Robust adaptive signal-level fusion for enhanced target detection

Ivan Kadar

doi:10.1117/12.243165

14 June 1996 Robust adaptive signal-level fusion for enhanced target detection

Ivan Kadar

Proceedings Volume 2755, Signal Processing, Sensor Fusion, and Target Recognition V; (1996) https://doi.org/10.1117/12.243165
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

In a series of prior papers Kadar et al have designed, developed and experimentally evaluated a family of centralized detection algorithms for multi-frequency radars operating in various degrees of clutter. Previously, two centralized fusion algorithms, called non-coherent integration (NCI) and T-squared (TSQ), followed by adaptive constant false alarm (ACFAR) post-processing were evaluated. In this paper, an optimum adaptive CFAR version of the T- squared algorithm, called ATSQ, is developed. The performance of ATSQ is compared with the NCI and TSQ algorithms using measured data in clutter. The fusion performance comparisons are presented in terms of receiver operating characteristics (ROC). It is shown that the new ATSQ algorithm is robust to changes of the background clutter. Its ROC performance is near the optimum achievable, and is significantly better than either NCI or TSQ.

Citation Download Citation

Ivan Kadar "Robust adaptive signal-level fusion for enhanced target detection", Proc. SPIE 2755, Signal Processing, Sensor Fusion, and Target Recognition V, (14 June 1996); https://doi.org/10.1117/12.243165

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