Extended objects generate multiple measurements and are characterised with their size or volume. They require methods able to deal with the data association problem and at the same time to estimate both their kinemati...
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ISBN:
(纸本)9781849198639
Extended objects generate multiple measurements and are characterised with their size or volume. They require methods able to deal with the data association problem and at the same time to estimate both their kinematic states and shape parameters. This paper presents a solution to the extended object tracking for rectangular extended objects, with the Box Particle filter (Box PF) approach. The Box PF is implemented based on dynamically calculated constraints. Promising results are demonstrated.
In this paper, we present a new method for data association in multi-targettracking situation in the framework of evidence theory. The representation and the fusion of the information in our method are based on the u...
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ISBN:
(纸本)9781632666642
In this paper, we present a new method for data association in multi-targettracking situation in the framework of evidence theory. The representation and the fusion of the information in our method are based on the use of belief function in the sense of Dempster-Shafer theory of evidence. The proposal generates two belief matrices using two different specialized basic belief mass assignments. While the decision making process is based on the extension of the frame of hypotheses. The method has been tested for a nearly constant velocity target in two ambiguous cases using Monte Carlo simulations.
. Extended objects generate multiple measurements and are characterised with their size or volume. They require methods able to deal with the data association problem and at the same time to estimate both their kinema...
详细信息
ISBN:
(纸本)9781632666642
. Extended objects generate multiple measurements and are characterised with their size or volume. They require methods able to deal with the data association problem and at the same time to estimate both their kinematic states and shape parameters. This paper presents a solution to the extended object tracking for rectangular extended objects, with the Box Particle filter (Box PF) approach. The Box PF is implemented based on dynamically calculated constraints. Promising results are demonstrated.
The main contribution of this paper is the general framework, termed multi-core Beamformer Particle Filter (multi-core BPF), for solving the ill-posed EEG inverse problem. The method combines a particle filter (statis...
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ISBN:
(纸本)9781849198639
The main contribution of this paper is the general framework, termed multi-core Beamformer Particle Filter (multi-core BPF), for solving the ill-posed EEG inverse problem. The method combines a particle filter (statistical approach) for reconstruction of the brain source spatial locations and a multi-core Beamformer (deterministic approach) for estimation of the corresponding dipole waveforms in a recursive way The intuition behind is to benefit from the advantages of both deterministic and statistical inverse problem solvers in order to improve the estimation accuracy without increasing the complexity and the computational cost. Our simulations show that the proposed algorithm can reconstruct reliably the few most active (the dominant) brain sources that have generated the registered EEG measurements. The main advantage of the method is that in contrast to conventional (single-core) Beamforming spatial filters, the proposed Multi-core Beamformer explicitly takes into consideration the potential temporal correlation between the dipoles.
. This paper provides an initial examination of the use of particle filters in tracking swarms of small targets such as Unmanned Aerial Vehicles using a radar. From the standpoint of conventional tracking solutions, s...
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ISBN:
(纸本)9781632666642
. This paper provides an initial examination of the use of particle filters in tracking swarms of small targets such as Unmanned Aerial Vehicles using a radar. From the standpoint of conventional tracking solutions, such swarms present a severe challenge — due not only to the quasi-erratic motion of the UAVs relative to the swarm trajectory as a whole, but also from the effects of the small target size upon radar resolution and detection probability. It is shown here that a particle filter is capable of providing a stable track on the swarm centroid, although not the individual constituent UAVs.
. In the context of multi-targettracking application, the concept of variance in the number of targets estimated in specified regions of the surveillance scene has been recently introduced for multi-object filters. T...
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ISBN:
(纸本)9781632666642
. In the context of multi-targettracking application, the concept of variance in the number of targets estimated in specified regions of the surveillance scene has been recently introduced for multi-object filters. This article has two main objectives. First, the regional variance is derived for a multi-object representation commonly used in the tracking literature, known as the multi-Bernoulli point process, in which the multi-target state is described with a set of hypothesised tracks with associated existence probabilities. This model is exploited in multi-targetapplications where it can be assumed that targets evolve independently of each other and generate sensor observations that are uncorrelated with other targets. An illustration of the concept of regional statistics (mean and variance) in target number, and how to interpret them in the broader context of multi-object filtering, it then provided. Possible applications include performance assessment and sensor control for multi-targettracking.
. In this paper we present a method to estimate a position in buildings without using absolute positioning data like WiFi signals. Unlike other state-of-the-art methods, we use probability estimations for possible ste...
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ISBN:
(纸本)9781632666642
. In this paper we present a method to estimate a position in buildings without using absolute positioning data like WiFi signals. Unlike other state-of-the-art methods, we use probability estimations for possible steps and 90° turns. The only information source we use is the data collected by a smartphone's accelerometer and gyroscope and the floor map information. The current position is tracked with the help of particle filtering. For this, we integrate the information of the previous state into the weight update step. In addition we show how the observation data can help within the state transition model.
This paper presents a sensor fusion approach to fusing Microsoft Kinect sensor and the built-in inertial sensors in a mobile device. A multi-rate Kalman filter is designed and applied for fusing the low-sampling-rate ...
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ISBN:
(纸本)9781632666642
This paper presents a sensor fusion approach to fusing Microsoft Kinect sensor and the built-in inertial sensors in a mobile device. A multi-rate Kalman filter is designed and applied for fusing the low-sampling-rate (30Hz) uncertain positions sensed by the Kinect sensor and the high-sampling-rate (90Hz) accelerations measured by the inertial sensors. These sensors have complementary properties. The Kinect can be applied for skeleton tracking, which gives the joints' positions. Meanwhile, the built-in inertial sensors in the mobile device sense the hand motion and the acceleration can be estimated through inertial sensor fusion. Firstly, convert the acceleration estimated with inertial sensors from the body frame into the Kinect coordinate system. Experimental results show that the hand accelerations estimated with the Kinect sensor and the inertial sensors are comparable. Secondly, design and apply a multi-rate Kalman filter for sensor fusion. The sensor fusion helps improve the accuracy of the system state estimation including the position, the velocity and the acceleration. This is of great benefit for combining inertial sensors and the external position sensing device for indoor augmented reality (AR) and other location-aware sensing applications.
Multitargettracking is fundamental in many security and surveillance applications. However, as algorithms have been applied in more and more challenging environments, traditional simplifications no longer even approx...
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ISBN:
(纸本)9781632666642
Multitargettracking is fundamental in many security and surveillance applications. However, as algorithms have been applied in more and more challenging environments, traditional simplifications no longer even approximately hold true. In particular, we consider the problem of maritime surveillance in which small targets (boats) are to be detected and tracked in the presence of highly structured noise (waves). In particular, we consider two problems. The first is that, given the resolution of modern radar systems, even small targets are extended, straddling multiple range or azimuth bins. The second is that sea clutter is not a uniform, Poisson-distributed noise process but is highly spatially varying. In this paper, we develop two extensions of the Probability Hypothesis Density (PHD) Filter. Using a generalised likelihood model, extended targets can be readily accounted for. Through the use of spatially varying clutter models, structured noise approximation is provided. The algorithms were developed and tested using a sea trial in which Rigid-Hulled Inflatable Boats (RHIBs), equipped with GPS receivers, were tracked using a radar system.
The main contribution of this paper is the general framework, termed multi-core Beamformer Particle Filter (multi-core BPF), for solving the ill-posed EEG inverse problem. The method combines a particle filter (statis...
详细信息
ISBN:
(纸本)9781632666642
The main contribution of this paper is the general framework, termed multi-core Beamformer Particle Filter (multi-core BPF), for solving the ill-posed EEG inverse problem. The method combines a particle filter (statistical approach) for reconstruction of the brain source spatial locations and a multi-core Beamformer (deterministic approach) for estimation of the corresponding dipole waveforms in a recursive way The intuition behind is to benefit from the advantages of both deterministic and statistical inverse problem solvers in order to improve the estimation accuracy without increasing the complexity and the computational cost. Our simulations show that the proposed algorithm can reconstruct reliably the few most active (the dominant) brain sources that have generated the registered EEG measurements. The main advantage of the method is that in contrast to conventional (single-core) Beamforming spatial filters, the proposed Multi-core Beamformer explicitly takes into consideration the potential temporal correlation between the dipoles.
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