Joint estimation of target state and ionospheric height bias in over-the-horizon radar target tracking

Up to now, all over-the-horizon radar-based tracking methods have the prerequisite that the virtual ionospheric height, as the key ionosphere state, should be known, or should be estimated from the provided ionospheric measurements, or at least the prior statistical information of the ionosphere should be given. However, the ionospheric height is intermittently evolving and hence deviates from its nominal value. Therefore, no matter the virtual ionospheric height is directly given by ionosondes or estimated from the ionospheric measurements or prior statistical information, it may have ionospheric height bias. Here, the authors propose the problem of joint estimation of target state and ionospheric height bias in clutters. The problem is reformulated as the multi-rate state estimation with random coefficient matrices. Then, the linear minimum mean square error estimator with causality constraints is designed and extended to the non-linear measurement model via iterative optimisation. The proposed method is shown effective in the simulation about tracking one target in the case of four resolvable propagation modes.