A. Vijaya ¹, S. Koteswara Rao ¹, A. Jawahar ¹, Sk. B. Karishma ²

Affiliations
1 Department of Electronics and Communication Engineering, KL University, Vaddeswaram, Guntur - 522502, Andhra Pradesh, IN
2 Department of Electronics and Communication Engineering, KL University, Vaddeswaram, Guntur - 522502, Andhra Pradesh

Abstract

Background/Objectives: This paper presents parameterized Modified Gain Bearings Only Extended Kalman Filter to track a torpedo using bearings-only measurements. Methods/Statistical Analysis: The parameterization is included to obtain fast convergence in estimated torpedo motion parameters. Findings: Observer uses estimated target kinematics to determine proper observer manuever calculates. Monte-Carlo simulation is carried out and the results are presented. Application/Improvements: It is noted that parameterization reduces the time of convergence and the results are satisfactory.

Keywords

Kalman Filter, Sonar, Torpedo

Full Text

   

K. Lakshmiprasanna ¹, S. Koteswara Rao ¹, A. Jawahar ¹, Sk. B. Karishma ¹

Affiliations
1 School of Electrical Sciences, KL University, Vaddeswaram – 522502, Andhra Pradesh, IN

Abstract

Background/Objectives: The necessity of ownship stratagies is essential during the attack of enemy torpedo. Methods/Statistical Analysis: Torpedo tacking using advanced particle filter is proposed in bearings-only tracking environment for underwater applications. The observer platform has to perform evasive maneuver as well as perform target motion analysis to track the incoming hostile torpedo. Particle Filter (PF) combined with Modified Gain Bearings-Only Extended Kalman Filter (MGBEKF) is proposed technique. Findings: Monte-Carlo simulation is carried out for performance evaluation of the algorithm and the obtained results are presented which agree that PF-MGBEKF is most suitable as a part of ownship strategy.

Keywords

Bearing Measurements, Estimation, Simulation, Sonar, Torpedo, Tracking

Full Text

   

K. Lakshmiprasanna ¹, S. Koteswara Rao ¹, A. Jawahar ¹, Sk. B. Karishma ¹

Affiliations
1 School of Electrical Sciences, KL University, Vaddeswaram, Guntur – 522502, Andhra Pradesh, IN

Abstract

Background/Objectives: Pseudo Linear Estimator (PLE) is developed for active sonar applications. Methods/Statistical analysis: The PLE offers features of Extended Kalman Filter (EKF). Findings: The results of PLE are compared with that of EKF. The results of MC simulation are presented for typical scenarios. Application/Improvements: In PLE, there is no need to initialize target state vector and its covariance matrix with prior (approximate) knowledge and hence its performance is found to be better than that of EKF.

Keywords

Estimation, Kalman Filter, Pseudo Linear Estimator, Simulation, Target Tracking

Full Text

   

K. Lakshmi Prasanna ¹, S. Koteswara Rao ¹, SK. B. Karishma ¹, A. Jawahar ¹

Affiliations
1 Koneru Lakshmaiah University, Guntur – 522502, Andhra Pradesh, IN

Abstract

Background/Objectives: The objective of this paper is to develop a novel estimation algorithm based target tracking simulator for underwater target tracking applications. Methods/Statistical Analysis: An own ship observes corrupted sonar bearings from a radiating target and finds out Target Motion Parameters (TMP) - viz., range, course, bearing and speed of the target. The issue is inherently nonlinear as the bearing measurement is non-linearly related to the target state. CKF is a new nonlinear filter for state estimation. The modeling of target state and measurement vectors is carried out. CKF is integrated into the model to result in evolution of simulator. Extensive performance evaluation of CKF with respect to bearings-only target tracking problem in Monte-Carlo simulation is carried out and the results are presented. Findings: CKF depends on spherical-radial cubature rule that makes it potential to numerically figure variable moment integrals encountered within the nonlinear filter. The underwater passive target tracking following Cubature Kalman filter is explored in this paper. Application/Improvements: The results obtained are satisfactory and UKF can be used in futuristic submarines in Indian Navy owing to its advantages as envisaged in this paper.

Keywords

Cubature Kalman (CKF) Filter, Estimation, Kalman Filter, Simulation, Sonar, Underwater Target Tracking.

Full Text

   

B. Omkar Lakshmi Jagan ¹, S. Koteswara Rao ¹, A. Jawahar ¹, SK. B. Karishma ¹

Affiliations
1 Koneru Lakshmaiah University, Vaddeswaram - 522502, Andhra Pradesh, IN

Abstract

Background/Objectives: Target tracking is an age old problem which demands robust statistical estimators which can effectively track the target within the acceptable limits of errors in target motion parameters. The objective of this paper is to develop a novel estimation algorithm based target tracking simulator for underwater target tracking applications. Methods/Statistical Analysis: The unscented transformation developed by Julier, et al., is applied to the body of Kalman filter to synthesize Unscented Kalman filter. The modeling of target state and measurement vectors is carried out. Unscented Kalman filter is integrated into the model to result in evolution of simulator. Extensive performance evaluation of UKF with respect to bearings-only target tracking problem in Monte-Carlo simulation is carried out and the results are presented. Findings: UKF algorithms effectively track the target with encouraging convergence time which is proved from the results obtained in single run and Monte-Carlo simulation. It is observed that UKF is suitable algorithm for bearings-only target tracking problem. Application/Improvements: The results obtained are satisfactory and UKF can be used in futuristic submarines in Indian Navy owing to its advantages as envisaged in this paper.

Keywords

Estimation Theory, Kalman Filter, Simulation, Sonar, Target Tracking.

Full Text

   

B. Omkar Lakshmi Jagan ¹, S. Koteswara Rao ¹, A. Jawahar ¹, SK. B. Karishma ¹

Affiliations
1 School of Electrical Sciences, KL University, Vaddeswaram, Guntur - 522502, Andhra Pradesh, IN

Abstract

Background/Objectives: The Extended Kalman Filter (EKF) using range and bearing measurements is analyzed for undersea target tracking. The Input estimation technique, developed by Bar-Shalom and Fortmann for radar applications is implemented for sonar applications. Methods/Statistical Analysis: Input estimation is used to estimate the target acceleration whenever the target makes a maneuver. Findings: The algorithm estimates target kinematics using zero mean chi-square distributed random sequence residual. Upon detection of target maneuver, this algorithm corrects the velocity and position components using acceleration components. Application/Improvements: Finally, the performance of this algorithm is evaluated in Monte-Carlo simulations and results conform the effectiveness of input estimation technique.

Keywords

Bearing Measurements, Estimation, Maneuvering, Range, Target Tracking.

Full Text