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Hopped-frequency waveform achieves high range resolution by transmitting a burst of narrowband pulses spanning the desired bandwidth. As its frequencies are irregularly or even sparsely distributed, hopped-frequency waveform could obtain a wider bandwidth with fixed pulses number and shows stronger anti-jamming ability than the linear stepped-frequency waveform; however, an undesired attribute associated...
This paper presents a novel library-based approach for real-time waveform adaption in spectral crowed environment. The suggested approach is to first design a serious of sub-waveforms that are of different durations and bandwidths (namely establish the library), and then assemble the properly selected sub-waveforms to generate the desired waveform for certain quantized spectrum condition. Analysis...
In this paper, we suggest the hopper-frequency (HopF) waveform for cognitive probing in spectral congested environment. Similar with stepped-frequency (SF) waveform, HopF waveform is very easy for implementation and processing. Besides, it also has the necessary degree of freedom (DOF) to achieve high spectrum efficiency and to suppress range sidelobes, thanks to its very flexible carriers. In this...
This paper presents an approach for designing continuous phase modulation (CPM) waveform with good spectral compatibility and low correlation properties. The suggested approach is to first design over-sampled polyphase code with stopband and correlation constraint, and then transform the polyphase code into CPM waveform. Applying over-sampling ensures that the resulting CPM waveform would well inherit...
This paper considers the coherent processing of a sparse frequency agility radar, aiming at achieving the advantages of both frequency agility and coherent processing in anti-jamming. Sparse frequency here refers to a discontinuous band occupation, which is intended to dilute the hostile jamming. A new Doppler processer based on nonuniform fast Fourier transform (NUFFT) is proposed to replace the...
In this paper, we apply a multiple-instance learning (MIL) method, MILES (multiple-instance learning via embedded instance selection), to human histological image classification. MILES converts a MIL problem to a supervised learning problem by an instance-based feature mapping. 1-norm SVM is then adopted to select features and construct a classifier simultaneously. MILES identifies the sub-images...
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