Share Email Print

Proceedings Paper

A pseudo oversampling-based C3PC algorithm for close space objects detection and localization
Author(s): Jing Hu; Fan Dong; Shinie Cai
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Target cluster brings about a light-spot which consists of several neighborhood pixels in image, therefore it is difficult to distinguish between the targets or locate them with sub-pixel accuracy. In this paper, a pseudo oversampling-based C3PC (Covariance Constrained Constructive Particle Clustering) method is proposed to solve the closely space objects problem. As a classical detection and location method, C3PC algorithm, presents a particle clustering decomposition technique. However, the particle distribution according to the pixel gray value yields pixel level accuracy, which will lead to location error. Thus, by using a particle distribution at sub-pixel level, substantially better position accuracy can be obtained. According the characteristic of oversampling, an improved interpolation algorithm which simulating the oversampling techniques of sensor is brought forward. Simulation experiment results show that the positioning accuracy of CSOs in our algorithm is higher than that of C3PC algorithm.

Paper Details

Date Published: 14 December 2015
PDF: 9 pages
Proc. SPIE 9812, MIPPR 2015: Automatic Target Recognition and Navigation, 981215 (14 December 2015); doi: 10.1117/12.2210934
Show Author Affiliations
Jing Hu, Huazhong Univ. of Science and Technology (China)
Fan Dong, Huazhong Univ. of Science and Technology (China)
Shinie Cai, Huazhong Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 9812:
MIPPR 2015: Automatic Target Recognition and Navigation
Nong Sang; Xinjian Chen, Editor(s)

© SPIE. Terms of Use
Back to Top