Fuzzy probabilistic data association filter and its application to single maneuvering target

Li, Xiaobin; Fan, En; Shen, Shigen; Hu, Keli; Li, Pengfei

doi:10.1186/s13634-016-0401-8

Cited by 2 publications

(7 citation statements)

References 26 publications

(25 reference statements)

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“…To track a single maneuvering target in situations with unknown measurement noise covariances, the FRLSF method proposed in [24] employs a fuzzy reference to adjust the fading factor of the recursive least squares filter (RLSF). It achieves good tracking performance if no clutter is present in surveillance.…”

Section: Fuzzy Recursive Least Squares Filter (Frlsf)mentioning

confidence: 99%

“…It achieves good tracking performance if no clutter is present in surveillance. Next, the filter is further extended for cluttered environments, and its simplified form is deduced in [24]. The main equations of the FRLSF are given as follows:…”

Section: Fuzzy Recursive Least Squares Filter (Frlsf)mentioning

confidence: 99%

“…whereẑ k,t is the fused measurement on the target t as described in reference [24], I is an n × n unit matrix and L is the number of the designed fuzzy rules. A l i and B l i denote the corresponding fuzzy sets for the measurement residual v k,t and heading change ϑ k,t , respectively.…”

Section: Fuzzy Recursive Least Squares Filter (Frlsf)mentioning

confidence: 99%

“…The fuzzy recursive least squares filter method (FRLSF) proposed in [22] utilizes measurement residuals and heading changes as two input variables of the designed fuzzy system, and this system is further employed to adjust the fading factor and realize single maneuvering target tracking. The improvement of FRLSF combined with the PDA algorithm is utilized in cluttered environments [24], thus providing an effective method to solve the MMTT problem.…”

Section: Introductionmentioning

confidence: 99%

“…The incorporation of the motion features on a moving target into a MMTT method is a good strategy to improve the accuracy of data association [22]. In particular, the observed angle is an important motion feature on maneuvering targets and is often employed in maneuvering target tracking [24,25]. Therefore, it can be introduced into the calculation of association probabilities to improve the association accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Improved Joint Probabilistic Data Association (JPDA) Filter Using Motion Feature for Multiple Maneuvering Targets in Uncertain Tracking Situations

et al. 2018

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To track multiple maneuvering targets in cluttered environments with uncertain measurement noises and uncertain target dynamic models, an improved joint probabilistic data association-fuzzy recursive least squares filter (IJPDA-FRLSF) is proposed. In the proposed filter, two uncertain models of measurements and observed angles are first established. Next, these two models are further employed to construct an additive fusion strategy, which is then utilized to calculate generalized joint association probabilities of measurements belonging to different targets. Moreover, the obtained probabilities are applied to replace the joint association probabilities calculated by the standard joint probabilistic data association (JPDA) method. Considering the advantage of the fuzzy recursive least squares filter (FRLSF) on tracking a single maneuvering target, which can relax the restrictive assumption of measurement noise covariances and target dynamic models, FRLSF is still used to update the state of each target track. Thus, the proposed filter can not only provide the advantage of FRLSF but can also adjust the weights of measurements and observed angles in the generalized joint association probabilities adaptively according to their uncertainty. The performance of the proposed filter is evaluated in two experiments with simulation data and real data. It is found to be better than the performance of other three filters in terms of the tracking accuracy and the average run time.

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Section: Fuzzy Recursive Least Squares Filter (Frlsf)mentioning

confidence: 99%

Section: Fuzzy Recursive Least Squares Filter (Frlsf)mentioning

confidence: 99%

Section: Fuzzy Recursive Least Squares Filter (Frlsf)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved Joint Probabilistic Data Association (JPDA) Filter Using Motion Feature for Multiple Maneuvering Targets in Uncertain Tracking Situations

et al. 2018

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Real-time adaptive fuzzy density clustering for multi-target data association

Nazari¹,

Pashazadeh²

2021

IDA

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The problem of data association for tracking multiple targets based on using the ship-borne radar is addressed in this study. A robust fuzzy density clustering algorithm is proposed, that contains three steps. At first, a customized form of adaptive density clustering is used to determine valid measurements for each target’s state. In the second step, the degree of fuzzy membership for each valid measurement is determined based on the maximum entropy approach. At the final step, the measurements with a maximum degree of membership are used for updating the position of the targets. The proposed approach does not require gating techniques and led to the reduction of steps in comparison with other data association methods. In addition, the effect of ship movement in the performance of the tracking filter, based on the adaptive extended Kalman filter (AEKF) was studied. The efficiency and effectiveness of the proposed algorithm are compared with the nearest neighbor (NN) with Mahalanobis distance and Fuzzy nearest neighbor (FNN) methods. The results demonstrate the main advantages of the proposed algorithm, including its simplicity and suitability for real-time target tracking in cluttered environments.

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Fuzzy probabilistic data association filter and its application to single maneuvering target

Cited by 2 publications

References 26 publications

Improved Joint Probabilistic Data Association (JPDA) Filter Using Motion Feature for Multiple Maneuvering Targets in Uncertain Tracking Situations

Improved Joint Probabilistic Data Association (JPDA) Filter Using Motion Feature for Multiple Maneuvering Targets in Uncertain Tracking Situations

Real-time adaptive fuzzy density clustering for multi-target data association

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