Nonlinear Tracking of Target Submarine Using Extended Kalman Filter (EKF)
This paper presents the effective method for submarine tracking using EKF. EKF is a Bayesian recursive filter based on the linearization of nonlinearities in the state and the measurement system. Here the sonar system is used to determine the position and velocity of the target submarine which is moving with respect to non moving submarine, and sonar is the most effective methods in finding the completely immersed submarine in deep waters. When the target submarines position and velocity is located from the reflected sonar, an extended Kalman filter is used as smoothening filters that describes the position and velocity of the ship with the noisy measurements given by sonar that is reflected back. By using the algorithm of extended Kalman filter we derived to estimate the position and velocity. Here the target motion is defined in Cartesian coordinates, while the measurements are specified in spherical coordinates with respect to sonar location. When the target submarine is located, the alert signal is sent to the own ship. This can be excessively used in military applications for tracking the state of the target submarine. Prediction of the state of the submarine is possible, with Gaussian noise to the input data. The simulation results show that proposed method is able to track the state estimate of the target, this was validated by plotting SNR vs MSE of state estimates. Here in this algorithm regressive iteration method is used to converge to the actual values from the data received.
KeywordsExtended Kalman Filter (EKF) Active sonar Passive sonar Non-linear filters Prediction methods
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