Sentence examples for methods for radar from inspiring English sources
Existing human gait classification methods for radar systems can be categorized as parametric and nonparametric approaches.
An automatic classification method for radar targets with micro-motions is proposed based on the idea of entropy and feature extraction from the spectrogram.
The authors in [9] proposed a robust method for radar emitter recognition based on the Wigner Ville distribution (WVD) and transfer learning, the average recognition rate (ARR) reaches more than 90% when signal-to-noise ratio (SNR) is 10 dB.
Consequently, it dynamically extends the lattice parametrization of fullband spectrum into subbands, and thereby arises as an useful and practical method for radar signal analysis/classification, speech analysis/synthesis, adaptive multiple frequency tracking, and cognitive radio spectrum sensing tasks.
In this paper, a new method for radar-based vibration estimation was presented, making the radar capable of more accurate vibration measurement.
Compared to other robust adaptive beamforming methods for MIMO radar, the proposed approach has the advantages of high efficiency and accuracy.
In addition to actual clutter removal, simple and operationally viable methods for monitoring radar performance and predicting clutter extent have been demonstrated.
The main goal of sparsity-based methods for MIMO radar systems ([21 24]) is to achieve accurate estimates for target parameters, whereas in this paper, we mainly focus on the CS MIMO radar problem to reduce the sampling rate lower than the Nyquist criterion by designing a suitable measurement matrix.
In general, the proposed method gives a different classification structure for classification methods of radar pulse modulations.
Observations are based on two data processing methods for synthetic aperture radar acquisitions: synthetic aperture radar interferometry (InSAR) and permanent scatterer InSAR (PS-InSAR) monitoring.
The design method for UHF radar system is presented.
