Research on an Anti-Perturbation Kalman Filter Algorithm

Abstract

Abstract. An improved Kalman filter algorithm is proposed by two kinds of anti-perturbation method which is derived according to the perturbation theorem of inverse matrix. Furthermore, direction-correcting has been merged into the algorithm by using multiple hypothesis testing theory which can detect the current direction of a target. Finally, Both quantitative and qualitative analysis are given in detail. The measurements and experiments based on indoor positioning demonstrate that the improved algorithm(named IKF) has great performance

Improved Image Alignment Algorithm Based on Projective Invariant for Aerial Video Stabilization

“…J et al [18] uses scale-invariant feature transform points to obtain a crude estimate of the projective transformation, and identifies the features from the moving objects by the difference in moving velocities between objects and the background. Cheng.H.P et al [19] achieves aerial video stabilization through detecting SIFT [22] points and calculating the parameters of the projective transformation in a RANSAC process, then a Gaussian distribution is used to create a background model and detect the distant moving objects, but this method only applicable to runway scene. Wang.…”

“…Kalman filters [14] has been used to compensate the unwanted shaking of camera without the intentional camera motion. We will adopt a Sage-Husa Kalman filter [31] where the correction vector for each image frame is obtained as the difference between the filtered and original positions. This assumption helps to distinguish and preserve the intended camera motion.…”

“…In this article, the innovation sequence [31] is chosen to predict the residual error. Measurement of residual error is as follow:…”

“…1, aims at presenting a novel optimal derivative filters and projective invariant based ME algorithm, which can generate the accurate locations of the corner points and distinguish more accurate matching points from the less accurate ones, and the most accurate points that belong to the planar background will be used to estimate transformation model. The proposed method 1)selects a set of accurate feature points in each frame based on optimal derivative filters method, 2)finds the matching points between the feature points in the frames, 3)uses the projective invariant method that can distinguish more accurate matching points from the less accurate ones, and the most accurate points that belong to the planar background will be used to estimate transformation model, 4)performs the motion compensation with Sage-Husa Kalman filter [22] to stabilize the sequence. In the following sections, related work in aerial video stabilization is reviewed, details of an automatic stabilization system are provided, and experimental results of the performance of the algorithm are presented and discussed.…”