Skip to Main Content
An algorithm based on grey innovation model GM (1, 1) of fixed length is introduced for the localization and tracking of moving targets. Kalman filter is an efficient computational method for tracking, but motion and noise assumption limits its process model to constant velocity model or constant acceleration model. The grey system theory uses the data characteristic of extrinsic randomicity and holistic regularity to find out the intrinsic rules of the system. It explores the law of subjectpsilas motivation by accumulation of raw data and builds up the differential equations to estimate the next states of the system. Therefore an object tracking algorithm based on grey innovation model GM (1, 1) of fixed length is proposed and studied in detail. The effectiveness and efficiency of the proposed method is revealed through the performance comparison of grey innovation model and Kalman filter with constant acceleration model. A further study advice is discussed at the end.