Bayesian filtering in electronic surveillance

Stefano Coraluppi; Craig Carthel

doi:10.1117/12.912964

7 May 2012 Bayesian filtering in electronic surveillance

Stefano Coraluppi, Craig Carthel

Proceedings Volume 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI; 839202 (2012) https://doi.org/10.1117/12.912964
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

Fusion of passive electronic support measures (ESM) with active radar data enables tracking and identification of platforms in air, ground, and maritime domains. An effective multi-sensor fusion architecture adopts hierarchical real-time multi-stage processing. This paper focuses on the recursive filtering challenges. The first challenge is to achieve effective platform identification based on noisy emitter type measurements; we show that while optimal processing is computationally infeasible, a good suboptimal solution is available via a sequential measurement processing approach. The second challenge is to process waveform feature measurements that enable disambiguation in multi-target scenarios where targets may be using the same emitters. We show that an approach that explicitly considers the Markov jump process outperforms the traditional Kalman filtering solution.

Citation Download Citation

Stefano Coraluppi and Craig Carthel "Bayesian filtering in electronic surveillance", Proc. SPIE 8392, Signal Processing, Sensor Fusion, and Target Recognition XXI, 839202 (7 May 2012); https://doi.org/10.1117/12.912964

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