The thesis summarizes a selection of my research within Synthetic Aperture Radar (SAR). Mainly the research is aimed at applying and developing signal processing methods to single channel and multi channel SAR for wideband systems. SAR systems can generate images looking very similar to optical pictures, i.e. photos, and sometimes with much finer resolution compared to optical systems orbiting Earth. SAR has also for instance been used to obtain fine resolution images of the moon, Venus and the satellites of Saturn. Other applications for SAR has is to detect changes in ice sheets and deforestation. In this thesis, SAR systems capable of very high resolution imaging are con- sidered, and data from such systems, namely the VHF system CARABAS-II and the UHF system LORA, is used. High resolution imaging in this thesis refers to high resolution with regard to wavelength, this independent of system operating frequency. Two of the topics in this thesis are related to detection and parameter estimation of moving objects in SAR, the first one using CARABAS-II data and the second with LORA data. On the CARABAS-II data, a speed estimation and refocusing method is introduced and applied to single channel CARABAS-II data. The results show good estimation accuracy as well as good ability to focus the object and suppress forest clutter by ap- plying the refocusing algorithm. The results on LORA data are satisfactory especially with regard to forest clutter suppression. The ability to detect and focus images of ships allow for surveillance of coastal areas and help in rescue of ships lost at sea. Detection and location of cars and trucks allow for traffic monitoring to obtain statistics of how many cars travel the roads and their speed. In the thesis, two more important aspects for SAR processing is presented. One paper presents windowing of UWB SAR images. A strong object such as a power line in a SAR image cause ringing on both sides of the power line. This ringing can cause a small house to be covered by these so called side lobes. Applying a window can make these side lobes in the image much suppressed, however if windowing too much, the power line will smear over the image, covering the small house. The last topic in the thesis concern with theoretical limits for measurement accuracy of parameters for a moving object in a SAR image. These parameters are position, velocity, radar cross section and phase. The theoretical expressions are verified using simulations for a single channel system for estimation accuracy of target speed and relative speed.

Space-time adaptive processing (STAP) for bistatic airborne radar offers several advantages, such as the higher possibility of detecting stealth targets. However, in a bistatic environment, the usual impediment and possible clutter in-homogeneity is further complicated by the rangedependent nature of the clutter ridge in the angle-Doppler plane induced by the physical geometry of the two aircrafts. This complicates the clutter suppression problem and leads to signi cant degradation in performance. The major objective of this thesis is to develop training methods for bistatic radar operation in a dense environment of ground-moving targets. The work is directed towards what may be called `small STAP', where the number of spatial channels is small and the array is non-uniform. The work is motivated by a desire to minimise the amount of navigational data associated with both the transmitter and receiver. Furthermore, it is directed towards environments where all range gates may contain targets. This thesis presents several novel STAP approaches, which can be classi ed into two main categories, to address the range dependency problem within a bistatic airborne radar framework. The rst category is on training strategies for joint-domain localised (JDL)-STAP in a bistatic environment. The JDL algorithm is originally proposed to reduce the computational complexity for monostatic radar by using a two-dimensional discrete Fourier transformation to transform the data from the space-time domain into the angle-Doppler domain. However, it has restrictions that essentially assume the receiving antenna to be an equi-spaced linear array of ideal, isotropic, point sensors. Two novel algorithms are proposed to overcome these two restrictions and they incorporate angle and Doppler compensation into the JDL processor to mitigate the bistatic clutter Doppler range dependency problem. In addition, a novel JDL in-the-gate processing approach is proposed, which forgoes the training data requirement and operates solely on the test data set. This single data set detection approach alleviates the high target density or heterogeneity problems associated with the training data requirement of conventional STAP algorithms. It is particularly applicable to heterogeneous environments where the clutter homogeneity assumption does not hold or independent training data is not readily available. The second category is on bistatic STAP training without navigation data. A novel technique is proposed to predict the range-dependent inverse covariance matrix, which is used to compute the STAP lter weights, by utilising linear prediction theory. The proposed technique provides mitigation against additional clutter notches resulting from range and Doppler ambiguities. It also allows for detection in other range gates under test without having to re-compute the prediction weights. Another novel technique is proposed to obtain an estimate of the rangedependent inverse covariance matrix by using an eigen-analysis based method. This technique involves applying eigen-decomposition to the covariance matrix in each range gate, sorting the eigenvalues by using maximum inner-product of the eigenvectors of the training range gate with respect to the test range gate and then averaging the resulting sorted eigenvalues. Both of the proposed techniques eliminate the requirement for a uniform linear array and can be applied to arrays of arbitrary con guration. No navigational data or parameter estimation is necessary as only the clutter data is required, thus reducing real-time computational costs.

Les traitements spatio-temporels adaptatifs (STAP) sont des traitements qui exploitent conjointement les deux dimensions spatiale et temporelle des signaux reçus sur un réseau d'antennes, contrairement au traitement d'antenne classique qui n'exploite que la dimension spatiale, pour leur filtrage. Ces traitements sont particulièrement intéressants dans le cadre du filtrage des échos reçus par un radar aéroporté en provenance du sol pour lesquels il existe un lien direct entre direction d'arrivée et fréquence Doppler. Cependant, si les principes des traitements STAP sont maintenant bien acquis, leur mise en œuvre pratique face à un environnement réel se heurte à des points durs non encore résolus dans le contexte du radar opérationnel. Le premier verrou, adressé par la thèse dans une première phase, est d'ordre théorique, et consiste en la définition de procédures d'estimation de la matrice de covariance du fouillis sur la base d'une sélection des données d'apprentissage représentatives, dans un contexte à la fois de fouillis non homogène et de densité parfois importante des cibles d'intérêts. Le second verrou est d'ordre technologique, et réside dans l'implémentation physique des algorithmes, lié à la grande charge de calcul nécessaire. Ce point, crucial en aéroporté, est exploré par la thèse dans une deuxième phase, avec l'analyse de la faisabilité d'une implémentation sur GPU des étapes les plus lourdes d'un algorithme de traitement STAP. / Space-time adaptive processing (STAP) is a processing that makes use of both the spatial and the temporal dimensions of the received signals by an antenna array, whereas conventional antenna processing only exploits the spatial dimension to perform filtering. These processing are very powerful to remove ground echoes received by airborne radars, where there is a direct relation between the arrival angle and the Doppler frequency. However, if the principles of STAP processing are now well understood, their performances are limited when facing practical situations. The first part of this thesis, is theoretical, and consists of defining effective procedures to estimate the covariance matrix of the clutter using a representative selection of training data, in a context of both non-homogeneous clutter and sometimes high density of targets. The second point studied in this thesis is technological, and lies in the physical implementation of the selected algorithms, because of their high computational workload requirement. This is a key point in airborne operations, and is explored by the thesis in a second phase, with the analysis of the feasibility of implementation on GPU of the heaviest stages of a STAP processing.

This dissertation deals with techniques that enhance the detection of ground targets by airborne radar. The methods employed deal with the problem of air to ground detection by breaking the problem into two broad categories. The first category deals with improving detection of moving targets by using space-time adaptive processing (STAP) in a multistatic configuration. Mult-static STAP provides increased detection performance by observing targets from multiple perspectives. Multiple viewing perspectives afford more opportunities to the combined system for observing radial velocity of the target more directly, thus increasing Doppler that helps distinguish the target from background clutter. Detection performance also improves through an increased number of independent observations of a target, which reduces the likelihood of the target fading for the combined system. Increasing detection performance by increasing the number of independent observations is referred to in communications theory as channel diversity. The second part of this dissertation deals with the problem of distinguishing stationary targets from background clutter within a Synthetic Aperture Radar image. Stationary target discrimination is accomplished by exploiting the statistical nature of multifaceted metallic objects within a scene. The performance improvement for both moving and non-moving improvement methods is characterized and compared to other systems that attempt to accomplish the same end using different means.

High-Frequency Surface Wave Radar (HFSWR) is a radar technology that offers numerous advantages for surveillance of coastal waters beyond the exclusive economic zone. However, target detection and tracking is primarily limited by ionospheric interference. Ionospheric clutter is characterized by a high degree of nonhomogeneity and nonstationarity, which makes its suppression difficult using conventional processing techniques. Space-time adaptive processing techniques have enjoyed great success in airborne radar, but have not yet been investigated in the context of HFSWR. This thesis is primarily concerned with the evaluation of existing STAP techniques in the HFSWR scenario and the development of a new multistage adaptive processing approach, dubbed the Fast Fully Adaptive (FFA) scheme, which was developed with the particular constraints of the HFSWR interference environment in mind. Three different spatio-temporal partitioning schemes are introduced and a thorough investigation of the performance of the FFA is conducted.

High-Frequency Surface Wave Radar (HFSWR) is a radar technology that offers numerous advantages for surveillance of coastal waters beyond the exclusive economic zone. However, target detection and tracking is primarily limited by ionospheric interference. Ionospheric clutter is characterized by a high degree of nonhomogeneity and nonstationarity, which makes its suppression difficult using conventional processing techniques. Space-time adaptive processing techniques have enjoyed great success in airborne radar, but have not yet been investigated in the context of HFSWR. This thesis is primarily concerned with the evaluation of existing STAP techniques in the HFSWR scenario and the development of a new multistage adaptive processing approach, dubbed the Fast Fully Adaptive (FFA) scheme, which was developed with the particular constraints of the HFSWR interference environment in mind. Three different spatio-temporal partitioning schemes are introduced and a thorough investigation of the performance of the FFA is conducted.

Standard open loop linear adaptive signal processing algorithms derived from the least squares minimization criterion require estimates of the N-dimensional input interference and noise statistics. Often, estimated statistics are biased by contaminant data (such as outliers and non-stationary data) that do not fit the dominant distribution, which is often modeled as Gaussian. In particular, convergence of sample covariance matrices used in block processed adaptive algorithms, such as the Sample Matrix Inversion (SMI) algorithm, are known to be affected significantly by outliers, causing undue bias in subsequent adaptive weight vectors. The convergence measure of effectiveness (MOE) of the benchmark SMI algorithm is known to be relatively fast (order K = 2N training samples) and independent of the (effective) rank of the external interference covariance matrix, making it a useful method in practice for non-contaminated data environments. Novel robust adaptive algorithms are introduced here that perform superior to SMI algorithms in contaminated data environments while some retain its valuable convergence independence feature. Convergence performance is shown to be commensurate with SMI in non-contaminated environments as well. The robust algorithms are based on the Gram Schmidt Cascaded Canceller (GSCC) structure where novel building block algorithms are derived for it and analyzed using the theory of Robust Statistics. Coined M – cancellers after M – estimates of Huber, these novel cascaded cancellers combine robustness and statistical estimation efficiency in order to provide good adaptive performance in both contaminated and non-contaminated data environments. Additionally, a hybrid processor is derived by combining the Multistage Wiener Filter (MWF) and Median Cascaded Canceller (MCC) algorithms. Both simulated data and measured Space-Time Adaptive Processing (STAP) airborne radar data are used to show performance enhancements. The STAP application area is described in detail in order to further motivate research into robust adaptive processing. / Ph. D.

The detection of weak, ground-moving targets can be improved through effective utilization of additional target signal energy collected over an extended dwell time. The signal model used in conventional radar processing limits integration of signal energy over an extended dwell. Two solutions that consider the complexity of the extended-dwell signal model and effectively combine signal energy collected over a long dwell are presented. The first solution is a single-channel algorithm that provides an estimate of the optimal detector to maximize output signal-to-interference-plus-noise ratio for the extended dwell time signal. Rather than searching for the optimal detector in an intractably large filter bank that contains all combinations of phase components, the single-channel algorithm projects dictionary entries against the data to estimate the signal’s linear and nonlinear phase components sequentially with small, phase-specific dictionaries in a multistage process. When used as the detector, the signal model formed from the estimated phase components yields near optimal performance for a wide range of target parameters for dwell times up to four seconds. In comparison, conventional radar processing methods are limited to an integration time of approximately 100 milliseconds. The second solution is a multichannel, multistage algorithm based on element-space pre-Doppler space-time-adaptive processing with two modifications that make it suitable for detection of weak targets whose energy is collected over an extended dwell time. The multichannel solution detects targets with lower radial velocities at significantly lower signal-to-noise ratios (SNRs) than conventional radar processing methods. The decrease in required input SNR for the multichannel solution as compared to conventional methods nearly doubles the detection range for a typical target of interest. Future related research includes extension of these concepts to other radar applications and investigation of algorithm performance for the multiple-target scenario.

<p>Space-Time Adaptive Processing (STAP) has been widely used in modern radar systems such as Ground Moving Target Indication (GMTI) systems in order to suppress jamming and interference. However, the high performance comes at a price of higher computational complexity, which requires extensive powerful hardware.</p><p>The new STI Cell Broadband Engine (CBE) processor combines PowerPC core augmented with eight streamlined high-performance SIMD processing engine offers an opportunity to implement the STAP baseband signal processing without any full custom hardware. This paper presents the implementation of an STAP baseband signal processing flow on the state-of-the-art STI CELL multiprocessor, which enables the concept of Software-Defined Radar (SDR). The potential of the Cell BE processor is studied so that kernel subroutine such as QR decomposition, Fast Fourier Transform (FFT), and FIR filtering of STAP are mapped to the SPE co-processors of Cell BE processor with variety of architectural specific optimization techniques.</p><p>This report starts with an overview of airborne radar technique and then the standard, specifically the third-order Doppler-factored STAP are introduced. Next, it goes with the thorough description of Cell BE architecture, its programming tool chain and parallel programming methods for Cell BE. In later chapter, how the STAP is implemented on the Cell BE processor is discussed and the simulation results are presented. Furthermore, based on the result of earlier benchmarking, an optimized task partition and scheduling method is proposed to improve the overall performance.</p>

We introduce the notion of diversity order as a performance measure for distributed radar systems. We define the diversity order of a radar network as the slope of the probability of detection (PD) versus SNR evaluated at PD =0.5. We prove that the communication bandwidth between the sensors and the fusion center does not affect the growth in diversity order. We also prove that the OR rule leads to the best performance and its diversity order grows as (log K). We then introduce the notion of a random radar network to study the effect of geometry on overall system performance. We approximate the distribution of the SINR at each sensor by an exponential distribution, and we derive the moments for a specific system model. We then analyze multistatic systems and prove that each sensor should be large enough to cancel the interference in order to exploit the available spatial diversity.