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A Protocol for Real-time 3D Single Particle Tracking

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Delay-Tolerant Distributed Inference in Tracking Networks.

Mohammadreza Alimadadi¹, Milica Stojanovic¹, Pau Closas¹

¹Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA.

Sensors (Basel, Switzerland)

|September 10, 2021

Summary

This summary is machine-generated.

This study addresses asynchronous distributed inference for object tracking with communication delays. An efficient data fusion method uses predictions to combine estimates, improving performance in real-world scenarios.

Keywords:

Kalman filter data fusion delay distributed learning object tracking partial estimators random access statistical inference wireless sensor network

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Area of Science:

Computer Vision
Robotics
Distributed Systems

Background:

Object tracking often relies on synchronized data, which is unrealistic in distributed systems.
Communication delays between distributed estimators and a central station are a significant challenge.

Purpose of the Study:

To develop an efficient data fusion method for asynchronous distributed object tracking.
To address the challenge of non-negligible communication delays in distributed inference.

Main Methods:

Introduced a novel data fusion technique for combining estimates from partial estimators.
Implemented state prediction based on the most current available information to compensate for delays.

Main Results:

Simulation results demonstrate the effectiveness of the proposed asynchronous inference method.
The data fusion approach successfully integrates delayed estimates for improved object tracking.

Conclusions:

The proposed method provides an efficient solution for asynchronous distributed object tracking under communication delays.
This work advances distributed inference techniques by accounting for practical communication constraints.