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This paper considers a model predictive control (MPC) strategy for mitigating the effects of Parkinson tremors on a movement-sensing, joystick controlled battery powered wheelchair with regenerative braking to extend its range between charges. Regenerative braking transforms the wheelchair model into a (switched) hybrid system. The wheelchair is represented as a joystick controlled wheeled mobile...
This paper proposes a new visual object tracking algorithm using a novel Bayesian Kalman filter (BKF) with simplified Gaussian mixture (BKF-SGM). The new BKF-SGM employs a GM representation of the state and noise densities and a novel direct density simplifying algorithm for avoiding the exponential complexity growth of conventional KFs using GM. Together with an improved mean shift (MS) algorithm,...
This paper considers distributed consensus filters for sensor networks. In such networks each sensor measures a signal corrupted by noise. We propose an algorithm which significantly improves the accuracy of time-varying signal tracking and attenuates high frequency noise. It is a modification of an existing algorithm. This new algorithm allows us to define the desired bandwidth of the distributed...
The multi-Bernoulli (MB) filter is a new attractive approach for multi-target filtering in the presence of clutter and detection uncertainty. However, the computational complexity grows as clutter density increases. The clutter measurements may also degrade the filtering accuracy. To eliminate the clutter measurements and reduce the computational complexity, gating technique for the Gaussian mixture...
Multiple hypothesis tracking (MHT) is considered to be the preferred solution for track formation and target tracking in complex environment. Main difficulty with MHT is it's high computing power requirements. This paper deals with several aspects of computational performance of this algorithm. Improvements like space division, multithread processing and fast hypotheses generation techniques are considered.
Aiming at data correlation and estimation problems in particle filtering multi-targets tracking, classical particle filtering is extended into multi-targets state estimation in the given several observation process. Gibbs sampling is regarded as the methods of estimation and allocation correlation vector. Target state vector and association probability was jointly estimated without list, trim, threshold...
Using multi-sensor to detect and track targets has been always a research focus in recent years. And the track-to-track association is an important part of the distributed multi-sensor tracking system. Presently, most of the association algorithms need to do the temporal alignment at the first step, which may bring more errors and degrade the algorithms' performance due to the impact of noise or inaccurate...
The individual characteristics of the AIS and satellite electronic information are analyzed, and proposes a track correlation algorithm of AIS and satellite electronic information based on point-track distance, aiming at the problem of low positioning accuracy, strong noise and discontinuous of satellite information. First, the satellite electronic information is clustered with attribute and filtered...
To improve Maneuver Target Tracking Algorithm, The multi-model intelligent input estimating method based on fuzzy logic is proposed. A fuzzy inference system is constructed with the input of the residual of observation and the residual variation. Then we could get the real-time estimate of the maneuver input of the model according to the output of the system. The algorithm is optimized by the parameter...
The Probability Hypothesis Density (PHD) filter is a more tractable alternative to the Random Finite Set (RFS) based optimal multitarget Bayes recursion. In this paper, a matrix reformulation of the Gaussian Mixture PHD (GM-PHD) filter is introduced. Thus a new multisensor GM-PHD filter is constructed based on the matrix reformulation. Simulation results show it can be used in some applications when...
Information fusion for nonlinear systems is one of the challenging topics in target tracking recently. Aiming at a kind of multi-sensor target tracking systems with correations between process and measurement noises, we study the design of a decentralized fusion algorithm based on the unscented strong tracking filter with correlated noises (USTF-CN). Firstly, the information form of USTF with correlated...
A robust tracking algorithm based on the particle filter with multi-cue adaptive fusion is proposed which can overcome the shortcoming of single visual cue in complex environments. The color and the texture based on the discrete wavelet transform (DWT) are used to describe the tracking target. The weights are adaptively adjusted using the democratic integration according to the current tracking situations,...
In this paper, geometry-based algorithms for device-free localization (DFL) of a single target are proposed. The algorithms exploit the change in attenuation caused by the device-free target on radio links passing through the deployment area. By solving for the intersections of some of the attenuated links and taking the centroid of the intersection points, an estimate of the target's location is...
In this paper, a cooperative mobile target estimation approach based on interacting multiple model (IMM) algorithm is presented. We propose a dual-IMM estimator structure to improve the accuracy and robustness of mobile target localization and tracking in wireless sensor networks. Suppose that two sensor systems are affected by different levels of noises, the measured data can be first processed at...
A long "V" shaped linear wake with lots of bubbles is formed behind a sailing ship, which provides a target feature of the ship. Ship wake acoustic detection is one of the most effective ways for the Autonomous Underwater Vehicle to automatically search, track and attack surface targets. In this paper, we processed the sonar image data of ship wake in the lake test, using the sonar image...
In this paper comparison between constant coefficient and variable coefficient tracking filters for radar has been presented. αβγ filter and Kalman filter are discussed as instances of constant coefficient and variable coefficient filters respectively. Both of the filters are sophisticated smoothing and prediction tracking filters which are used in sampled data target trackers. As polynomial predictor...
Multisensor target tracking is finding many applications these days, due to its advantages like accurate target tracking and cheaper in cost. Range and range rate measurements from sensor often used for tracking target. In estimating target location in central station, Kalman filter and its extensions (like extended Kalman Filter) are generally preferred, because if we go to the Multilateration process...
Tracking or more generally state estimation of dynamic systems are tasks that appear in many different contexts - for instance in surveillance with wireless sensor networks. Usually the state-evolution equations are assumed to be known excepting some parameters. In this case, particle filters and related approaches have been applied with great success. Very few attempts, however, have been made so...
According to the nonlinear maneuvering characteristics of flying targets, unscented Kalman filter is used to achieve a two-dimensional trajectory tracking and predicting in the coordinates of camera view field. By calculating the target centroid position in the imaging focal plane, the third-order constant acceleration motion model of two-dimensional is established. Because of the nonlinear characteristics...
A time-of-arrival (TOA) measurements-based tracking method for passive bistatic radar (PBR) in the presence of glint noise is proposed. To improve the TOA-based tracking precision in glint, an improved particle filter (PF) based on differential evolution algorithm is used for target tracking. It is shown that the proposed method can improve the tracking precision of PBR in glint noise compared with...
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