Knowledge-Aided STAP Using Low Rank and Geometry Properties

This paper presents knowledge-aided space-time adaptive processing (KA-STAP) algorithms that exploit the low-rank dominant clutter and the array geometry properties (LRGP) for airborne radar applications. The core idea is to exploit the fact that the clutter subspace is only determined by the space-time steering vectors, {red}{where the Gram-Schmidt orthogonalization approach is employed to compute the clutter subspace. Specifically, for a side-looking uniformly spaced linear array, the} algorithm firstly selects a group of linearly independent space-time steering vectors using LRGP that can represent the clutter subspace. By performing the Gram-Schmidt orthogonalization procedure, the orthogonal bases of the clutter subspace are obtained, followed by two approaches to compute the STAP filter weights. To overcome the performance degradation caused by the non-ideal effects, a KA-STAP algorithm that combines the covariance matrix taper (CMT) is proposed. For practical applications, a reduced-dimension version of the proposed KA-STAP algorithm is also developed. The simulation results illustrate the effectiveness of our proposed algorithms, and show that the proposed algorithms converge rapidly and provide a SINR improvement over existing methods when using a very small number of snapshots.Comment: 16 figures, 12 pages. IEEE Transactions on Aerospace and Electronic Systems, 201

A bayesian approach to adaptive detection in nonhomogeneous environments

We consider the adaptive detection of a signal of interest embedded in colored noise, when the environment is nonhomogeneous, i.e., when the training samples used for adaptation do not share the same covariance matrix as the vector under test. A Bayesian framework is proposed where the covariance matrices of the primary and the secondary data are assumed to be random, with some appropriate joint distribution. The prior distributions of these matrices require a rough knowledge about the environment. This provides a flexible, yet simple, knowledge-aided model where the degree of nonhomogeneity can be tuned through some scalar variables. Within this framework, an approximate generalized likelihood ratio test is formulated. Accordingly, two Bayesian versions of the adaptive matched filter are presented, where the conventional maximum likelihood estimate of the primary data covariance matrix is replaced either by its minimum mean-square error estimate or by its maximum a posteriori estimate. Two detectors require generating samples distributed according to the joint posterior distribution of primary and secondary data covariance matrices. This is achieved through the use of a Gibbs sampling strategy. Numerical simulations illustrate the performances of these detectors, and compare them with those of the conventional adaptive matched filter

Adaptive Illumination Patterns for Radar Applications

The fundamental goal of Fully Adaptive Radar (FAR) involves full exploitation of the joint, synergistic adaptivity of the radar\u27s transmitter and receiver. Little work has been done to exploit the joint space time Degrees-of-Freedom (DOF) available via an Active Electronically Steered Array (AESA) during the radar\u27s transmit illumination cycle. This research introduces Adaptive Illumination Patterns (AIP) as a means for exploiting this previously untapped transmit DOF. This research investigates ways to mitigate clutter interference effects by adapting the illumination pattern on transmit. Two types of illumination pattern adaptivity were explored, termed Space Time Illumination Patterns (STIP) and Scene Adaptive Illumination Patterns (SAIP). Using clairvoyant knowledge, STIP demonstrates the ability to remove sidelobe clutter at user specified Doppler frequencies, resulting in optimum receiver performance using a non-adaptive receive processor. Using available database knowledge, SAIP demonstrated the ability to reduce training data heterogeneity in dense target environments, thereby greatly improving the minimum discernable velocity achieved through STAP processing

Passive Radar Clutter Modeling and Emitter Selection for Ground Moving Target Indication

Moving target detection with a passive radar system relies on many competing and coupled variables. When simulating a passive bistatic radar (PBR) system for ground moving target indication (GMTI) a three-dimensional model is critical. The signal path geometry induced from separating the radar receiver and transmitter causes several performance effects that change with location. Since a performance prediction is only as good as the model, the choice of how to model clutter becomes important. Measured data of bistatic clutter shows that the received clutter power depends on scattering angles. Therefore, a new in-plane out-of-plane (IPOP) interpolation model was developed. The IPOP model causes high clutter returns to reside in regions near an in-plane orientation (forward or backward scattering). The model produces a more localized clutter spectrum in angle-Doppler space when compared to monostatic radar. Generally, the stationary transmitter is modeled as a communication emitter due to the availability. These continuous waveforms must be partitioned as pulses spaced at constant intervals over the coherent processing interval (CPI). This diverse pulse train is non-ideal for pulse-Doppler radars. The waveform produces high range sidelobes and causes colored noise to spread in Doppler. It is shown for the first time that these waveform effects can be modeled through a covariance matrix taper (CMT). Choosing an optimal emitter becomes an interesting problem when multiple emitters are present. A common metric for GMTI when using space-time adaptive processing (STAP) is signal-to-interference-plus-noise ratio (SINR). However, SINR changes based off relative geometries, and GMTI depends on where a target's location and two-dimensional velocity maps into angle-Doppler space. Therefore, average SINR, weighted average SINR, minimum SINR, and usable velocity space fraction (UVSF) are the newly developed metrics proposed for down-selecting to an optimal emitter. The choice of metric is extremely dependent on the scenario. Finally, in STAP large clutter discretes (LCDs) can cause either false alarms or missed detections. Ultimately, they contaminate the data, and it is very desirable, yet very hard, to remove LCDs. However, the clutter structure in angle-Doppler space for PBR can offer a benefit for removing an LCD. Due to the fact that bistatic clutter can be more localized in angle-Doppler, the detection and estimation of an LCD can be accomplished for an out-of-plane geometry. Then the LCD can be successfully removed from the data, and new application of spectral estimation techniques have been developed for this purpose

Mismatched Processing for Radar Interference Cancellation

Matched processing is a fundamental filtering operation within radar signal processing to estimate scattering in the radar scene based on the transmit signal. Although matched processing maximizes the signal-to-noise ratio (SNR), the filtering operation is ineffective when interference is captured in the receive measurement. Adaptive interference mitigation combined with matched processing has proven to mitigate interference and estimate the radar scene. A known caveat of matched processing is the resulting sidelobes that may mask other scatterers. The sidelobes can be efficiently addressed by windowing but this approach also comes with limited suppression capabilities, loss in resolution, and loss in SNR. The recent emergence of mismatch processing has shown to optimally reduce sidelobes while maintaining nominal resolution and signal estimation performance. Throughout this work, re-iterative minimum-mean square error (RMMSE) adaptive and least-squares (LS) optimal mismatch processing are proposed for enhanced signal estimation in unison with adaptive interference mitigation for various radar applications including random pulse repetition interval (PRI) staggering pulse-Doppler radar, airborne ground moving target indication, and radar & communication spectrum sharing. Mismatch processing and adaptive interference cancellation each can be computationally complex for practical implementation. Sub-optimal RMMSE and LS approaches are also introduced to address computational limitations. The efficacy of these algorithms is presented using various high-fidelity Monte Carlo simulations and open-air experimental datasets

Ground moving target tracking with space-time adaptive radar

Ground moving target tracking by airborne radar provides situational awareness of vehicle movements in the supervised region. Vehicles are detected by applying space time adaptive processing to the received multi channel radar data. The detections are then fed to a tracking algorithm that processes them to tracks. In literature, radar signal processing and ground target tracking are treated as two separate topics and results are not validated by experimental data. The first objective of this thesis is to provide a closer link between these fields. The second objective is to show that tracking performance can be improved by providing additional data from the radar signal processing to the tracking step. The third objective is to validate the algorithm and the performance improvement using experimental data. As a result this thesis presents a unified treatment of ground moving target tracking from radar raw data to established tracks. A complete reference algorithm for ground moving target tracking based on the Gaussian mixture probability hypothesis density filter is presented. In particular, Jacobians of the observation process are derived. They are presented in such a form that immediate implementation in a programming language is possible. In the course of this thesis a measurement campaign with the experimental radar PAMIR of Fraunhofer FHR was conducted. The experiment included two GPS equipped reference vehicles and a multitude of targets of opportunity. Tracking results obtained with this experimental data and the reference tracking algorithm of this thesis are shown. The thesis also enhances the reference target tracking algorithm by a parameter that characterizes the variance of the direction of arrival measurement of the target signal. This parameter is determined adaptively depending on the estimated signal strength and the clutter background. The major contribution with regard to this enhancement is a thorough experimental validation: Firstly, a comparison between GPS based measurements and radar based measurements of the direction of arrival shows that this variance captures the distribution of measurement errors excellently. Secondly, tracking results are compared to the GPS tracks of the ground truth vehicles. It is found that the enhanced algorithm yields superior track quality with respect to both track accuracy and track continuity.Bodenzielverfolgung mit luftgestütztem Radar liefert das Lagebild von Fahrzeug­bewegungen innerhalb des beobachteten Gebiets. Fahrzeuge werden durch die Anwendung von Raum-Zeit adaptiver Signalverarbeitung (STAP) entdeckt. Die Entdeckungen werden dann von einem Zielverfolgungsalgorithmus zu Zielspuren verarbeitet. In der Literatur werden Radarsignalverarbeitung und Zielverfolgung als zwei getrennte Forschungsfelder behandelt und die Bodenzielverfolgung wird nicht anhand von Realdaten validiert. Das erste Ziel dieser Arbeit ist, eine engere Verbindung zwischen beiden Feldern herzustellen. Das zweite Ziel ist zu zeigen, dass die Qualität der Zielverfolgung durch das Verwenden zusätzlicher, durch die Radarsignalverarbeitung gewonnene Information verbessert werden kann. Das dritte Ziel ist, die Funktionalität der Zielverfolgung und die Verbesserung der Leistung durch experimentelle Realdaten zu belegen. Somit stellt diese Arbeit eine Gesamtbehandlung der Bodenzielverfolgung von den Radar-Rohdaten bis zu Zielspuren dar. Es wird ein vollständiger, auf dem Gaussian Mixture Probability Hypothesis Density Filter basierender Referenzalgorithmus für die Bodenzielverfolgung entwickelt. Insbesondere werden Jacobimatrizen der Beobachtungsfunktion hergeleitet. Sie werden in der Arbeit so dargestellt, dass sie direkt in einer Programmiersprache implementiert werden können. Im Zuge dieser Arbeit wurde ein Zielverfolgungs-Experiment mit dem Experimentalsystem PAMIR des Fraunhofer FHR durchgeführt. In dem Experiment wurden neben einer Vielzahl von Gelegenheitszielen zwei mit GPS-Geräten ausgerüstete Fahrzeuge von dem Radar beobachtet. Auf Basis dieses Experiments und des Referenzalgorithmus werden Zielverfolgungsergebnisse vorgestellt. Darüber hinaus erweitert diese Arbeit den Referenzalgorithmus um einen Parameter, der die Varianz der Richtungsschätzung des Zielsignals charakterisiert. Dieser Parameter wird adaptiv anhand der geschätzten Signalstärke und der Stärke störender Bodenrückstreuungen festgelegt. Der wesentliche Beitrag dieser Arbeit in Bezug auf diese Erweiterung ist eine gründliche experimentelle Validierung. Erstens zeigt der Vergleich von GPS- und Radar-basierten Richtungsschätzungen, dass dieser Parameter die Verteilung des Messfehlers exzellent beschreibt. Zweitens werden Zielverfolgungsergebnisse mit den GPS-Spuren verglichen. Es zeigt sich, dass der erweiterte Algorithmus sowohl in Bezug auf die Spurgenauigkeit als auch in Bezug auf die Spurkontinuität die Zielverfolgung verbessert

The Atmospheric Imaging Radar for High Resolution Observations of Severe Weather

Mobile weather radars often utilize rapid scan strategies when collecting obser- vations of severe weather. Various techniques have been used to improve volume update times, including the use of agile and multi-beam radars. Imaging radars, similar in some respects to phased arrays, steer the radar beam in software, thus requiring no physical motion. In contrast to phased arrays, imaging radars gather data for an entire volume simultaneously within the field-of-view of the radar, which is defined by a broad transmit beam. As a result, imaging radars provide update rates significantly exceeding those of existing mobile radars, including phased arrays. The Atmospheric Radar Research Center at the University of Oklahoma is engaged in the design, construction and testing of a mobile imaging weather radar system called the Atmospheric Imaging Radar (AIR).Initial tests performed with the AIR demonstrate the benefits and versatility of utilizing beamforming techniques to achieve high spatial and temporal resolution. Specifically, point target analysis was performed using several digital beamform- ing techniques. Adaptive algorithms allow for the improved resolution and clutter rejection when compared to traditional techniques. Additional experiments were conducted during three severe weather events in Oklahoma, including an isolated cell event with high surface winds, a squall line, and a non-tornadic cyclone. Sev- eral digital beamforming techniques were tested and analyzed, producing unique, simultaneous multi-beam measurements using the AIR.The author made specific contributions to the field of radar meteorology in several areas. Overseeing the design and construction of the AIR was a signif- icant effort and involved the coordination of many smaller teams. Interacting with the members of each group and ensuring the success of the project was a primary focus throughout the venture. Meteorological imaging radars of the past have typically focused on boundary layer or upper atmospheric phenomena. The AIR's primary focus is to collect precipitation data from severe weather. Ap- plying well defined beamforming techniques, ranging from Fourier to adaptive algorithms like robust Capon and Amplitude and Phase Estimation (APES), to precipitation phenomena was a unique effort and has served to advance the use of adaptive array processing in radar meteorology. Exploration of irregular antenna spacing and drawing from the analogies between temporal and spatial process- ing led to the development of a technique that reduced the impact of grating lobes by unwrapping angular ambiguities. Ultimately, the author leaves having created a versatile platform capable of producing some of the highest resolution weather data available in the research community today, with opportunities to significantly advance the understanding of rapidly evolving weather phenomena and severe storms

Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)

Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression