We briefly review recent results on photoemission spectroscopy based on the deep and vacuum ultraviolet diode pumped solid-state lasers which we have developed. Cascaded second harmonic generation with the nonlinear crystal KBe2BO3F2 (KBBF) is used to generate deep ultraviolet and vacuum ultraviolet laser radiation, which complements traditional incoherent light sources such as gas discharge lamps and synchrotron radiation, and has greatly improved resolution with respect to energy, momentum, and spin of photoemission spectroscopy. Many new functions have been developed with the advantages of high photon energy, narrow linewidth, high photon flux density, and so on. These have led to the observation of various new phenomena and the amassment of new data in the fields of high temperature superconductivity, topological electronics, Fermi semi-metals, and so forth. These laser systems have revived the field of photoemission spectroscopy and provided a new platform in this frontier research field.

A robust generalized sidelobe canceller is proposed to combat direction of arrival (DOA) mismatches. To estimate the interference-plus-noise (IPN) statistics characteristics, conventional signal of interest (SOI) extraction methods usually collect a large number of segments where only the IPN signal is active. To avoid that collection procedure, we redesign the blocking matrix structure using an eigenanalysis method to reconstruct the IPN covariance matrix from the samples. Additionally, a modified eigenanalysis reconstruction method based on the rank-one matrix assumption is proposed to achieve a higher reconstruction accuracy. The blocking matrix is obtained by incorporating the effective reconstruction into the maximum signal-to-interferenceplus-noise ratio (MaxSINR) beamformer. It can minimize the influence of signal leakage and maximize the IPN power for further noise and interference suppression. Numerical results show that the two proposed methods achieve considerable improvements in terms of the output waveform SINR and correlation coefficients with the desired signal in the presence of a DOA mismatch and a limited number of snapshots. Compared to the first proposed method, the modified one can reduce the signal distortion even further.

Cross-eye jamming is an electronic attack technique that induces an angular error in the monopulse radar by artificially creating a false target and deceiving the radar into detecting and tracking it. Presently, there is no effective anti-jamming method to counteract cross-eye jamming. In our study, through detailed analysis of the jamming mechanism, a multi-target model for a cross-eye jamming scenario is established within a random finite set framework. A novel anti-jamming method based on multitarget tracking using probability hypothesis density filters is subsequently developed by combining the characteristic differences between target and jamming with the releasing process of jamming. The characteristic differences between target and jamming and the releasing process of jamming are used to optimize particle partitioning. Particle identity labels that represent the properties of target and jamming are introduced into the detection and tracking processes. The release of cross-eye jamming is detected by estimating the number of targets in the beam, and the distinction between true targets and false jamming is realized through correlation and transmission between labels and estimated states. Thus, accurate tracking of the true targets is achieved under severe jamming conditions. Simulation results showed that the proposed method achieves a minimum delay in detection of cross-eye jamming and an accurate estimation of the target state.

Identifying code has been widely used in man-machine verification to maintain network security. The challenge in engaging man-machine verification involves the correct classification of man and machine tracks. In this study, we propose a random forest (RF) model for man-machine verification based on the mouse movement trajectory dataset. We also compare the RF model with the baseline models (logistic regression and support vector machine) based on performance metrics such as precision, recall, false positive rates, false negative rates, F-measure, and weighted accuracy. The performance metrics of the RF model exceed those of the baseline models.

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