Algorithm for passive localization based on MVEKF (modified covariance extended Kalman filter)

MVEKF algorithm utilizes phase-difference rate of change to implement passive localization. In the full paper, we explain our MVEKF algorithm in detail; in this abstract, we just add some pertinent remarks to listing the three topics of explanation: (1) the passive localization principles based on phase-difference rate of change, (2) the details of MVEKF algorithm, and (3) the application of MVEKF to passive localization; in topic 1, eqs.(1) and (2) in the full paper are taken from the open literature; in topic 2, eqs.(3) through (7) in the full paper are also taken from the open literature; in topic 3, we select suitable state variables for a fixed target on ground and derive state equation, i.e., eq.(10) in the full paper; then, also in topic 3, using the passive localization principles based on phase-difference rate of change, we derive the system of filter equations for MVEKF, i.e., eqs.(11) through (18) in the full paper. The simulation results, given in Figs.1 and 2 in the full paper, show that, compared with EKF algorithm, MVEKF algorithm converges faster, possesses much better filtering stability, and, moreover, it is not easily influenced by the selection of initial state. The above-mentioned results also show that almost the same filtering effect as MGEKF can be achieved by MVEKF without the necessity of searching for modification function for measurement equation.