Adaptive Radar Sensor Model for Tracking Structured Extended Objects

We propose a tracking framework jointly estimating the position of a single extended object and the set of radar reflectors that it contains. The reflectors are assumed to lie on a line structure, but the number of reflectors and their positions on the line are unknown. Additionally, we incorporate an accurate radar sensor model considering the resolution capabilities of the sensor. The evaluation of the framework on radar measurements shows promising results

Variational Bayesian Expectation Maximization for Radar Map Estimation

For self-localization, a detailed and reliable map of the environment can be used to relate sensor data to static features with known locations. This paper presents a method for construction of detailed radar maps that describe the expected intensity of detections. Specifically, the measurements are modelled by an inhomogeneous Poisson process with a spatial intensity function given by the sum of a constant clutter level and an unnormalized Gaussian mixture. A substantial difficulty with radar mapping is the presence of data association uncertainties, i.e., the unknown associations between measurements and landmarks. In this paper, the association variables are introduced as hidden variables in a variational Bayesian expectation maximization (VBEM) framework, resulting in a computationally efficient mapping algorithm that enables a joint estimation of the number of landmarks and their parameters

Joint inference of dominant scatterer locations and motion parameters of an extended target in high range-resolution radar

A target of interest measured by a high range resolution radar may be modelled by multiple dominant points of reflections referred to as dominant scatterers. In this paper a non-linear state space setting is used to model the states and measurements of a target moving in the down- and cross-range dimensions. A resample-move particle filter with simulated annealing is successfully used to jointly infer the locations of the dominant scatterers and the motion parameters of the target. A novel technique for the initialization of the particle filter for the given application is presented. The location estimates of scatterers using the particle filter method are compared to those obtained using standard range-Doppler inverse synthetic aperture radar (ISAR) imaging when using the same radar returns for both cases. The particle filter infers the location of scatterers more accurately than range-Doppler ISAR processing, and the processing can be performed online as opposed to ISAR processing, which requires batching. It is relatively straightforward to extend the method to perform localisation and tracking of scatterers in three dimensions, whereas such an extension is challenging in range-Doppler ISAR processing. However, several challenges need be addressed to make this algorithm suitable for practical implementation and these challenges are discussed. This method may be used to obtain very accurate estimates of target state, which may in turn be used for accurate ISAR motion compensation. Given enough computing resources this algorithm may in future become the basis of a new radar target imaging scheme.King Abdulaziz City for Science and Technology (KACST) in the Kingdom of Saudi Arabia and the Council for Scientific and Industrial Research (CSIR) in South Africa.http://digital-library.theiet.org/content/journals/iet-rsnhb201

Variational Bayesian Expectation Maximization for Radar Map Estimation

Abstract-For self-localization, a detailed and reliable map of the environment can be used to relate sensor data to static features with known locations. This paper presents a method for construction of detailed radar maps that describe the expected intensity of detections. Specifically, the measurements are modelled by an inhomogeneous Poisson process with a spatial intensity function given by the sum of a constant clutter level and an unnormalized Gaussian mixture. A substantial difficulty with radar mapping is the presence of data association uncertainties, i.e., the unknown associations between measurements and landmarks. In this paper, the association variables are introduced as hidden variables in a variational Bayesian expectation maximization (VBEM) framework, resulting in a computationally efficient mapping algorithm that enables a joint estimation of the number of landmarks and their parameters

A Survey on Modelling of Automotive Radar Sensors for Virtual Test and Validation of Automated Driving

Radar sensors were among the first perceptual sensors used for automated driving. Although several other technologies such as lidar, camera, and ultrasonic sensors are available, radar sensors have maintained and will continue to maintain their importance due to their reliability in adverse weather conditions. Virtual methods are being developed for verification and validation of automated driving functions to reduce the time and cost of testing. Due to the complexity of modelling high-frequency wave propagation and signal processing and perception algorithms, sensor models that seek a high degree of accuracy are challenging to simulate. Therefore, a variety of different modelling approaches have been presented in the last two decades. This paper comprehensively summarises the heterogeneous state of the art in radar sensor modelling. Instead of a technology-oriented classification as introduced in previous review articles, we present a classification of how these models can be used in vehicle development by using the V-model originating from software development. Sensor models are divided into operational, functional, technical, and individual models. The application and usability of these models along the development process are summarised in a comprehensive tabular overview, which is intended to support future research and development at the vehicle level and will be continuously updated

Multidimensional Frequency Estimation with Applications in Automotive Radar

This thesis considers multidimensional frequency estimation with a focus on computational efficiency and high-resolution capability. A novel framework on multidimensional high-resolution frequency estimation is developed and applied to increase the range, radial velocity, and angular resolution capcability of state-of-the-art automotive radars

Channel Prediction and Target Tracking for Multi-Agent Systems

Mobile moving agents as part of a multi-agent system (MAS) utilize the wireless communication channel to disseminate information and to coordinate between each other. This channel is error-prone and the transmission quality depends on the environment as well as on the configuration of the transmitter and the receiver. For resource allocation and task planning of the agents, it is important to have accurate, yet computationally efficient, methods for learning and predicting the wireless channel. Furthermore, agents utilize on-board sensors to determine both their own state and the states of surrounding objects. To track the states over time, the objects’ dynamical models are combined with the sensors’ measurement models using a Bayesian filter. Through fusion of posterior information output by the agents’ filters, the awareness of the agents is increased. This thesis studies the uncertainties involved in the communication and the positioning of MASs and proposes methods to properly handle them.A framework to learn and predict the wireless channel is proposed, based on a Gaussian process model. It incorporates deterministic path loss and stochastic large scale fading, allowing the estimation of model parameters from measurements and an accurate prediction of the channel quality. Furthermore, the proposed framework considers the present location uncertainty of the transmitting and the receiving agent in both the learning and the prediction procedures. Simulations demonstrate the improved channel learning and prediction performance and show that by taking location uncertainty into account a better communication performance is achieved. The agents’ location uncertainties need to be considered when surrounding objects (targets) are estimated in the global frame of reference. Sensor impairments, such as an imperfect detector or unknown target identity, are incorporated in the Bayesian filtering framework. A Bayesian multitarget tracking filter to jointly estimate the agents’ and the targets’ states is proposed. It is a variant of the Poisson multi-Bernoulli filter and its performance is demonstrated in simulations and experiments. Results for MASs show that the agents’ state uncertainties are reduced by joint agent-target state trackingcompared to tracking only the agents’ states, especially with high-resolution sensors. While target tracking allows for a reduction of the agents’ state uncertainties, highresolution sensors require special care due to multiple detections per target. In this case, the tracking filter needs to explicitly model the dimensions of the target, leading to extended target tracking (ETT). An ETT filter is combined with a Gaussian process shape model, which results in accurate target state and shape estimates. Furthermore, a method to fuse posterior information from multiple ETT filters is proposed, by means of minimizing the Kullback-Leibler average. Simulation results show that the adopted ETT filter accurately tracks the targets’ kinematic states and shapes, and posterior fusion provides a holistic view of the targets provided by multiple ETT filters

Tracking Extended Objects with Active Models and Negative Measurements

Extended object tracking deals with estimating the shape and pose of an object based on noisy point measurements. This task is not straightforward, as we may be faced with scarce low-quality measurements, little a priori information, or we may be unable to observe the entire target. This work aims to address these challenges by incorporating ideas from active contours and exploiting information from negative measurements, which tell us where the target cannot be

Tracking Extended Objects with Active Models and Negative Measurements

Beim Tracking von ausgedehnten Objekten (auf Englisch ‚extended object tracking‘, kurz EOT) geht es darum, die Form und Lage eines Zielobjekts anhand von verrauschten Punktmessungen zu schätzen. EOT wird traditionell zur Verfolgung von Großobjekten wie Flugzeugen, Schiffen, oder Autos verwendet. Allerdings ermöglichen Technologiefortschritte bei Tiefenkameras wie Microsoft Kinects mittlerweile sogar Laien, Punktwolken aus ihrer Umgebung aufzunehmen. Das stellt eine neue Herausforderung für EOT-Ansätze dar, die in modernen Anwendungen, wie z.B. Objektmanipulation in Augmented Reality oder in der Robotik, Zielobjekte mit vielen möglichen Formen anhand von Messungen unterschiedlicher Qualität verfolgen müssen. In diesem Kontext ist die Auswahl der Formmodelle ausschlaggebend, denn sie bestimmen, wie robust und leistungsfähig der Schätzer sein wird, was wiederum eine sorgfältige Betrachtung der Modalitäten und Qualität der verfügbaren Informationen erfordert. Solch ein Informationsparadigma kann als ein Spektrum visualisiert werden: auf der einen Seite, eine große Anzahl an genauen Messungen, und auf der anderen Seite, nur wenige verrauschte Beobachtungen. Allerdings haben sich die Verfahren in der Literatur traditionell auf einen schmalen Teil dieses Spektrums konzentriert. Einerseits assoziieren ‚gierige‘ Verfahren, die auf der Methode der kleinsten Quadrate basieren, Messungen mit der nächsten Quelle auf der Form. Diese Verfahren sind effizient und liefern sogar für komplizierte Formen akkurate Ergebnisse, allerdings nur solange das Messrauschen niedrig bliebt. Ansonsten kann nicht gewährleistet werden, dass der nächste Punkt immer noch eine passende Approximation der wahren Quelle ist, was zu verzerrten Ergebnissen führt. Andererseits sind probabilistische Modelle wie Raumverteilungen präzise für einfache Formen, sogar bei extrem hohem Messrauschen, allerdings werden sie schon für wenig komplexe Formen unlösbar oder numerisch instabil. Die Schwierigkeit besteht darin, dass in vielen modernen Trackingszenarien die Menge an verfügbarer Information sich drastisch mit der Zeit ändern kann. Das unterstreicht den Bedarf an Ansätzen, die nicht nur die Stärken beider Modelle kombinieren, sondern auch alle Bereiche des Spektrums und nicht nur dessen Grenzfälle abdecken können. Das Ziel dieser Arbeit ist es, diese Lücke zu füllen und somit die oben angesprochenen Herausforderungen zu lösen. Dazu schlagen wir vier Beiträge vor, die den aktuellen Stand der Technik signifikant erweitern. Zuerst schlagen wir Level-set Partial Information Models vor, einen probabilistischen Ansatz zur erwartungstreuen Formschätzung für Szenarien mit Verdeckungen und hohem Messrauschen. Zusätzlich führen wir Level-set Active Random Hypersurface Models ein, die von Konzepten aus EOT und Computervision inspiriert sind, eine flexible Formparametrisierung für konvexe und nicht-konvexe Formen ermöglichen, und die auch mit wenig Information umgehen können. Darüber hinaus machen Negative Information Models sogenannte ‚negative‘ Information nutzbar, indem Messungen verarbeitet werden, die uns sagen, wo das Zielobjekt nicht sein kann. Schließlich zeigen wir eine einfach zu implementierende Erweiterung von diesen Beiträgen, Extrusion Models, um dreidimensionale Objekte mit realen Sensordaten zu verfolgen