Low-power HF surface-wave radar: statistical analysis of data and detection performances

This thesis concerns with the analysis of data collected by two low-power surface wave (SW) over-the-horizon (OTH) radars. The two systems, developed at the University of Hamburg and named Wellen Radar (WERA), concurrently operate in a real maritime surveillance scenario in the Bay of Brest (France), with the double purpose of both sea current sensing and vessel detection. The research activity presented in this dissertation develops following the second topic and is interested in the statistical analysis of recorded data and then in the consequent ship detection. Part I deals with the statistical analysis of sea clutter in the HF band. Detailed investigations have been carried out on which is the best statistical model. Numerical results have shown interesting features, both before and after beamforming. The clutter amplitude is Rayleigh or Weibull distributed for the majority of near and middle-range cells, while it cannot be fitted very accurately by the most common amplitude distributions at long distances. This is caused by the highly impulsive nature of interferences, both man-made and natural, which mask the clutter signal. The resulting signal, however, can still be modeled as a compound-Gaussian (CG) process, as verified by an in depth analysis of the probability density functions (PDFs) of both the speckle and texture signal components. The spectral features of the signal have been studied as well. They demonstrated to be a precious tool for recovering information about the nature of non-Gaussianities. Finally, the signal spectrum has been described as an auto-regressive (AR) model. Part II is instead focused on the application of decision-based techniques. Starting from the previous results, our investigation develops on the analysis of detection algorithms based on the normalized adaptive matched filter (NAMF) and the impact of clutter non-stationarities on detection performance. To deal with the unknown clutter statistics, three covariance matrix estimators, namely the sample covariance matrix (SCM), the normalized sample covariance matrix (NSCM) and the fixed point covariance matrix (FPCM), or approximate maximum likelihood (AML), are investigated and their performances evaluated. The idea is to capitalize these results and exploit WERA for detecting ships well beyond the horizon in a complex integrated maritime surveillance (IMS) scenario. The final superposition of WERA detection maps and automatic identification system (AIS) ground truth data will allow to quantify the goodness of the algorithms. In this sense, guidelines about future work are provided as well.