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Annabattula, Jawahar
- Advanced Submarine Integrated Weapon Control System
Authors
1 School of Electrical Sciences, KLEF, KL University, Guntur - 522502, Andhra Pradesh, IN
2 Department of ECE, Vignan’s Institute of Information Technology, Visakhapatnam - 530046, Andhra Pradesh, IN
Source
Indian Journal of Science and Technology, Vol 8, No 35 (2015), Pagination:Abstract
Background/Objectives: In underwater, a single submarine processes noisy sonar bearings from a radiating target (ship/submarine/torpedo) in passive listening mode. Methods/Statistical Analysis: The target is assumed to be at constant velocity or maneuvering in its course/speed/course and speed. An adaptive Kalman filter estimates target motion parameters. Findings: Data fusion and data association in multi target and multi sensor scenario are carried out, using Chi-squared innovation process. Applications/Improvements: Finally improved torpedo intercept guidance algorithm with homing capabilities is developed for attacking the target.Keywords
Data Fusion, Doppler, Fire Control System, Towed Array- Multi-Sensor Submarine Surveillance System using MGBEKF
Authors
1 School of Electrical Sciences, KLEF, KL University, Guntur - 522502, Andhra Pradesh, IN
2 Department of ECE, Vignan’s Institute of Information Technology, Visakhapatnam - 530046, Andhra Pradesh, IN
Source
Indian Journal of Science and Technology, Vol 8, No 35 (2015), Pagination:Abstract
Background/Objectives: The modern submarines use mutlitple sensors for tracking multiple targets in sea environment. In general, multiple sensor data can be handled in two ways: measurement fusion and state vector fusion. Methods/ Statistical Analysis: Measurement fusion is not practical for implementation due to various reasons and hence state vector fusion is proposed. Target trackingusing Modified Gain Bearings only Extended Kalman Filter in generic two-dimensional platform is carried out in each channel. Findings: In this approach, a state vector and its corresponding covariance matrix are extracted from the sensor measurements by an estimator equipped on each sensor. The output of each channel is transported via a data link in to order to be reach the fusion center. Applications/Improvements: A composite target state vector is obtained by performing track-to-track correlation and fusion at the fusion center.Monte-Carlo simulation is carried outand the results are presented for a typical scenario.Keywords
Estimation, Fusion, Kalman Filter, Simulation, Tracking- Underwater Passive Target Tracking in Constrained Environment
Authors
1 School of Electrical Sciences, KLEF, KL University, Guntur - 522502, Andhra Pradesh, IN
2 Department of ECE, Vignan’s Institute of Information Technology, Visakhapatnam - 530046, Andhra Pradesh, IN
Source
Indian Journal of Science and Technology, Vol 8, No 35 (2015), Pagination:Abstract
In underwater scenario, observer manoeuvre is required to find out target motion parameters in bearings only passive target tracking. Sometimes, due to tactical constraints, observer is not able to carry out manoeuvre. In this paper, it is shown that target motion parameters can be obtained in such situation, if the knowledge of any one of the target motion parameters is available. There are other practical problems like spurious bearings are generated by sonar, etc. In addition, auto tracking fails often and some bearings will be missed. Pseudo Linear Kalman Filter is made flexible to address these practical problems.Keywords
Constrained Environment, Direction of Arrival Estimation, Kalman Filtering, Monte Carlo Simulation, Passive Target Tracking, Target Detection- Some Insightful Aspects in Underwater Bearings-Only Tracking
Authors
1 Sanketika Institute of Technology and Management, Visakhapatnam, Andhra Pradesh, IN
2 Naval Technological Research Laboratories, Visakhapatnam, Andhra Pradesh, IN
Source
Fuzzy Systems, Vol 10, No 5 (2018), Pagination: 109-116Abstract
In bearings-only under water target tracking, observer has to carry out one or more maneuvers to estimate target motion parameters. In general, two dimensional bearings-only tracking is used for underwater applications. The basic assumptions are that the submarine motion is unrestricted and the target (another submarine or ship) moves at constant velocity. The target and observer are assumed to be in the same horizontal plane. In passive target tracking, a single observer monitors a sequence of target bearing measurements, which are assumed to be available at equi-spaced discrete time intervals. The bearing measurements are corrupted with white Gaussian noise and this noise is assumed to be less when compared to the actual value of the bearing. The observer can search for the target within some specified look angle (and not total 360 deg) to avoid its own self noise zone hence traditional S-maneuver is not preferable always. In this research work, a procedure is tried out to understand the scenario online and recommend the maneuver accordingly.
Keywords
Sonar, Estimation Theory, Target Tracking, Observer, Maneuver.- Characterisation Studies of Underwater Target Tracking
Authors
1 Sanketika Institute of Technology and Management, Visakhapatnam, Andhra Pradesh, IN
2 Naval Research and Technological Laboratories, Visakhapatnam, Andhra Pradesh, IN
Source
Fuzzy Systems, Vol 10, No 5 (2018), Pagination: 117-125Abstract
Passive target tracking using bearings-only measurements is used to track underwater targets. Unscented Kalman Filter (UKF) is proved to be efficient non-linear estimator to estimate the target motion parameters. An effort is made to find out the Range Uncertainty Ellipse Zone (RUEZ) of the target using UKF covariance matrix in Monte-Carlo simulation. Underwater warfare today constitutes one of the greatest threats to the freedom of the seas. In under water, a single submarine processes noisy sonar bearings from a radiating target (ship/submarine/torpedo) in passive listening mode. The target is assumed to be at constant velocity. Bearing measurements by passive sensors are inaccurate. The observer processes these measurements and finds out Target Motion Parameters (TMP) - viz., range, course, bearing and speed of the target. This process is called Target Motion Analysis (TMA). Here the measurement is non-linear, making the whole process non-linear. Once the target’s RUEZ becomes less than that of homing zone of the weapon to be fired on to target, weapon can be released to destroy the target.