Distributed Estimation with Information-Seeking Control in Agent Network

We introduce a distributed, cooperative framework and method for Bayesian estimation and control in decentralized agent networks. Our framework combines joint estimation of time-varying global and local states with information-seeking control optimizing the behavior of the agents. It is suited to nonlinear and non-Gaussian problems and, in particular, to location-aware networks. For cooperative estimation, a combination of belief propagation message passing and consensus is used. For cooperative control, the negative posterior joint entropy of all states is maximized via a gradient ascent. The estimation layer provides the control layer with probabilistic information in the form of sample representations of probability distributions. Simulation results demonstrate intelligent behavior of the agents and excellent estimation performance for a simultaneous self-localization and target tracking problem. In a cooperative localization scenario with only one anchor, mobile agents can localize themselves after a short time with an accuracy that is higher than the accuracy of the performed distance measurements.Comment: 17 pages, 10 figure

Multisensor Poisson Multi-Bernoulli Filter for Joint Target-Sensor State Tracking

In a typical multitarget tracking (MTT) scenario, the sensor state is either assumed known, or tracking is performed in the sensor's (relative) coordinate frame. This assumption does not hold when the sensor, e.g., an automotive radar, is mounted on a vehicle, and the target state should be represented in a global (absolute) coordinate frame. Then it is important to consider the uncertain location of the vehicle on which the sensor is mounted for MTT. In this paper, we present a multisensor low complexity Poisson multi-Bernoulli MTT filter, which jointly tracks the uncertain vehicle state and target states. Measurements collected by different sensors mounted on multiple vehicles with varying location uncertainty are incorporated sequentially based on the arrival of new sensor measurements. In doing so, targets observed from a sensor mounted on a well-localized vehicle reduce the state uncertainty of other poorly localized vehicles, provided that a common non-empty subset of targets is observed. A low complexity filter is obtained by approximations of the joint sensor-feature state density minimizing the Kullback-Leibler divergence (KLD). Results from synthetic as well as experimental measurement data, collected in a vehicle driving scenario, demonstrate the performance benefits of joint vehicle-target state tracking.Comment: 13 pages, 7 figure

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

Multipath-assisted maximum-likelihood indoor positioning using UWB signals

Multipath-assisted indoor positioning (using ultrawideband signals) exploits the geometric information contained in deterministic multipath components. With the help of a-priori available floorplan information, robust localization can be achieved, even in absence of a line-of-sight connection between anchor and agent. In a recent work, the Cramér-Rao lower bound has been derived for the position estimation variance using a channel model which explicitly takes into account diffuse multipath as a stochastic noise process in addition to the deterministic multipath components. In this paper, we adapt this model for position estimation via a measurement likelihood function and evaluate the performance for real channel measurements. Performance results confirm the applicability of this approach. A position accuracy better than 2.5 cm has been obtained in 90% of the estimates using only one active anchor at a bandwidth of 2GHz and robustness against non-line-of-sight situations has been demonstrated

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

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.Comment: Globecom 2018 paper with corrected figure # 7 (RMSE now significantly higher than CRB

Distributed channel prediction for multi-agent systems

Los sistemas multiagente (MAS) se comunican a través de una red inalámbrica para coordinar sus acciones e informar sobre el estado de su misión. La conectividad y el rendimiento del sistema pueden mejorarse mediante la predicción de la ganancia del canal. Presentamos un esquema basado en regresión de procesos gaussianos (GPR) distribuidos para predecir el canal inalámbrico en términos de la potencia recibida en el MAS. El esquema combina una máquina de comité bayesiano con un esquema de consenso medio, distribuyendo así no sólo la memoria sino también la carga computacional y de comunicación. A través de simulaciones de Monte Carlo, demostramos el rendimiento del GPR propuesto.RACHEL TEC2013-47141-C4-4-RMulti-agent systems (MAS) communicate over a wireless network to coordinate their actions and to report their mission status. Connectivity and system-level performance can be improved by channel gain prediction. We present a distributed Gaussian process regression (GPR) framework for channel prediction in terms of the received power in MAS. The framework combines a Bayesian committee machine with an average consensus scheme, thus distributing not only the memory, but also computational and communication loads. Through Monte Carlo simulations, we demonstrate the performance of the proposed GPR

Performance Bounds for Multipath-assisted Indoor Localization on Backscatter Channels

Abstract—In this paper, we derive the Cramér-Rao lower bound (CRLB) on the position error for an RFID tag localization system exploiting multipath on backscatter radio channels. The backscatter channel is modeled with a hybrid deterministic/stochasticchannelmodel.Inthisway, boththegeometry of the deterministic multipath components (MPCs) and the interfering diffuse multipath are taken into account. Computational results show the influence of the room geometry on the bound and the impact of the diffusemultipath.Time reversal (TR) processing on the uplink channel is analyzed using the deterministic MPCs to overcome the degenerate nature of the backscatter channel. The CRLB shows the potential gain obtained from TR processing as well as its strong dependence on the geometry. Such TR processing has been proposed for TX waveform adaptation in the perception-action cycle of a cognitive radar. The results of this paper illustrate that it can indeed influence beneficially the measurement noise of the received signal, yielding control over the localization system

Chalmers Publication Library Location-Aware Formation Control in Swarm Navigation Location-Aware Formation Control in Swarm Navigation

Abstract-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 locationaware 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