SUBMARINE TATICAL GEOMETRIES DURING ENEMY VEHICLE ATTACK USING NOVEL STATISTICAL STOCHASTIC NON LINEAR FILTER

Particle filter is proposed for tracking a torpedo using bearings-only measurements when torpedo is attacking an ownship. Towed array is used to generate torpedo bearing measurements. Ownship evasive maneuver is used for observability of the bearings-only process. Particle filter combined with Modified Gain Bearings-Only Extended Kalman Filter is used to estimate torpedo motion parameters, which are used to calculate optimum ownship evasive maneuver. Monte-Carlo simulation is carried out and the results are presented for typical scenarios

INVESTIGATION OF ADVANCED NON-LINEAR CONTROL AND ESTIMATION ALGORITHM FOR ROCKET BASED APPLICATIONS

Online Object tracking is an important task in radar and sonar signal processing As it  is a challenging problem due to the presence of noise, and dynamic changes. a variety of Stochastic algorithms for tracking targets have been proposed and implemented to reach  these challenges. Approaches towards   highly nonlinear  applications  is an   advanced   task . In this paper, we devote the effort to use the Particle Filtering with estimation of various states of a vehicle launched from an idealized spherical, airless, non-rotating earth to improve tracking efficiency. The simulation results show that the PF improved the tracking performance compared to the Kalman based Filters (EKF, UKF) for the rocket launch application

Comparative Analysis of Non Linear Estimation Schemes used for Undersea Sonar Applications

    The performance evaluation of various passive underwater target tracking algorithms like Pseudo Linear Estimator, Maximum Likelihood Estimator, Modified Gain Bearings-only Extended Kalman Filter, Unscented Kalman Filter, Parameterized Modified Gain Bearings-only Extended Kalman Filter and Particle Filter coupled with Modified Gain Bearings-only Extended Kalman Filter using bearings-only measurements is carried out with various scenarios in Monte Carlo Simulation. The performance of Parameterized Modified Gain Bearings-only Extended Kalman Filter is found to be better than all estimates

ANALYSIS OF EXTENED KALMAN FILTER USING RANGE AND LINE OF SIGHT MEASUREMENT FOR UNDERSEA TARGET LOCALISATION

The feasibility of the extended Kalman filter using range and bearing measurements is explored for underwater applications. The Input estimation technique, developed by Bar-Shalom and Fortmann for radar applications is implemented for sonar applications. Input estimation is used to estimate the target acceleration whenever the target makes a maneuver. The algorithm estimates target motion parameters and detects target maneuver 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. Finally, the performance of this algorithm is evaluated in Monte-Carlo simulations and results are shown for various typical geometries and found that this input estimation technique can be used for underwater applications

IMPROVED NON-LINEAR SIGNAL ESTIMATION TECHNIQUE FOR UNDERSEA SONAR BASED NAVAL APPLICATIONS

The aim of this work is to develop passive target tracking algorithm, suitable for implementation in target motion analysis for underwater applications. The vehicle is assumed to be standstill in underwater watching for any target ship using bearings only measurements. Using these measurements, the algorithm calculates the course of the target, which is further used to find out target range and speed. Provision is given to generate range and course if the speed of the target is known by some other means. Pseudo Linear Estimator (PLE) is developed to reduce the noise in the measurements and to find out target motion parameters. Though PLE offers a biased estimate in certain scenarios, it has an advantage as it hardly diverges. It offers the features of Kalman filter viz., sequential processing, flexibility to adopt the variance of each measurement etc.  The Monte-Carlo simulation results are presented for a typical scenario and it is shown that this algorithm is useful for naval underwater applications.Â

PARAMETERIZED MODIFIED GAIN BEARINGS-ONLY EXTENDED KALMAN FILTER UNDERSEA TARGET TRACKING

Inclusion of range, course and speed parameterization is proposed for Modified Gain Bearings Only Extended Kalman Filter to track a torpedo using bearings-only measurements. Parameterization is included to obtain fast convergence in estimated torpedo motion parameters. The ownship is assumed to be under torpedo attack and the bearing measurements are available from hull mounted array of the ownship. Observer uses estimated torpedo motion parameters to calculate optimum evasive maneuver.  Monte-Carlo simulation is carried out and the results are presented for typical scenarios with and without parameterization. It is noted that parameterization reduces the time of convergence and the results are satisfactory.Â