Worldwide air traffic levels are growing at a rate expected to double the current traffic level. In order to control and ensure the aircrafts, most large airport surface have multisensors coverage, with not necessaril...
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Worldwide air traffic levels are growing at a rate expected to double the current traffic level. In order to control and ensure the aircrafts, most large airport surface have multisensors coverage, with not necessarily synchronized sensors, so a data fusion scheme is essential to provide a coherent and stable output. The multi-sensors include radars, VHF data link, and satellites, and form a surveillance network. The current technology Air Traffic Control fusion systems are stretched to their limit and are prone to large delays during the peak summer travel season. There is doubt that the current systems can be scaled up to meet the expected demand levels and surveillance trajectory. Many Air Traffic Management automation systems have been proposed to increase controller capability, and some are in operation. While ATM automation systems will help handle more traffic, it is still doubtful that they can grow to meet the doubling in traffic levels foreseen. In order to complete the function, the data come from the multi-sensors must be processed and then fused, so that the fusion trajectory comes into being. Fusion data come from PSR, SSR, ADS and VDL, time-scales are different. Before the 4D fusion trajectory comes into view, time-registration of the data must be implemented. Thereby registration is the vital problem for the data fusion of the multi-sensors network. This paper presents a novel algorithm of time-registration for 4D trajectory based on GNSS's time for ATM. Because of the difference of surveillance data's time-scales, general timeregistration methods have many difficulties and risks for so many kind data. We find that there is connection between the surveillance data and GNSS's time, so the data's time-scales can be unified to GNSS's time benchmark respectively, and then the unified data can be fused, the 4D fusion trajectory comes true. Using the GNSS's time fusion systems can further improve the controller trajectory accuracy and can be used to increa
Ground Penetrating Radar (GPR) technique has been successfully used in the detection and identification of void under airfield pavement, but the estimation of void thickness is still in need of solving. In this paper,...
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A direction of arrival (DOA) estimator for wideband coherent signals with known waveforms and unknown amplitude is presented in this paper, which is extended from the estimation algorithm for time delay, referred to a...
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ISBN:
(纸本)9781849190107
A direction of arrival (DOA) estimator for wideband coherent signals with known waveforms and unknown amplitude is presented in this paper, which is extended from the estimation algorithm for time delay, referred to as WRELAX. The method can be called coherent space-time RELAX (CST-RELAX), which is a relaxation-based minimizer of a complicated nonlinear least squares cost function and is implemented in both space and time domain. Numerical examples in the presence of white and colored noise respectively are provided to demonstrate its performance and compare with other well-known algorithms including CSM (coherent subspace method) and wideband RELAX. The proposed method improves the estimation accuracy and has an excellent resolution.
In the near-infrared (NIR) light imaging manner, finger-vein images are always degraded greatly due to optical scattering in the biological tissue. This directly leads to difficulty in reliable finger-vein feature rep...
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Shadow detection is crucial for robust and reliable visual surveillance system. The shadow detection method based on gray level and color information will fail when object parts have very similar properties with real ...
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We investigate the performances of different types of transformations in image watermarking algorithm using subsampling which include Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Discrete Mul...
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As a newly emergent biometric technology, finger-vein recognition has attracted more attentions in personal identification. Generally, finger-vein images have low contrast and uneven illumination due to finger-vein im...
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Shadows in images always cause problems to computer visual tasks, so how to remove shadow is an important topic of imageprocessing. In this paper, we propose a new shadow removal method based on Retinex theory. We fi...
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Finger-vein recognition is widely applied on access control system due to the high user acceptance and convince. Improving the integrity of finger-vein is helpful for increasing the finger-vein recognition accuracy. D...
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Finger-vein recognition is widely applied on access control system due to the high user acceptance and convince. Improving the integrity of finger-vein is helpful for increasing the finger-vein recognition accuracy. During the process of finger-vein imaging, foreign objects may be attached on fingers, which directly affects the integrity of finger-vein images. In order to effectively extract finger-vein networks, the integrity of venous networks is still not ideal after preprocessing of finger vein images. In this paper, we propose a novel deep learning based image restoration method to improve the integrity of finger-vein networks. First, a region detecting method based on adaptive threshold is presented to locate the incomplete region. Next, an encoder-decoder model is used to restore the venous networks of the finger-vein images. Then we analyze the restoration results using several different methods. Experimental results show that the proposed method is effective to restore the venous networks of the finger-vein images.
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