Gaussian Sum Shifted Rayleigh Filter for Underwater Bearings-Only Target Tracking Problems

Radhakrishnan, R.; Bhaumik, Shovan; Tomar, Nutan Kumar

doi:10.1109/joe.2018.2814218

Cited by 40 publications

(20 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The filters are all initialized using the method given in []. The position estimates of the relative state vector are initialized based on the first bearing measurement θ 0 and initial range guess, r .…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The filters are all initialized using the method given in [20,23]. The position estimates of the relative state vector are initialized based on the first bearing measurement 0 and initial range guess, r. Depending on how the target is closing in on the observer, the initial course estimate can be defined asc = 0 + and the initial assumed target speed as s. Parameters r , s and c are the possible errors in assumed range, target speed and target initial course.…”

Section: Filter Initializationmentioning

confidence: 99%

“…However, due to the nonlinear nature of the measurement model, the solution becomes mathematically intractable and hence we have to go for suboptimal solutions. Almost all suboptimal solutions like the extended Kalman filter (EKF) [], unscented Kalman filter (UKF) [], cubature Kalman filter (CKF) [], Gauss‐Hermite filter (GHF) [], sparse‐grid Gauss‐Hermite filter (SGHF) [], particle filter (PF) [], shifted Rayleigh filter (SRF) [], their Gaussian sum extensions [] have been subjected to exhaustive study for TMA problems. Of these filters, SRF has proved to be highly accurate and computationally less demanding than many other popular filters [].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Continuous‐discrete filters for bearings‐only underwater target tracking problems

Radhakrishnan

Bhaumik

Tomar

2019

Asian Journal of Control

Self Cite

View full text Add to dashboard Cite

In this work, we develop a continuous-discrete shifted Rayleigh filter (CD-SRF) and a continuous-discrete sparse-grid Gauss-Hermite filter (CD-SGHF) for a real-life passive underwater bearings-only target tracking problem. The stochastic difference equation describing the process model is derived from its continuous equivalent using Ito-Taylor expansion of order 1.5. The performance of the proposed filters is compared in terms of root mean square error (RMSE), track divergence and computational time. For a fair comparison, popular filters like the unscented Kalman filter (UKF), cubature Kalman filter (CKF) andGauss-Hermite filter (GHF) are implemented. The effect of initial uncertainty, measurement noise covariance and sampling time on filtering accuracy is also studied. Finally, RMSEs of all the filters are evaluated in comparison with the Cramer-Rao lower bound (CRLB). From simulation results, it was observed that CD filters performed with higher accuracy than their discrete equivalents, with CD-SRF proving to be the most accurate among all the filters.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Section: Filter Initializationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Continuous‐discrete filters for bearings‐only underwater target tracking problems

Radhakrishnan

Bhaumik

Tomar

2019

Asian Journal of Control

Self Cite

View full text Add to dashboard Cite

show abstract

“…Here I 2×2 is an identity matrix of order 2 and O 2×2 is a zero matrix of order 2. The process noise vector η k is assumed to follow a normal distribution with zero mean and covariance Q, where Q is the same as mentioned in [23].…”

Section: Simulation Resultsmentioning

confidence: 99%

A New Method for Generating Sigma Points and Weights for Nonlinear Filtering

Radhakrishnan

Yadav

Date

et al. 2018

IEEE Control Syst. Lett.

Self Cite

View full text Add to dashboard Cite

In this paper, a new method termed as new sigma point Kalman filter (NSKF), is proposed for generating sigma points and weights for estimating the states of a stochastic nonlinear dynamic system. The sigma points and their corresponding weights are generated such that the points nearer to the mean (in inner product sense) have a higher probability of occurrence, and the mean vector and covariance matrix are matched exactly. Performance of the new algorithm is compared with the existing unscented Kalman filter (UKF), the cubature Kalman filter (CKF), the cubature quadrature Kalman filter (CQKF) and higher order unscented filter (HOUF) for two different problems. Comparison is done by calculating the root mean square error (RMSE), relative computational time and track-loss. From simulation results, it can be concluded that the proposed algorithm performs with superior estimation accuracy when compared to the UKF, CKF, CQKF and HOUF.

show abstract

“…Bearings‐only tracking (BOT) is a classical method in passive target tracking, which only uses the angle parameter to realize the target localization and tracking. BOT is widely applied for reconnaissance operations in aviation, navigation, and submarines .…”

Section: Introductionmentioning

confidence: 99%

Robust maneuver strategy of observer for bearings‐only tracking

Chen

et al. 2019

Asian Journal of Control

View full text Add to dashboard Cite

For the bearings-only tracking problem, an observer's maneuver can enhance the accuracy of estimation. However, it may be idealistic to expect the observer to move in the free space during reconnaissance missions, because threats in the motion space can restrict the motion space of the observer. In this study, considering the constraint of threats avoidance on the motion space of the observer, an optimal maneuver strategy was proposed. Firstly, the adaptive cubature Kalman Filter method was established to improve the robustness and accuracy of state estimation. Then the finite horizon Markov Decision VProcess approach was improved to generate the optimal maneuver policy, in which the quantization method was responsible for discretizing the process and providing the transition matrix for Markov Decision Process, and the novel reward function was proposed as the criterion for optimization. Finally, the framework of the method was established. The numerical results verified the feasibility and advantage of the proposed method by comparing them with state of the art research in the field.KEYWORDS bearing-only tracking, cubature Kalman filter, Markov decision process, maneuver strategy, robustness ---

show abstract

Gaussian Sum Shifted Rayleigh Filter for Underwater Bearings-Only Target Tracking Problems

Cited by 40 publications

References 22 publications

Continuous‐discrete filters for bearings‐only underwater target tracking problems

Continuous‐discrete filters for bearings‐only underwater target tracking problems

A New Method for Generating Sigma Points and Weights for Nonlinear Filtering

Robust maneuver strategy of observer for bearings‐only tracking

Contact Info

Product

Resources

About