OPTIMAL RECURSIVE DATA PROCESSING ALGORITHM USING BAYESIAN INFERENCE FOR UNDERWATER VEHICLE LOCALISATION AND NAVIGATION SYSTEMS

In the ocean environment, two dimensional Range & Bearings target motion analysis (TMA) is generally used. In the underwater scenario, the active sonar, positioned on a observer, is capable of sensing the sound waves reflected from the target in water. The sonar sensors in the water pick up the target reflected signal in the active mode. The observer is assumed to be moving in straight line and the target is assumed to be moving mostly in straight line with maneuver occasionally. The observer processes the measurements and estimates the target motion parameters, viz., Range, Bearing, Course and Speed of the target. It also generates the validity of each of these parameters. Here we try to apply Kalman Filter for the sea scenario using the input estimation technique to detect target maneuver, estimate target acceleration and correct the target state vector accordingly.              There are mainly two versions of Kalman Filter – a linearised Kalman Filter (LKF) in which polar measurements are converted into Cartesian coordinates and the well-known Extended Kalman Filter (EKF). Recently S. T. Pork and L. E. Lee presented a detailed theoretical comparative study of the above two methods and stated that both the methods perform well. Here, EKF is used through out

SIMULATION AND CONTROL OF INDUCTION MOTOR DRIVE USING ADVANCED SOFT COMPUTING TECHNIQUES

Induction motor drives have certain advantages like less cost, ruggedness and required low maintenance. Field oriented control provides good solution for industrial applications. Normally in order to implement a vector control operation we generally require number of position sensors like speed, voltage, current sensors. But if we use the position sensors then the cost and size will be increased. So, to overcome this we need to use limited number of sensors. Reducing the number of sensors will increase the reliability of the system. So, if we eliminate the number of sensors we need to estimate the required quantity. The estimation can be done by using different strategies like model based and signal based out of this model based estimation the best method to estimate the speed by using Model Reference Adaptive System (MRAS).Â

IMPROVED NON LINEAR STOCHASTIC ESTIMATOR FOR ELECTRONIC SUPPORT MEASUREMENTS IN ELECTRONIC WARFARE

In Electronic Warfare (EW) Electronic Support Measurement (ESW) systems, the transmissions made by radar on a target ship are assumed to be intercepted by an EW system of ownship. Time to estimate the target motion parameters with reasonable accuracy is highly dependent on the values used for initialization of target state vector components. Inclusion of range, course and speed parameterization is proposed in target state vector of Modified Gain Bearings-Only Extended Kalman Filter to obtain fast convergence and to track a stationary/non stationary target. The performance of the algorithm is evaluated in simulation and results are presented for two selected scenarios

INVESTIGATION OF DATA PROCESSING FOR PASSIVE ACCOUSTIC AND ELECTROMAGNETIC UNDERWATER LOCALISATION AND CLASSIFICATION

In our earlier work, data fusion with specific application to underwater tracking environment is explored. The target can be tracked using array bearings, while it is moving with constant velocity and maneuvering occasionally. In this paper, it is shown that if data fusion is carried out using the bearing measurements available from Towed Array (TA) along with hull mounted array‟s bearings, then tracking of a continuously moving target can be carried out easily. This algorithm is independent of ownship maneuver for the observability of the process. Song and Speyer's & Galkowski and Islam‟s modified gain algorithms are utilized with some modifications for estimation. Monte Carlo simulation is performed and results are shown for various typical geometries

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

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

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

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

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

INVESTIGATION OF TARGET MOTION PARAMETERS USING OPTIMAL RECURSIVE ESTIMATION TECHNIQUE FROM PASSIVE SONAR IN UNDERWATER NAVIGATION SYSTEMS

In under water an observer pre-processes the noisy bearing measurements available from passive sonar and then the data is used by Kalman filter to find out target motion parameters. The pre-processing reduces the amplitude of the noise, replaces the missed bearings with estimated bearings, supplies the estimated bearings if the bearing measurement is not available or incorrect and finally it finds out mean and variance of the noisy data. The statistical characteristics of the data are used in Kalman filter which finds out the target motion parameters. On line estimation of bearing measurement is carried out using Pseudo linear estimator. Finally, the whole algorithm is evaluated in Monte- Carlo simulation and the results for one typical scenario are presented