Prediction of chaotic time series based on wavelet neural network
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Abstract:
Wavelet neural network possesses the best function approximation ability, that is to say it has the ability to identify the model. Because the constricting model algorithm is different from common artificial neural network BP algorithm, it can effectively overcome intrinsic defect of common artificial neural network. Therefore the better prediction effect can be reached effectively. The paper gives a method of prediction model of chaotic time series based on wavelet neural network that enables prediction model to have not only wavelet good approximation property, but also neural network self-learning adaptive quality. The authors make use the method to predict sea clutter data.Keywords:
Function Approximation
In this paper we present an overview of research studies aimed at deriving quantitative measured for image clutter. Image clutter is identified as a perceptual effect and therefore quantitative measures for clutter should be derived on the basis of models for perception. Previous studies in the field have produced limited success since they concentrated on the signal level interpretation in clutter characterization. In this study, we promote examination of the clutter problem at a higher level, where preattentive cues form the basis for their definition. Specifically, we derive clutter measures based upon preattentive texture features. Results of an extensive experimental study highlight the limitations of signal level clutter measures and the utility of texture based measures.
Characterization
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Limitations on achievable clutter rejection due to ambiguous range clutter are described. The profile of clutter power versus range is shown to limit achievable clutter rejection. Ambiguous range effects are discussed in the context of sea clutter, using a model that includes propagation conditions, and rain clutter. Limitations in moving target indication systems are illustrated for sea clutter, where propagation is subject to evaporation ducts. Benefits of fill pulses are illustrated for rain and sea clutter.< >
Radar horizon
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Abstract The presence of clutter in through‐the‐wall images significantly degrades image quality and hampers the performance of algorithms used for data processing in tasks such as target detection, identification, or reconstruction. Real building walls exhibit inherent inhomogeneities, featuring varying frequency and spatial properties that defy the assumption of a smooth surface. Consequently, they produce nonuniform wall surface reflections at each scanning position. Furthermore, these walls often incorporate supply pipes that introduce substantial clutter, which, although stronger than the target responses, remains weaker than the wall surface clutter. Additionally, this clutter often exhibits signatures similar to those of the target. In real‐world scenarios, target reflections can manifest as wide, flat hyperbolas or nearly straight lines, and they may be positioned near the wall. Consequently, distinguishing targets from clutter becomes a complex challenge. Various clutter reduction methods have been proposed in recent years, showing varying degrees of success. However, the effectiveness of these methods in the literature is inconsistent and sometimes contradictory. To address this issue, a comprehensive investigation of well‐established clutter reduction methods was conducted to evaluate their performance under identical conditions. These methods were rigorously assessed using practical radar‐measured data acquired in the presence of actual building wall materials and contrasting targets. Evaluation criteria include the target‐to‐clutter ratio and peak signal‐to‐noise ratio. The results of the experiment revealed that independent component analysis outperformed other clutter reduction methods, demonstrating superior performance in mitigating clutter and enhancing target detection.
Stationary target indication
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This paper presents a site specific ground clutter simulation developed for ground-based radars. The simulation provides clutter power maps according to digital terrain relief and landcover information. The comparison between simulated clutter and actual clutter shows that a good accuracy in clutter simulation can be achieved even for complex landscapes.
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In theory, properties of clutter can be defined globally or locally. However, in the literature, the distinction between local and global clutter is arbitrary, where the standard approach of setting the local domain to twice the expected target size, in applying local clutter metrics, is adopted without any justification. This paper addresses this problem and considers the implications for the application of clutter metrics. It was found that the size of the local clutter region around a target has a strong effect on the probability of detection of that target and that this is affected by regions much larger than twice the target size. It was also discovered that this effect was much stronger for targets subtending less than 0.8 degrees of visual angle than for larger targets. In the case of the former, the fall-off in human visual performance with clutter region size was approximately quadratic, compared to a slight linear fall-off for larger targets. A simple model is presented explaining these phenomena, indicating that the auto-covariance function characterising the clutter is the main determinant of the size of the region of local clutter.
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Eye tracking results from a recent visual search experiment suggest 1) people avoid searching in highly cluttered regions of displays, and 2) people tend to start searching in regions with lower clutter and progress to regions with higher clutter as needed. Subjects searched for a symbol randomly placed in displays containing varying amounts of clutter, measured with the C3 clutter metric (Lohrenz et al., 2009). Displays were categorized as having low, medium, or high “global” clutter (average clutter for the display). Displays were gridded into 100 cells, and a “local” C3 value was calculated for each cell. An eye-tracker monitored search behavior. On average, with low and medium global-clutter displays, subjects searched in areas with local clutter similar to the displays' global clutter; with high global-clutter displays, subjects searched in areas with significantly lower local clutter (relative to global clutter). The average clutter of cells in which subjects searched plateaued at C3=6.4 (on a scale from 0 to 12), suggesting a measurable limit to the amount of clutter through which subjects are able (or willing) to search.
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A method of estimating the representative values of clutter height, the dominant parameter in Recommendation ITU-R P.452 for calculating clutter losses, is proposed. Our method estimates clutter heights from average building heights and terrain roughness of the area being investigated. We also derive the parameter values needed for the proposed method and discuss its validity by using the validation method of clutter losses that is based on 3D maps.
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A display can appear cluttered because it shows a lot of information, the information is irrelevant for the task at hand or the information is poorly organized. In this paper, we review past research on pilot's performance under various levels of visual clutter. We found that this research lacks a common rationale to manipulate clutter and we suggest such a rationale. We show that models defining clutter as the number of elements shown on the display fail to explain recent empirical results where displays with minimal clutter do not give the best technical flight performance. The paper ends with three suggestions to de-clutter a display.
Empirical Research
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Display clutter is a problem that affects operators in various data-rich environments. Clutter measurement techniques such as image processing and performance measures can provide an estimate of clutter but are largely not suited to tracing the effects of clutter on the dynamic allocation of attention. Eye tracking is a promising process-oriented tool that can help assess in real-time the attentional costs associated with the different aspects of clutter. In this experiment, we investigated which of a number of eye tracking metrics in the literature are sensitive to clutter. Twenty-two participants were asked to look for a target in static and dynamic images that were classified as either high or low in clutter. Response time and error rate were recorded, and an eye tracker was used to compute the identified eye tracking metrics. Results showed that, in both the static and dynamic conditions, a large number of eye tracking metrics were significantly affected by an increase in clutter. This suggests that eye tracking can be used to supplement other clutter measurement techniques by providing information about dynamic attention allocation.
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