Tracking multiple maneuvering targets using a sequential multiple target Bayes filter with jump Markov system models

Liu, Zongxiang; Zhang, Qi-quan; Li, Liang-qun; Xie, Weixin

doi:10.1016/j.neucom.2016.07.028

Cited by 17 publications

(6 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most common problem of MJLSs is the state estimation according to the measurements. This problem originates from inevitable variables in the states of MJLSs in the target tracking (Geng et al, 2017;Liu et al, 2016), signal processing (Gao, et al, 2018;Shen et al, 2017), fault detection (Dong et al, 2018;Habibi et al, 2017;Wang, et al, 2015), and so on (Luan et al, 2013;Luan et al, 2018;Zhou et al, 2018Zhou et al, , 2019. Moreover, when non-white additive noises perturb the sensor measurements, the complexity of the estimation problem increases.…”

Section: Introductionmentioning

confidence: 99%

Finite-time higher-order moment state estimation for Markov jump linear system with time-correlated measurement noise

Zhou

Luan

Liu

2021

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

In the present study, a higher-order moment state estimation problem in a finite time interval for the discrete-time Markov jump linear systems (MJLS) is investigated. Moreover, time-correlated noise in measurements is considered. Initially, the measruement differencing approach is applied to convert the time-correlated measurement noise to an uncorrelated noise. Then the cumulant generating function is utilized to solve the stochastic jumping problem of MJLS, by which the discrete-time MJLS is transformed into a deterministic system. In this way, the transformed deterministic system has the same norm with a higher-order moment of the original state. Finally, a finite-time state estimation algorithm is proposed to guarantee that the higher-order moment of error trajectory remains within a pre-specified bound over a given time interval. In order to evaluate the performance of the proposed method, some test cases are applied. Obtained results prove the accuracy and efficiency of the proposed method.

show abstract

Section: Introductionmentioning

confidence: 99%

Finite-time higher-order moment state estimation for Markov jump linear system with time-correlated measurement noise

Zhou

Luan

Liu

2021

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

show abstract

“…T ARGET tracking is a fundamental task in sensor-based applications, such as radar, sonar, and navigation [1]. Numerous mathematical models have been developed to approximate the motion trajectory of the target [2], such as the commonly used non-manoeuvring model, i.e., constant velocity (CV) model and highly manoeuvring model, i.e., coordinated turn (CT) model.…”

Section: Introductionmentioning

confidence: 99%

A Gaussian Process based Method for Multiple Model Tracking

Sun

Davies

Proudler

et al. 2020

2020 Sensor Signal Processing for Defence Conference (SSPD)

View full text Add to dashboard Cite

Manoeuvring target tracking faces the challenge caused by the target motion model uncertainty, i.e., unknown model types or uncertain model parameters. Multiple-model (MM) methods have been generally considered to deal with this challenge, in which a bank of elemental filters is run simultaneously to provide a joint decision and estimation of motion model and localisation. However, if the uncertainty of the target trajectory increases, such as the target moves under mixed manoeuvring behaviours with time-varying parameters, more filters with different model assumptions have to be taken into account to match the motion of the target, which may lead to prohibitive computational complexity. To address this problem, we establish a training based algorithm which can learn the actual motion model as a Gaussian process (GP) regression. Then, by integrating the trained GP into the particle filter (PF), a GP-PF based tracking method is developed to track the manoeuvring targets in real-time. Monte Carlo experiments show that the proposed method had much lower tracking root mean square error (RMSE) and robustness compared with the most commonly used MM methods.

show abstract

“…The maneuvering target tracking problem has drawn immense attention for many years in areas such as aircraft surveillance, radar tracking, and road vehicle navigation [ 1 , 2 ]. In the military field, to avoid being attacked, most of the enemy’s targets have strong maneuvers.…”

Section: Introductionmentioning

confidence: 99%

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

Ding

Liu

et al. 2018

Sensors

View full text Add to dashboard Cite

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.

show abstract

Tracking multiple maneuvering targets using a sequential multiple target Bayes filter with jump Markov system models

Cited by 17 publications

References 29 publications

Finite-time higher-order moment state estimation for Markov jump linear system with time-correlated measurement noise

Finite-time higher-order moment state estimation for Markov jump linear system with time-correlated measurement noise

A Gaussian Process based Method for Multiple Model Tracking

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

Contact Info

Product

Resources

About