Template updating kalman filter what is a normal age for kids to start dating
This paper first presents the Mean Shift algorithm, then the Mean Shift algorithm iterative weight is modified with main information more prominent, secondary information suppressed, avoiding the tedious root, improving the real-time and effectiveness of target tracking.The target template updating algorithm is present to solve change of background and target shape change.The nominal template is incorporated into an extended Kalman filter which corrects the nominal template acceleration with the filter states to predict future thrust acceleration, velocity and position. Such a strategy minimizes the requirements for warhead and decoy discrimination, and allows for multi-layered defense opportunities.The correction can compensate for motor burn variations and missile energy management (lofted/depressed trajectory). A missile defense system supporting this strategy must include an accurate boost phase target state estimator.Then a Kalman filter in the horizontal position and the vertical position is established to solve the problem of target tracking completely covered.Simulation results show that target tracking algorithm on the condition of target template update has higher tracking accuracy , higher real-time property and at the same time is robust than the traditional Mean Shift tracking algorithm .Once the user has provided a calling routine and the required application-specific subfunctions, the application-specific Kalman-filter software can be compiled and executed immediately.During execution, the generic Kalman-filter function is called from a higher-level “navigation” or “estimation” routine that preprocesses measurement data and postprocesses output data.
Secondly, we present an improved Kalman filter for approximate estimating the motion trail of the target and a modified similarity evaluation function for exact matching.
The generic Kalman-filter function uses the aforementioned data structures and five implementation-specific subfunctions, which have been developed by the user on the basis of the aforementioned templates.
The GKF software can be used to develop many different types of unfactorized Kalman filters.
The scheme considers the following differences: the weight differences in two successive frames; different types of affine transformation applied to templates.
Finally, experiments demonstrate that the proposed algorithm is robust to appearance variation of fast motion target and achieves real-time performance on middle/low-range computing platform.