28 research outputs found
Joint Probabilistic Data Association-Feedback Particle Filter for Multiple Target Tracking Applications
This paper introduces a novel feedback-control based particle filter for the
solution of the filtering problem with data association uncertainty. The
particle filter is referred to as the joint probabilistic data
association-feedback particle filter (JPDA-FPF). The JPDA-FPF is based on the
feedback particle filter introduced in our earlier papers. The remarkable
conclusion of our paper is that the JPDA-FPF algorithm retains the innovation
error-based feedback structure of the feedback particle filter, even with data
association uncertainty in the general nonlinear case. The theoretical results
are illustrated with the aid of two numerical example problems drawn from
multiple target tracking applications.Comment: In Proc. of the 2012 American Control Conferenc
A Comprehensive Mapping and Real-World Evaluation of Multi-Object Tracking on Automated Vehicles
Multi-Object Tracking (MOT) is a field critical to Automated Vehicle (AV) perception systems. However, it is large, complex, spans research fields, and lacks resources for integration with real sensors and implementation on AVs. Factors such those make it difficult for new researchers and practitioners to enter the field.
This thesis presents two main contributions: 1) a comprehensive mapping for the field of Multi-Object Trackers (MOTs) with a specific focus towards Automated Vehicles (AVs) and 2) a real-world evaluation of an MOT developed and tuned using COTS (Commercial Off-The-Shelf) software toolsets. The first contribution aims to give a comprehensive overview of MOTs and various MOT subfields for AVs that have not been presented as wholistically in other papers. The second contribution aims to illustrate some of the benefits of using a COTS MOT toolset and some of the difficulties associated with using real-world data. This MOT performed accurate state estimation of a target vehicle through the tracking and fusion of data from a radar and vision sensor using a Central-Level Track Processing approach and a Global Nearest Neighbors assignment algorithm. It had an 0.44 m positional Root Mean Squared Error (RMSE) over a 40 m approach test.
It is the authors\u27 hope that this work provides an overview of the MOT field that will help new researchers and practitioners enter the field. Additionally, the author hopes that the evaluation section illustrates some difficulties of using real-world data and provides a good pathway for developing and deploying MOTs from software toolsets to Automated Vehicles
Acoustic multi target tracking using direction-of-arrival batches
In this paper, we propose a particle filter acoustic direction-of-arrival (DOA) tracker to track multiple maneuvering targets using a state space approach. The particle filter determines its state vector using a batch of DOA estimates. The filter likelihood treats the observations as an image, using template models derived from the state update equation, and also incorporates the possibility of missing data as well as spurious DOA observations. The particle filter handles multiple targets, using a partitioned state-vector approach. The particle filter solution is compared with three other methods: the extended Kalman filter, Laplacian filter, and another particle filter that uses the acoustic microphone outputs directly. We discuss the advantages and disadvantages of these methods for our problem. In addition, we also demonstrate an autonomous system for multiple target DOA tracking with automatic target initialization and deletion. The initialization system uses a track-before-detect approach and employs the matching pursuit idea to initialize multiple targets. Computer simulations are presented to show the performances of the algorithms
Simultaneous Target and Multipath Positioning
<p>In this work, we present the Simultaneous Target and Multipath Positioning (STAMP) technique to jointly estimate the unknown target position and uncertain multipath channel parameters. We illustrate the applications of STAMP for target tracking/geolocation problems using single-station hybrid TOA/AOA system, monostatic MIMO radar and multistatic range-based/AOA based localization systems. The STAMP algorithm is derived using a recursive Bayesian framework by including the target state and multipath channel parameters as a single random vector, and the unknown correspondence between observations and signal propagation channels is solved using the multi-scan multi-hypothesis data association. In the presence of the unknown time-varying number of multipath propagation modes, the STAMP algorithm is modified based on the single-cluster PHD filtering by modeling the multipath parameter state as a random finite set. In this case, the target state is defined as the parent process, which is updated by using a particle filter or multi-hypothesis Kalman filter. The multipath channel parameter is defined as the daughter process and updated based on an explicit Gaussian mixture PHD filter. Moreover, the idenfiability analysis of the joint estimation problem is provided in terms of Cramér-Rao lower bound (CRLB). The Fisher information contributed by each propagation mode is investigated, and the effect of Fisher information loss caused by the measurement origin uncertainty is also studied. The proposed STAMP algorithms are evaluated based on a set of illustrative numeric simulations and real data experiments with an indoor multi-channel radar testbed. Substantial improvement in target localization accuracy is observed.</p>Dissertatio
Satellite Cluster Tracking via Extent Estimation
Clusters of closely-spaced objects in orbit present unique tracking and prediction challenges. Association of observations to individual objects is often not possible until the objects have drifted sufficiently far apart from one another. This dissertation proposes a new paradigm for initial tracking of these clusters of objects: instead of tracking the objects independently, the cluster is tracked as a single entity, parameterized by its centroid and extent, or shape. The feasibility of this method is explored using a decoupled centroid and extent estimation scheme. The dynamics of the centroid of a cluster of satellites are studied, and a set of modified equinoctial elements is shown to minimize the discrepancy between the motion of the centroid and the observation-space centroid. The extent estimator is formulated as a matrix-variate particle filter. Several matrix similarity measures are tested as the filter weighting function, and the Bhattacharyya distance is shown to outperform the others in test cases. Finally, the combined centroid and extent filter is tested on a set of three on-orbit breakup events, generated using the NASA standard breakup model and simulated using realistic force models. The filter is shown to perform well across low-Earth, geosynchronous, and highly-elliptical orbits, with centroid error generally below five kilometers and well-fitting extent estimates. These results demonstrate that a decoupled centroid and extent filter can effectively track clusters of closely-spaced satellites. This could improve spaceflight safety by providing quantitative tracking information for the entire cluster much earlier than would otherwise be available through typical means
Localisation of humans, objects and robots interacting on load-sensing floors
International audienceLocalisation, tracking and recognition of objects and humans are basic tasks that are of high value in applications of ambient intelligence. Sensing floors were introduced to address these tasks in a non-intrusive way. To recognize the humans moving on the floor, they are usually first localized, and then a set of gait features are extracted (stride length, cadence, pressure profile over a footstep). However, recognition generally fails when several people stand or walk together, preventing successful tracking. This paper presents a detection, tracking and recognition technique which uses objects' weight. It continues working even when tracking individual persons becomes impossible. Inspired by computer vision, this technique processes the floor pressure-image by segmenting the blobs containing objects, tracking them, and recognizing their contents through a mix of inference and combinatorial search. The result lists the probabilities of assignments of known objects to observed blobs. The concept was successfully evaluated in daily life activity scenarii, involving multi-object tracking and recognition on low resolution sensors, crossing of user trajectories, and weight ambiguity. This technique can be used to provide a probabilistic input for multi-modal object tracking and recognition systems
Architectures for embedded multimodal sensor data fusion systems in the robotics : and airport traffic suveillance ; domain
Smaller autonomous robots and embedded sensor data fusion systems often suffer from limited
computational and hardware resources. Many ‘Real Time’ algorithms for multi modal sensor data
fusion cannot be executed on such systems, at least not in real time and sometimes not at all, because
of the computational and energy resources needed, resulting from the architecture of the
computational hardware used in these systems. Alternative hardware architectures for generic
tracking algorithms could provide a solution to overcome some of these limitations. For tracking and
self localization sequential Bayesian filters, in particular particle filters, have been shown to be able to
handle a range of tracking problems that could not be solved with other algorithms. But particle filters
have some serious disadvantages when executed on serial computational architectures used in most
systems. The potential increase in performance for particle filters is huge as many of the computational
steps can be done concurrently. A generic hardware solution for particle filters can relieve the central
processing unit from the computational load associated with the tracking task.
The general topic of this research are hardware-software architectures for multi modal sensor data
fusion in embedded systems in particular tracking, with the goal to develop a high performance
computational architecture for embedded applications in robotics and airport traffic surveillance
domain. The primary concern of the research is therefore: The integration of domain specific concept
support into hardware architectures for low level multi modal sensor data fusion, in particular
embedded systems for tracking with Bayesian filters; and a distributed hardware-software tracking
systems for airport traffic surveillance and control systems.
Runway Incursions are occurrences at an aerodrome involving the incorrect presence of an aircraft,
vehicle, or person on the protected area of a surface designated for the landing and take-off of aircraft.
The growing traffic volume kept runway incursions on the NTSB’s ‘Most Wanted’ list for safety
improvements for over a decade. Recent incidents show that problem is still existent. Technological
responses that have been deployed in significant numbers are ASDE-X and A-SMGCS. Although these
technical responses are a significant improvement and reduce the frequency of runway incursions,
some runway incursion scenarios are not optimally covered by these systems, detection of runway
incursion events is not as fast as desired, and they are too expensive for all but the biggest airports.
Local, short range sensors could be a solution to provide the necessary affordable surveillance accuracy
for runway incursion prevention. In this context the following objectives shall be reached. 1) Show the
feasibility of runway incursion prevention systems based on localized surveillance. 2) Develop a design
for a local runway incursion alerting system. 3) Realize a prototype of the system design using the
developed tracking hardware.Kleinere autonome Roboter und eingebettete Sensordatenfusionssysteme haben oft mit stark
begrenzter Rechenkapazität und eingeschränkten Hardwareressourcen zu kämpfen. Viele
Echtzeitalgorithmen für die Fusion von multimodalen Sensordaten können, bedingt durch den hohen
Bedarf an Rechenkapazität und Energie, auf solchen Systemen überhaupt nicht ausgeführt werden,
oder zu mindesten nicht in Echtzeit. Der hohe Bedarf an Energie und Rechenkapazität hat seine
Ursache darin, dass die Architektur der ausführenden Hardware und der ausgeführte Algorithmus
nicht aufeinander abgestimmt sind. Dies betrifft auch Algorithmen zu Spurverfolgung. Mit Hilfe von
alternativen Hardwarearchitekturen für die generische Ausführung solcher Algorithmen könnten sich
einige der typischerweise vorliegenden Einschränkungen überwinden lassen. Eine Reihe von Aufgaben,
die sich mit anderen Spurverfolgungsalgorithmen nicht lösen lassen, lassen sich mit dem Teilchenfilter,
einem Algorithmus aus der Familie der Bayesschen Filter lösen. Bei der Ausführung auf traditionellen
Architekturen haben Teilchenfilter gegenüber anderen Algorithmen einen signifikanten Nachteil,
allerdings ist hier ein großer Leistungszuwachs durch die nebenläufige Ausführung vieler
Rechenschritte möglich. Eine generische Hardwarearchitektur für Teilchenfilter könnte deshalb die
oben genannten Systeme stark entlasten.
Das allgemeine Thema dieses Forschungsvorhabens sind Hardware-Software-Architekturen für die
multimodale Sensordatenfusion auf eingebetteten Systemen - speziell für Aufgaben der
Spurverfolgung, mit dem Ziel eine leistungsfähige Architektur für die Berechnung entsprechender
Algorithmen auf eingebetteten Systemen zu entwickeln, die für Anwendungen in der Robotik und
Verkehrsüberwachung auf Flughäfen geeignet ist. Das Augenmerk des Forschungsvorhabens liegt
dabei auf der Integration von vom Einsatzgebiet abhängigen Konzepten in die Architektur von
Systemen zur Spurverfolgung mit Bayeschen Filtern, sowie auf verteilten Hardware-Software
Spurverfolgungssystemen zur Überwachung und Führung des Rollverkehrs auf Flughäfen.
Eine „Runway Incursion“ (RI) ist ein Vorfall auf einem Flugplatz, bei dem ein Fahrzeug oder eine Person
sich unerlaubt in einem Abschnitt der Start- bzw. Landebahn befindet, der einem Verkehrsteilnehmer
zur Benutzung zugewiesen wurde. Der wachsende Flugverkehr hat dafür gesorgt, das RIs seit über
einem Jahrzehnt auf der „Most Wanted“-Liste des NTSB für Verbesserungen der Sicherheit stehen.
Jüngere Vorfälle zeigen, dass das Problem noch nicht behoben ist. Technologische Maßnahmen die in
nennenswerter Zahl eingesetzt wurden sind das ASDE-X und das A-SMGCS. Obwohl diese Maßnahmen
eine deutliche Verbesserung darstellen und die Zahl der RIs deutlich reduzieren, gibt es einige RISituationen
die von diesen Systemen nicht optimal abgedeckt werden. Außerdem detektieren sie RIs
ist nicht so schnell wie erwünscht und sind - außer für die größten Flughäfen - zu teuer. Lokale Sensoren
mit kurzer Reichweite könnten eine Lösung sein um die für die zuverlässige Erkennung von RIs
notwendige Präzision bei der Überwachung des Rollverkehrs zu erreichen. Vor diesem Hintergrund
sollen die folgenden Ziele erreicht werden. 1) Die Machbarkeit eines Runway Incursion
Vermeidungssystems, das auf lokalen Sensoren basiert, zeigen. 2) Einen umsetzbaren Entwurf für ein
solches System entwickeln. 3) Einen Prototypen des Systems realisieren, das die oben gennannte
Hardware zur Spurverfolgung einsetzt