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The traditional time‐frequency analysis (TFA) techniques are instruments for target classification, which can reflect the feature of the target in the time‐frequency domain. However, it will lead to a serious decrease in the recognition accuracy as the decline of the sampling rate. To ease this problem, in this article, the sparse time‐frequency feature analysis (STFFA) is implemented for aircraft classification, and the genetic algorithm is adopted to solve the sparse problem quickly for saving time. Firstly, the sparse time‐frequency decomposition and recovery signal are obtained by matching pursuit. Then, three novel kinds of aircraft features are extracted from the sparse time‐frequency decomposition, which are sparse recovery time‐frequency entropy (SRTFE), frequency entropy by time, and first‐order sparse time‐frequency moment. Thus, the feature combination modes based on the three features are applied to realize the classification of aircraft and compared with the traditional TFA techniques. Besides, a support vector machine is also used to classify the three kinds of aircraft. The accuracy and efficiency of the STFFA method have been investigated by employing the parametric model data and electromagnetic scattering model simulated trials. Furthermore, in contrast to the traditional TFA instrument, our method can reach a recognition accuracy of more than 90% from the numerical experiment results, which demonstrates that the feature extraction by sparse time‐frequency analysis improves the accuracy of aircraft classification under a low sampling rate. [ABSTRACT FROM AUTHOR]

In radar detection for micro‐drones, the performance of the characteristics and features of micro‐drones distinctly varies depending on their motion states. Therefore, this study presents an extraction method for spectral signatures of radar cross section (RCS) to classify and discriminate between typical motion states of micro‐drones. The features related to the RCS of the micro‐drones are transformed and converted into a modified Mel frequency cepstral coefficient (MFCC), which is proposed to detailedly characterize the spectral change of RCS along with different movements. Then the hidden Markov model (HMM) is used to recognize the typical movements of micro‐drones based on the modified MFCC. Experiments in X, Ku bands and HH, VV polarization modes are conducted to investigate the effectiveness of the proposed method, and the classification results with average accuracy above 97%$\%$ are also demonstrated in experiments in discriminating between five different motion scenarios of micro‐drone. The works involved will aid in maximizing classification accuracy for micro‐drones in real scenarios and subsequent situation awareness. [ABSTRACT FROM AUTHOR]

In this paper, an algorithm named the sparse Wigner‐Ville distribution (WVD) is applied for the time‐frequency analysis of signals. In this algorithm, the sampling for ambiguity function (AF) is regarded as a sparsity measurement of the WVD, and the Fourier transform matrix is treated as a sparsity redundant dictionary. The effectiveness of the algorithm is verified by using the liner frequency modulation and sinusoidal frequency modulation signals. Then the algorithm is employed to deal with the time‐frequency analysis of helicopters. The experiment results show that the time‐frequency images of helicopters obtained by utilizing this algorithm exhibit higher resolution without cross‐interference terms compared with that by WVD. [ABSTRACT FROM AUTHOR]

A wideband monostatic and bistatic radar cross‐section (RCS) reduction of microstrip antenna array is proposed and investigated. By using coding metasurface on the proposed antenna, the reflected energy is diffused scattering to more directions. A remarkable monostatic RCS reduction is achieved from 6 to 7.6 GHz and 9.5 to 26 GHz covering the working frequency band, and the bistatic RCS of proposed antenna is also effectively suppressed. The proposed antenna still maintains in a compact structure, while the antenna radiation performance is preserved. Both the reference and proposed antennas are fabricated and measured in anechoic chamber, and the results show that there is a good agreement between simulations and measurements. [ABSTRACT FROM AUTHOR]

A time‐domain inverse scattering algorithm for determining unknown plasma resonance frequency and electron collision frequency of unmagnetized plasma media object has been proposed. Finite‐difference time‐domain (FDTD) method in conjunction with Lagrange multipliers and gradient‐based minimization method are used in the proposed algorithm. The reconstruction of the parameters is based on the minimization of a cost function that describes the discrepancy between simulation and measured data of the electric field at the observation points. In order to obtain the gradient value of the unknown plasma parameters, an augmented cost function has been given by means of Lagrange multipliers with the Maxwell curl equations and constitutive relation of the unmagnetized plasma media. Then, the gradient value could be obtained after a series of transformations. At last, the Ploak‐Ribiere conjugate gradient algorithm has been applied to update the plasma parameters iteratively. The effectiveness of the proposed algorithm has been validated through the simulation test. [ABSTRACT FROM AUTHOR]

A three‐dimensional unconditionally stable five‐step locally one‐dimensional finite‐difference time‐domain (LOD‐FDTD) method for anisotropic magnetized plasma medium is presented. Firstly, we search for a diagonal matrix to transform Maxwell coefficient matrix of plasma into skew symmetric matrix which is beneficial to analyze the properties of the proposed algorithm. Then, the detailed formula derivation and numerical stability analysis is presented. At last, the resonance frequency of a microwave cavity partially filled with a plasma slab and the rotation angle of an infinite plasma slab are simulated to verify the accuracy and stability of the proposed method. The effectiveness of the proposed method is further demonstrated by comparing with the three‐step LOD‐FDTD method. [ABSTRACT FROM AUTHOR]