Channel Prediction and Target Tracking for Multi-Agent Systems
Doctoral thesis, 2018
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 tracking
compared 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.
extended target tracking
Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems
Channel Prediction with Location Uncertainty for Ad-Hoc Networks
IIEEE Transactions on Signal and Information Processing over Networks,; Vol. 4(2018)p. 349-361
Channel gain prediction for multi-agent networks in the presence of location uncertainty
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings,; (2016)p. 3911-3915
Paper in proceedings
Cooperative Localization of Vehicles without Inter-vehicle Measurements
2018 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC),; (2018)
Paper in proceedings
M. Fröhle, C. Lindberg, K. Granström, and H. Wymeersch, “Multisensor Poisson Multi-Bernoulli Filtering for Joint Target-Sensor State Tracking”
Multiple Target Tracking With Uncertain Sensor State Applied To Autonomous Vehicle Data
2018 IEEE Statistical Signal Processing Workshop (SSP),; (2018)p. 628-632
Paper in proceedings
M. Fröhle, K. Granström, and H.Wymeersch, “Decentralized Poisson Multi-Bernoulli Filtering for Extended Target Tracking”
Furthermore, to enhance the vehicles’ awareness, information is shared wirelessly among vehicles. This could be, for example, a “here I am” message or the information that “the pedestrian in the red suit enters the crosswalk with 95 percent accuracy within the next 800 milliseconds.” The wireless communication channel is error-prone. In order to enable a reliable communication between vehicles, an accurate method to predict the communication quality is needed.
An understanding of the scene, in the form of positions and dimensions of other road users, is obtained by processing the vehicle’s sensor measurements by an algorithm, a so-called Bayesian filter. When the vehicles share their information about the environment, either in the form of sensor measurements or the outputs from the filters, a holistic view is generated compared to the information provided by the sensors of a single vehicle alone. Furthermore, combining the shared information allows to increase the accuracies of the position estimates of other road users as well as the vehicles themselves.
In this thesis, we develop a framework to predict the quality of the wireless communication channel between moving vehicles which determine their own positions using sensors. We demonstrate in simulations that, with our approach of incorporating the accuracy of the position estimates, a more reliable communication is achieved. Furthermore, for various sensors, we develop different types of Bayesian filters to determine the presence, positions and dimensions of other road users as well as the vehicles’ positions. We combine the information output by multiple such filters to increase the overall accuracy. We demonstrate the performance of the proposed filters in simulations and in a real driving experiment. A technical abstract of this thesis can be found on page i.
Multi-technology positioning professionals (MULTI-POS)
European Commission (FP7), 2012-10-01 -- 2016-09-30.
Cooperative Situational Awareness for Wireless Networks (COOPNET)
European Commission (FP7), 2011-05-01 -- 2016-04-30.
COPPLAR CampusShuttle cooperative perception & planning platform
VINNOVA, 2016-01-01 -- 2018-12-31.
High precision positioning for cooperative ITS applications
European Commission (Horizon 2020), 2015-01-01 -- 2017-12-31.
Areas of Advance
Information and Communication Technology
ReVeRe (Research Vehicle Resource)
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4518
Chalmers University of Technology
Room EA, EDIT building
Opponent: Dr. Paolo Braca, Centre for Maritime Research and Experimentation , Nato Science and Technology Organization