Tracking an underwater maneuvering target using an adaptive Kalman filter

Li, Wei; Li, Yiping; Ren, Siying; Feng, Xisheng

doi:10.1109/tencon.2013.6718493

Cited by 6 publications

(6 citation statements)

References 9 publications

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“…It adopts a Taylor series to linearize the nonlinear part and obtains a sub-optimal estimation. An example of the use of EKF in underwater target tracking can be found in [1,4,15,19]. An UKF approximates the nonlinear distribution using a set of discrete, weighted samples, called particles and makes an approximation of the posterior probability density function (PDF) of the state.…”

Section: Target Tracking In Sonar Anti-collisionmentioning

confidence: 99%

“…The usefulness of the PF for underwater target tracking was proven for example in [14,20]. Some other interesting modifications and adaptations of the KF are reported in the literature, and are mostly dedicated to specific applications, such as maneuvering target tracking or multiple-target tracking [19,21]. An interesting approach to underwater target tracking is also use of the neural networks, proposed for example in [22] as a modification of radar target tracking methods.…”

Section: Target Tracking In Sonar Anti-collisionmentioning

confidence: 99%

“…However, because the vector state model was modified by the coordinate transformation, the resulting system noise was given as the variation of the bearing. This acceleration based concept was also expanded in an interesting way in [19], in which an interacting multiple model (IMM) filter was constructed. A constant velocity model and constant acceleration model were incorporated in the IMM.…”

Section: Process Noise Modelsmentioning

confidence: 99%

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Determination of Process Noise for Underwater Target Tracking with Forward Looking Sonar

Kazimierski¹,

Zaniewicz²

2021

Remote Sensing

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Target tracking is a process that provides information about targets in a specific area and is one of the key issues affecting the safety of any vehicle navigating in water. The main sensor used for underwater target tracking is sonar, with one of the most popular configurations being forward looking sonar (FLS). The target tracking state vector is usually estimated with the use of numerical filter algorithms, such as the Kalman filter (KF) and its modification, or the particle filter (PF). This requires the definition of a process model, including process noise, and a measurement model. This study focused on process noise definition. It is usually implemented as Gaussian noise, with a covariance matrix defined by the author. An analytical and empirical analysis was conducted, including a verification of the existing approaches and a survey of the published literature. Additionally, a theoretical analysis of the factors influencing process noise was conducted, which was followed by an empirical verification. The results were discussed, leading to the conclusions. The results of the theoretical analysis were confirmed by the empirical experiment and the results were compared with commonly used values of process noise in underwater target tracking processes.

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Section: Target Tracking In Sonar Anti-collisionmentioning

confidence: 99%

Section: Target Tracking In Sonar Anti-collisionmentioning

confidence: 99%

Section: Process Noise Modelsmentioning

confidence: 99%

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Determination of Process Noise for Underwater Target Tracking with Forward Looking Sonar

Kazimierski¹,

Zaniewicz²

2021

Remote Sensing

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“…An adaptive KF is proposed for tracking underwater maneuvering target in [ 88 ]. The algorithm uses another KF to estimate the process noise variance of the main KF, which effectively improves the tracking accuracy and reduces the response time of the filter for maneuvering behaviors of the target.…”

Section: Classification Underwater Acoustic Target Tracking Algorimentioning

confidence: 99%

Underwater Acoustic Target Tracking: A Review

Luo

Han

Fan

2018

Sensors

110

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Advances in acoustic technology and instrumentation now make it possible to explore marine resources. As a significant component of ocean exploration, underwater acoustic target tracking has aroused wide attention both in military and civil fields. Due to the complexity of the marine environment, numerous techniques have been proposed to obtain better tracking performance. In this paper, we survey over 100 papers ranging from innovative papers to the state-of-the-art in this field to present underwater tracking technologies. Not only the related knowledge of acoustic tracking instrument and tracking progress is clarified in detail, but also a novel taxonomy method is proposed. In this paper, algorithms for underwater acoustic target tracking are classified based on the methods used as: (1) instrument-assisted methods; (2) mode-based methods; (3) tracking optimization methods. These algorithms are compared and analyzed in the aspect of dimensions, numbers, and maneuvering of the tracking target, which is different from other survey papers. Meanwhile, challenges, countermeasures, and lessons learned are illustrated in this paper.

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“…Different than the existing works such as [12,13], which require parallel two filters running to both estimate the AUV dynamics and adapt the filter, the proposed algorithm has only one filter running. Thus it does not require a cumbersome additional computational load opposed to the two-filter based adaptive algorithms [12,13]. There are other single-filter based adaptive algorithms proposed for AUV state estimation in the literature [14].…”

Section: Introductionmentioning

confidence: 99%

Q-Adaptation of UKF Algorithm for Estimation of the Autonomous Underwater Vehicles Dynamics

Hajiyev¹,

Söken²

2018

International Conference of Control, Dynamic Systems, and Robotics

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Accurate estimation for the pose and orientation of the autonomous underwater vehicles (AUVs) is necessary in most of the applications. This is same for both the missile type AUVs or the vehicles used for research purposes deep in the sea or ocean. A nonlinear version of the Kalman Filter as the unscented Kalman filter (UKF) gives satisfactory estimation results for this purpose in the normal operation conditions. However, in the deep sea, changes in the environment (process noise) either instantaneously or periodically are very likely. In such case, the UKF must be adapted to become robust against such changes and provide accurate estimation results even in this case. In this study, we propose process noise covariance matrix adaptation (Q-adaptation) for the UKF algorithm. The main aim is to make the algorithm adaptive against the changes in the process noise covariance. The Adaptive UKF (AUKF) estimates the AUVs dynamics. We investigate the performance of the algorithm when the process noise increases and decreases, which are very likely as a result of changes in the AUV dynamics in different environments. The results are compared with those of a non-Q-adaptive version of the UKF algorithm.

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Tracking an underwater maneuvering target using an adaptive Kalman filter

Cited by 6 publications

References 9 publications

Determination of Process Noise for Underwater Target Tracking with Forward Looking Sonar

Determination of Process Noise for Underwater Target Tracking with Forward Looking Sonar

Underwater Acoustic Target Tracking: A Review

Q-Adaptation of UKF Algorithm for Estimation of the Autonomous Underwater Vehicles Dynamics

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