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The basic principle in SAS is to use an array which is small in length to create a long synthetic array thus the better resolution is achieved through the use of signal processing. Additionally, the resolution that is independent of range and signal frequency, makes SAS a advantageous tool for applications. Although the origin of SAS comes from SAR, SAS still needs to overcome all constraints for real-world application. In a previous study by Sung and prof. Liu, published results of the along track resolution experiments which were well done however there was still much room in range resolution, the purpose of this research is to achieve high range resolution at any ranges. Indeed there are many existing factors affecting the capability of resolution which including characteristic of the target, certain arrangements of targets, bandwidth, waveforms and pulse duration and etc. High range resolution is obtained using pulse compression techniques. The experiments were carried out using the transducers of AST MK VI 192 kHz which were employed to transmit and receive signals, scanned various copper balls at anechoic water tank(4 m ¡Ñ 3.5 m ¡Ñ 2 m) in NSYSU. From the equipment we have now results were evaluated based on both simulated and real data: for the range resolution the pulse length is very important the shortest pulse length on an object would be 2L/c theoretically, the measured range resolution is about 7.5 cm for the 20-kHz bandwidth signals and 5 cm for along track resolution. As all the experiments have been successful in the Water Tank, we intent to launch further investigation of this research to the real world application of SAS i.e. in Sizihwan Bay Marine Test Field.

This thesis focuses upon automatic target recognition (ATR) with radar sensors. Recent advancements in ATR have included the processing of target signatures from multiple, spatially-diverse perspectives. The advantage of multiple perspectives in target classification results from the angular sensitivity of reflected radar transmissions. By viewing the target at different angles, the classifier has a better opportunity to distinguish between target classes. This dissertation extends recent advances in multi-perspective target classification by: 1) leveraging bistatic target reflectivity signatures observed from multiple, spatially-diverse radar sensors; and, 2) employing a statistical distance measure to identify radar sensor locations yielding improved classification rates. The algorithms provided in this thesis use high resolution range (HRR) profiles – formed by each participating radar sensor – as input to a multi-sensor classification algorithm derived using the fundamentals of statistical signal processing. Improvements to target classification rates are demonstrated for multiple configurations of transmitter, receiver, and target locations. These improvements are shown to emanate from the multi-static characteristics of a target class’ range profile and not merely from non-coherent gain. The significance of dominant scatterer reflections is revealed in both classification performance and the “statistical distance” between target classes. Numerous simulations have been performed to interrogate the robustness of the derived classifier. Errors in target pose angle and the inclusion of camouflage, concealment, and deception (CCD) effects are considered in assessing the validity of the classifier. Consideration of different transmitter and receiver combinations and low signal-to-noise ratios are analyzed in the context of deterministic, Gaussian, and uniform target pose uncertainty models. Performance metrics demonstrate increases in classification rates of up to 30% for multiple-transmit, multiple-receive platform configurations when compared to multi-sensor monostatic configurations. A distance measure between probable target classes is derived using information theoretic techniques pioneered by Kullback and Leibler. The derived measure is shown to suggest radar sensor placements yielding better target classification rates. The predicted placements consider two-platform and three-platform configurations in a single-transmit, multiple-receive environment. Significant improvements in classification rates are observed when compared to ad-hoc sensor placement. In one study, platform placements identified by the distance measure algorithm are shown to produce classification rates exceeding 98.8% of all possible platform placements.

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Frequency Modulated Continuous Wave (FMCW) radar has wide application areas in both civil and military use. The range resolution is a critical concept for these FMCW radars as for the other radar types. There are theoretical restrictions in the range resolution. In addition, the non-ideal properties of the modules used in the systems negatively affects the range resolution. The transmitter leakage, non-linear frequency sweep, FM to AM distortion and measurement errors are some of the critical non-ideal properties. The problems arising from these non-ideal properties further restrict the range resolution of FMCW radars. Another important concept for the range resolution that can be obtained from FMCW radars is the signal processing method. This thesis deals with the non-ideal properties of the system modules and techniques to reduce their effects on the range resolution. Furthermore, the signal processing methods used for FMCW radar signals and the possible improvement techniques for these methods are discussed. Moreover, a simple signal processing unit called zero crossing counter which can be used for short range FMCW radars is implemented and range resolution performance of this zero crossing counter is investigated by carrying out measurements on a prototype FMCW radar at 2200MHz.

In high range-resolution (HRR) radar systems, the returns from a single target may fall in multiple adjacent range bins which individually vary in amplitude. A target following this representation is commonly referred to as an extended target and results in more information about the target. However, extracting this information from the radar returns is challenging due to several complexities. These complexities include the single dimensional nature of the radar measurements, complexities associated with the scattering of electromagnetic waves, and complex environments in which radar systems are required to operate. There are several applications of HRR radar systems which extract target information with varying levels of success. A commonly used application is that of imaging referred to as synthetic aperture radar (SAR) and inverse SAR (ISAR) imaging. These techniques combine multiple single dimension measurements in order to obtain a single two dimensional image. These techniques rely on rotational motion between the target and the radar occurring during the collection of the single dimension measurements. In the case of ISAR, the radar is stationary while motion is induced by the target. There are several difficulties associated with the unknown motion of the target when standard Doppler processing techniques are used to synthesise ISAR images. In this dissertation, a non-standard Dop-pler approach, based on Bayesian inference techniques, was considered to address the difficulties. The target and observations were modelled with a non-linear state space model. Several different Bayesian techniques were implemented to infer the hidden states of the model, which coincide with the unknown characteristics of the target. A simulation platform was designed in order to analyse the performance of the implemented techniques. The implemented techniques were capable of successfully tracking a randomly generated target in a controlled environment. The influence of varying several parameters, related to the characteristics of the target and the implemented techniques, was explored. Finally, a comparison was made between standard Doppler processing and the Bayesian methods proposed. / Dissertation (MEng)--University of Pretoria, 2013. / gm2014 / Electrical, Electronic and Computer Engineering / unrestricted

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