This paper deals with the detection of small unmanned air vehicles (UAVs) and drones in high-density target scenarios such as airport terminal areas. In fact, in such conditions, strong reflections by high radar cross...
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ISBN:
(纸本)9781728168135
This paper deals with the detection of small unmanned air vehicles (UAVs) and drones in high-density target scenarios such as airport terminal areas. In fact, in such conditions, strong reflections by high radar cross section (RCS) targets are likely to prevent the detection of very weak target echoes. In this work, we aim at overcoming this issue by taking advantage of long coherent processing intervals (CPIs) and by implementing a CLEAN-like multistage algorithm to remove the strongest target contributions. The effectiveness of the proposed strategy is first demonstrated against simulated data representing a typical scenario. Then, a preliminary experimental result is shown, obtained against data collected by the DVB-T based AULOS (R) passive radar developed by Leonardo S.p.A..
Video surveillance has been using in daily life widely and has significant impact in security field. Reading time information in video has becoming an essential part. Because of the complex background of timestamp and...
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The classification and recognition of underwater target signal are based on the samples. In the practical field, the amount of available training samples is limited. In this paper, based on analyzing the radiated nois...
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ISBN:
(纸本)9781728154466
The classification and recognition of underwater target signal are based on the samples. In the practical field, the amount of available training samples is limited. In this paper, based on analyzing the radiated noise signals of two types of underwater targets, the Bayesian dynamic modification model for underwater acoustic signals is established, which follows the idea of virtual sample generation. According to idea of virtual sample generation, a Bootstrap-Bayesian dynamic modification model for the small-sample-size problem is proposed, based on Bootstrap method and Bayesian parameter estimation. Through the processing of experimental data and comparative analysis of the experimental results, the effectiveness of the feature extraction method and the dynamically modified classification model proposed in this paper are verified. The applicability of the classification model for two types of underwater acoustic signals has also been confirmed.
The theoretical model representing the signal reflected from rotating blades of aerial objects with different rotor configurations is described. The proposed model is used concerning the radar operating in UHF-band fo...
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While traditional electromagnetic stealth materials/metasurfaces can render a target virtually invisible to some extent, they lack flexibility and adaptability, and can only operate within a limited frequency and angl...
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Real-time small infrared (IR) target detection is critical to the performance of the situational awareness system in high-altitude aircraft. However, current IR target detection systems are generally hardware-unfriend...
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Real-time small infrared (IR) target detection is critical to the performance of the situational awareness system in high-altitude aircraft. However, current IR target detection systems are generally hardware-unfriendly and have difficulty in achieving a robust performance in datasets with clouds occupying a large proportion of the image background. In this paper, we present new results by using an efficient method that extracts the candidate targets in the pre-processing stage and fuses the local scale, blob-based contrast map and gradient map in the detection stage. We also developed mid-wave infrared (MWIR) and long-wave infrared (LWIR) cameras for data collection experiments and algorithm evaluations. Experimental results using both publicly available datasets and image sequences acquired by our cameras clearly demonstrated that the proposed method achieves high detection accuracy with the mean AUC being at least 22.3% higher than comparable methods, and the computational cost beating the other methods by a large margin.
The importance of visual N2 Event-Related Potential (ERP) component in the study of cognitive processes lies in its interpretation as a measure of the allocation of visual attention to possible targets. Unfortunately,...
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ISBN:
(纸本)9783030316358;9783030316341
The importance of visual N2 Event-Related Potential (ERP) component in the study of cognitive processes lies in its interpretation as a measure of the allocation of visual attention to possible targets. Unfortunately, the N2 component has a small amplitude and the domain of validity of methods used for its estimation is difficult to assess if real data are considered. Here, we develop a computer simulator of ERP measurements emulating the variability of the visual N2 component and use the synthetic data to evaluate the performance of four popular literature ERP estimation methods. Results confirmed that a solid simulation framework could allow identifying a reliable method to detect small-amplitude ERP components and quantifying its accuracy.
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