This thesis deals with the detection of hidden objects using a short-range frequency-modulated continuous wave (FMCW) radar. The detection is carried out by examining the estimated Power Spectral Density (PSD) functions of sampled returns, the peaks of which theoretically correspond to the reflecting surfaces of hidden objects. Fourier and non-Fourier PSD estimation algorithms are applied to the radar returns to extract information on the hidden surfaces. The Fourier methods used are Direct, Blackman-Tukey, Bartlett, and Smoothed Periodograms. The different PSDs are compared, and the validity of each PSD is then discussed. The study is new for this type of radar and the results are used as references for other PSD estimations. Non-Fourier methods offer many advantages. Firstly the Autoregressive Process (AR) is used for this particular application. As well as PSDs the noise spectra are also produced to show the performance of the chosen models. An alternative approach to the conventional forward-backward residuals ( e. g. Burg's method) or autocorrelation and covariance methods ( as those used in speech analysis ) is introduced in this thesis. The stability and good resolution of the PSDs is obtained by a better estimation of the autocovariance coefficients (ACF) from the data available : averaging two p-shifted ACF calculated by covariance method. Once the covariance coefficients are found, the Levinson-Durbin recursive algorithm is used to get the model parameters and the PSDs. Two other non-conventional methods are also attempted to show the image of hidden objects. They are Pisarenko Harmonic Decomposition method and Prony energy spectrum density estimation. In addition to the one-dimensional processing stated above, this thesis extends it to two-dimensional cases, which give more information on the shape of hidden objects.

Synthetic Aperture Radar (SAR) is an active microwave remote sensing system able to acquire high resolution images of the scattering behaviour of an observed scene. The contribution of SAR polarimetry (POLSAR) in detection and classification of objects is described and found to add valuable information compared to previous approaches. In this thesis, a new target detection/classification methodology is developed that makes novel use of the polarimetric information of the backscattered field from a target. The detector is based on a geometrical perturbation filter which correlates the target of interest with its perturbed version. Specifically, the operation is accomplished with a polarimetric coherence representing a weighted and normalised inner product between the target and its perturbed version, where the weights are extracted from the observables. The mathematical formulation is general and can be applied to any deterministic (point) target. However, in this thesis the detection is primarily focused on multiple reflections and oriented dipoles due to their extensive availability in common scenarios. An extensive validation against real data is provided exploiting different datasets. They include one airborne system: E-SAR L-band (DLR, German Aerospace Centre); and three satellite systems: ALOS-PALSAR L-band (JAXA, Japanese Aerospace Exploration Agency), RADARSAT-2 C-band (Canadian Space Agency) and TerraSAR-X X-band (DLR). The attained detection masks reveal significant agreement with the expected results based on the theoretical description. Additionally, a comparison with another widely used detector, the Polarimetric Whitening Filter (PWF) is presented. The methodology proposed in this thesis appears to outperform the PWF in two significant ways: 1) the detector is based on the polarimetric information rather than the amplitude of the return, hence the detection is not restricted to bright targets; 2) the algorithm is able to discriminate among the detected targets (i.e. target recognition).

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'Modern' radar systems have come in for much criticism in recent years, particularly in the aftermath of the Falklands campaign. There have also been notable failures in commercial designs, including the well-publicised 'Nimrod' project which was abandoned due to persistent inability to meet signal processing requirements. There is clearly a need for improvement in radar signal processing techniques as many designs rely on technology dating from the late 1970's, much of which is obsolete by today’s standards. The Durham Radar Automatic Track Extraction System (RATES) is a practical implementation of current microprocessor technology, applied to plot extraction of surveillance radar data. In addition to suggestions for the design of such a system, results are quoted for the predicted performance when compared with a similar product using 1970's design methodology. Suggestions are given for the use of other VLSI techniques in plot extraction, including logic arrays and digital signal processors. In conclusion, there is an illustrated discussion concerning the use of systolic arrays in RATES and a prediction that this will represent the optimum architecture for future high-speed radar signal processors.

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.