Bearing‐only underwater uncooperative target tracking for non‐Gaussian environment using fast particle filter

Hou, Xianghao; Yang, Long; Yang, Yixin; Zhou, Jianbo; Qiao, Gang

doi:10.1049/rsn2.12198

Cited by 4 publications

(3 citation statements)

References 26 publications

(47 reference statements)

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“…In this case, it is assumed that the target maneuvers with a constant and known turn rate, ω. The state transition matrix of this model is expressed as [23,29,30]…”

Section: Constant Turn Modelmentioning

confidence: 99%

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Tracking an Underwater Object with Unknown Sensor Noise Covariance Using Orthogonal Polynomial Filters

Kumar

Bhaumik

Arulampalam

2022

Sensors

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In this manuscript, an underwater target tracking problem with passive sensors is considered. The measurements used to track the target trajectories are (i) only bearing angles, and (ii) Doppler-shifted frequencies and bearing angles. Measurement noise is assumed to follow a zero mean Gaussian probability density function with unknown noise covariance. A method is developed which can estimate the position and velocity of the target along with the unknown measurement noise covariance at each time step. The proposed estimator linearises the nonlinear measurement using an orthogonal polynomial of first order, and the coefficients of the polynomial are evaluated using numerical integration. The unknown sensor noise covariance is estimated online from residual measurements. Compared to available adaptive sigma point filters, it is free from the Cholesky decomposition error. The developed method is applied to two underwater tracking scenarios which consider a nearly constant velocity target. The filter’s efficacy is evaluated using (i) root mean square error (RMSE), (ii) percentage of track loss, (iii) normalised (state) estimation error squared (NEES), (iv) bias norm, and (v) floating point operations (flops) count. From the simulation results, it is observed that the proposed method tracks the target in both scenarios, even for the unknown and time-varying measurement noise covariance case. Furthermore, the tracking accuracy increases with the incorporation of Doppler frequency measurements. The performance of the proposed method is comparable to the adaptive deterministic support point filters, with the advantage of a considerably reduced flops requirement.

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“…In this case, it is assumed that the target maneuvers with a constant and known turn rate, ω. The state transition matrix of this model is expressed as [23,29,30]…”

Section: Constant Turn Modelmentioning

confidence: 99%

“…Remark 4. Although, throughout our work, we consider the measurement noises to be Gaussian, it may not always be so [30,39,40]. In the case of non-Gaussian noise, it can be approximated by a Gaussian distribution and we can apply the proposed method.…”

Section: Measurement Updatementioning

confidence: 99%

Tracking an Underwater Object with Unknown Sensor Noise Covariance Using Orthogonal Polynomial Filters

Kumar

Bhaumik

Arulampalam

2022

Sensors

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“…The direction-of-arrival (DOA) estimation and tracking is an important research topic in sonar signal processing [1][2][3][4][5]. For the scenario of moving targets, the traditional DOA estimation methods cut the measurement of the output of sonar array signal into small periods in time to process, which ignores the kinematic characteristics of the targets [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise

Zhang

Hou²,

Yang³

et al. 2023

Front. Phys.

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The direction-of-arrival (DOA) tracking of underwater targets is an important research topic in sonar signal processing. Considering that the underwater DOA tracking is a typical multi-target problem under unknown underwater environment with missing detection, false alarm, and uncertain measurement noise, a robust underwater multi-target DOA tracking method for uncertain measurement noise is proposed. First, a kinematic model of the multiple underwater targets and bearing angle measurement model with missing detection and false alarms are established. Then, the multi-target DOA tracking algorithm is derived by using the cardinalized probability hypothesis density (CPHD) filter, the performance of which largely depends on the accuracy of the parameter of measurement noise variance. In addition, the variational Bayesian approach is used to adaptively estimate the uncertain measurement of noise variance for each measurement of target in the real time of tracking. Thus, the robust underwater multi-target DOA tracking is carried out. Finally, comprehensive experimental validations and discussions are made to prove that the proposed algorithm can provide robust DOA tracking in the multi-target tracking scenario with uncertain measurement noise.

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Robust cardinalized probability hypothesis density filter based underwater multi-target direction-of-arrival tracking with uncertain measurement noise

Yang,

Zhang,

Hou

2024

Applied Acoustics

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Bearing‐only underwater uncooperative target tracking for non‐Gaussian environment using fast particle filter

Cited by 4 publications

References 26 publications

Tracking an Underwater Object with Unknown Sensor Noise Covariance Using Orthogonal Polynomial Filters

Tracking an Underwater Object with Unknown Sensor Noise Covariance Using Orthogonal Polynomial Filters

Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise

Robust cardinalized probability hypothesis density filter based underwater multi-target direction-of-arrival tracking with uncertain measurement noise

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