Bearing-only 2D maneuvering target tracking using smart interacting multiple model filter

Ebrahimi, Mohsen; Ardeshiri, Mahdi; Khanghah, Sedigheh Alaie

doi:10.1016/j.dsp.2022.103497

Cited by 14 publications

(7 citation statements)

References 35 publications

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“…In addition, a novel MCC-based Rauch-Tung-Striebel smoother [22] is proposed to solve the state estimation problem under non-Gaussian process and measurement noises. For the maneuvering target tracking, the multiple-model-based filter is currently the mainstream method [23][24][25]. Unfortunately, the above MCC-based filters are designed based on a single model, and they cannot solve the multiple-model state estimation problem under outlier interference.…”

Section: Introductionmentioning

confidence: 99%

Weighted Maximum Correntropy Criterion-Based Interacting Multiple-Model Filter for Maneuvering Target Tracking

Huai,

Li,

Yun

et al. 2023

Remote Sensing

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During the process of maneuvering target tracking, the measurement may be disturbed by outliers, which leads to a decrease in the state estimation performance of the classic interacting multiple-model (IMM) filter. To solve this problem, a weighted maximum correntropy criterion (WMCC)-based IMM filter is proposed. In the proposed filter, the fusion state is used as the input of each sub-model to reduce the computational complexity of state interaction and the WMCC is adopted to derive the sub-model state update and state fusion to improve the state estimation performance under outlier interference. Through principal analysis, the superiority of the proposed filter over the classic IMM filter in fusion strategy is revealed. The specific form of the proposed filter in radar maneuvering target tracking is provided. Two experimental cases of maneuvering target tracking are tested to illustrate the effectiveness of the proposed filter.

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Section: Introductionmentioning

confidence: 99%

Weighted Maximum Correntropy Criterion-Based Interacting Multiple-Model Filter for Maneuvering Target Tracking

Huai,

Li,

Yun

et al. 2023

Remote Sensing

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show abstract

“…Target tracking for unmanned systems in terrestrial, aerial or underwater environment, such as unmanned aerial vehicle (UAV), unattended air system (UAS), autonomous underwater vehicle (AUV) and etc. mostly utilizes artificial intelligence, positional information collection and environmental understanding [1][2][3], considering 2D or 3D kinematic information [4][5].…”

Section: Introductionmentioning

confidence: 99%

An Improved Target Tracking Scheme based on MC-MPMC Method for Mobile Wireless Sensor Networks

Zhu

Zhang

2022

Preprint

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Target tracking is crucial to many applications in wireless sensor networks (WSNs). Existing tracking schemes used in WSNs can basically be classified two categories, clustering and predicting. Considering network clustering consumes much energy for limited-energy WSNs, a predicting target tracking scheme is proposed called MC-MPMC (Measurement Compensation based Mixture Population Monte Carlo) method which tracks the target based on predicted locations in this work. Adaptive mixture PMC model for generating proposals varying from each iteration is proposed to guarantee the sample diversity. And also, extra measurements or observations generating method is introduced to compensate missing predict locations or false estimations, avoiding tracking behavior degradation. Firstly, samples drawn from the proposals of next iteration can generated by a mixture method to avoid sample degeneracy. Secondly, sample weights are jointly computed based on adaptive fusion of compensation measurement and true measurements. Thirdly, the HTC method is combined to MC-MPMC scheme to decrease energy consumption in WSNs. Then, the proposed method is verified through comprehensive experiments about tracking error, delay and consumption predictions. Moreover, performance comparisons of MC-MPMC with other tracking schemes are also proposed.

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“…However, if the course and speed of the target change significantly or abruptly, multiple motion models are required. Interacting multiple model (IMM) estimation is a representative estimation algorithm that employs multiple models, and it has been successfully used for solving the problem of tracking maneuvering targets [32]- [35]. However, existing IMM-based localization algorithms are centralized estimation algorithms, which implies that all measurement information is centralized in a computation center (algorithm).…”

Section: Introductionmentioning

confidence: 99%

Distributed Frobenius-Norm Finite Memory Interacting Multiple Model Estimation for Mobile Robot Localization

et al. 2022

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This paper proposes a novel state estimation algorithm, called the distributed Frobenius-norm finite memory interacting multiple model (DFFM-IMM) estimation algorithm, for mobile robot localization in wireless sensor networks (WSNs). The proposed algorithm involves finite memory estimation based on recent finite measurements; such estimation facilitates robust localization in cases of missing measurements and robot kidnapping. Furthermore, the proposed algorithm employs IMM, which facilitates accurate localization if a mobile robot abruptly changes its speed and course. Notably, average-consensus-based distributed processing renders the proposed DFFM-IMM algorithm computationally efficient, and hence, real-time processing for very short sampling times of the WSN is possible. The proposed algorithm's performance is demonstrated by comparing it with a centralized Frobenius-norm finite memory IMM (CFFM-IMM) estimation algorithm and a localization algorithm on the basis of simulations and experiments.

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Bearing-only 2D maneuvering target tracking using smart interacting multiple model filter

Cited by 14 publications

References 35 publications

Weighted Maximum Correntropy Criterion-Based Interacting Multiple-Model Filter for Maneuvering Target Tracking

Weighted Maximum Correntropy Criterion-Based Interacting Multiple-Model Filter for Maneuvering Target Tracking

An Improved Target Tracking Scheme based on MC-MPMC Method for Mobile Wireless Sensor Networks

Distributed Frobenius-Norm Finite Memory Interacting Multiple Model Estimation for Mobile Robot Localization

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