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.

Space-Time Adaptive Processing (STAP) has been widely used in spaceborne and airborne radar platforms in order to track ground moving targets. Jammer is an hostile electronic countermeasure that is being used to degrade radar detection and tracking performance. STAP adapts radar&rsquo / s antenna radiating pattern in order to reduce jamming effectiveness. Jamming power that enters the system is decreased with respect to the adapted radiation pattern. In this thesis, a generic STAP radar model is developed and implemented in simulation environment. The implemented radar model demonstrates that, STAP can be used in order to suppress wideband jammer effectiveness together with ground clutter effects.

Space-time Adaptive Processing (STAP) is a two-dimensional filtering technique for antenna array with multiple spatial channels. The name "space-time" describes the coupling of these spatial channels with pulse-Doppler waveforms. Applications for STAP includes ground moving target indicator (GMTI) for airborne radar systems. Today, there are strong interests to develop STAP algorithms for operations in “sample starved” environments, where intense environmental interference can reduce STAP capacity to detect and track ground targets. Careful applications of STAP can effectively overcome these conditions by suppressing these interferences and maximize the signal to interference plus noise ratio (SINR). The Multi-stage Wiener filter (MWF) and principal component signal dependent (PC-SD) algorithm are two such methods that can suppress these interference through truncation of the signal subspace. This thesis makes contribution in several ways. First it details the importance of rank compression and sample compression for effective STAP operations in “sample starved” environments. Second, it shows how MWF and PC-SD could operate in this type of environment. Third it details how a “soft stop” technique like diagonal loading (DL) could improve STAP performance in target detection for MWF and PC-SD. Fourth, this thesis contrasts the performance of several existing “hard stop” techniques in rank compression and introduces a new one using a-priori knowledge.

Under a number of practical operating scenarios, traditional moving target indicator (MTI) systems inadequately suppress ground clutter in airborne radar systems. Due to the moving platform, the clutter gains a nonzero relative velocity and spreads the power across Doppler frequencies. This obfuscates slow-moving targets of interest near the "direct current" component of the spectrum. In response, space-time adaptive processing (STAP) techniques have been developed that simultaneously operate in the space and time dimensions for effective clutter cancellation. STAP algorithms commonly operate under the assumption of homogeneous clutter, where the returns are described by complex, white Gaussian distributions. Empirical evidence shows that this assumption is invalid for many radar systems of interest, including high-resolution radar and radars operating at low grazing angles. We are interested in these heterogeneous cases, i.e., cases when the Gaussian model no longer suffices. Hence, the development of reliable STAP algorithms for real systems depends on the accuracy of the heterogeneous clutter models. The clutter of interest in this work includes heterogeneous texture clutter and point clutter. We have developed a cell-based clutter model (CCM) that provides simple, yet faithful means to simulate clutter scenarios for algorithm testing. The scene generated by the CMM can be tuned with two parameters, essentially describing the spikiness of the clutter scene. In one extreme, the texture resembles point clutter, generating strong returns from localized range-azimuth bins. On the other hand, our model can also simulate a flat, homogeneous environment. We prove the importance of model-based STAP techniques, namely knowledge-aided parametric covariance estimation (KAPE), in filtering a gamut of heterogeneous texture scenes. We demonstrate that the efficacy of KAPE does not diminish in the presence of typical spiky clutter. Computational complexities and susceptibility to modeling errors prohibit the use of KAPE in real systems. The computational complexity is a major concern, as the standard KAPE algorithm requires the inversion of an MNxMN matrix for each range bin, where M and N are the number of array elements and the number of pulses of the radar system, respectively. We developed a Gram Schmidt (GS) KAPE method that circumvents the need of a direct inversion and reduces the number of required power estimates. Another unavoidable concern is the performance degradations arising from uncalibrated array errors. This problem is exacerbated in KAPE, as it is a model-based technique; mismatched element amplitudes and phase errors amount to a modeling mismatch. We have developed the power-ridge aligning (PRA) calibration technique, a novel iterative gradient descent algorithm that outperforms current methods. We demonstrate the vast improvements attained using a combination of GS KAPE and PRA over the standard KAPE algorithm under various clutter scenarios in the presence of array errors.

No description available.

Le Synthetic Aperture Radar (SAR) aéroporté permet d’obtenir des images hautes résolutions, en compensant un déphasage lié au déplacement de l’avion. Il n’est cependant pas adapté à l’imagerie des cibles mobiles, celles-ci introduisant un déphasage supplémentaire, dépendant de leur vitesse et de leur accélération. En utilisant un système SAR multivoies, il est cependant possible de réaliser des traitements adaptés aux cibles mobiles, dont les principes sont proches du Space-Time Adaptive Processing (STAP). Le Synthetic Aperture Radar (SAR) aéroporté permet d’obtenir des images hautes résolutions, en compensant un déphasage lié au déplacement de l’avion. Il n’est cependant pas adapté à l’imagerie des cibles mobiles, celles-ci introduisant un déphasage supplémentaire, dépendant de leur vitesse et de leur accélération. En utilisant un système SAR multivoies, il est cependant possible de réaliser des traitements adaptés aux cibles mobiles, dont les principes sont proches du Space-Time Adaptive Processing (STAP). / Airborne Synthetic Aperture Radar (SAR) provides high-resolution images, by compensating a phase shift linked to the platform movement. However, this processing is not suited for imaging moving target, for they introduce an additional phase shift, depending on their velocity and acceleration. By using a multichannel SAR system, it is possible to correctly process moving targets. Such a processing is closely related to Space-Time Adaptive Processing (STAP) principles. Airborne Synthetic Aperture Radar (SAR) provides high-resolution images, by compensating a phase shift linked to the platform movement. However, this processing is not suited for imaging moving target, for they introduce an additional phase shift, depending on their velocity and acceleration. By using a multichannel SAR system, it is possible to correctly process moving targets. Such a processing is closely related to Space-Time Adaptive Processing (STAP) principles.