Ionospheric Decontamination Algorithm Based on Maximum-likelihood Method in Over-the-horizon Radar

Ionospheric phase decontamination is a key technology in signal processing of sky-wave Over-The- Horizon Radar (OTHR). Due to the inaccuracy of the models and the complexity of the ionosphere, the accuracy of the existing algorithms is not satisfactory when the phase changes too fast. A new ionospheric phase decontamination algorithm is proposed based on the Maximum-Likelihood (ML) method. In this algorithm, the signal is modeled as a phase polynomial, and estimation of the perturbation phase is achieved by maximizing the likelihood function. To avoid matrix inversion in the ML method, the ML issue is further transformed to a least-squares issue. The coefficients of phase are solved by the genetic algorithm. The simulation results show that, compared with the traditional methods, the proposed algorithm has the following advantages: compared with the HRR algorithm and the CED algorithm, the algorithm proposed in this paper has higher accuracy, and the signal spectrum after decontamination is more sharp. Under the situation of serious phase contamination, the proposed algorithm still has higher precision, accordingly, the proposed algorithm is more advantageous to extract the target information. This algorithm adopts higher-order polynomials, which avoids segmented processing and computing the inverse of matrix, thus the computation process is simplified.