An adaptive IMM filter for jump Markov systems with inaccurate noise covariances in the presence of missing measurements

A proper filtering method for jump Markov system (JMS) is an effective approach for tracking a maneuvering target. Since the coexisting of heavy-tailed measurement noises (HTMNs) and one-step random measurement delay (OSRMD) in the complex scenarios of the surface maneuvering target tracking, the effectiveness of typical interacting multiple model (IMM) techniques may decline severely. To solve the state estimation problem in JMSs with HTMN and OSRMD simultaneously, this article designs a novel robust IMM filter utilizing the variational Bayesian (VB) inference framework. This algorithm models the HTMNs as student’s t-distribuitons, and presents a random Bernoulli variable to describe the OSRMD in JMSs. By transforming measurement likelihood function form from weighted summation to exponential product, this paper constructs hierarchical Gaussian state space models. Then, the state vectors, random Bernoulli vairable, and model probability are inferred jointly according to VB inference. The surface maneuvering target tracking simulation example result indicates that the presented IMM filter achieves superior target state estimation accuracy among existing IMM filters.

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“…Using the Kullback-Leibler average fusion method [33], the summation term in Equation ( 33) is approximated as follows:…”

Section: Design Of the Proposed Filtermentioning

confidence: 99%

A Novel Robust IMM Filtering Method for Surface-Maneuvering Target Tracking with Random Measurement Delay

Chen

Zhou

Gao

2023

JMSE

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“…In order to adapt to the emerging trend of enhanced maneuverability in the target domain, it is necessary to enhance the tracking performance of PD radar systems when dealing with highly maneuverable targets. The interacting multiple model (IMM) algorithm has gradually become a mainstream maneuvering target tracking algorithm due to its superior robustness, accuracy, flexibility, and scalability advantages compared with other single-model tracking algorithms [4][5][6][7][8]. As a result, it has been widely applied in various types of PD radars.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive IMM Algorithm for a PD Radar with Improved Maneuvering Target Tracking Performance

Xu,

Xiao,

et al. 2024

Remote Sensing

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A pulse-Doppler (PD) radar has the advantage of strong anti-interference ability, and it is often used as a solution for maneuvering target tracking. In the application of target monitoring and tracking in PD radars, the interacting multiple model algorithm (IMM) has become the main and preferred choice due to its flexibility and high accuracy. However, the probability transfer matrix in classical IMM algorithms generally depends on constant prior knowledge, and if a PD radar is tracking a strong maneuvering target, it is inevitable to encounter some limitations, such as the possibility of target tracking trajectory deviation, and even a loss of the target. The Markov probability transfer matrix is proposed with an adaptive modification ability in real time to overcome the above problems in this paper. Additionally, for improving the speed of switching between the models, the fuzzy control system for secondary updating of model probability is adopted. By this means, the tracking accuracy of maneuvering targets is enhanced. Compared with the classical IMM algorithm, the corresponding simulation results for the PD radar indicate that the overall tracking accuracy of the proposed adaptive IMM algorithm is improved by 19.6%. In conclusion, the continuity and accuracy of the target trajectory can be effectively improved with the proposed adaptive IMM algorithm in PD radar cases.

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“…Tracking surface targets can be considered a state estimation issue in JMSs. There is no existing optimal Bayesian solution to estimate the states of JMSs, since they introduce problems of nondeterministic polynomial difficulties and computational intractability [10]. In recent decades, a series of sub-optimal estimators have been proposed, such as particle filters, the generalized pseudo-Bayesian method, and the interacting multiple model (IMM) approach, etc.…”

Section: Introductionmentioning

confidence: 99%

Robust IMM Filtering Approach with Adaptive Estimation of Measurement Loss Probability for Surface Target Tracking

Chen

Zhou

Gao

2023

JMSE

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A suitable jump Markov system (JMS) filtering approach provides an efficient technique for tracking surface targets. In complex surface target tracking situations, due to the joint influences of lost measurements with an unknown probability and heavy-tailed measurement noise (HTMN), the estimation accuracy of conventional interacting multiple model (IMM) methods may be seriously degraded. Aiming to address the filtering issues in JMSs with HTMNs and random measurement losses, this paper presents an IMM filtering approach with the adaptive estimation of unknown measurement loss probability. In this study, we assumed that the measurement noises obey student’s t-distributions and then proposed Bernoulli random variables (BRVs) to characterize the random measurement loss. Notably, by converting the two likelihood functions from the weighted sum form to exponential multiplication, we established hierarchical Gaussian state space models to directly utilize the variational inference method. The system state vectors, unknown distribution parameters, BRVs, and unknown measurement loss probabilities were estimated simultaneously according to the variational Bayesian inference in the IMM framework. The results of maneuvering target tracking simulations verified that the presented filtering approach demonstrated superior estimation accuracy compared to existing IMM filters.

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An adaptive IMM filter for jump Markov systems with inaccurate noise covariances in the presence of missing measurements

Cited by 12 publications

References 34 publications

A Novel Robust IMM Filtering Method for Surface-Maneuvering Target Tracking with Random Measurement Delay

A Novel Robust IMM Filtering Method for Surface-Maneuvering Target Tracking with Random Measurement Delay

An Adaptive IMM Algorithm for a PD Radar with Improved Maneuvering Target Tracking Performance

Robust IMM Filtering Approach with Adaptive Estimation of Measurement Loss Probability for Surface Target Tracking

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