Alpha beta filter

About: Alpha beta filter is a research topic. Over the lifetime, 5653 publications have been published within this topic receiving 128415 citations.

Distributed extended Kalman filter with nonlinear consensus estimate

Abstract: This paper is concerned with the distributed filtering problem for discrete-time nonlinear systems over a sensor network. In contrast with the distributed filters with linear consensus estimate, a distributed extended Kalman filter (EKF) is developed with nonlinear consensus estimate. Specifically, a new nonlinear consensus protocol with polynomial form is proposed to generate the consensus estimate. By using the variance-constrained approach, the Kalman gain matrix is determined for each node to guarantee an optimized upper bound on the state estimation error covariance despite consensus terms and linearization errors. It is shown that the Kalman gain matrix can be derived by solving two Riccati-like difference equations. The effectiveness of the proposed filter is evaluated on an indoor localization of a mobile robot with visual tracking systems.

A Kalman filter approach to direct depth estimation incorporating surface structure

Abstract: The problem of depth-from-motion using a monocular image sequence is considered A pixel-based model is developed for direct depth estimation within a Kalman filtering framework A method is proposed for incorporating local surface structure into the Kalman filter Experimental results are provided to illustrate the effect of structural information on depth estimation

Stereo Visual-Inertial Odometry With Multiple Kalman Filters Ensemble

Abstract: In this paper, we present a stereo visual-inertial odometry algorithm assembled with three separated Kalman filters, i.e., attitude filter, orientation filter, and position filter. Our algorithm carries out the orientation and position estimation with three filters working on different fusion intervals, which can provide more robustness even when the visual odometry estimation fails. In our orientation estimation, we propose an improved indirect Kalman filter, which uses the orientation error space represented by unit quaternion as the state of the filter. The performance of the algorithm is demonstrated through extensive experimental results, including the benchmark KITTI datasets and some challenging datasets captured in a rough terrain campus.

Speech enhancement using a masking threshold constrained Kalman filter and its heuristic implementations

Abstract: A masking threshold constrained Kalman filter for speech enhancement is derived in the paper. A key step in a traditional Kalman filter requires minimizing an estimation error variance between a clean signal and its estimation. Our new method is to minimize the estimation error variance under the constraint that the energy of the estimation error is smaller than a masking threshold, computed from both time-domain forward masking and frequency-domain simultaneous masking properties of human auditory systems. The new Kalman filter provides a theoretical base for the application of the masking properties in Kalman filtering for speech enhancement. Due to the high computation cost of the proposed perceptually constrained Kalman filter, a perceptual post-filter concatenated with a standard Kalman filter is also proposed as a heuristic alternative for real-time implementation. The post-filter is constructed to make the estimation error obtained from the Kalman filter lower than the masking threshold. A wavelet Kalman filter with post-filtering is introduced to further reduce the computational load. Experimental results with colored noise show that the new constrained Kalman filter method produces the best performance when compared with other recent methods, and that the proposed heuristics with post-filtering can also produce a significant performance gain over other recent methods.

On the use of the Kalman filter in timescales

Abstract: The Kalman filter is a very useful tool of estimation theory, successfully adopted in a wide variety of problems. As a recursive and optimal estimation technique, the Kalman filter seems to be the correct tool also for building precise timescales, and various attempts have been made in the past giving rise, for example, to the TA(NIST) timescale. Despite the promising expectations, a completely satisfactory implementation has never been found, due to the intrinsic non-observability of the clock time readings, which makes the clock estimation problem underdetermined. However, the case of the Kalman filter applied to the estimation of the difference between two clocks is different. In this case the problem is observable and the Kalman filter has proved to be a powerful tool. A new proposal with interesting results, concerning the definition of an independent timescale, came with the GPS composite clock, which is based on the Kalman filter and has been in use since 1990 in the GPS system. In the composite clock the indefinite growth of the covariance matrix due to the non-observability is controlled by the so-called `transparent variations'—squeezing operations on the covariance matrix that do not interfere with the estimation algorithm. A useful quantity, the implicit ensemble mean, is defined and the `corrected clocks' (physical clocks minus their predicted bias) are shown to be observable with respect to this quantity. We have implemented the full composite clock and we discuss some of its advantages and criticalities. More recently, the Kalman filter is generating new interest, and a few groups are proposing new implementations. This paper gives an overview of what has been done and of what is currently under investigation, pointing out the peculiar advantages and the open questions in the application of this attractive technique to the generation of a timescale.