The state-of-the-art navigation frequently fails to provide precise outdoor localization especially in urban areas due to GPS errors. Conventional approaches tracking vehicle positions have used the particlefilter al...
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ISBN:
(纸本)9781450344166
The state-of-the-art navigation frequently fails to provide precise outdoor localization especially in urban areas due to GPS errors. Conventional approaches tracking vehicle positions have used the particle filter algorithm, and we focus on magnetic fingerprint features for increasing performance of the algorithm as landmarks. We investigated the magnetic features in two conditions such as in-vehicle and at an intersection. More diverse traffic environments and an effective design of the particle filter algorithm will be complemented in the future research. We expect these magnetic fingerprints significantly improve outdoor localization algorithms especially in urban areas.
Target tracking is important for pedestrian detection in on-board vision application preventing traffic accidents effectively. Facing complex traffic scene including background change, various pedestrian appearance an...
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ISBN:
(纸本)9781479986330
Target tracking is important for pedestrian detection in on-board vision application preventing traffic accidents effectively. Facing complex traffic scene including background change, various pedestrian appearance and multi-targets etc., existing target tracking algorithms such as Kalman and particlefilters expose shortcomings in accuracy, robustness and availability. This paper proposes an improved particle filter algorithm for multi-target tracking in far-infrared (FIR) pedestrian detection, where a heuristic tracking scheme including feature model learning and target tracking iteratively is used. Partial least squares regression (PLSR) and heuristic computation are adopted to learn and update feature models for each pedestrian. The proposed particle filter algorithm combines adaptive searching region and double feature models, to achieve higher target tracking performance. Experiment on several FIR video sequences demonstrates the improved scheme outperforms comparing with other particle filter algorithms when multi-pedestrian tracking, even with partial occlusion, scale and posture variation.
In typical Wi-Fi based indoor positioning systems employing fingerprint model,plentiful fingerprints need to be trained by trained experts or technician,which extends labor costs and restricts their *** this paper,a n...
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In typical Wi-Fi based indoor positioning systems employing fingerprint model,plentiful fingerprints need to be trained by trained experts or technician,which extends labor costs and restricts their *** this paper,a novel approach based on crowd paths to solve this problem is presented,which collects and constructs automatically fingerprints database for anonymous buildings through common crowd ***,the accuracy degradation problem may be introduced as crowd customers are not professional trained and ***,we define two concepts:fixed landmark and hint landmark,to rectify the fingerprint database in the practical system,in which common corridor crossing points serve as fixed landmark and cross point among different crowd paths serve as hint *** techniques are utilized for short range approximation around fixed landmarks and fuzzy logic decision technology is applied for searching hint landmarks in crowd traces ***,the particle filter algorithm is also introduced to smooth the sample points in crowd *** implemented the approach on off-the-shelf smartphones and evaluate the *** results indicate that the approach can availably construct WiFi fingerprint database without reduce the localization accuracy.
For nonlinear state space models to resolve the state estimation problem is difficult or these problems usually do not admit analytic solution. The Extended Kalman filter (EKF) algorithm is the widely used method for ...
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For nonlinear state space models to resolve the state estimation problem is difficult or these problems usually do not admit analytic solution. The Extended Kalman filter (EKF) algorithm is the widely used method for solving nonlinear state estimation applications. This method applies the standard linear Kalman filteralgorithm with linearization of the nonlinear system. This algorithm requires that the process and observation noises are Gaussian distributed. The Unscented Kalman filter (UKF) is a derivative-free alternative method, and it is using one statistical linearization technique. The particlefilter (PF) methods are recursive implementations of Monte-Carlo based statistical signal processing. The PF algorithm does not require either of the noises to be Gaussian and the posterior probabilities are represented by a set of randomly chosen weighted samples.
For detecting and tracking the infrared dim-small target with the low SNR, the particle filter algorithm is applied to the problem of infrared dim-small target's tracking before detection in this paper. The infrar...
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ISBN:
(纸本)9781479902606
For detecting and tracking the infrared dim-small target with the low SNR, the particle filter algorithm is applied to the problem of infrared dim-small target's tracking before detection in this paper. The infrared dim-small target's tracking before detection based on the full view sampling's particle filter algorithm is proposed. And the hardware system is composed of an infrared imager, the ICETEK-DM642-PCI development board and the monitor. The hardware system is configured by using DSP/BIOS which is a real-time operating system. The experimental results show that the system achieves better detection and tracking's effect than the traditional methods.
Microtunneling is a trenchless technology method used for installing new pipelines. The inherent advantages of this method over open-cut trenching have led to its increasing use. This paper presents a general model fo...
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Microtunneling is a trenchless technology method used for installing new pipelines. The inherent advantages of this method over open-cut trenching have led to its increasing use. This paper presents a general model for microtunneling decision support system (MDS) that can be used as a basis for developing more effective microtunneling design and construction. The model objectives are to: (1) develop a description of local geology that reflects the uncertainty of the information on which it is based and (2) provide the input data necessary for other decision support systems. MDS is composed of two main modules: (1) geology prediction model (GPM) module which is based on Neural-Autoregressive Hidden Markov Model and (2) excavation method selection module to select appropriate excavation method based on GPM result. In order to validate the proposed model, a microtunneling project: Zhong-he drainage water tunnel in Taiwan, was used as a case study. The result shows that the MDS model achieves these objectives to a satisfactory degrese. (C) 2010 Elsevier Ltd. All rights reserved.
Distributed optimization can be formulated as an n-player coordination game. One of the most common learning techniques in game theory is fictitious play and its variations. However, fictitious play is founded on an i...
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Distributed optimization can be formulated as an n-player coordination game. One of the most common learning techniques in game theory is fictitious play and its variations. However, fictitious play is founded on an implicit assumption that opponents' strategies are stationary. In this paper we present a new variation of fictitious play in which players predict opponents' strategy using a particle filter algorithm. This allows us to use a more realistic model of opponent strategy. We used pre-specified opponents' strategies to examine if our algorithm can efficiently track the strategies. Furthermore, we have used these experiments to examine the impact of different values of our algorithm parameters on the results of strategy tracking. We then compared the results of the proposed algorithm with those of stochastic and geometric fictitious play in three different strategic form games: a potential game and two climbing hill games, one with two players and the other with three players. We also tested our algorithm in two different distributed optimization scenarios, a vehicle-target assignment game and a disaster management problem. Our algorithm converges to the optimum faster than both the competitor algorithms in the strategic form games and the vehicle-target assignment game. Hence by placing a greater computational demand on the individual agents, less communication is required between the agents. In the disaster management scenario we compared the results of particlefilter fictitious play with the ones of Matlab's centralized algorithm bintprog and the centralized pre-planning algorithm of (Gelenbe, E. and Timotheou, S. (2008) Random neural networks with synchronized interactions. Neural Comput., 20(9), 2308-2324). In this scenario our algorithm performed better than the pre-planning algorithm in two of the three performance measures we used.
We propose a new psychometric model for two-dimensional stimuli, such as color differences, based on parameterizing the threshold of a one-dimensional psychometric function as ail ellipse. The P Bayesian adaptive esti...
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We propose a new psychometric model for two-dimensional stimuli, such as color differences, based on parameterizing the threshold of a one-dimensional psychometric function as ail ellipse. The P Bayesian adaptive estimation method applied to this model yields trials that vary in multiple stimulus dimensions simultaneously. Simulations indicate that this new procedure can be much more efficient than the more conventional procedure of estimating the psychometric function on one-dimensional lines independently, requiring only one-fourth or less the number of trials for equivalent performance in typical situations. In a real psychophysical experiment with a yes-no task, as few as 22 trials per estimated threshold ellipse were enough to consistently demonstrate certain color appearance phenomena. We discuss the practical implications of the multidimensional adaptation. In order to make the application of the model practical, we present two significantly faster algorithms for running the P method: a discretized algorithm utilizing the Fast Fourier Transform for better scaling with the sampling rates and a Monte Carlo particle filter algorithm that should be able to scale into even more dimensions. (c) 2006 Elsevier Inc. All rights reserved.
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