Generalized bias compensated pseudolinear Kalman filter for colored noisy bearings-only measurements

Kaba, Utku; Temeltaş, Hakan

doi:10.1016/j.sigpro.2021.108331

Cited by 8 publications

(2 citation statements)

References 35 publications

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“…Aidala [22] pioneered the use of Kalman filtering for target state estimation. Subsequently, non-linear filtering methods like extended Kalman filtering (EKF) gained widespread adoption as they employ recursive algorithms, resulting in a substantial reduction in computational requirements [23][24][25][26]. Nevertheless, the aforementioned algorithms cannot completely obtain unbiased estimation, are more sensitive to the initial value or the measured value.…”

Section: Related Workmentioning

confidence: 99%

Bearing‐only motion analysis of target based on low‐quality bearing‐time recordings map

Zhang,

Wang,

Zhang

et al. 2023

IET Radar Sonar & Navi

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Accurate bearing‐time recordings play a crucial role in hydroacoustic target motion analysis and situation estimation. The target bearing estimated by vector hydrophone in the interference environment suffers from poor display and low signal‐to‐noise ratio. To solve this problem, a two‐stage bearing‐only motion analysis method is proposed. Firstly, the bearing peak information and bearing dispersion are extracted for each sampling moment of the bearing‐time recording. Custom constraint regions and adaptive parameters are then set based on this information. The order truncate average algorithm is improved to enhance the quality of the low‐quality bearing‐time recording map. Secondly, a bearing‐only target motion model is established based on the bearing‐time recording distribution features, and a bearing‐time recording trust interval is constructed. Then a modified pseudo‐linear estimation algorithm is proposed to solve the target motion situation information, including target abeam time, target abeam bearing, target course, velocity‐to‐initial‐distance ratio, and so forth. Finally, by comparing the solved values of the sea trial data with the automatic identification system calculation results, the enhancement effect of the target bearing‐time recording map is significant and the solving error of the target motion elements meets the needs of engineering applications.

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Section: Related Workmentioning

confidence: 99%

Bearing‐only motion analysis of target based on low‐quality bearing‐time recordings map

Zhang,

Wang,

Zhang

et al. 2023

IET Radar Sonar & Navi

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“…One study [5] proposes the Sequential Extended Kalman Filter (SEKF) for the target tracking problem using Doppler measurement, where the measurement conversion Kalman filter was first used to filter the position measurement linearly. Then, the Extended Kalman Filter (EKF) [6][7][8] was used to process the Doppler measurement. However, discarding high-order terms above second order during the EKF linearization process can lead to more significant errors when dealing with strong nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

A Method to Track Moving Targets Using a Doppler Radar Based on Converted State Kalman Filtering

Zhao,

Liu

et al. 2024

Electronics

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Strong nonlinearity between Doppler measurement and target motion in Doppler radar target tracking leads to the inadequate utilization of measurement information and limited tracking accuracy. We solved this problem by combining converted state Kalman filtering and the Interacting Multiple Model. This maneuvering target tracking method is suitable for Doppler measurement. First, we converted the target motion in the Cartesian coordinate to the polar coordinate. Then, we expanded the measurement equation to include Doppler measurement, making target motion linearly related to the Doppler radar observation vectors and allowing efficient utilization of measurement information. Next, we used unscented transformation to calculate the statistical characteristics of the process noise in the polar coordinate. This process helps to reduce the noise error caused by the coordinate system transformation in the original converted state Kalman filter. Finally, the system effectively tracks targets that may perform maneuvers with unknown motion during actual tracking. Using the converted state Kalman filter with Doppler measurement as a sub-filter, an Interacting Multiple Model tracking method can be constructed to adjust the model probabilities without going through nonlinear transformation. Simulation results show that the technique can achieve effective target tracking in Doppler measurement application scenarios and has higher tracking accuracy in non-maneuvering and maneuvering scenarios.

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Maximum correntropy-based pseudolinear Kalman filter for passive bearings-only target tracking

Urooj,

Radhakrishnan

2024

Control Theory Technol.

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Generalized bias compensated pseudolinear Kalman filter for colored noisy bearings-only measurements

Cited by 8 publications

References 35 publications

Bearing‐only motion analysis of target based on low‐quality bearing‐time recordings map

Bearing‐only motion analysis of target based on low‐quality bearing‐time recordings map

A Method to Track Moving Targets Using a Doppler Radar Based on Converted State Kalman Filtering

Maximum correntropy-based pseudolinear Kalman filter for passive bearings-only target tracking

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