A New Pseudolinear Filter for Bearings-Only Tracking without Requirement of Bias Compensation

Bu, Shizhe; Meng, Aiqiang; Zhou, Gongjian

doi:10.3390/s21165444

Cited by 7 publications

(4 citation statements)

References 32 publications

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“…The third simulation is about sensor trajectory planning. For fairness and completeness, we use the pseudolinear Kalman filter method in [30] for target localization as the platform to compare the performance of trajectories. One fitness function,…”

Section: Examplementioning

confidence: 99%

Target Localization and Sensor Movement Trajectory Planning with Bearing-Only Measurements in Three Dimensional Space

et al. 2022

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In order to improve the accuracy of bearing-only localization in three dimensional (3D) space, this paper proposes a novel bias compensation method and a new single-sensor maneuvering trajectory algorithm, respectively. Compared with traditional methods, the bias compensation method estimates the unknown variance of bearing noise consistently, which is utilized in pseudo-linear target localization to achieve higher precision. The sensor maneuvering algorithm designs the next moment sensor location in consideration of all the past sensor locations, unlike other approaches that only consider finite past locations. Research shows that the trajectories generated by our algorithm have greater Fisher information matrix (FIM) determinants and better localization accuracy.

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Section: Examplementioning

confidence: 99%

Target Localization and Sensor Movement Trajectory Planning with Bearing-Only Measurements in Three Dimensional Space

et al. 2022

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“…The extended Kalman filter (EKF) is a classical method for the nonlinear tracking problem [ 7 ] but often diverges when the model nonlinearity is strong. The pseudolinear Kalman filter (PLKF) was introduced in [ 8 , 9 ], with better convergence than the EKF. However, the estimate is biased, which is highly dependent on sensor geometry [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Optimal Geometry and Motion Coordination for Multisensor Target Tracking with Bearings-Only Measurements

Wang

et al. 2023

Sensors

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This paper focuses on the optimal geometry and motion coordination problem of mobile bearings-only sensors for improving target tracking performance. A general optimal sensor–target geometry is derived with uniform sensor–target distance using D-optimality for arbitrary n (n≥2) bearings-only sensors. The optimal geometry is characterized by the partition cases dividing n into the sum of integers no less than two. Then, a motion coordination method is developed to steer the sensors to reach the circular radius orbit (CRO) around the target with a minimum sensor–target distance and move with a circular formation. The sensors are first driven to approach the target directly when outside the CRO. When the sensor reaches the CRO, they are then allocated to different subsets according to the partition cases through matching the optimal geometry. The sensor motion is optimized under constraints to achieve the matched optimal geometry by minimizing the sum of the distance traveled by the sensors. Finally, two illustrative examples are used to demonstrate the effectiveness of the proposed approach.

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“…These variants of PLKF based on bias compensation are not always perfect when the measurement noise is large and the geometry is unfavorable. Based on the PLKF, Bu et al [26] proposes a new pseudolinear filter under the minimum mean square error (PL-MMSE) framework without offset compensation, which shows better tracking performance under the large measurement noise than the above algorithms.…”

Section: Introductionmentioning

confidence: 99%

Non-Gaussian Pseudolinear Kalman Filtering-Based Target Motion Analysis with State Constraints

Tang

Zhang

et al. 2022

Applied Sciences

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For the bearing-only target motion analysis (TMA), the pseudolinear Kalman filter (PLKF) solves the complex nonlinear estimation of the motion model parameters but suffers serious bias problems. The pseudolinear Kalman filter under the minimum mean square error framework (PL-MMSE) has a more accurate tracking ability and higher stability compared to the PLKF. Since the bearing signals are corrupted by non-Gaussian noise in practice, we reconstruct the PL-MMSE under Gaussian mixture noise. If some prior information, such as state constraints, is available, the performance of the PL-MMSE can be further improved by incorporating state constraints in the filtering process. In this paper, the mean square and estimation projection methods are used to incorporate PL-MMSE with linear constraints, respectively. Then, the linear approximation and second-order approximation methods are applied to merge PL-MMSE with nonlinear constraints, respectively. Simulation results demonstrate that the constrained PL-MMSE algorithms result in lower mean square errors and bias norms, which demonstrates the superiority of the constrained algorithms.

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A New Pseudolinear Filter for Bearings-Only Tracking without Requirement of Bias Compensation

Cited by 7 publications

References 32 publications

Target Localization and Sensor Movement Trajectory Planning with Bearing-Only Measurements in Three Dimensional Space

Target Localization and Sensor Movement Trajectory Planning with Bearing-Only Measurements in Three Dimensional Space

Optimal Geometry and Motion Coordination for Multisensor Target Tracking with Bearings-Only Measurements

Non-Gaussian Pseudolinear Kalman Filtering-Based Target Motion Analysis with State Constraints

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