Analysis of space–time adaptive processing performance using K-means clustering algorithm for normalisation method in non-homogeneity detector process

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Analysis of space–time adaptive processing performance using K-means clustering algorithm for normalisation method in non-homogeneity detector process

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This study describes the performance analysis of the non-homogeneity detector (NHD) with various normalisation methods for the space–time adaptive processing (STAP) of airborne radar signals under the non-homogeneous clutter environments. The authors can calculate a threshold value from the statistical analysis of generalised inner product (GIP) using the normalisation method using mean, median and the K-means clustering algorithm of training data snapshots in the NHD process. The selected homogeneous data using the threshold value are used to recalculate covariance matrix of the total interference. To evaluate the performance of the covariance matrix, the authors calculated the eigenspectra and signal to interference noise ratio (SINR) loss. The accuracy of the recalculated covariance matrix is verified by the modified sample matrix inversion (MSMI) test statistic for the target detection. Projection statistics (PS) based on GIP is also used to compare the performance of detecting single and multiple targets. The authors’ simulation results demonstrate that the K-means clustering algorithm as a normalisation method for both GIP and GIP-based PS can improve the STAP performance in the severe non-homogeneous clutter environment even under the multiple targets scenarios, compared to the other normalisation methods.

Inspec keywords: space-time adaptive processing; statistical analysis; airborne radar

Other keywords: airborne radar signals; K-means clustering algorithm; covariance matrix; generalised inner product; statistical analysis; projection statistics; space-time adaptive processing; modified sample matrix inversion test statistic; non-homogeneity detector; target detection; normalisation method

Subjects: Other topics in statistics; Radar equipment, systems and applications; Signal processing and detection; Radar theory

References

    1. 1)
      • J.R. Guerci . (2003) Space–time adaptive processing.
    2. 2)
    3. 3)
    4. 4)
      • Rangaswamy, M., Himed, B., Michels, J.H.: `Statistical analysis of the nonhomogeneity detector', Proc. Conf. IEEE Asilomar Signals, Systems and Computers, 2000, Pacific Grove, CA, p. 1117–1121.
    5. 5)
      • Schoenig, G.N., Picciolo, M.L., Mili, L.: `Improved detection of strong nonhomogeneities for STAP via projection statistics', Proc. Int. Conf. IEEE Radar, May 2005, p. 720–725.
    6. 6)
      • Melvin, W.L., Wicks, M.C.: `Improving practical space–time adaptive radar', Proc. National Conf. IEEE Radar, May 1997, NY, US, p. 48–53.
    7. 7)
      • Melvin, W.L., Wicks, M.C., Brown, R.D.: `Assessment of multichannel airborne radar measurements for analysis and design of space–time processing architectures and algorithms', Proc. National Conf. IEEE Radar, May 1996, Michigan, US, p. 130–135.
    8. 8)
    9. 9)
      • W. Stahel , S. Weisberg . (1991) Robust distance: simulations and cutoff values: Part 2 – directions in robust statistics and diagnostics.
    10. 10)
      • Titi, G., Marshall, D.: `The ARPA/NAVY mountaintop program: adaptive signal processing for airborne early warning radar', Proc. Int. Conf. IEEE Acoustics, Speech, and Signal Processing, May 1996, Atlanta, US, 2, p. 1165–1168.
    11. 11)
    12. 12)
      • Wicks, M.C., Melvin, W.L., Chen, P.: `An efficient architecture for nonhomogeneity detection in space–time adaptive processing airborne early warning radar', Proc. Int. Conf. IEE Radar, October 1997, Edinburgh, UK, p. 295–299, no. 449.
    13. 13)
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