The high-resolution techniques, which based on the orthogonality between noise subspace and signal subspace. In general, there are three categories of array signal processing approaches to solving the problem, high-resolution techniques, maximum-likelihood (ML) techniques and neural network techniques.
#NEURAL NETWORK RADAR HOW TO#
How to improve the performance of direction of arrival (DOA) estimation, many experts and scholars have made a great deal of research. Hence, the phenomena of model ‘mismatch’ and beam split occur under the strong multipath effect. And the direct signal is difficult to be distinguished from the received signals in spatial, temporal and Doppler domains. The complex multipath signal is highly correlated with the direct signal in theory. The received signals contain direct signal and multipath signals reflected by ground or sea surface, especially for low-elevation target. However, with characteristics of long wavelength, large beamwidth and limited antenna aperture, VHF radar often exhibits poor performance in angle resolution and angle estimation. Very high-frequency (VHF) radar plays an important role in the anti-stealth system and early-warning system. Combining the DNN approach with altitude measurement of a low-elevation target for VHF radar is meaningful to explore.
The results of the practical data show the practicability of the proposed method for the low-elevation target under the serious multipath effect. The results of simulation data verify the validity of the proposed method. In the test procedure, the characteristic of data is extracted by the well-trained network and projected into the constructed characteristic space. A new characteristic space is constructed in the training procedure. The approach of the deep neural network is applied to learn the received data's characteristics from a different elevation. This is the highlight of the proposed method.
However, the characteristics of the multipath signal are exploited and used to improve the precision in this study. It is generally considered that the serious multipath effect reduces the precision of elevation estimation.
The classical methods are all based on the classical multipath signal model, hence, it often causes the problem of model mismatch and results in poor performance in estimation.
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