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Tesi etd-04232010-141439

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