In the target localization of skywave over-the-horizon radar (OTHR), the error of the ionospheric parameters is one main error source. To reduce the error of ionospheric parameters, a method using both the information of reference sources (e.g., terrain features, ADS-B) in ground coordinates and the corresponding OTHR measurements is proposed to estimate the ionospheric parameters. Describing the ionospheric electron density profile by the quasi-parabolic model, the estimation of the ionospheric parameters is formulated as an inverse problem, and is solved by a Markov chain Monte Carlo method due to the complicated ray path equations. Simulation results show that, comparing with using the a prior value of the ionospheric parameters, using the estimated ionospheric parameters based on four airliners in OTHR coordinate registration process, the ground range RMSE of interested targets is reduced from 2.86 to 1.13 km and the corresponding improvement ratio is up to 60.39%. This illustrates that the proposed method using reference sources is able to significantly improve the accuracy of target localization.

In this article, we consider the problem of optimally selecting a subset of transmitters from a transmitter set available to a multiple-input and multiple-output radar network. The aim is to minimize the location estimation error of underlying targets under a power constraint. We formulate it as a minimum-variance estimation problem and show that the underlying variance reduction function is submodular. From the properties of submodularity, we present a balanced selection policy which minimizes the worst-case error value using a minimax strategy. A greedy algorithm with guaranteed performance with respect to optimal solutions is given to efficiently implement the scheduling policy. The effectiveness and the efficiency of the proposed algorithm are demonstrated in simulated examples.

A radar HRRP recognition algorithm based on time-spectrogram feature and multi-scale convolutional neural network is proposed to address the difficult feature extraction and low accuracy in space target recognition. Firstly, the normalization is used to eliminate the intensity sensitivity, the absolute alignment of multiple dominant scatterers is used to eliminate the translation sensitivity, and the radar Doppler velocity is used to eliminate the widening effect, distortion and wave crest splitting on HRRP caused by high-speed motion of the target. Then, the method applies the time-frequency analysis to the preprocessed HRRP to extract the time-frequency diagram. Finally, the time-frequency features are extracted with different scales of fineness and different directions through asymmetric convolution of different scales. The data processing results demonstrate that the present method has a high target recognition accuracy. In addition, the present improves the anti-posture sensitivity and target recognition on the same platform.