Floating small target detection in sea clutter via normalised Doppler power spectrum. Issue 4 (1st April 2016)

• Record Type: Journal Article
• Authors: Li, Dong‐Chen; Shui, Peng‐Lang
• Is Part Of: IET radar, sonar & navigation. Volume 10:Issue 4(2016)
• Page Start: 699
• Page End: 706
• Publication Date: 2016-04-01
• Subjects: radar clutter -- Doppler radar -- marine radar -- radar detection; floating small target detection -- sea clutter -- normalised Doppler power spectrum -- Doppler power spectra -- DPS -- cell under test -- first‐stage detection -- second‐stage detection -- IPIX datasets; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2015.0259
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : This paper, proposes a detector based upon normalised Doppler power spectrum (NDPS) is proposed to find floating small targets in sea clutter. Doppler power spectra (DPS) of sea clutter are modelled by a positive stochastic process in Doppler bins. To measure the power fluctuation of sea clutter at each Doppler bin, the NDPS is constructed which equals the DPS of the received time series subtracting the mean function and divided by the standard deviation function of the stochastic process. In view of the Doppler spread when the target return power is integrated within a long observation time, a double detection scheme is developed. The NDPS at the cell under test (CUT) is first estimated from the DPS at the reference range cells and CUT. Then, the first‐stage detection is conducted by the shape‐parameter‐dependent thresholds to yield the thresholded NDPS at individual Doppler bins. The second‐stage detection is made by selective integration of the thresholded NDPS. The proposed detector is compared with the fractal‐based and tri‐feature‐based detectors by using the real high‐resolution sea clutter datasets. The experimental results show that the proposed detector attains better performance than the fractal‐based detector and has comparable performance with the tri‐feature‐based detector but lower computational complexity.