Air- and spaceborne radars play an important role for civilian and military use. There are numerous applications such as earth observations, surveillance and others. High performance clutter suppression is a crucial part of many of these radar systems. Space time adaptive processing(STAP)has become a topic of interest for clutter suppression applications. Although for most moving target indication(MTI) radars other applications are used for clutter suppression. This master thesis analyses STAP on two antenna configuration for airborne radar applications. The first configuration is based on auxiliary antennas, the second configuration is based on a multitapering method called discrete prolate spheroidal sequences(DPSS). This theses shows that both antenna configurations are valid choices for STAP applications. Although the later configuration, DPSS, has a higher clutter suppression performance in general. However, there are fundamental limitations with the DPSS configuration. These limitations are shortly discussedin this theses but more work should be done before implementing the DPSS configuration

Commercial concealed weapon detection systems are large and expensive and are not suitable to be used as a portable system. Currently, new methods of concealed weapon detection are being developed to build small and compact systems. One such method is based upon the natural resonances of objects; however, no such system has made it to the market due to the low quality of the signals used in the detection algorithms. In this thesis, a prototype concealed weapon detection system is developed and tested for operation in a cluttered environment. This system utilizes the late-time portion of a radar return to extract the resonance information of an unknown target. After proper signal processing and clutter suppression, the signals are classified to determine if the object is a threat. Multiple measurements with frequency-sweep and time-domain systems are used to verify the algorithm. Microwave tissue imaging techniques aim to reconstruct the internal dielectric distribution of the tissue and rely on the dielectric contrast between healthy and malignant tissues. This contrast has been shown to be weak, and therefore, the signals are easily susceptible to noise. This thesis proposes and validates a method for signal-to-noise ratio analysis of complex S-parameter data sets that are used for microwave imaging. A study of de-noising and artifact reduction techniques for microwave holographic imaging is also presented. / Thesis / Master of Applied Science (MASc)

Passive coherent location (PCL) radars employ illuminators of opportunity to detect and track targets. This silent operating mode provides many advantages such as low cost and interception immunity. Many radiation sources have been exploited as illumination sources such as broadcasting and telecommunication transmitters. The classical architecture of the bistatic PCL radars involves two receiving channels: a reference channel and a surveillance channel. The reference channel captures the direct-path signal from the transmitter, and the surveillancesignal collects the possible target echoes. The two major challenges for the PCL radars are the reference signal noise and the surveillance signal static clutter. A noisy reference signal degrades the detection probability by increasing the noise-floor level of the detection filter output. And the static clutter presence in the surveillance signal reduces the detector dynamic range and buries low magnitude echoes.In this thesis, we consider a PCL radar based on the digital video broadcasting-terrestrial (DVB-T) signals, and we propose a set of improved methods to deal with the reference signal noise and the static clutter in the surveillance signal. The DVB-T signals constitute an excellentcandidate as an illumination source for PCL radars; they are characterized by a wide bandwidth and a high radiated power. In addition, they provide the possibility of reconstructing the reference signal to enhance its quality, and they allow a straightforward static clutter suppressionin the frequency domain. This thesis proposes an optimum method for the reference signal reconstruction and an improved method for the static clutter suppression.The optimum reference signal reconstruction minimizes the mean square error between the reconstructed signal and the exact one. And the improved static clutter suppression method exploits the possibility of estimating the propagation channel. These two methods extend thefeasibility of a single receiver PCL radar, where the reference signal is extracted from the direct-path signal present in the surveillance signal. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

Staggering the pulse repetititon intervals is a widely used solution to alleviate the blind speed problem in Moving Target Indication (MTI) radar systems. It is possible to increase the first blind speed on the order of ten folds with the use of non-uniform sampling. Improvement in blind speed results in passband fluctuations that may degregade the detection performance for particular Doppler frequencies. Therefore, it is important to design MTI filters with non-uniform interpulse periods that have minimum passband ripples with sufficient clutter attenuation along with good range and blind velocity performance. In this thesis work, the design of MTI filters with non-uniform interpulse periods is studied through the least square, convex and min-max filter design methodologies. A trade-off between the contradictory objectives of maximum clutter suppression and minimum desired signal attenuation is established by the introduction of a weight factor into the designs. The weight factor enables the adaptation of MTI filter to different operational scenarios such as the operation under low, medium or high clutter power. The performances of the studied designs are investigated by comparing the frequency response characteristics and the average signal-to-clutter suppression capabilities of the filters with respect to a number of defined performance measures.Two further approaches are considered to increase the signal-to-clutter suppression performance. First approach is based on a modified min-max filter design whereas the second one focuses on the multiple filter implementations. In addition, a detailed review and performance comparison with the non-uniform MTI filter designs from the literature are also given.

Space-Time Adaptive Processing (STAP) is an effective method in Ground Moving Target Indicator (GMTI) operation of airborne radars. Clutter suppression is the key to successful MTI operation. Airborne radars are different than the ground based ones in regard to clutter due to the displacement of the platform during operation. When STAP methods are to be investigated, one needs to have accurate signal models while evaluating performance. In this thesis, a comprehensive received signal model is developed first for an airborne antenna array. The impacts of the aircraft motion and irregularities in it, aircraft displacement during reception, intrinsic clutter motion and radar parameters have been accounted in the model and incorporated into a simulator environment. To verify the correctness of the signal simulator, the classical DPCA approach and optimum STAP methods are inspected.

In this thesis, a maximum likelihood receiver to obtain the target range profile that uses the clutter prediction derived from the target-free previous observations is suggested as a fast time processor for pulse compression radar systems. The maximum likelihood receiver is proposed to overcome the range sidelobe problem, which is introduced by the pulse compression method. Conventional methods, such as the matched filter receiver, as fast time processor result in the targets with high radar cross sec- tion masking the low radar cross section targets at the neighboring range cells / since sidelobes of the matched filter is determined by the autocorrelation of the spreading code and linearly proportional to target signal power. An unbiased estimator, like the maximum likelihood receiver in this thesis work does not su&crarr / er from such issues. In addition to that, to suppress the signal dependent interference, namely the clutter, at the output of fast time processor / the previous target-free observations are col- lected and utilized to predict the clutter signal for next time instant. This prediction is used in the maximum likelihood receiver. The clutter prediction is done for the stationary case and the internal clutter motion case, and their SINR performances with the maximum likelihood receiver are evaluated. In conclusion, such an approach managed to have an unbiased estimation of target range profile and the clutter suppression advantage in the fast time.

Radar signal processing techniques aim to suppress clutter to enable target detection. Many clutter suppression techniques have been developed to improve the detection performance in literature. Among these methods, the most widely known is MTI plus coherent integrator, which gives sufficient radar performance in various scenarios. However, when the correlation coefficient of clutter is small or the spectral separation between the target and clutter is small, classical approaches to clutter suppression fall short. In this study, we consider the ARMA spectral estimation performance in sea clutter modelled by compound K-distribution through Monte Carlo simulations. The method is applied for varying conditions of clutter spikiness and auto correlation sequences (ACS) depending on the radar operation. The performance of clutter suppression using ARMA spectral estimator, which will be called ARMA-CS in this work, is analyzed under varying ARMA model orders. To compare the clutter suppression of ARMA-CS with that of conventional methods, we use improvement factor (IF) which is the ratio between the output Signal to Interference Ratio (SIR) and input SIR as performance measure. In all cases, the performance of ARMA-CS method is better than conventional clutter suppression methods when the correlation among clutter samples is small or the spectral separation between target and clutter is small.