Object recognition and identification using ESM data

Recognition and identification of unknown targets is a crucial task in surveillance and security systems. Electronic Support Measures (ESM) are one of the most effective sensors for identification, especially for maritime and air-to-ground applications. In typical surveillance systems multiple ESM sensors are usually deployed along with kinematic sensors like radar. Different ESM sensors may produce different types of reports ready to be sent to the fusion center. The focus of this paper is to develop a new architecture for target recognition and identification when non-homogeneous ESM and possibly kinematic reports are received at the fusion center. A Bayesian version of the new fusion architecture is evaluated using simulations to show the benefit of utilizing different ESM reports such as attribute and signal level ESM data.

[1]  R. Yager.  On the dempster-shafer framework and new combination rules , 1987, Inf. Sci..

[2]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[3]  Liang Juan.  ESM-radar track correlation algorithms based on maximum likelihood estimation , 2007 .

[4]  C. L. Davies,et al.  Automatic processing for ESM , 1982 .

[5]  A. Kawalec,et al.  Knowledge-based signal processing for radar identification , 2008, 2008 International Conference on "Modern Problems of Radio Engineering, Telecommunications and Computer Science" (TCSET).

[6]  Subhash Challa,et al.  Joint target tracking and classification using radar and ESM sensors , 2001 .

[7]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .

[8]  David L Adamy,et al.  Ew 101: A First Course in Electronic Warfare , 2001 .

[9]  T Smestad,et al.  ESM-Sensors for Tactical Information in Air Defence Systems , 2001 .

[10]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[11]  James B. Y. Tsui,et al.  Electronic Warfare Receiver with Multiple FFT Frame Sizes , 2012, IEEE Transactions on Aerospace and Electronic Systems.

[12]  Parameswaran Ramanathan,et al.  Distributed target classification and tracking in sensor networks , 2003 .

[13]  A. Cohen,et al.  GMM-based target classification for ground surveillance Doppler radar , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[14]  David Barber,et al.  Bayesian Classification With Gaussian Processes , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  N. Collins,et al.  Terrain aided passive estimation , 1989, Proceedings of the IEEE National Aerospace and Electronics Conference.

[16]  R. Owczarek,et al.  Specific emitter identification using intrapulse data , 2004, First European Radar Conference, 2004. EURAD..

[17]  Lotfi A. Zadeh,et al.  A Simple View of the Dempster-Shafer Theory of Evidence and Its Implication for the Rule of Combination , 1985, AI Mag..

[18]  Pascal Vasseur,et al.  Introduction to Multisensor Data Fusion , 2005, The Industrial Information Technology Handbook.

[19]  Aarnout Brombacher,et al.  Probability... , 2009, Qual. Reliab. Eng. Int..

[20]  V. Aidala.  Kalman Filter Behavior in Bearings-Only Tracking Applications , 1979, IEEE Transactions on Aerospace and Electronic Systems.

[21]  Frans Voorbraak,et al.  A Computationally Efficient Approximation of Dempster-Shafer Theory , 1988, Int. J. Man Mach. Stud..

[22]  A. Gad,et al.  Data fusion architecture for Maritime Surveillance , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[23]  Hugues Demers,et al.  Identity multiassignment in ESM to radar fusion , 2006, 2006 9th International Conference on Information Fusion.

[24]  Yaakov Bar-Shalom,et al.  Decorrelated unbiased converted measurement Kalman filter , 2014, IEEE Transactions on Aerospace and Electronic Systems.

[25]  P. Smets,et al.  Target classification approach based on the belief function theory , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[26]  Branko Ristic,et al.  Target Classification with Imprecise Likelihoods: Mahler's Approach , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[27]  J. Roe,et al.  Knowledge-based signal processing for radar ESM systems , 1990 .

[28]  P. Smets,et al.  Target identification using belief functions and implication rules , 2005, IEEE Transactions on Aerospace and Electronic Systems.