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Sparsity aware micro-Doppler analysis for radar target classification

Sparsity aware micro-Doppler analysis for radar target classification

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In this chapter, two sparsity-driven algorithms of micro-Doppler analysis were presented for radar classification of rigid-body and nonrigid body targets, respectively. The first algorithm aimed to accurately estimate the micro-Doppler parameters of a rigid-body target. A parametric dictionary, which is dependent on the unknown angular speed of the target, was designed to decompose the radar echo into several dominant micro -Doppler components. By doing so, the problem of micro-Doppler parameter estimation was converted into the problem of sparse signal recovery with a parametric dictionary. To avoid the time-consuming full search, the POMP algorithm was presented by embedding the pruning process into the OMP procedure. Simulation results have demonstrated that the POMP algorithm can yield more accurate micro -Doppler parameter estimates and better time - frequency resolution in comparison with some well-recognized algorithms based on WVD and Hough transform. The second algorithm, referred to as the Gabor- Hausdorff algorithm, was presented for micro -Doppler feature extraction and applied to radar recognition of nonrigid body targets such as hand gestures. Taking advantage of the sparse properties of radar echoes reflected from dynamic hand gestures, the Gabor decomposition was used to extract the time -frequency locations and corresponding coefficients of the dominant signal components. The extracted micro-Doppler features were inputted into modified-Hausdorff-distance based NN classifier to determine the type of dynamic hand gestures. Experimental results based on real radar data have shown that the Gabor-Hausdorff algorithm outperforms the PCA-based and the DCNN-based methods in conditions of small training dataset.

Chapter Contents:

  • 7.1 Introduction
  • 7.2 Micro-Doppler parameter estimation via PSR
  • 7.2.1 Signal model
  • 7.2.2 Description of the POMP algorithm
  • 7.2.3 Discussions
  • 7.2.3.1 Connection with dictionary learning
  • 7.2.3.2 Computational complexity
  • 7.2.3.3 Comparison with Hough-kind algorithms
  • 7.2.4 Simulation results
  • 7.2.4.1 Analysis of parameter estimation accuracy in the noise-free case
  • 7.2.4.2 Analysis of parameter estimation accuracy in noisy environment
  • 7.2.4.3 Analysis of resolution
  • 7.3 Dynamic hand gesture recognition via Gabor-Hausdorff algorithm
  • 7.3.1 Measurements of dynamic hand gestures
  • 7.3.2 Sparsity-driven recognition of hand gestures
  • 7.3.2.1 Extraction of the time-frequency trajectory
  • 7.3.2.2 Clustering for central time-frequency trajectory
  • 7.3.2.3 The nearest neighbor classifier based on modified Hausdorff distance
  • 7.3.3 Experimental results
  • 7.3.3.1 Recognition results with varying sparsity
  • 7.3.3.2 Recognition results with varying time-frequency resolution
  • 7.3.3.3 Recognition results with the varying size of training data
  • 7.3.3.4 Recognition results for unknown personnel targets
  • 7.3.3.5 Analysis of the computational complexity
  • 7.4 Conclusion
  • References

Inspec keywords: principal component analysis; radar imaging; time-frequency analysis; feature extraction; image classification; convolutional neural nets; gesture recognition; Hough transforms

Other keywords: radar echoes; time-frequency resolution; micro-Doppler parameters; DCNN-based methods; modified-Hausdorff-distance based NN classifier; Hough transform; Gabor-Hausdorff algorithm; radar recognition; parametric dictionary; sparsity-driven algorithms; POMP algorithm; Gabor decomposition; micro-Doppler feature extraction; dominant micro-Doppler components; radar classification; micro-Doppler parameter estimation; dynamic hand gestures; micro-Doppler analysis

Subjects: Other topics in statistics; Integral transforms; Image recognition; Radar theory; Radar equipment, systems and applications; Mathematical analysis

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