Joint Multiple Hypothesis Tracking and Identification (JMHTI) With GMTI/HRR Data

"MRLets Technologies, Inc.proposes an innovative approach for joint target tracking and identification (ID).The innovation is composed of three major components: exploitation of local and global motions,joint multiple hypothesis tracking and identification (JMHTI) and wavelets domain statistic processingof local motion information. The proposed research embarks upon the first research of this kind indiscovering the information coupling mechanism between target kinematics and ID spaces, andthe key is the introduction of global motion and local motion. Local motion carries certain kinematics informationand rich ID information and therefore, naturally serves as an informationcoupling mechanism. One of the challenges is the separation of local motion fromcomposite motion information delivered by GMTI and HRR sensors. We proposed to use a waveletsdomain statistic processing technique based on the hidden Markov trees (HMT) approach forlocal motion information decomposition.A new and powerful joint tracking and identification -- joint multiple hypothesis tracking andidentification (JMHTI) -- is proposed as a main processing engine for track and IDestimation. One of the main advantages of JMHTI is the multiscan information accrual in bothkinematics and ID spaces which effectively alleviates the impact of noisy aspect angles.The proposed JMHTI is expected to benefit significantly both tracking and ATR communities. The proposed joint GMTI/HRR