bullet Sensors & Transducers Journal

    (ISSN 1726- 5479)

205.767

2008 e-Impact Factor

25 Top Downloaded Articles

Best Selling Articles 2012

Journal Subscription 2013

Editorial Calendar 2013

Submit an Article

Editorial Board

Current Issue

S&T journal's cover

Sensors & Transducers Journal 2011

Sensors & Transducers Journal 2010

Sensors & Transducers Journal 2009

Sensors & Transducers Journal 2008

Sensors & Transducers Journal 2007

2000-2002 S&T e-Digest Contents

2003 S&T e-Digest Contents

2004 S&T e-Digest Contents

2005 S&T e-Digest Contents

2006 S&T e-Digest Contents

 

Best Articles 2011

 

 

 

Vol. 22, Special Issue, June 2013, pp. 58-65

 

Bullet

 

Performance of Track-to-Track Association Algorithms Based on Mahalanobis Distance
 
1 Xi LIU, 2 Hao YIN, 3 Hai-Yan LIU, 1 Ze-Min WU

1 College of Communications Engineering, PLA University of Science and Technology, Biaoying Road, Nanjing, 210007, China

Tel.: +86-25-84607181

2 Institute of China Electronic System Engineering Company, Dacheng Road, Beijing, 100141, China

3 College of Sciences, PLA University of Science and Technology, Shuanglong Street, Nanjing, 211101, China

E-mail: liuxi_ice@163.com

 

Received: 15 April 2013   /Accepted: 20 June 2013   /Published: 28 June 2013

Digital Sensors and Sensor Sysstems

 

Abstract: In multi-sensor tracking system, the track-to-track association problem is to determine whether a set of local tracks from different sensor systems are represent the same target. This problem is usually formulated as a binary hypothesis test, and the most common statistics is defined as the squared Mahalanobis distance (SMD) between the kinematic state estimates of two tracks. In this paper, three types of SMD algorithms are investigated, i.e., the SMD algorithm, the cumulative SMD algorithm, and the Discrete Wavelet Transform (DWT) algorithm which can be regarded as a generalized SMD ratio algorithm. The first one can be looked as singlescan algorithm, and the rest two are multiscan approaches. From another viewpoint, the first two are time domain algorithms, and the last one is a transform domain algorithm. The probability distribution functions of statistics defined by these algorithms have been discussed under the assumption that the estimates errors are independent across time. The Operating Characteristic Function is used to describe association performance. It shows that the multiscan algorithm performs better than the singlescan algorithm. As to multiscan algorithms, the DWT algorithm is superior to time domain algorithm. But better algorithm is more sensitive to the residual bias because the statistic based on SMD of target state estimates is directly contaminated by the bias.

 

Keywords: Multi-sensor data fusion, Multi-target tracking, Mahalanobis distance, Track-to-track association, Discrete wavelet transform, Operating characteristic function.

 

Acrobat reader logo Click <here> or title of paper to download the full pages article in pdf format

 

 

 

 

 


1999 - 2018 Copyright , International Frequency Sensor Association (IFSA). All Rights Reserved.


Home - News - Links - Archives - Tools - Voltage-to-Frequency Converters - Standardization - Patents - Marketplace - Projects - Wish List - e-Shop - Sensor Jobs - Membership - Videos - Publishing - Site Map - Subscribe - Search

 Members Area -Sensors Portal -Training Courses - S&T Digest - For advertisers - Bookstore - Forums - Polls - Submit Press Release - Submit White Paper - Testimonies - Twitter - Facebook - LinkedIn