Multi-Channel Calibration for Airborne PostDoppler Space-Time Adaptive Processing

This paper presents a fast and efficient multichannel calibration algorithm for along-track systems, which in particular is evaluated for the post-Doppler space-time adaptive processing (PD STAP) technique. The calibration algorithm corrects the phase and magnitude offsets among the receiving channels, estimates and compensates the Doppler centroid variation caused by atmospheric turbulences by using the attitude angles of the antenna array. Important parameters and offsets are estimated directly from the radar rangecompressed data. The proposed algorithm is compared with the state-of-the-art Digital Channel Balancing technique based on real multi-channel X-band data acquired by the DLR’s airborne system F-SAR. The experimental results are shown and discussed in the frame of traffic monitoring applications

Novel post-Doppler STAP with a priori knowledge information for traffic monitoring applications

The road traffic has worsened over time in most cities, and the methods employed for monitoring and counting the vehicles on the roads (e.g., cameras, induction loops, or even people manually counting) are expensive and limited in spatial coverage. Synthetic aperture radars (SAR) provide an effective solution for this problem due to the wide-area coverage and the independence from daylight and weather conditions. Special attention is given in case of large scale events or catastrophes, when mobile internet is unavailable and phone communication is impossible. In this particular scenario, the traffic monitoring with real-time information ensures the safety of the road users and can even save lives. For that reason, this paper presents a novel a priori knowledge-based algorithm for traffic monitoring, where the powerful post-Doppler space-time adaptive processing (PD STAP) is combined with a road network obtained from the freely available OpenStreetMap (OSM) database. The incorporation of a known road network into the processing chain presents great potential for real-time processing, since only the acquired data related to the roads need to be processed. As a result, decreased processing hardware complexity and low costs compared to state-of-the-art systems can be achieved. In addition, it is a promising solution for detecting effectively the road vehicles and estimating their positions, velocities and moving directions with high accuracy. The PD STAP is well-known for its very good clutter suppression, its sensitivity also to low vehicle velocities, and its accurate target position estimation capabilities. The road information is applied after the PD STAP, where the OSM database fused with a digital elevation model (DEM) is applied in order to recognize and to reject false detections, and moreover, to reposition the vehicles detected in the vicinity of the roads. In other words, the distance between the estimated position of the target and its closest road point is measured and compared to a relocation threshold for deciding whether the target corresponds to a true road vehicle or to a false detection. If the first condition is fulfilled, the target is repositioned to its closest road point; otherwise it is discarded. The relocation threshold is computed adaptively for each detection by using an appropriate performance model. The proposed algorithm was tested using real 4-channel aperture switching data acquired by DLR’s airborne system F-SAR. In the radar data takes examined so far, the PD STAP detected vehicles as slow as 7 km/h, with an overall position estimation accuracy better than 10 m. Besides, the estimated velocities of the vehicles were in very good agreement with the differential GPS reference data. To sum up, the experimental results revealed a powerful algorithm that detects even slow vehicles and discards most of the false detections, being suitable for many traffic monitoring applications. We will not limit our further investigations to the data takes whose results are shown in this paper. We have a large pool of multi-channel F-SAR data takes containing real highway traffic scenarios with dozens or even hundreds of vehicles

A Priori Knowledge-Based Post-Doppler STAP for Traffic Monitoring with Airborne Radar

Die Verkehrsüberwachung gewinnt aufgrund des weltweiten Anstiegs der Verkehrsteilnehmer immer mehr an Bedeutung. Sicherer und effizierter Straßenverkehr erfordert detaillierte Verkehrsinformationen. Häufig sind diese lediglich stationär, räumlich stark begrenzt und meist nur auf Hauptverkehrsstraßen verfügbar. In dieser Hinsicht ist ein Ausfall des Telekommunikationsnetzes, beispielsweise im Falle einer Katastrophe, und der damit einhergehende Informationsverlust als kritisch einzustufen. Flugzeuggetragene Radarsysteme mit synthetischer Apertur (eng. Synthetic Aperture Radar - SAR) können für dieses Szenario eine Lösung darstellen, da sie großflächig hochauflösende Bilder generieren können, unabhängig von Tageslicht und Witterungsbedingungen. Sie ermöglichen aufgrund dieser Charakteristik die Detektion von Bewegtzielen am Boden (eng. ground moving target indication – GMTI). Moderne GMTI-Algorithmen und -Systeme, die prinzipiell für die Verkehrsüberwachung verwendbar sind, wurden in der Literatur bereits diskutiert. Allerdings ist die Robustheit dieser Systeme oft mit hohen Kosten, hoher Hardwarekomplexität und hohem Rechenaufwand verbunden. Diese Dissertation stellt einen neuartigen GMTI-Prozessor vor, der auf dem Radar-Mehrkanalverfahren post-Doppler space-time adaptive processing (PD STAP) basiert. Durch die Überlagerung einer Straßenkarte mit einem digitalen Höhenmodell ist es mithilfe des PD STAP möglich, Falschdetektionen zu erkennen und auszuschließen sowie die detektierten Fahrzeuge ihren korrekten Straßenpositionen zu zuordnen. Die präzisen Schätzungen von Position, Geschwindigkeit und Bewegungsrichtung der Fahrzeuge können mit vergleichsweise geringerer Hardware-Komplexität zu niedrigeren Kosten durchgeführt werden. Ferner wird im Rahmen dieser Arbeit ein effizienter Datenkalibrierungsalgorithmus erläutert, der das Ungleichgewicht zwischen den Empfangskanälen sowie die Variation des Dopplerschwerpunkts über Entfernung und Azimut korrigiert und so das Messergebnis verbessert. Darüber hinaus werden neue und automatisierte Strategien zur Erhebung von Trainingsdaten vorgestellt, die für die Schätzung der Clutter-Kovarianzmatrix wegen ihres direkten Einflusses auf die Clutter-Unterdrückung und Zieldetektion essentiell für PD STAP sind. Der neuartige PD STAP Prozessor verfügt über drei verschiedene Betriebsarten, die für militärische und zivile Anwendungen geeignet sind, darunter ein schneller Verarbeitungsalgorithmus der das Potential für eine zukünftige Echtzeit-Verkehrsüberwachung hat. Alle Betriebsarten wurden erfolgreich mit Radar-Mehrkanaldaten des flugzeuggetragenen F-SAR-Radarsensors des DLR getestet

Frequency Diverse Array Radar: Signal Characterization and Measurement Accuracy

Radar systems provide an important remote sensing capability, and are crucial to the layered sensing vision; a concept of operation that aims to apply the right number of the right types of sensors, in the right places, at the right times for superior battle space situational awareness. The layered sensing vision poses a range of technical challenges, including radar, that are yet to be addressed. To address the radar-specific design challenges, the research community responded with waveform diversity; a relatively new field of study which aims reduce the cost of remote sensing while improving performance. Early work suggests that the frequency diverse array radar may be able to perform several remote sensing missions simultaneously without sacrificing performance. With few techniques available for modeling and characterizing the frequency diverse array, this research aims to specify, validate and characterize a waveform diverse signal model that can be used to model a variety of traditional and contemporary radar configurations, including frequency diverse array radars. To meet the aim of the research, a generalized radar array signal model is specified. A representative hardware system is built to generate the arbitrary radar signals, then the measured and simulated signals are compared to validate the model. Using the generalized model, expressions for the average transmit signal power, angular resolution, and the ambiguity function are also derived. The range, velocity and direction-of-arrival measurement accuracies for a set of signal configurations are evaluated to determine whether the configuration improves fundamental measurement accuracy

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

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

Processing of ultra-wideband low-frequency signals, for application in Foliage Penetration (FOPEN) Synthetic Aperture Radar (SAR) systems

Foliage Penetration (FOPEN) radar systems were introduced in 1960, and have been constantly improved by several organizations since that time. The use of Synthetic Aperture Radar (SAR) approaches for this application has important advantages, due to the need for high resolution in two dimensions. The design of this type of systems, however, includes some complications that are not present in standard SAR systems. FOPEN SAR systems need to operate with a low central frequency (VHF or UHF bands) in order to be able to penetrate the foliage. High bandwidth is also required to obtain high resolution. Due to the low central frequency, large integration angles are required during SAR image formation, and therefore the Range Migration Algorithm (RMA) is used. This project thesis identifies the three main complications that arise due to these requirements. First, a high fractional bandwidth makes narrowband propagation models no longer valid. Second, the VHF and UHF bands are used by many communications systems. The transmitted signal spectrum needs to be notched to avoid interfering them. Third, those communications systems cause Radio Frequency Interference (RFI) on the received signal. The thesis carries out a thorough analysis of the three problems, their degrading effects and possible solutions to compensate them. The UWB model is applied to the SAR signal, and the degradation induced by it is derived. The result is tested through simulation of both a single pulse stretch processor and the complete RMA image formation. Both methods show that the degradation is negligible, and therefore the UWB propagation effect does not need compensation. A technique is derived to design a notched transmitted signal. Then, its effect on the SAR image formation is evaluated analytically. It is shown that the stretch processor introduces a processing gain that reduces the degrading effects of the notches. The remaining degrading effect after processing gain is assessed through simulation, and an experimental graph of degradation as a function of percentage of nulled frequencies is obtained. The RFI is characterized and its effect on the SAR processor is derived. Once again, a processing gain is found to be introduced by the receiver. As the RFI power can be much higher than that of the desired signal, an algorithm is proposed to remove the RFI from the received signal before RMA processing. This algorithm is a modification of the Chirp Least Squares Algorithm (CLSA) explained in [4], which adapts it to deramped signals. The algorithm is derived analytically and then its performance is evaluated through simulation, showing that it is effective in removing the RFI and reducing the degradation caused by both RFI and notching. Finally, conclusions are drawn as to the importance of each one of the problems in SAR system design

Passive radar on moving platforms exploiting DVB-T transmitters of opportunity

The work, effort, and research put into passive radar for stationary receivers have shown significant developments and progress in recent years. The next challenge is mounting a passive radar on moving platforms for the purpose of target detection and ground imaging, e.g. for covert border control. A passive radar on a moving platform has many advantages and offers many benefits, however there is also a considerable drawback that has limited its application so far. Due to the movement the clutter returns are spread in Doppler and may overlap moving targets, which are then difficult to detect. While this problem is common for an active radar as well, with a passive radar a further problem arises: It is impossible to control the exploited time-varying waveform emitted from a telecommunication transmitter. A conventional processing approach is ineffective as the time-varying waveform leads to residuals all over the processed data. Therefore a dedicated clutter cancellation method, e.g. the displaced phase centre antenna (DPCA) approach, does not have the ability to completely remove the clutter, so that target detection is considerably limited. The aim must be therefore to overcome this limitation by exploiting a processing technique, which is able to remove these residuals in order to cope with the clutter returns thus making target detection feasible. The findings of this research and thesis show that a reciprocal filtering based stage is able to provide a time-invariant impulse response similar to the transmissions of an active radar. Due to this benefit it is possible to achieve an overall complete clutter removal together with a dedicated DPCA stage, so that moving target detection is considerably improved, making it possible in the first place. Based on mathematical analysis and on simulations it is proven, that by exploiting this processing in principle an infinite clutter cancellation can be achieved. This result shows that the reciprocal filter is an essential processing stage. Applications on real data acquired from two different measurement campaigns prove these results. By the proposed approach, the limiting factor (i.e. the time-varying waveform) for target detection is negotiated, and in principle any clutter cancellation technique known from active radar can be applied. Therefore this analysis and the results provide a substantial contribution to the passive radar research community and enables it to address the next questions

Forward-Looking Radar Clutter Suppression Using Frequency Diverse Arrays

This thesis introduces a new array structure, the Frequency Diverse Array (FDA), where each channel transmits and receives at a different frequency. The resulting range-dependent FDA antenna pattern is proposed to improve forward-looking clutter suppression. The planar FDA radar data model is derived and analytically verified to be equivalent to the constant frequency data model when each element frequency is set to the same value. The linear FDA at high platform altitude provides significant benefits? by reducing the range ambiguous clutter contribution, improving target detection by up to 10 dB. At low altitudes without range ambiguous clutter the linear FDA achieved a small but consistent performance improvement of 1 to 2 dB attributed to sample support data homogeneity. Planar FDA showed up to a 20 dB detection improvement for a high altitude platform with an airborne target. The simulation results show the FDA provides considerable benefit for low relative velocity targets, improving ground target detection for platforms such as Joint Surveillance and Target Attack Radar System (JSTARS) and Unmanned Aerial Vehicles (UAV)

The University Defence Research Collaboration In Signal Processing: 2013-2018

Signal processing is an enabling technology crucial to all areas of defence and security. It is called for whenever humans and autonomous systems are required to interpret data (i.e. the signal) output from sensors. This leads to the production of the intelligence on which military outcomes depend. Signal processing should be timely, accurate and suited to the decisions to be made. When performed well it is critical, battle-winning and probably the most important weapon which you’ve never heard of. With the plethora of sensors and data sources that are emerging in the future network-enabled battlespace, sensing is becoming ubiquitous. This makes signal processing more complicated but also brings great opportunities. The second phase of the University Defence Research Collaboration in Signal Processing was set up to meet these complex problems head-on while taking advantage of the opportunities. Its unique structure combines two multi-disciplinary academic consortia, in which many researchers can approach different aspects of a problem, with baked-in industrial collaboration enabling early commercial exploitation. This phase of the UDRC will have been running for 5 years by the time it completes in March 2018, with remarkable results. This book aims to present those accomplishments and advances in a style accessible to stakeholders, collaborators and exploiters