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Chinese Journal of Acoustics
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Knowledge-aided Method in Partially Homogeneous Clutter Enhances Detection Performances
Update time: 2017/09/06
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In last decades, the problem of detecting a signal vector, has received increasing attention in radar community.

In partially homogeneous clutter, researchers from the Institute of Acoustics of the Chinese Academy of Sciences together with researchers from Italy have recently worked out a knowledge-aided adaptive detection method for a point-like target. The researchers reformulate the problem by using the structure of the clutter covariance matrix jointly, and design new detectors for partially homogeneous environment.

In partially homogeneous environment model, the primary and secondary data share the same covariance matrix up to an unknown power scaling factor. The possibility of signal presence is accepted for the primary data, while the secondary inputs are assumed to contain only noise, independent of and statistically identical to the noise components of the primary data.

For the sake of deriving the new detectors, researchers jointly exploit the persymmetric structure of the clutter covariance matrixas well as the symmetry in the clutter spectral characteristics.

In fact, the modern adaptive techniques are very restrictive, because they require the environment to “remain stationary and homogeneous” during adaptation. Poor training data selection in such adaptive filters can produce bad output signals.

A possible way to circumvent this drawback is the real-time exploitation of a priori knowledge concerning the environment. Priori knowledge is a basic concept of knowledge-aided or cognitive radar. Namely, it is important to abstract the priori information from sample data.

As first step toward detector designs, researchers transfer the data test problem from the complex to the real domain by two consecutive transformations. The new receivers would work under less number of secondary data, when compared with traditional detectors, and exhibit superior detection performance.

Then researchers derive two adaptive detectors, by resorting to the Rao test and the two-step modifications of the generalized likelihood ratio test.

The performance analysis, conducted on both simulated and real radar data, confirms the superiority of the newly proposed receivers over the traditional state-of-the-art counterparts, which ignore either the persymmetry or the symmetric spectrum.

Funding for this research came from the National Natural Science Foundation of China(No. 61571434).


Goffredo Foglia, HAO Chengpeng, Gaetano Giunta, Danilo Orlando. Knowledge-Aided Adaptive Detection in Partially Homogeneous Clutter: Joint Exploitation of Persymmetry and Symmetric Spectrum. Digital Signal Processing (Volume 67, August 2017, Pages 131-138). DOI:10.1016/j.dsp.2017.04.003


HAO Chengpeng

Institute of Acoustics, Chinese Academy of Sciences, 100190 Beijing, China


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