High-Frequency (HF) radar systems are currently used to collect wave data. By applying spectral analysis methods, such as the Fast Fourier Transform (FFT) method, to the radar backscatter from the ocean surface, the so-called Doppler spectrum is calculated, and from this the directional wave spectrum and wave measurements are obtained. Because of the random nature of the ocean surface, spectral measurements are subject to random variability. In order to reduce variability, and hence to obtain relatively precise estimates, each spectrum is usually calculated by averaging a number of FFT estimates. Naturally, this method requires long data series, and problems may arise. In rapidly varying sea conditions, for example, successive FFT estimates may be quite inconsistent with each other (in non-stationary conditions), and then the spectrum estimate obtained by averaging is not only difficult to interpret but it may also be distorted. It is known that the more recent spectral analysis methods such as methods based on autoregressive (AR) and autoregressive-moving average (ARMA) stochastic models can provide stable estimates from short data sets. Thus these methods are potentially good alternatives to the FFT, as they avoid problems inherent to the use of large data sets. The aim of this thesis is to investigate how some of the modem spectral analysis methods may be used to obtain reliable spectral estimates from small data sets. Unlike the FFT method, the AR- and ARMA-based methods presuppose specific parametric forms for the spectral function, and therefore consist in estimating certain parameters from the data (as opposed to estimating the function itself). The modified covariance method and Burg's method are among several methods of estimating the parameters of the spectral function.

The main effort in locating and rescuing survivors of a maritime incident is borne by merchant shipping. This research shows that search and rescue is a task that will face most seafarers, but as they generally lack the necessary levels of skill and knowledge required the task will often be poorly performed. A remedy to this unsatisfactory situation lies in proper training and guidance for ships' officers. This thesis evaluates, using illuminative techniques, the first simulator course devised to provide such training. The evaluation will be of particular use to others called upon to provide similar training. It also shows a requirement for the adoption of improved procedures in merchant ship searches, makes relevant recommendations, and identifies areas for further research. More significantly the study has allowed, through simulation, an opportunity unparalleled in the real situation to assess the guidance contained in the Merchant Ship Search and Rescue Manual (MERSAR). This International Maritime Organization manual is the primary aid available to seafarers facing search and rescue responsibilities. The assessment concludes there is scope for extensive amendment to MERSAR amounting to overall rather than piecemeal revision. Positive recommendations are made, particularly in the areas of communications, control and co-ordination. It is anticipated that this original research will have an important role to play in MERSAR's revision, and through this improve the effectiveness of maritime search and rescue.

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The phased array multi-function radar is an effective solution to the requirement for simultaneous surveillance and multiple target tracking. However, since it is performing the jobs usually undertaken by several dedicated radars its radar time and energy resources are limited. For this reason, and also due to the large cost of active phased array antennas, it is important for the strategies adopted in the control of the radar to be efficient. This thesis investigates and develops efficient strategies for multi-function radar control and tracking. Particularly the research has focused on the use of rotating array antennas and simultaneous multiple receive beam processing. The findings of the research challenge the traditional view that three or four fixed (static) array faces is the best antenna configuration for a multi-function radar system. By developing novel methods for the comparison of systems utilising different antenna configurations it is shown that a rotating array multi-function radar performs the surveillance function with a greater efficiency in its use of radar time than a static array system. Also, a rotating array system benefits from the ability to distribute the radar resources over the angular coverage in a way that is impossible with a static array system. A novel strategy is presented to achieve this, which allows the rotating array system to better support the realistic situation of a high concentration of radar tasks in a narrow angular sector. It is shown that the use of broadened transmit beams coupled with simultaneous multiple narrow receive beams can eliminate the compromise on radar beamwidth between the surveillance and tracking functions that is associated with multi-function radars. This technique would allow construction of multi-function radar systems with narrow beamwidths, giving improved tracking performance, without extending search frame times excessively. Efficient tracking strategies for both static array and rotating array multi-function radars are developed. They are applied through computer simulation to demonstrate tracking of highly manoeuvrable targets with a narrow beam multi-function radar. Track robustness is attained through the use of multiple beam track updating strategies at little cost in terms of radar time.

The problem of improved radar detection of targets embedded in spiky clutter is addressed. Two main areas where improvements may be possible are investigated, namely improved clutter suppression by doppler filtering, and improved Constant False Alarm Rate (CFAR) processing. The clutter suppression performance of several doppler processors is quantified under a wide range of conditions. It is shown that in spatially homogeneous clutter ideal optimal (Hsiao) filters offer 2 to 3 dB higher improvement factor than conventional techniques. Adaptive Hsiao filters are evaluated under conditions of spatially heterogeneous clutter, and it is shown that practical losses due to filter adaptivity and spectral heterogeneity will outweigh the superior performance of ideal Hsiao filters in homogeneous clutter. It is concluded that improved doppler filtering offers little scope for improving detection performance in spiky clutter, and that more significant benefits are to be gained through improved CFAR processing. The performance of three current generation CFAR processors is evaluated in spatially uncorrelated K-distributed clutter to quantify detection losses. It is shown that losses of in excess of 10 dB can be expected in spiky clutter. Reducing the loss by exploitation of any spatial correlation of the underlying clutter power is investigated. To this end a mathematically rigorous model for spatially correlated K-distributed clutter is derived. An improved CFAR processor based on optimal weighting of reference cells is formulated and evaluated. It is shown that in highly correlated clutter CFAR loss can be reduced by 2 to 5 dB compared to Cell Averaging CFAR processors. An alternative "RDT-CFAR" processor is formulated to eliminate reliance on spatial correlation, and this is shown to reduce CFAR loss by more than 10 dB in spectrally homogeneous spiky clutter. However, an increase in false alarm rate in clutter without constant spectrum is demonstrated. The RDT-CFAR processor has been modified to eliminate dependence on surrounding range bins. The resulting "δ-CFAR" processor reduces CFAR loss by more than 10 dB in even moderately spiky clutter. It is also immune to extraneous targets and clutter edges, and its false alarm performance is insensitive to clutter spikiness.

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