Paper
16 April 2008 A distributed database view of network tracking systems
Author Affiliations +
Abstract
In distributed tracking systems, multiple non-collocated trackers cooperate to fuse local sensor data into a global track picture. Generating this global track picture at a central location is fairly straightforward, but the single point of failure and excessive bandwidth requirements introduced by centralized processing motivate the development of decentralized methods. In many decentralized tracking systems, trackers communicate with their peers via a lossy, bandwidth-limited network in which dropped, delayed, and out of order packets are typical. Oftentimes the decentralized tracking problem is viewed as a local tracking problem with a networking twist; we believe this view can underestimate the network complexities to be overcome. Indeed, a subsequent 'oversight' layer is often introduced to detect and handle track inconsistencies arising from a lack of robustness to network conditions. We instead pose the decentralized tracking problem as a distributed database problem, enabling us to draw inspiration from the vast extant literature on distributed databases. Using the two-phase commit algorithm, a well known technique for resolving transactions across a lossy network, we describe several ways in which one may build a distributed multiple hypothesis tracking system from the ground up to be robust to typical network intricacies. We pay particular attention to the dissimilar challenges presented by network track initiation vs. maintenance and suggest a hybrid system that balances speed and robustness by utilizing two-phase commit for only track initiation transactions. Finally, we present simulation results contrasting the performance of such a system with that of more traditional decentralized tracking implementations.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jason Yosinski and Randy Paffenroth "A distributed database view of network tracking systems", Proc. SPIE 6969, Signal and Data Processing of Small Targets 2008, 696915 (16 April 2008); https://doi.org/10.1117/12.778039
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Cited by 1 scholarly publication.
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KEYWORDS
Databases

Sensors

Detection and tracking algorithms

Monte Carlo methods

Data modeling

Network architectures

Distributed computing

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