Blind MIMO System Identification for Improved Emitter Classification/Identification

Classification and identification of radio frequency (RF) emissions onboard US Navy ships is an important ingredient in keeping our sailors and marines safe around the world. Techniques to increase the number of emitter parameters measured by shipboard electronic support measure (ESM) systems are being introduced into the fleet. However, this approach brings new issues such as measurement distortions due to propagation and environmental effects. The main objective of the proposal is to estimate the distortions from environmental and propagation phenomena and to reduce or eliminate their affect. Since the quantitative description of distortion phenomena is usually unknown in advance and emitter signals are inaccessible to the signal analyst, this problem is best addressed by utilizing recent advances in the signal processing area of blind system identification. Our approach is based on algorithms for blind estimation and equalization of multiple-input multiple-output (MIMO) systems. In this effort we will develop signal processing techniques that aim to improve the measurement and estimation of higher order emitter parameters by employing MIMO system estimation, equalization and advanced higher-order parameter extraction. The proposed signal processing techniques will be studied and tested under different scenarios and for a wide range of system parameters and assumptions.