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2D HRR Radar Data Modeling and Processing

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Abstract

High Range Resolution (HRR) -based Automatic Target Recognition (ATR) has attracted increasing attention due to a number of potential advantages over alternative radar techniques in moving target identification. Most current HRR-based ATR studies have been conducted using 1D HRR signatures. However, these 1D HRR signatures are generally plagued by scintillation effects, and thus demonstrate highly irregular behavior that dramatically degrades the performance and robustness of algorithms based on these signatures. In order to circumvent this difficulty, an alternative HRR radar data representation and processing technique is presented in this paper. This technique models and extracts the target characteristics directly, based on the 2D HRR raw data. In this paper, we first derive a general, but complex HRR radar model, and then simplify this model by instantiating a set of real-world radar and target parameters for the model. This simplification process produces two HRR radar models with different degrees of simplicity. After establishing this set of models, the typical HRR data processes, such as feature extraction and clutter suppression, are reduced to one problem, which is model-parameter estimation. Based upon the most simplified HRR model we proposed, we devise two model- parameter estimation algorithms. One is a scatterer extraction algorithm based on available 1D Parameter Estimation (1DPE), while the other is based on 2D discrete Fourier Transform (2DFT). In order to examine the performance of these two algorithms a set of simulations are conducted. The experimental results are presented, and the performance comparison between 1DPE and 2DFT is presented.

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Authors
  1. Junshui Ma
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  2. Xun Du
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  3. Stanley C. Ahalt
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Ma, J., Du, X. & Ahalt, S.C. 2D HRR Radar Data Modeling and Processing. Multidimensional Systems and Signal Processing 14, 25–48 (2003). https://doi.org/10.1023/A:1022216924086

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