Parameter estimation algorithms based on a physics-based HRR moving target model

In contrast to Synthetic Aperture Radar (SAR), High Range Resolution (HRR) radar may economically provide satisfactory target resolution when applied to moving targets scenarios. We have devised a series of new physics-based HRR moving target models with different degrees of simplification. These models represent the scatterers from both targets and clutter equally. By employing these models, we can unify the studies of both clutter suppression and target feature extraction into a single topic of model parameter estimation. Therefore, finding reliable parameter estimation algorithms based on these models becomes an important topic for target identification using HRR signatures. This paper derives and presents two feasible parameter estimation algorithms. The first algorithm (1DPE) reduces the 2D-estimation problem to two 1D-estimation problems, and solves the problems by employing some mature 1D-estirnation algorithms. The second algorithm (2DFT) utilizes the 2D Discrete Fourier Transform (DFT) to estimate the model parameters by simply applying the 2D DFT to the HRR data, and obtaining the estimation of model parameters from the peaks of the 2D DFT. In order to verify the performance of these algorithms, we performed a series of simulation experiments and the experimental results are presented in this paper. Finally, a brief comparison of these two algorithms is also presented.