213 research outputs found
Decentralized Poisson Multi-Bernoulli Filtering for Vehicle Tracking
A decentralized Poisson multi-Bernoulli filter is proposed to track multiple
vehicles using multiple high-resolution sensors. Independent filters estimate
the vehicles' presence, state, and shape using a Gaussian process extent model;
a decentralized filter is realized through fusion of the filters posterior
densities. An efficient implementation is achieved by parametric state
representation, utilization of single hypothesis tracks, and fusion of vehicle
information based on a fusion mapping. Numerical results demonstrate the
performance.Comment: 14 pages, 5 figure
A track-before-detect labelled multi-Bernoulli particle filter with label switching
This paper presents a multitarget tracking particle filter (PF) for general
track-before-detect measurement models. The PF is presented in the random
finite set framework and uses a labelled multi-Bernoulli approximation. We also
present a label switching improvement algorithm based on Markov chain Monte
Carlo that is expected to increase filter performance if targets get in close
proximity for a sufficiently long time. The PF is tested in two challenging
numerical examples.Comment: Accepted for publication in IEEE Transactions on Aerospace and
Electronic System
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 objectsâ dynamical models are combined with the sensorsâ measurement models using a Bayesian filter. Through fusion of posterior information output by the agentsâ 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 agentsâ 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 agentsâ and the targetsâ 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 agentsâ state uncertainties are reduced by joint agent-target state trackingcompared to tracking only the agentsâ states, especially with high-resolution sensors. While target tracking allows for a reduction of the agentsâ 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 targetsâ kinematic states and shapes, and posterior fusion provides a holistic view of the targets provided by multiple ETT filters
Forum Bildverarbeitung 2022
Bildverarbeitung verknĂŒpft das Fachgebiet die Sensorik von Kameras â bildgebender Sensorik â mit der Verarbeitung der Sensordaten â den Bildern. Daraus resultiert der besondere Reiz dieser Disziplin. Der vorliegende Tagungsband des âForums Bildverarbeitungâ, das am 24. und 25.11.2022 in Karlsruhe als Veranstaltung des Karlsruher Instituts fĂŒr Technologie und des Fraunhofer-Instituts fĂŒr Optronik, Systemtechnik und Bildauswertung stattfand, enthĂ€lt die AufsĂ€tze der eingegangenen BeitrĂ€ge
- âŠ