Results 231 to 240 of about 42,098 (267)
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RADAR 2002, 2002
The networked radar air picture is built using input from radars in many different locations. In an ideal world, each radar can track every target continuously. However, the laws of physics do not permit this. Target fades, terrain blockage, and spurious signals all conspire to make the situation not ideal. As a result, in general, no one radar is able
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The networked radar air picture is built using input from radars in many different locations. In an ideal world, each radar can track every target continuously. However, the laws of physics do not permit this. Target fades, terrain blockage, and spurious signals all conspire to make the situation not ideal. As a result, in general, no one radar is able
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Efficient sampling for radar sensor networks
International Journal of Sensor Networks, 2015Compressive sensing CS is an excellent technique for data acquisition and reconstruction in radar sensor networks RSNs with a high computational capability. This paper presents a new efficient and effective signal compression and reconstruction algorithm based on CS principles for applications in real-world RSNs, in which the signals are obtained in ...
Junjie Chen 0002, Qilian Liang
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Studies on Radar and Non-radar Sensor Networks
2006Abstract : During the period of 12/1/2005--5/30/2006, we expanded our research from generic wireless sensor networks to radar sensor networks. For radar sensor networks, we performed the following preliminary studies: (1) Waveform design and diversity in radar sensor networks with applications to automatic target recognition with or without delay ...
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Detection in Passive MIMO Radar Networks
IEEE Transactions on Signal Processing, 2014This paper addresses target detection in passive multiple-input multiple-output (MIMO) radar networks comprised of non-cooperative transmitters and multichannel receivers. A generalized likelihood ratio test is derived, and approximate test statistic distributions are presented for both hypotheses under common scenario conditions.
Daniel E. Hack +3 more
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Compressive Sensing for Radar Sensor Networks
2010 IEEE Global Telecommunications Conference GLOBECOM 2010, 2010Motivated by recent advances on Compressive Sensing (CS) and high data redundancy among radars in radar sensor networks, we study CS for radar sensor networks. We demonstrate that the sense-through- foliage UWB radar signals are very sparse, which means CS could be applied to radar sensor networks to tremendously reduce the sampling rate. We propose to
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Compressive Sensing for Radar and Radar Sensor Networks
2013Abstract : In this project, compressive sensing for radar and radar sensor networks were studied. Significant results have been achieved in the following aspects: Compressive Sensing in Radar Sensor Networks Using Pulse Compression Waveforms; Theoretical Performance Bounds for Compressive Sensing with Random Noise; Compressive Sensing in Radar Sensor ...
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NeXtRAD and RHINO radar: Harnessing the herd for networked radar
2014 International Radar Conference, 2014This paper demonstrates the use of open source hardware and software to implement the digital back end of a network of radars. i.e. RHINO platform - a highly capable reconfigurable platform serving both digitisation / synthesis and packetisation functions for a dual X and L band radar. The system is capable of arbitrary waveform generation for transmit,
Michael Inggs, Andrew van der Byl
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Radar target imaging using distributed radar networks
2009 IEEE Antennas and Propagation Society International Symposium, 2009Target images can be clearly reconstructed from the monostatic and bistatic range profiles generated by a DRN with enough radar sensors. Because the images are instantly formulated, no synthetic aperture processing or motion compensation is needed.
Hai Deng, Braham Himed
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Convolutional Neural Networks for Radar Detection
2002The use of convolutional neural networks (CNN's) for radar detection is evaluated. The detector includes a time-frequency block that has been implemented by the Wigner-Ville distribution and the Short-Time Fourier Transform to test the suitability of both techniques. The CNN detectors are compared with the classic multilayer perceptron and with several
Gustavo López-Risueño +3 more
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