Extended K alman Filter for Bearings Only T racking

Jahan, Kausar; Rao, S. Koteswara

doi:10.35940/ijeat.f8088.088619

Cited by 7 publications

(6 citation statements)

References 3 publications

(3 reference statements)

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“…A mathematical model of UKF is presented and detailed derivations are provided (Lakshmi et al , 2019; Tudoroiu and Khorasani, 2005; Jahan and Rao, 2019b).…”

Section: Proposed Methodologymentioning

confidence: 99%

“…For example, the computational load becomes heavy due to an increase in the Jacobian Matrix dimension with the nonlinearity of the system, resulting in poor accuracy of estimation. A new KF-based nonlinear estimation technique, UKF (Gupta et al , 2015; Jahan and Rao, 2019b; Lakshmi et al , 2019; Tudoroiu and Khorasani, 2005), was proposed by researchers to develop the efficiency of the EKF process. Based on unscented transformation (UT), a method (UKF) for generating mean and covariance that can be propagated through a nonlinear transformation is developed, which handles nonlinearities in a better way.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Shifted Rayleigh filter: a novel estimation filtering algorithm for pervasive underwater passive target tracking for computation in 3D by bearing and elevation measurements

Kavitha

Koteswara

Subrahmanyam

2021

IJPCC

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Purpose Marine exploration is becoming an important element of pervasive computing underwater target tracking. Many pervasive techniques are found in current literature, but only scant research has been conducted on their effectiveness in target tracking. Design/methodology/approach This research paper, introduces a Shifted Rayleigh Filter (SHRF) for three-dimensional (3 D) underwater target tracking. A comparison is drawn between the SHRF and previously proven method Unscented Kalman Filter (UKF). Findings SHRF is especially suitable for long-range scenarios to track a target with less solution convergence compared to UKF. In this analysis, the problem of determining the target location and speed from noise corrupted measurements of bearing, elevation by a single moving target is considered. SHRF is generated and its performance is evaluated for the target motion analysis approach. Originality/value The proposed filter performs better than UKF, especially for long-range scenarios. Experimental results from Monte Carlo are provided using MATLAB and the enhancements achieved by the SHRF techniques are evident.

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“…A mathematical model of UKF is presented and detailed derivations are provided (Lakshmi et al , 2019; Tudoroiu and Khorasani, 2005; Jahan and Rao, 2019b).…”

Section: Proposed Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Shifted Rayleigh filter: a novel estimation filtering algorithm for pervasive underwater passive target tracking for computation in 3D by bearing and elevation measurements

Kavitha

Koteswara

Subrahmanyam

2021

IJPCC

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“…PF also has the disadvantage of sample degeneracy and sample impoverishment. This is reduced by several resampling techniques available in literature [9,22]. However, these techniques are not suitable for all types of applications.…”

Section: Particle Filter Combined With Other Filtersmentioning

confidence: 99%

“…, 4 for SST The particles of the target state vector for PFEKF and PFMGBEKF filters are generated by adding random noise to the initial state vector as in (58) for TST. Similarly, for SST the initial state vector is added with a column matrix of random numbers with four rows as in Equation (64) [22].…”

Section: Implementation and Simulationmentioning

confidence: 99%

Fusion of Angle Measurements from Hull Mounted and Towed Array Sensors

Jahan

Rao

2020

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Two sensor arrays, hull-mounted array, and towed array sensors are considered for bearings-only tracking. An algorithm is designed to combine the information obtained as bearing (angle) measurements from both sensor arrays to give a better solution. Using data from two different sensor arrays reduces the problem of observability and the observer need not follow the S-maneuver to attain observability of the process. The performance of the fusion algorithm is comparable to that of theoretical Cramer–Rao lower bound and with that of the algorithm when bearing measurements from a single sensor array are considered. Different filters are used for analyzing both algorithms. Monte Carlo runs need to be done to evaluate the performance of algorithms more accurately. Also, the performance of the fusion algorithm is evaluated in terms of solution convergence time.

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“…In recent days, active research is being carried on UTT for passive target tracking. Different filters such as Kalman filter (KF) and its derivatives (Extended Kalman filter (EKF), Unscented Kalman filter (UKF) and Particle filter (PF) algorithms) are executed for tracking using only bearing measurements commonly known as Bearings-Only Tracking (BOT) applications (Koteswara Rao, 2018; Jahan and Koteswara Rao, 2019a, b; Ristic et al , 2004). In this research work, authors have extended their contribution to implementing nonlinear filtering algorithms for DBT by using Doppler-Bearing measurements.…”

Section: Introductionmentioning

confidence: 99%

Measure of nonlinearity for underwater target tracking using hull-mounted sensor

Jagan¹,

Rao²

2021

IJICC

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PurposeDoppler-Bearing Tracking (DBT) is commonly used in target tracking applications for the underwater environment using the Hull-Mounted Sensor (HMS). It is an important and challenging problem in an underwater environment.Design/methodology/approachThe system nonlinearity in an underwater environment increases due to several reasons such as the type of measurements taken, the speeds of target and observer, environmental conditions, number of sensors considered for measurements and so on. Degrees of nonlinearity (DoNL) for these problems are analyzed using a proposed measure of nonlinearity (MoNL) for state estimation.FindingsIn this research, the authors analyzed MoNL for state estimation and computed the conditional MoNL (normalized) using different filtering algorithms where measurements are obtained from a single sensor array (i.e. HMS). MoNL is implemented to find out the system nonlinearity for different filtering algorithms and identified how much nonlinear the system is, that is, to measure nonlinearity of a problem.Originality/valueAlgorithms are evaluated for various scenarios with different angles on the target bow (ATB) in Monte-Carlo simulation. Computation of root mean squared (RMS) errors in position and velocity is carried out to assess the state estimation accuracy using MATLAB.

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Extended K alman Filter for Bearings Only T racking

Cited by 7 publications

References 3 publications

Shifted Rayleigh filter: a novel estimation filtering algorithm for pervasive underwater passive target tracking for computation in 3D by bearing and elevation measurements

Shifted Rayleigh filter: a novel estimation filtering algorithm for pervasive underwater passive target tracking for computation in 3D by bearing and elevation measurements

Fusion of Angle Measurements from Hull Mounted and Towed Array Sensors

Measure of nonlinearity for underwater target tracking using hull-mounted sensor

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