摘要
Navigation and surveillance applications require tracking constant input/bias targets. When the target＇s trajectory follows a constant input/bias constraint, model mismatching caused by conventional tracking algorithms can be handled by a delayed update filter （DUF）. The statistical convergence and stability properties of the delayed update filter were studied to insure the rationality of its steady-state analysis. A steady-state filter gain was then designed for a constant-gain DUF to reduce the computations without much performance loss. Simulations demonstrate the potential of the constant-gain DUF, and the CGDUF is nearly 60% faster than the DUF without much loss in steady-state tracking accuracy.
Navigation and surveillance applications require tracking constant input/bias targets. When the target＇s trajectory follows a constant input/bias constraint, model mismatching caused by conventional tracking algorithms can be handled by a delayed update filter （DUF）. The statistical convergence and stability properties of the delayed update filter were studied to insure the rationality of its steady-state analysis. A steady-state filter gain was then designed for a constant-gain DUF to reduce the computations without much performance loss. Simulations demonstrate the potential of the constant-gain DUF, and the CGDUF is nearly 60% faster than the DUF without much loss in steady-state tracking accuracy.
