Stochastic models for the state vector of the closely integrated navigation system for the general case as well as for the orthodromic motion case have been built. Ways of computational cost optimization have been ide...
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A map-aided navigation method is considered. The main features of this method are discussed;an overview of the algorithms used to solve navigation problems is given. Considerable attention is focused on the algorithms...
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This paper presents a new method of nonlinear finetwork with Gaussian kernel functions. In practice, signal enhancement filters are usually adopted as a preprocessor of signal processing system. For this purpose, an a...
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ISBN:
(数字)9783319265551
ISBN:
(纸本)9783319265551;9783319265544
This paper presents a new method of nonlinear finetwork with Gaussian kernel functions. In practice, signal enhancement filters are usually adopted as a preprocessor of signal processing system. For this purpose, an approach of nonlinear filtering using a network with Gaussian kernel functions is proposed for the efficient enhancement of noisy signals. In this method, the condition for signal enhancement is obtained by using the phase space analysis of signal time series. Then, from this analysis, the structure of nonlinear filter is determined and a network with Gaussian kernel functions is trained in such a way of obtaining the clean signal. This procedure can be repeated to obtain the multilayer (or deep) structure of nonlinear filters. As a result, the proposed nonlinear filter has demonstrated significant merits in signal enhancement compared with other conventional preprocessing filters.
The Gaussian particle filter (GPF) is a type of particle filter that employs the Gaussian filter approximation as the proposal distribution. However, the linearization errors are introduced during the calculation of t...
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The Gaussian particle filter (GPF) is a type of particle filter that employs the Gaussian filter approximation as the proposal distribution. However, the linearization errors are introduced during the calculation of the proposal distribution. In this article, a progressive transform-based GPF (PT-GPF) is proposed to solve this problem. First, a progressive transformation is applied to the measurement model to circumvent the necessity of linearization in the calculation of the proposal distribution, thereby ensuring the generation of optimal Gaussian proposal distributions in sense of linear minimum mean-square error (LMMSE). Second, to mitigate the potential impact of outliers, a supplementary screening process is employed to enhance the Monte Carlo approximation of the posterior probability density function. Finally, simulations of a target tracking example demonstrate the effectiveness and superiority of the proposed method.
Change detection is an important problem in the analysis of optical remote sensing images. The usual way of approaching this problem is by thresholding a difference image in order to obtain a detection mask, but the c...
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Change detection is an important problem in the analysis of optical remote sensing images. The usual way of approaching this problem is by thresholding a difference image in order to obtain a detection mask, but the choice of this threshold is not always easy as the distribution of the values of changed and unchanged pixels may overlap. Therefore, an automatic detector can lead to a high number of false alarms. In this paper, we propose to improve this technique by designing a nonlinear filtering step that highlights the changes in the difference image. In order to better accomplish this process, a previous segmentation stage using texture information from the original images is required. This information can also be used to dismiss areas that do not contain changes with a high likelihood. We show that the process separates the distribution of values in the changed region from the unchanged region and make the choice of the threshold more robust. This results in a significantly lower error than obtaining the mask from the difference image without previous nonlinearfiltering. The proposed technique has been used with success in the detection of new constructions on non-urban soil from very-high-resolution aerial images.
This note addresses Distributed State Estimation (DSE) over sensor networks. Two existing consensus approaches for DSE, i.e., consensus on information (CI) and consensus on measurements (CM), are combined to provide a...
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This note addresses Distributed State Estimation (DSE) over sensor networks. Two existing consensus approaches for DSE, i.e., consensus on information (CI) and consensus on measurements (CM), are combined to provide a novel class of hybrid consensus filters (named Hybrid CMCI) which enjoy the complementary benefits of CM and CI. Novel theoretical results, limitedly to linear systems, on the guaranteed stability of the Hybrid CMCI filters under collective observability and network connectivity are proved. Finally, the effectiveness of the proposed class of consensus filters is evaluated on a target tracking case study with both linear and nonlinear sensors.
The problem of gravity anomaly (GA) estimation from aircraft is considered. The corresponding filtering problem is formulated under the assumption that satellite data about the aircraft altitude are available and the ...
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Stochastic models for the state vector of the closely integrated navigation system for the general case as well as for the orthodromic motion case have been built. Ways of computational cost optimization have been ide...
详细信息
ISBN:
(纸本)9785919950233
Stochastic models for the state vector of the closely integrated navigation system for the general case as well as for the orthodromic motion case have been built. Ways of computational cost optimization have been identified. The results of numerical simulation for the filtering algorithm are discussed.
The problem of estimating parameters of random sequences used to describe sensor errors is formulated and solved in the context of the Bayesian approach as a nonlinear filtering problem. The algorithms that provide th...
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We consider a model composed of a signal process X given by a classic stochastic differential equation and an observation process Y, which is supposed to be correlated to the signal process. We assume that process Y i...
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We consider a model composed of a signal process X given by a classic stochastic differential equation and an observation process Y, which is supposed to be correlated to the signal process. We assume that process Y is observed from time 0 to s>0 at discrete times and aim to estimate, conditionally on these observations, the probability that the non-observed process X crosses a fixed barrier after a given time t>0. We formulate this problem as a usual nonlinear filtering problem and use optimal quantization and Monte Carlo simulations techniques to estimate the involved quantities.
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