685 research outputs found
Coded Distributed Tracking
We consider the problem of tracking the state of a process that evolves over
time in a distributed setting, with multiple observers each observing parts of
the state, which is a fundamental information processing problem with a wide
range of applications. We propose a cloud-assisted scheme where the tracking is
performed over the cloud. In particular, to provide timely and accurate
updates, and alleviate the straggler problem of cloud computing, we propose a
coded distributed computing approach where coded observations are distributed
over multiple workers. The proposed scheme is based on a coded version of the
Kalman filter that operates on data encoded with an erasure correcting code,
such that the state can be estimated from partial updates computed by a subset
of the workers. We apply the proposed scheme to the problem of tracking
multiple vehicles. We show that replication achieves significantly higher
accuracy than the corresponding uncoded scheme. The use of maximum distance
separable (MDS) codes further improves accuracy for larger update intervals. In
both cases, the proposed scheme approaches the accuracy of an ideal centralized
scheme when the update interval is large enough. Finally, we observe a
trade-off between age-of-information and estimation accuracy for MDS codes.Comment: Accepted for publication at IEEE GLOBECOM 201
A Distributed Tracking Algorithm for Reconstruction of Graph Signals
The rapid development of signal processing on graphs provides a new
perspective for processing large-scale data associated with irregular domains.
In many practical applications, it is necessary to handle massive data sets
through complex networks, in which most nodes have limited computing power.
Designing efficient distributed algorithms is critical for this task. This
paper focuses on the distributed reconstruction of a time-varying bandlimited
graph signal based on observations sampled at a subset of selected nodes. A
distributed least square reconstruction (DLSR) algorithm is proposed to recover
the unknown signal iteratively, by allowing neighboring nodes to communicate
with one another and make fast updates. DLSR uses a decay scheme to annihilate
the out-of-band energy occurring in the reconstruction process, which is
inevitably caused by the transmission delay in distributed systems. Proof of
convergence and error bounds for DLSR are provided in this paper, suggesting
that the algorithm is able to track time-varying graph signals and perfectly
reconstruct time-invariant signals. The DLSR algorithm is numerically
experimented with synthetic data and real-world sensor network data, which
verifies its ability in tracking slowly time-varying graph signals.Comment: 30 pages, 9 figures, 2 tables, journal pape
Distributed tracking control of leader-follower multi-agent systems under noisy measurement
In this paper, a distributed tracking control scheme with distributed
estimators has been developed for a leader-follower multi-agent system with
measurement noises and directed interconnection topology. It is supposed that
each follower can only measure relative positions of its neighbors in a noisy
environment, including the relative position of the second-order active leader.
A neighbor-based tracking protocol together with distributed estimators is
designed based on a novel velocity decomposition technique. It is shown that
the closed loop tracking control system is stochastically stable in mean square
and the estimation errors converge to zero in mean square as well. A simulation
example is finally given to illustrate the performance of the proposed control
scheme.Comment: 8 Pages, 3 figure
Energy-aware distributed tracking in wireless sensor networks
We consider a wireless sensor network engaged in the task of distributed tracking. Here, multiple remote sensor nodes estimate a physical process (for example, a moving object) and transmit quantized estimates to a fusion center for processing. At the fusion node a BLUE (Best Linear Unbiased Estimation) approach is used to combine the sensor estimates and create a final estimate of the state. In this framework, the uncertainty of the overall estimate is derived and shown to depend on the individual sensor transmit energy and quantization levels. Since power and bandwidth are critically constrained resources in battery operated sensor nodes, we attempt to quantify the trade-off between the lifetime of the network and the estimation quality over time. A unique feature of this work is that instead of merely allowing a greedy minimization of uncertainty in each time instance, the lifetime of the wireless sensor network is improved by incorporating a heuristic scaling on the operating capability of each node. This heuristic in turn depends on the remaining energy, equivalent to the past history of power and quantization decisions. Simulation results demonstrate the quality of the state estimate as well as the extended lifetime of the network when power and quantization levels are dynamically updated
Non-overlapping Distributed Tracking System Utilizing Particle Filter
Tracking people across multiple cameras is a challenging research area in visual computing, especially when these cameras have non-overlapping field of views. The important task is to associate a current subject with other prior appearances of the same subject across time and space in a camera network. Several known techniques rely on Bayesian approaches to perform the matching task. However, these approaches do not scale well when the dimension of the problem increases; e.g. when the number of subject or possible path increases. The aim of this paper is to propose a unified tracking framework using particle filters to efficiently switch between visual tracking (field of view tracking) and track prediction (non-overlapping region tracking). The particle filter tracking system utilizes a map (known environment) to assist the tracking process when targets leave the field of view of any camera. We implemented and tested this tracking approach in an in-house multiple cameras system as well as using on-line data. Promising results were obtained which suggested the feasibility of such an approach
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