Range-Parameterized Range and Velocity Constrained State Estimation

Abstract

To enhance the estimation accuracy, in this letter, we proposed a range-parameterized, constrained state estimation technique for a bearings-only underwater tracking problem. After executing the range-parameterized filtering method by running a number of traditional filters in parallel, each having a different initial estimate of range, the weighted average estimate of the filters is calculated. On the weighted averaged outcome, the range and the velocity constrained optimization problem are solved using the Lagrange multiplier. The constraints are determined using the range and the velocity limits known to the observer. The method is implemented in two underwater tracking scenarios, and the results are compared in terms of root mean square error, percentage of track loss, and relative execution time. The proposed method has been observed to perform better than the respective range-parameterized and traditional filters.