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.

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.

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.

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. 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. 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.

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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.