Target tracking algorithm based on adaptive strong tracking particle filter

Li, Jiaqiang; Zhao, Ronghua; Chen, Jinli; Zhao, Chongbin; Yanping, Zhu

doi:10.1049/iet-smt.2016.0044

Cited by 44 publications

(20 citation statements)

References 22 publications

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“…On the basis of the model fusion method in the traditional multiple model algorithm, the premise membership function is identified by Equations (19) and (20), the model probability is obtained by Equation (12), and the xfalse^ki and Pki are obtained by strong tracking [38]. Therefore, the state and covariance updates of the proposed T-S fuzzy model are as follows:x˜k=true∑i=1Nfμkixfalse^ki P˜k=true∑i=1Nfμkifalse[Pki+false(x˜k−xfalse^kifalse)false(x˜k−xfalse^kifalse)Tfalse]…”

Section: The Proposed Algorithmmentioning

confidence: 99%

“…T-S fuzzy model parameter identificationConsequence parameter identification: The strong tracking algorithm [38] is used to identify the consequence parameters.Premise parameter identification: As shown in Algorithm 1.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

“…Consequence parameter identification: The strong tracking algorithm [38] is used to identify the consequence parameters.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

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A Novel FEM Based T-S Fuzzy Particle Filtering for Bearings-Only Maneuvering Target Tracking

Wang

Xie

2019

Sensors

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In this paper, we propose a novel fuzzy expectation maximization (FEM) based Takagi-Sugeno (T-S) fuzzy particle filtering (FEMTS-PF) algorithm for a passive sensor system. In order to incorporate target spatial-temporal information into particle filtering, we introduce a T-S fuzzy modeling algorithm, in which an improved FEM approach is proposed to adaptively identify the premise parameters, and the model probability is adjusted by the premise membership functions. In the proposed FEM, the fuzzy parameter is derived by the fuzzy C-regressive model clustering method based on entropy and spatial-temporal characteristics, which can avoid the subjective influence caused by the artificial setting of the initial value when compared to the traditional FEM. Furthermore, using the proposed T-S fuzzy model, the algorithm samples particles, which can effectively reduce the particle degradation phenomenon and the parallel filtering, can realize the real-time performance of the algorithm. Finally, the results of the proposed algorithm are evaluated and compared to several existing filtering algorithms through a series of Monte Carlo simulations. The simulation results demonstrate that the proposed algorithm is more precise, robust and that it even has a faster convergence rate than the interacting multiple model unscented Kalman filter (IMMUKF), interacting multiple model extended Kalman filter (IMMEKF) and interacting multiple model Rao-Blackwellized particle filter (IMMRBPF).

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Section: The Proposed Algorithmmentioning

confidence: 99%

Section: The Proposed Algorithmmentioning

confidence: 99%

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A Novel FEM Based T-S Fuzzy Particle Filtering for Bearings-Only Maneuvering Target Tracking

Wang

Xie

2019

Sensors

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“…The EKF adopts the Taylor series expansion to obtain the linear approximation of the nonlinear systems, and then the standard Kalman filter is applied. However, the accuracy of EKF is unsatisfactory for strong nonlinear systems in which the linearity error of the model may seriously affect filtering accuracy and even lead to the filter divergence [4]. Moreover, the EKF requires differentiation of the nonlinear system models where the Jacobian matrix needs to be calculated, which is computationally cumbersome in many applications [5].…”

Section: Introductionmentioning

confidence: 99%

Time‐Varying Noise Statistic Estimator Based Adaptive Simplex Cubature Kalman Filter

Li¹,

Yang²,

Ding³

2017

Mathematical Problems in Engineering

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To address the problem that filtering accuracy is reduced with the inaccurate time-varying noise statistic in conventional cubature Kalman filter, a noise statistic estimator based adaptive simplex cubature Kalman filter is put forward in this paper. First, the simplex cubature rule is adopted to approximate the intractable nonlinear Gaussian weighted integral in the filter. Secondly, a suboptimal unbiased constant noise statistic estimator is derived based on the maximum a posteriori estimation criterion. For the time-varying noise, the above estimator is modified using an exponential weighted attenuation method to realize the oblivion of stale data which results in a fading memory estimator, which has the ability to estimate the time-varying noise statistic to revise the filter online. The simulation results indicate that the proposed filter can achieve higher accuracy than conventional filters with inaccurate noise statistic.

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“…In target tracking problem, the process model is generally linear, while the measurement model, mainly including the measured range and bearing angle, is nonlinear [2,3]. The essence of the target tracking is to use a series of measured ranges and bearing angle information to estimate the position and velocity of the target in real time; hence, it belongs to the nonlinear filtering problem, which has always been dealt with using the nonlinear Kalman filters [4].…”

Section: Introductionmentioning

confidence: 99%

A Novel Fifth-Degree Strong Tracking Cubature Kalman Filter for Two-Dimensional Maneuvering Target Tracking

Yang

Ding

2018

Mathematical Problems in Engineering

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A novel fifth-degree strong tracking cubature Kalman filter is put forward to improve the two-dimensional maneuvering target tracking accuracy. First, a new fifth-degree cubature rule, with only one point more than the theoretical lower bound, is used to approximate the intractable nonlinear Gaussian weighted integral in the nonlinear Kalman filtering framework, and a novel fifthdegree cubature Kalman filter is proposed. Then, the suboptimal fading factor is designed for the filter to adjust the filtering gain matrix online and force the residual sequences mutually orthogonal, thus improving the ability of the filter to track the mutation state, and the fifth-degree strong tracking cubature Kalman filter is derived. The suboptimal fading factor is calculated in a new method, which reduces the number of calculations for the cubature points from three times to twice without calculating the Jacobian matrix. The simulation results indicate that the proposed filter has the ability to track the maneuvering target and achieve higher target tracking accuracy and thus verifies the effectiveness of the proposed filter.

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Target tracking algorithm based on adaptive strong tracking particle filter

Cited by 44 publications

References 22 publications

A Novel FEM Based T-S Fuzzy Particle Filtering for Bearings-Only Maneuvering Target Tracking

A Novel FEM Based T-S Fuzzy Particle Filtering for Bearings-Only Maneuvering Target Tracking

Time‐Varying Noise Statistic Estimator Based Adaptive Simplex Cubature Kalman Filter

A Novel Fifth-Degree Strong Tracking Cubature Kalman Filter for Two-Dimensional Maneuvering Target Tracking

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