Pseudomeasurement Kalman filter in underwater target motion analysis & Integration of bearing-only and active-range measurement

Abstract

Target motion analysis of the underwater target tracking by the UUV (Unmanned underwater vehicle) usually based on the bearing-only observations including azimuth and elevation angles. However, low angular resolution of hydroacoustic sonars does not enough for the good qiality of tracking. Moreover, angular observations lead to nonlinear filtering such as Extended Kalman Filtering (EKF) which usually produce estimations with unknown bias and quadratic errors. Moreover, in bearing-only observations, as it was mentioned long ago, possible unobservability could take place, therefore, some special observer’s motion become necessary. Other filters like the particle or unscented ones need the additional computer resources and also could produce the tracking loss. At the same time the pseudomeasurements Kalman filtering (PKF) method which transforms the estimation problem to the linear one and gives the current coordinates estimation with almost same accuracy could be modified to evaluate the moving target coordinates and velocities without bias. Since PKF gives unbiased estimate for the motion and the quadratic error it provides the good means for integration of various measurements methods such as passive (bearing-only) and active (range) metering. Using this filtering approach the good quality of TMA for randomly moving target may be achieved.