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Sparse representation has been introduced to tackle the target detection problem in hyperspectral imagery. While using windows to build the sparse dictionary, there exists target contamination problem. In our approach, we utilize a learning method based on convex optimization to build a dictionary for sparse target detection. Through its application, prior information such as the size of windows can...
Traditional supervised target detection methods need target spectra as prior knowledge. When the target spectra can only be acquired from the lab or field, they may be very different from the real target spectra obtained from images, which results in low accuracy of these target detection methods. In order to solve this problem, a new preprocessing method used for target detection in hyperspectral...
This paper focuses on the optimal waveform design for target detection in scenario of range-spread target. The previously published papers, which focused on optimal waveform design, assumed the target impulse response is perfect or known. However, the target impulse response used for next transmitted waveform is the previous target impulse response. The transmitted waveform designed should aim to...
The radar performs three main functions: search, tracing and weapon engagement. Each of functions occupies amount of time, energy and computation resource of the radar system. For cognitive radar, it should have the basic function which is learning. The radar needs to manage its resources dynamically and interactively between the setting of radar parameters to optimize the tasks to be carried out...
Dim point target from an image sequence has been merged into the clutter. So the image has a low SNR and the detection of the dim point target under the low conditions becomes the sticking point. We introduce a novel tracking system based on dynamic programming algorithm (DPA). This algorithm can detect the dim point targets under the condition of low SNR. The dynamic programming technique can detect...
The problem of adaptive waveform selection can be viewed as a problem of stochastic dynamic programming. However, the computational cost of solution of optimality equations is very high as a result of large state variable space. So far few suitable scheduling algorithms have been proposed. To account for the aboved shortcoming, we can use approximate dynamic programming (ADP) to solve adaptive waveform...
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