Multi-target detection and estimation with the use of massive independent, identical sensors

Tiancheng Li; Juan M. Corchado; Javier Bajo; Genshe Chen

doi:10.1117/12.2177973

22 May 2015 Multi-target detection and estimation with the use of massive independent, identical sensors

Tiancheng Li, Juan M. Corchado, Javier Bajo, Genshe Chen

Proceedings Volume 9469, Sensors and Systems for Space Applications VIII; 94690G (2015) https://doi.org/10.1117/12.2177973
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

Abstract

This paper investigates the problem of using a large number of independent, identical sensors jointly for multi-object detection and estimation (MODE), namely massive sensor MODE. This is significantly different to the general target tracking using few sensors. The massive sensor data allows very accurate estimation in theory (but may instead go conversely in fact) but will also cause a heavy computational burden for the traditional filter-based tracker. Instead, we propose a clustering method to fuse massive sensor data in the same state space, which is shown to be able to filter clutter and to estimate states of the targets without the use of any traditional filter. This non-Bayesian solution as referred to massive sensor observation-only (O2) inference needs neither to assume the target/clutter model nor to know the system noises. Therefore it can handle challenging scenarios with few prior information and do so very fast computationally. Simulations with the use of massive homogeneous (independent identical distributed) sensors have demonstrated the validity and superiority of the proposed approach.

Citation Download Citation

Tiancheng Li, Juan M. Corchado, Javier Bajo, and Genshe Chen "Multi-target detection and estimation with the use of massive independent, identical sensors", Proc. SPIE 9469, Sensors and Systems for Space Applications VIII, 94690G (22 May 2015); https://doi.org/10.1117/12.2177973

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available