Variable Structure Interacting Multiple Model Research Articles

Improved Variable Structure Interacting Multimodels for Target Trajectory Tracking and Extrapolation

To improve the lengthy computation time of conventional variable structure interacting multiple model (VSIMM) algorithm and increase the precision of target prediction and extrapolation, the target state and flight intent information captured by the Automatic Dependent Surveillance-Broadcast (ADS-B) are used as the model’s prior information; combining this information with VSIMM theoretical framework, we purpose an intent variable structure interacting multiple model (INT-VSIMM) algorithm. Firstly, the motion pattern of the target in the flight phase of the flight path is decomposed, and complete sets of motion models are established. Secondly, according to the principle of directed graph switching, a model set switching method is designed, which is mainly based on “hard” switching and supplemented by “soft” switching. Finally, the INT-VSIMM algorithm is used to track the trajectory of the target aircraft, and short-term trajectory extrapolation is performed based on the target state estimation. The simulation results show that the target tracking performance computational time based on the INT-VSIMM algorithm is superior to the comparative existing methods, and the extrapolated trajectory has less error in the short term, which can satisfy the needs of conflict detection.

Moving Horizon Estimation With Variable Structure Interacting Multiple Model for Surrounding Vehicle States in Complex Environments

Moving Horizon Estimation With Variable Structure Interacting Multiple Model for Surrounding Vehicle States in Complex Environments

Variable structure interacting multiple model for highly aperiodic sensors with poor measurement precision

The issue of aircraft tracking using passive sensors with poor measurement precision and a variable update rate is investigated. First, the need for a time-dependent Markov chain transition matrix for the Interacting Multiple Model (IMM) is thoroughly evaluated using simulation and real data from an experimental multilateration system. Three different methods for computing the time-dependent matrix are compared, showing their superiority for highly aperiodic sensors. Second, the Variable Structure Interacting Multiple Model (VS-IMM) for passive sensors with poor geometry is proposed, in which the mode set update is based on local dilution of precision or distance root mean square error. The performance of the estimator is compared with the IMM using real data. The VS-IMM results in the best performance for areas of both high and low measurement precision.

Variable structure Human Intention Estimator with mobility and vision constraints as model selection criteria

In this paper, a novel method for early estimation of a human’s action intention is presented. Human intention, modeled as a goal location associated with a hand motion and eye gaze dynamics, is inferred by fusing information from collected hand motion and gaze motion data. The algorithm, called Human Intention Estimator with Variable Structure (HIEVS), uses two variable structure Interacting Multiple Model (VS-IMM) filters in parallel to process the hand motion and gaze data and generate posterior model probabilities associated with a finite set of action models. The posterior model probabilities from each filter are then fused at the end of each iteration and the current intention is estimated as the model, which has the highest fused posterior model probability. Two model set augmentation (MSA) algorithms are presented to select the active models for each VS-IMM during each iteration. For the hand motion filter, an MSA algorithm which computes the human’s reachable workspace is used. The MSA algorithm for the gaze filter utilizes the human’s visual span to determine the active models. This method allows for accurate early prediction of the human’s intention even when the total model set is large. A real world experiment is performed to validate the proposed method.

A novel variable structure multi-model approach based on error-ambiguity decomposition

Model Set Adaptation (MSA) plays a key role in the Variable Structure Multi-Model tracking approach (VSMM). In this paper, the Error-Ambiguity Decomposition (EAD) principle is adopted to derive the EAD-MSA criterion that is optimal in the sense of minimizing the square error between the estimate and the truth. Consequently, the EAD Variable Structure first-order General Pseudo Bayesian (EAD-VSGPB1) algorithm and the EAD Variable Structure Interacting Multiple Model (EAD-VSIMM) algorithm are constructed. The proposed algorithms are tested in two groups of maneuvering target tracking scenarios under different modes and observation error conditions. The simulation results demonstrate the effectiveness of the EAD-VSMM approach and show that, compared to some existing multi-model algorithms, the proposed EAD-VSMM algorithms achieve more robust and accurate tracking results.

Steady State Filters Bank Based Interacting Multiple-Model-Extended Viterbi Algorithm for Maneuvering Target Tracking

The performance of Interacting Multiple-Model (IMM) algorithm depends on model set and filters bank. Most of the improved IMM algorithms originate from designing more reasonable and effective model set structures. One example in case is the Interacting Multiple-Model-Extended Viterbi (IMMEV) algorithm, which can not only inherit the effective cooperation strategies of the IMM algorithm, but also adapt to the outside world like variable structure IMM algorithm by eliminating those mode transition probabilities that are harmful. However, this design approach is difficult to achieve significant tracking performance and substantial complexity reduction, simultaneously. In this paper, the steady state filters bank based Interacting Multiple-Model-Extended Viterbi algorithm for maneuvering target tracking is presented. The main idea of the algorithm is to further improve the IMM-EV algorithm performance and reduce its complexity by designing a new filters bank. The new approach is based on the steady state filters (ab andabg filters) bank using IMM-EV algorithm design instead of a Kalman filters (second and third order filters) bank. Real maneuvering target tracking application experiments demonstrate that the SS-IMM–EV algorithm with some suitable choice of ”m” is superior to the IMM, Fast-IMM and IMM-EV algorithms, which can obtain a considerable tracking performance and reduce complexity markedly.

Tracking Moving Target on Airport Surface Based on Variable-Structure IMM Algorithm

In order to track the moving target precisely on the airport surface, the Variable Structure Interacting Multiple Model Algorithm (VS-IMM) is applied to realize the Surface Movement Radar (SMR) tracking of target. Firstly, according to the actual movement of aircraft, the paper establishes a constant acceleration motion model, constant turn motion model and constant velocity motion model. Then, the VS-IMM algorithm is applied to the airport surface movement target tracking. Finally, VS-IMM is compared with IMM. Simulation results show that VS-IMM algorithm is more reliable than IMM algorithm on tracking of moving target on airport surface

Tracking Moving Target on Airport Surface Based on Variable-Structure IMM Algorithm

In order to track the moving target precisely on the airport surface, the Variable Structure Interacting Multiple Model Algorithm (VS-IMM) is applied to realize the Surface Movement Radar (SMR) tracking of target. Firstly, according to the actual movement of aircraft, the paper establishes a constant acceleration motion model, constant turn motion model and constant velocity motion model. Then, the VS-IMM algorithm is applied to the airport surface movement target tracking. Finally, VS-IMM is compared with IMM. Simulation results show that VS-IMM algorithm is more reliable than IMM algorithm on tracking of moving target on airport surface

An expected-mode augmentation-based approach for multiple-fault detection and diagnosis in flight control systems

Aircraft fault detection and diagnosis (FDD) is a key technology to ensure flight safety. However, multiple simultaneous fault detection is still a difficult problem. To deal with it, a new FDD approach is proposed in this article, in which the expected-mode augmentation (EMA) approach is applied for the variable-structure interacting multiple model (VSIMM) estimation. The EMA approach is used for online model set adaption. In the EMA–VSIMM FDD approach, the failure model set is composed of a basic fixed model set and an adaptive EMA set. The EMA set is generated based on the basic fixed model set according to their predicted probabilities. Single or multiple simultaneous faults can be detected and diagnosed by applying the EMA set properly. The simulation results show that the EMA–VSIMM method is effective in detecting faults of various types and estimating unknown fault parameters.

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

In order to track ground moving target, a variable structure interacting multiple model (VS-IMM) using mean shift unscented particle filter (MS-UPF) is proposed in this paper. In model-conditioned filtering, sample particles obtained from the unscented particle filter are moved towards the maximal posterior density estimation of the target state through mean shift. On the basis of stop model in VS-IMM, hide model is proposed. Once the target is obscured by terrain, the prediction at prior time is used instead of the measurement at posterior time; in addition, the road model set used is not changed. A ground moving target indication (GMTI) radar is employed in three common simulation scenarios of ground target: entering or leaving a road, crossing a junction and no measurement. Two evaluation indexes, root mean square error (RMSE) and average normalized estimation error squared (ANEES), are used. The results indicate that when the road on which the target moving changes, the tracking accuracy is effectively improved in the proposed algorithm. Moreover, track interruption could be avoided if the target is moving too slowly or masked by terrain.

A novel interacting multiple model algorithm

For maneuvering target tracking, the interacting multiple model (IMM) algorithm employs a fixed model set. The performance of this algorithm depends on the model set adopted. The result of using too many models is as bad as the case of too few models. Therefore, a variable structure IMM (VSIMM) was presented and applied to ground target tracking. This algorithm improves performance and reduces computational load with using auxiliary information. But it is difficult to extend the VSIMM to other scenario (for example, aerial target), where there is not auxiliary information such as a map. A novel interacting multiple model (Novel-IMM) algorithm was presented to solve the problem of model set adaptation without auxiliary information. The Novel-IMM algorithm consists of N independent IMM filters operating in parallel, and each independent IMM filter also consists of multiple sub-filters, which operate interactively. In every time index, only one IMM output of a certain model set is used; but for a long time, the algorithm will alternatively choose an output of the model set to be the optimum final output. The Novel-IMM approach was illustrated in detail with an aerial complex maneuvering target tracking example.

New assignment-based data association for tracking move-stop-move targets

We present a new assignment-based algorithm for data association in tracking ground targets employing evasive move-stop-move maneuvers using ground moving target indicator (GMTI) reports obtained from an airborne sensor. To avoid detection by the GMTI sensor, the targets deliberately stop for some time before moving again. The sensor does not detect a target when the latter's radial velocity (along the line-of-sight from the sensor) falls below a certain minimum detectable velocity (MDV). Even in the absence of move-stop-move maneuvers, the detection has a less-than-unity probability (P/sub D/<1) due to obscuration and thresholding. Then, it is of interest, when a target is not detected, to develop a systematic technique that can distinguish between lack of detection due to P/sub D/<1 and lack of detection due to a stop (or a near stop). Previously, this problem was solved using a variable structure interacting multiple model (VS-IMM) estimator with a stopped target model (VS-IMM-ST) without explicitly addressing data association. We develop a novel assignment approach for move-stop-move targets that considers both the problem of data association as well as filtering. Typically, in assignment-based data association a measurement is used to denote the nondetection event. The use of the standard single-dummy assignment, which does not handle move-stop-move motion explicitly, can result in broken tracks. The new algorithm proposed here handles the evasive move-stop-move motion by introducing a second dummy measurement to represent nondetection due to the MDV. We also present a likelihood-ratio-based track deletion scheme for move-stop-move targets. Using this two-dummy data association algorithm, the track corresponding to a move-stop-move target is kept alive' during missed detections both due to MDV and due to P/sub D/<1. In addition, one can obtain reductions in both rms estimation errors as well as the total number of track breakages.

Tracking evasive move-stop-move targets with a GMTI radar using a VS-IMM estimator

We present the design of a variable structure interacting multiple model (VS-IMM) estimator for tracking evasive ground targets using ground moving target indicator (GMTI) reports obtained from an airborne sensor. In order to avoid detection by the GMTI sensor, the targets use a strategy, where a target deliberately stops or moves at a very low speed for some time before accelerating again. In this case, when the target's radial velocity (along the line of sight from the sensor) falls below a certain minimum detectable velocity, the target is not detected by the sensor. Under these conditions, the use of an estimator, which does not take care of this move-stop-move motion explicitly, will result in broken tracks. The tracker proposed here handles the evasive move-stop-move motion via the VS-IMM estimator, where the tracker mode set is augmented with a model when the estimated speed of the target falls below a certain threshold. Using this additional stopped-target model, the target track is kept alive even in the absence of a measurement. A simulated scenario is used to illustrate the selection of design parameters and the operation of the tracker. Performance measures are presented to contrast the benefits of the VS-IMM estimator, which uses the stopped-target model, over a standard IMM estimator.

Multiple-Model Based Fault Detection and Diagnosis of Internal Sensors in Mobile Robot. Detection and Diagnosis of Hard/Noise Failures of Sensor via Variable Structure Interacting Multiple-Model Estimator.

This paper proposes a multi-model based approach to detection and diagnosis of hard/noise failures of internal sensors in mobile robot. Three system modes (normal mode, hard failure mode, and noise failure modes)of each sensor are modeled. Changes of the three system modes are also modeled as switching from one mode to another in a probabilistic manner. The mode probabilities and sensor outputs are estimated based on a bank of Kalman filters, and they are interacted with each other effectively. To provide better fault decision, the model sets are switched according to the robot motion. The proposed fault detection and diagnosis (FDD) algorithm is formulated based on the variable structure interacting multiple-model (VSIMM) algorithm. The FDD algorithm is incorporated into a dead-reckoning system of our mobile robot with five internal sensors (four wheel-encoders and a yaw-rate gyro). Experimental results show that the FDD algorithm gives the correct fault decision of the sensors and the dead-reckoning system allows the robust self-localization of the robot subject to sensor failures.

Ground target tracking with variable structure IMM estimator

In this paper we present the design of a Variable Structure Interacting Multiple Model (VS-IMM) estimator for tracking groups of ground targets on constrained paths using Moving Target Indicator (MTI) reports obtained from an airborne sensor. The targets are moving along a highway, with varying obscuration due to changing terrain conditions. In addition, the roads can branch, merge or cross-the scenario represents target convoys along a realistic road network with junctions, changing terrains, etc. Some of the targets may also move in an open field. This constrained motion estimation problem is handled using an IMM estimator with varying mode sets depending on the topography, The number of models in the IMM estimator, their types and their parameters are modified adaptively, in real-time, based on the estimated position of the target and the corresponding road/visibility conditions. This topography-based variable structure mechanism eliminates the need for carrying all the possible models throughout the entire tracking period as in the standard IMM estimator, significantly improving performance and reducing computational load. Data association is handled using an assignment algorithm. The estimator is designed to handle a very large number of ground targets simultaneously. A simulated scenario consisting of over one hundred targets is used to illustrate the selection of design parameters and the operation of the tracker. Performance measures are presented to contrast the benefits of the VS-IMM estimator over the Kalman filter and the standard IMM estimator, The VS-IMM estimator is then combined with multidimensional assignment to gain time-depth. The additional benefit of using higher dimensional assignment algorithms for data association is also evaluated.