variable structure multiple model (FAVSMM) algorithm for tracking a high maneuvering target. The algorithm can optimize the model parameters using the model probability and construct an optimal model set quickly, and the fuzzy-logic IMM algorithm included in the FAVSMM algorithm is adopted for states estimation. The simulation results show that the proposed algorithm can match well with the actual target trajectory with less computational complexity and better accuracy.

Feedback structure based entropy approach for multiple-model estimation

The variable-structure multiple-model (VSMM) approach, one of the multiple-model (MM) methods, is a popular and effective approach in handling problems with mode uncertainties. The model sequence set adaptation (MSA) is the key to design a better VSMM. However, MSA methods in the literature have big room to improve both theoretically and practically. To this end, we propose a feedback structure based entropy approach that could find the model sequence sets with the smallest size under certain conditions. The filtered data are fed back in real time and can be used by the minimum entropy (ME) based VSMM algorithms, i.e., MEVSMM. Firstly, the full Markov chains are used to achieve optimal solutions. Secondly, the myopic method together with particle filter (PF) and the challenge match algorithm are also used to achieve sub-optimal solutions, a trade-off between practicability and optimality. The numerical results show that the proposed algorithm provides not only refined model sets but also a good robustness margin and very high accuracy.

Variable structure multiple model for articulated human motion tracking from monocular video sequences

A new model-based human body tracking framework with learning-based theory is introduced in this paper. We propose a variable structure multiple model (VSMM) framework to address challenging problems such as uncertainty of motion styles, imprecise detection of feature points, and ambiguity of joint locations. Key human joint points are detected automatically and the undetected points are estimated with Kalman filters. Multiple motion models are learned from motion capture data using a ridge regression method. The model set that covers the total motion set is designed on the basis of topological and compatibility relationships, while the VSMM algorithm is used to estimate quaternion vectors of joint rotation. Experiments using real image sequences and simulation videos demonstrate the high efficiency of our proposed human tracking framework.

A New Hybrid Grid Multiple Model Estimation Based on STF

The paper presents a hybrid model grid variable structure multiple model algorithm basing on strong tracking filter (STF-VSMM) which is used to state estimation for complicated system. The total model set for STF-VSMM is the combination of a coarse model grid and an adaptive fine grid moving freely in the system mode space. During the produce of the fine model grid, the paper uses a strong tracking filter to get the center position. Then, STF-VSMM can form a two-double bestrow of the system mode space. At last, the paper realizes the accurate tracking of maneuvering target using STF-VSMM. Simulation results demonstrate that STF-VSMM estimator outperforms the corresponding fixed structure multiple model (FSMM) at a negligible extra computational cost.

Best Model Augmentation for Variable-Structure Multiple-Model Estimation

A new approach, referred to as best model augmentation (BMA), for variable-structure multiple-model (VSMM) estimation is presented. Here the original set of models is augmented by a variable set of models intended to best match the unknown true mode. Based on the Kullback-Leiber (KL) information, two versions of the criterion serving as a metric of the closeness between candidate models and the true mode are derived in the space of states and measurements, respectively. The model set adaptation (MSA) in BMA turns out to be an online optimization problem based on the KL criterion, which can be solved easily. The performance of the proposed BMA approach is evaluated via several scenarios for maneuvering target tracking. Simulation results demonstrated the effectiveness of BMA compared with the interacting multiple-model (IMM) algorithm and the expected-mode augmentation (EMA) algorithm.