Variable Dimension Filter Research Articles

Position Prediction Based Frequency Control of Beacons in Vehicular Ad Hoc Networks

In VANETs, frequent beacon broadcasting can lead to high bandwidth consumption and channel congestion. In this paper, a position prediction based beacon approach is proposed to reduce beacon frequency and decrease bandwidth consumption. Vehicles track their neighbors using the predicted position instead of using periodic beacon broadcasting. Only when the prediction error is higher than a predefined tolerance will a beacon broadcasting be triggered. For improving the prediction accuracy, we classify the motion of vehicles into two typical patterns: a constant speed pattern and a maneuvering pattern. A maneuver detection module is responsible for recognizing current motion patterns, and a variable dimension filter that can switch dynamically between the two patterns is employed to generate high accurate position prediction. The simulation results show the proposed scheme can reduce significantly the number of beacons than three existing beacon approaches.

Switched Kalman filter‐interacting multiple model algorithm based on optimal autoregressive model for manoeuvring target tracking

A manoeuvring target tracking algorithm based on the autoregressive (AR) model is proposed. First, the AR model is incorporated into the Kalman filter (KF) for target tracking. The closed-form solution of the AR model coefficients is obtained by minimising the mean-square tracking error, and subject to the polynomial constraint of target motion. Then, based on the AR model, the proposed algorithm is constructed by combining the KF with the interacting multiple model (IMM) filter, coupled with the proposed detection schemes for manoeuvre occurrence and termination, as well as for switching initialisation. Simulations are performed to demonstrate the effectiveness of the AR model, and the proposed algorithm is compared with the IMM filter and variable-dimension filter in the manoeuvring scenario.

Tracking a maneuvering target using neural fuzzy network.

A fast target maneuver detecting and highly accurate tracking technique using a neural fuzzy network based on Kalman filter is proposed in this paper. In the automatic target tracking system, there exists an important and difficult problem: how to detect the target maneuvers and fast response to avoid miss-tracking? The traditional maneuver detection algorithms, such as variable dimension filter (VDF) and input estimation (IE) etc., are computation intensive and difficult to implement in real time. To solve this problem, neural network algorithms have been issued recently. However, the normal neural networks such as backpropagation networks usually produce the extra problems of low convergence speed and/or large network size. Furthermore, the way to decide the network structure is heuristic. To overcome these defects and to make use of neural learning ability, a developed standard Kalman filter with a self-constructing neural fuzzy inference network (KF-SONFIN) algorithm for target tracking is presented in this paper. By generating possible target trajectories including maneuver information to train the SONFIN, the trained SONFIN can detect when the maneuver occurred, the magnitude of maneuver values and when the maneuver disappeared. Without having to change the structure of Kalman filter nor modeling the maneuvering target, this new algorithm, SONFIN, can always find itself an economic network size with a fast learning process. Simulation results show that the KF-SONFIN is superior to the traditional IE and VDF methods in estimation accuracy.

Image mosaicing using sequential bundle adjustment

Abstract We describe the construction of accurate panoramic mosaics from multiple images taken with a rotating camera, or alternatively of a planar scene. The novelty of the approach lies in (i) the transfer of photogrammetric bundle adjustment techniques to mosaicing; (ii) a new representation of image line measurements enabling the use of lines in camera self-calibration, including computation of the radial and other non-linear distortion; and (iii) the application of the variable state dimension filter to obtain efficient sequential updates of the mosaic as each image is added. We demonstrate that our method achieves better results than the alternative approach of optimising over pairs of images.

A new model and efficient tracker for a target with curvilinear motion

A new model for targets moving in two dimensions is presented. Target motion is modelled as constant velocity with occasional along-track and cross-track acceleration inputs. Simulations show that a tracker based on this new model produces estimates superior to both the variable-dimension filter (VDF) and the circular-turn-based interacting multiple-model (IMM) tracker, By using nested multiple models, a modified tracker is created which has considerable computational savings compared with a standard IMM tracker.

Tracking using the variable-dimension filter with input estimation

An improved method for tracking a maneuvering target is proposed. The proposed tracking filter is constructed by combining the recursive formulation of the input estimation approach with the variable-dimension filtering approach. In the proposed approach, the filter also provides the estimate of time instant at which a target starts to maneuver, when a target maneuver is detected. Using this estimated starting time of the maneuver, the maneuver input is also estimated and the tracking system changes to the maneuvering model. The computational load of the proposed procedure is comparable to that of the input estimation approach. Simulations are performed to demonstrate the efficiency of the proposed tracking filter, and comparisons with the interacting multiple model (IMM) filter, the input estimation filter and the variable-dimension filter are made.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Enhanced variable dimension filter for maneuvering target tracking

An enhancement of the variable dimension (VD) filter for maneuvering-target tracking is presented. The use of measurement concatenation, a procedure whereby fast sampled measurements are stacked while maintaining their proper relationships with the states, leads to significant reduction in estimation error by low processing rate algorithms. The use of double decision logic (DDL) for the maneuver onset and ending detection as well as appropriate procedures for reinitialization of the estimation filters results in improved maneuver detection and filter adaptation. Simulation results show the performance of the proposed enhanced variable dimension (EVD) filter. >

Fixed-gain two-stage estimators for tracking maneuvering targets

The two-stage alpha-beta-gamma estimator is proposed as an alternative to adaptive gain versions of the alpha, beta and alpha, beta, and gamma filters for tracking maneuvering targets. The aim is to achieve fixed-gain, variable dimension filtering. The two-stage alpha-beta-gamma estimator is derived from the two-stage Kalman estimator, and the noise variance reduction matrix and steady-state error covariance matrix are given as a function of the steady-state gains. A procedure for filter parameter selection is also given along with a technique for maneuver response and a gain scheduling technique for initialization. The kinematic constraint for constant speed targets is also incorporated into the two-stage estimator to form the two-stage alpha-beta-gamma-lambda estimator. Simulation results are given to compare the performances of other estimators with that of the alpha-beta-gamma filter. >