A low-complexity interacting multiple model filter for maneuvering target tracking

Khalid, Syed Safwan; Abrar, Shafayat

doi:10.1016/j.aeue.2017.01.011

Cited by 15 publications

(9 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It assumes that, given a finite state of dynamic models (each of them corresponding to a particular behavior), a particular target can jump from one model to the other according to a set of transition probabilities. The interacting multiple model (IMM) algorithm [7,24,33] was the first to propose this approach through jump Markov process modeling. Then, the multiple model particle filter (MMPF) framework [23,34] came as a promising alternative outperforming the IMM algorithm in a bearings-only tracking problem [4].…”

Section: Related Workmentioning

confidence: 99%

Multi-model particle filter-based tracking with switching dynamical state to study bedload transport

Micheaux

Ducottet

Frey

2018

Machine Vision and Applications

View full text Add to dashboard Cite

Multi-object tracking is a difficult problem underlying many computer vision applications. In this work, we focus on bedload sediment transport experiments in a turbulent flow were sediments are represented by small spherical calibrated glass beads. The aim is to track all beads over long time sequences to obtain sediment velocities and concentration. Classical algorithms used in fluid mechanics fail to track the beads over long sequences with a high precision because they incorrectly handle both miss-detections and detector imprecision. Our contribution is to propose a particle filter-based algorithm including a multiple motion model adapted to our problem. Additionally, this algorithm includes several improvements such as the estimation of the detector confidence to account for the lack of precision of the detector. The evaluation was made using two test sequences-one from our experimental setup and one from a simulation created numerically-with their dedicated ground truths. The results show that this algorithm outperforms state-of-the-art concurrent algorithms. Keywords Visual object tracking • multiple targets tracking • particle filter • switching dynamical state • detector confidence • bedload transport

show abstract

Section: Related Workmentioning

confidence: 99%

Multi-model particle filter-based tracking with switching dynamical state to study bedload transport

Micheaux

Ducottet

Frey

2018

Machine Vision and Applications

View full text Add to dashboard Cite

show abstract

“…Some relevant theoretical analysis and simulation results are given in our prior works. 13 The adaptive law shown in equations (21) and (22) has been used in the MMAE to update the weights. The residualỹ ð Þ k, s is used as the metric to evaluate the performance of the parallel filters.…”

Section: Weight Updatementioning

confidence: 99%

“…Consequently, VS-IMM filters utilize a smaller model set and still achieve adequate performance. Another promising development is the low-complexity interacting multiple-model (IMM) filter with constant gain for linear time-invariant systems, 22 where the steady-state Kalman gain is computed off-line and inserted into the filtering algorithm, such that matrix inversions are not required during the online operation.…”

Section: Introductionmentioning

confidence: 99%

Parallel model adaptive Kalman filtering for autonomous navigation with line-of-sight measurements

Xiong¹,

Wei²,

Zhang³

2018

Proceedings of the Institution of Mechanical Engineers, Part G:

View full text Add to dashboard Cite

In this paper, a parallel model adaptive Kalman filtering algorithm is presented for multiple sensors estimation fusion when the measurement noise statistics are uncertain. As a typical adaptive filtering algorithm, the multiple model adaptive estimation tries to reduce the dependency of the filter on the noise parameters. It utilizes multiple models with different noise levels to estimate the state and combines the model-dependent estimates with model probability. However, with the increase in the number of active sensors, a large number of models are required to cover the entire range of the possible noise parameter values, which can become computationally infeasible. The main goal of this work is to incorporate the noise statistic estimator in the framework of the multiple model adaptive estimation, such that only two models are required for each sensor, which significantly reduce the complexity of the estimator. The advantage of the presented algorithm to deal with the model uncertainty is studied analytically. The high performance of the parallel model adaptive Kalman filtering for autonomous satellite navigation using inter-satellite line-of-sight measurements is illustrated in comparison with a robust Kalman filter, an intrinsically Bayesian robust Kalman filter, and the traditional multiple model adaptive estimation.

show abstract

“…The commonly used estimation model is a Kalman filter. At the same time, one must select a target's motion model, like the current statistical model, the Singer model, or the interactive multi-model scheme [3]. For polishing up the estimating performance of the target's maneuvers, observability of the interception problem with LOS angular velocity measurement is analyzed [4].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Robust Guidance Scheme Based on the Sliding Mode Control in an Aircraft Pursuit-Evasion Problem

Chen¹,

Zheng²,

Ren³

et al. 2018

Adaptive Robust Control Systems

View full text Add to dashboard Cite

In this chapter, a robust guidance scheme utilizing a line-of-sight (LOS) observation is presented. Initial relative speed and distance, and error boundaries of them are estimated in accordance with the interceptor-target relative motion kinematics. A robust guidance scheme based on the sliding mode control (SMC) is developed, which requires the boundaries of the target maneuver, and inevitably has jitter phenomenon. For solving abovementioned problems, an estimation to the target acceleration's boundary is developed for enhancing robustness of the guidance scheme and the Lyapunov stabilization is analyzed. The proposed robust guidance scheme's brief characteristic is to reduce the effect of relative speed and distance, to reduce the effect of target maneuverability on the guidance precision, and to strengthen the influence of line-of-sight angular velocity. The proposed scheme's performances are validated by the simulations of different target maneuvers under two worst-case conditions.

show abstract

A low-complexity interacting multiple model filter for maneuvering target tracking

Cited by 15 publications

References 22 publications

Multi-model particle filter-based tracking with switching dynamical state to study bedload transport

Multi-model particle filter-based tracking with switching dynamical state to study bedload transport

Parallel model adaptive Kalman filtering for autonomous navigation with line-of-sight measurements

Adaptive Robust Guidance Scheme Based on the Sliding Mode Control in an Aircraft Pursuit-Evasion Problem

Contact Info

Product

Resources

About