Channel Prediction and Target Tracking for Multi-Agent Systems
Doktorsavhandling, 2018

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 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.

Gaussian processes

multitarget tracking

channel prediction

extended target tracking

Multi-agent system

posterior fusion

location uncertainty

Room EA, EDIT building
Opponent: Dr. Paolo Braca, Centre for Maritime Research and Experimentation , Nato Science and Technology Organization


Markus Fröhle

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Channel Prediction with Location Uncertainty for Ad-Hoc Networks

IIEEE Transactions on Signal and Information Processing over Networks,; Vol. 4(2018)p. 349-361

Artikel i vetenskaplig tidskrift

Channel gain prediction for multi-agent networks in the presence of location uncertainty

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings,; Vol. 2016-May(2016)p. 3911-3915

Paper i proceeding

Cooperative Localization of Vehicles without Inter-vehicle Measurements


Paper i proceeding

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 i proceeding

M. Fröhle, K. Granström, and H.Wymeersch, “Decentralized Poisson Multi-Bernoulli Filtering for Extended Target Tracking”

Soon, cars and trucks will be able to drive autonomously on our roads. A car will not just park on a free parking spot, but also drive us to work in the morning while we prepare for an important meeting, and bring us home safely so that we can relax. To enable a safe operation, the vehicle needs to know not only its own position accurately, but also, the environment and the positions of other road users in its vicinity. The vehicle’s onboard sensors provide this information. For example, a Global Positioning System (GPS) receiver allows to determine the vehicle’s position with some level of accuracy, and a radar can measure angle and distance to an object.

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.

High precision positioning for cooperative ITS applications

Europeiska kommissionen (EU) (EC/H2020/636537), 2015-01-01 -- 2017-12-31.

Multi-technology positioning professionals (MULTI-POS)

Europeiska kommissionen (EU) (EC/FP7/316528), 2012-10-01 -- 2016-09-30.


Europeiska kommissionen (EU) (EC/FP7/258418), 2011-05-01 -- 2016-04-30.

COPPLAR CampusShuttle cooperative perception & planning platform

VINNOVA (2015-04849), 2016-01-01 -- 2018-12-31.


Informations- och kommunikationsteknik






ReVeRe (Research Vehicle Resource)



Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4518



Room EA, EDIT building

Opponent: Dr. Paolo Braca, Centre for Maritime Research and Experimentation , Nato Science and Technology Organization

Mer information

Senast uppdaterat