6,290 research outputs found
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
Vehicle infrastructure cooperative localization using Factor Graphs
Highly assisted and Autonomous Driving is dependent on the accurate localization of both the vehicle and other targets within the environment. With increasing traffic on roads and wider proliferation of low cost sensors, a vehicle-infrastructure cooperative localization scenario can provide improved performance over traditional mono-platform localization. The paper highlights the various challenges in the process and proposes a solution based on Factor Graphs which utilizes the concept of topology of vehicles. A Factor Graph represents probabilistic graphical model as a bipartite graph. It is used to add the inter-vehicle distance as constraints while localizing the vehicle. The proposed solution is easily scalable for many vehicles without increasing the execution complexity. Finally simulation indicates that incorporating the topology information as a state estimate can improve performance over the traditional Kalman Filter approac
Optimal Information-Theoretic Wireless Location Verification
We develop a new Location Verification System (LVS) focussed on network-based
Intelligent Transport Systems and vehicular ad hoc networks. The algorithm we
develop is based on an information-theoretic framework which uses the received
signal strength (RSS) from a network of base-stations and the claimed position.
Based on this information we derive the optimal decision regarding the
verification of the user's location. Our algorithm is optimal in the sense of
maximizing the mutual information between its input and output data. Our
approach is based on the practical scenario in which a non-colluding malicious
user some distance from a highway optimally boosts his transmit power in an
attempt to fool the LVS that he is on the highway. We develop a practical
threat model for this attack scenario, and investigate in detail the
performance of the LVS in terms of its input/output mutual information. We show
how our LVS decision rule can be implemented straightforwardly with a
performance that delivers near-optimality under realistic threat conditions,
with information-theoretic optimality approached as the malicious user moves
further from the highway. The practical advantages our new
information-theoretic scheme delivers relative to more traditional Bayesian
verification frameworks are discussed.Comment: Corrected typos and introduced new threat model
Gossip Algorithms for Distributed Signal Processing
Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area. We describe convergence rate results, which are related to the number of
transmitted messages and thus the amount of energy consumed in the network for
gossiping. We discuss issues related to gossiping over wireless links,
including the effects of quantization and noise, and we illustrate the use of
gossip algorithms for canonical signal processing tasks including distributed
estimation, source localization, and compression.Comment: Submitted to Proceedings of the IEEE, 29 page
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