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

Cooperative Localization of Vehicles without Inter-vehicle Measurements

While cooperation among vehicles can improve localization, standard communication technologies (e.g., 802.11p) cannot provide reliable range or angle measurements. To allow cooperation without explicit inter-vehicle measurements, we propose a cooperative localization method whereby vehicles track mobile features in the environment and use associations of features among vehicles to improve the vehicles\u27 localization accuracy. The proposed algorithm, which scales linearly in the number of vehicles and quadratically in the number of tracked features, shows superior localization performance compared to a non-cooperative approach

Multiple Target Tracking With Uncertain Sensor State Applied To Autonomous Vehicle Data

In a conventional multitarget tracking (MTT) scenario, the sensor position is assumed known. When the MTT sensor, e.g., an automotive radar, is mounted to a moving vehicle with uncertain state, it becomes necessary to relax this assumption and model the unknown sensor position explicitly. In this paper, we compare a recently proposed filter that models the unknown sensor state [1], to two versions of the track-oriented marginal MeMBer/Poisson (TOMB/P) filter: the first does not model the sensor state uncertainty; the second models it approximately by artificially increasing the measurement variance. The results, using real measurement data, show that in terms of tracking performance, the proposed filter can outperform TOMB/P without sensor state uncertainty, and is comparable to TOMB/P with increased variance

Decentralized Scheduling for Cooperative Localization With Deep Reinforcement Learning

Cooperative localization is a promising solution to the vehicular high-accuracy localization problem. Despite its high potential, exhaustive measurement and information exchange between all adjacent vehicles are expensive and impractical for applications with limited resources. Greedy policies or hand-engineering heuristics may not be able to meet the requirement of complicated use cases. In this paper, we formulate a scheduling problem to improve the localization accuracy (measured through the Cram\ue9r-Rao lower bound) of every vehicle up to a given threshold using the minimum number of measurements. The problem is cast as a partially observable Markov decision process and solved using decentralized scheduling algorithms with deep reinforcement learning, which allow vehicles to optimize the scheduling (i.e., the instants to execute measurement and information exchange with each adjacent vehicle) in a distributed manner without a central controlling unit. Simulation results show that the proposed algorithms have a significant advantage over random and greedy policies in terms of both required numbers of measurements to localize all nodes and achievable localization precision with limited numbers of measurements

Channel Prediction with Location Uncertainty for Ad-Hoc Networks

Multi-agent systems (MAS) rely on positioning technologies to determine their physical location, and on wireless communication technologies to exchange information. Both positioning and communication are affected by uncertainties, which should be accounted for. This paper considers an agent placement problem to optimize end-to-end communication quality in a MAS, in the presence of uncertainties. Using Gaussian processes (GPs), operating on the input space of location distributions, we are able to model, learn, and predict the wireless channel. Predictions, in the form of distributions, are fed into the communication optimization problems. This approach inherently avoids regions of the workspace with high position uncertainty and leads to better average communication performance. We illustrate the benefits of our approach via extensive simulations, based on real wireless channel measurements. Finally, we demonstrate the improved channel learning and prediction performance, as well as the increased robustness in agent placement

5G mmWave Downlink Vehicular Positioning

5G new radio (NR) provides new opportunities for accurate positioning from a single reference station: large bandwidth combined with multiple antennas, at both the base station and user sides, allows for unparalleled angle and delay resolution. Nevertheless, positioning quality is affected by multipath and clock biases. We study, in terms of performance bounds and algorithms, the ability to localize a vehicle in the presence of multipath and unknown user clock bias. We find that when a sufficient number of paths is present, a vehicle can still be localized thanks to redundancy in the geometric constraints. Moreover, the 5G NR signals enable a vehicle to build up a map of the environment

Channel Gain Prediction for Cooperative Multi-Agent Systems

In a cooperative multi-agent system (MAS), agents communicate with each other using the wireless medium. As agents move in the environment in order to fulfill the MAS\u27 higher level task, their location changes and so does the wireless communication channel they experience. To enable a successful coordination, it is paramount for the agents to retain connectivity among themselves. In order to achieve this, the availability of explicit channel knowledge for the MAS\u27 future configuration is needed.\ua0Since the agents determine their location from sensors, any expected residual location uncertainty for the MAS\u27 future configuration will have an implication on the channel knowledge.\ua0For this reason, a computationally attractive yet accurate method to predict the wireless ad-hoc communication channel for any configuration and location uncertainty of the agents is needed.In this thesis, we employ Gaussian processes (GPs) for learning of channel model parameters and for channel prediction at arbitrary (unvisited) transmitter (TX) and receiver (RX) locations.\ua0In an indoor measurement campaign, we investigate the ad-hoc wireless communication channel and its properties with respect to path-loss and shadowing from obstacles. We derive a suitable GP model, where we incorporate spatial correlation of communication links caused by shadowing. The effectiveness of our approach in a cooperative MAS is demonstrated, where the bit error rate (BER) among the agents\u27 communication links is minimized. Furthermore, we extend our GP framework allowing to make distributed predictions using a consensus scheme. We found that the incorporation of location uncertainty into channel prediction allows to outperform approaches where this is neglected. The incorporation of location uncertainty at both, the TX and the RX location, leads not only to robust estimates of the underlying channel parameters, but also to realistic channel predictions with respect to the agents\u27 true location uncertainty. Applied to a cooperative MAS, we see that the BER and BER uncertainty can be significantly reduced. Finally, with a distributed channel prediction, we observe a trade-off between computation complexity and accuracy of prediction.Natural extensions of our GP channel prediction framework could include distributed parameter learning and efficient methods to handle a high number of measurements

On the separation of timescales in radio-based positioning

Radio-based positioning methods are generally based on a separation of timescales, where each position update assumes the availability of synchronous measurements. This approach is convenient in the design of positioning algorithms, but fails to account for outdated information. We describe threedistinct ways how the separation of timescales can adverselyimpact the design and execution of positioning methods, andquantify the extent of these impacts analytically

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

Coordination among mobile agents relies on communication over a wireless channel and can thus be improved by channel prediction. We present a Gaussian process framework to learn channel parameters and predict the channel between arbitrary transmitter and receiver locations. We explicitly incorporate location uncertainty in both learning and prediction phases. Simulation results show that if location uncertainty is not modeled appropriately, it has a degenerative effect on the prediction quality

Location-Aware Formation Control in Swarm Navigation

Goal-seeking and information-seeking are canonical problems in mobile agent swarms. We study the problem of collaborative goal-approaching under uncertain agent position information. We propose a framework that establishes location-aware formations, resulting in a controller that accounts for agent position uncertainty with a realistic ranging model. Simulation results confirm that, as the outcome of the controller, the swarm moves towards its goal, while emerging formations conducive to high-quality localization