Variable numerous types of equipment/subsystems may exist in complex systems, which makes it difficult to analyze and identify electromagnetic emission sources. A theory named “basic emission waveform theory” is presented in this paper to solve this problem. This innovation theory characterizes emission with four basic waveforms, including square wave, sine wave, damped oscillation, and spike wave. Then, the effectiveness of the theory is discussed in both theoretical and engineering ways. In particular, the basic waveforms reflect the physical characteristics of the equipment. Furthermore, the basic waveforms generally do not vary with the locations or loading conditions. Therefore, the identification and analysis of an emission source can be realized by analyzing the basic waveforms from the emission of a complex system. Finally, the applications of damped oscillation and square wave in emission source identification field are used as validations of the proposed theory. Moreover, an example of emission source identification for an electric vehicle is also presented. The examples show that the proposed theory can be used to simply and accurately identify and analyze the emission sources of complex systems.

This paper investigates the problem of unmanned aerial vehicle (UAV) recognition in the presence of WiFi interference using passive radio frequency (RF) detection. The proposed method relies on machine learning based RF recognition and considers the scenario in the bandwidth of the video signal (VS) and WiFi are identical. Our machine learning strategy involves extracting 31 features from the WiFi signal and the UAV VS, which are then input to the classifier. Among the 31 features, 30 are statistical in the time and frequency domain, while the remaining one involves the effective subcarrier feature. We evaluate four different machine learning (ML) classifier variants and demonstrate through simulation and experiments that the proposed method can accurately recognize UAV VS in the presence of WiFi interference. We also improve the feature-vector compactness and reduce the 31-feature vector to a 6-feature vector composed of the most significant features and demonstrate that the recognition performance of random forest method (RandF) classifier is not compromised. The most appealing performance for the 6-feature vector case is attained by the RandF, managing a recognition rate of 100% in the indoor experiment and 96.26% in the 2 km outdoor experiment. The recognition rate of the four ML classifiers is larger than 95.52% in the 2 km outdoor experiment, which is better than other UAV detection methods such as radar, acoustic and video.

By introducing an additional comb beyond its dual-comb predecessor, tri-comb consisting of three combs with different mode spacing has shown its unique potential in several metrology areas such as nonlinear spectroscopy, frequency and distance measurement. However, tri-comb generation based on three frequency-stabilized comb lasers would be overly complicated and costly for many potential applications. Previous studies had realized dual-comb generation from a single-cavity laser by leveraging multiplexing in one of the waveguiding dimensions of the optical pulses. Such sources with built-in passive mutual coherence could greatly reduce system complexity. Yet, squeezing more combs into one multiplexing dimension becomes increasingly difficult without significant sacrifice in performance, which could jeopardize the usefulness of such a scheme. In this paper, it is proposed and demonstrated that multi-comb generation could be realized by tapping into the multidimensional joint parameter space. Up to four wavelength/polarization multiplexed, asynchronous ultrashort pulse sequences can be generated with good stability from an all-fiber, mode-locked ring-cavity laser. This could offer an attractive alternative solution as the low-complexity light sources for field-deployable multi-comb metrology applications.

The identification and localization of large-range, wide-band electromagnetic interference (EMI) sources have always been both costly and time-consuming. The measurements at different times and places are often required before a typical system can locate a target. In this paper, we proposed a 2D electromagnetic imaging system to localize interference sources and identify the EMI frequency in real time. In this system, an offset paraboloid with a diameter of three meters is designed for large-range EMI imaging, while a multi-channel digital signal acquisition system is developed for wide-band EMI localization. The located interference source is segmented by the maximum entropy method based on particle swarm optimization, and the modified generalized regression neural network (MGRNN) is applied to identify the EMI frequency effectively by excluding misleading effects of outliers. The experiment which has been completed on our dataset indicates that our approach not only increases accuracy by 5% compared with the standard generalized regression neural network approaches for identification, but also exerts a large-range wide-band localization of the EMI source detection method.

A novel broadband high-sensitivity optical E-field sensor is proposed in this letter. To solve the problem of bandwidth sensitivity, thin-film LiNbO ₃ (TFLN) is employed to fabricate the photonic device. Herein, the inductively coupled plasma (ICP) etching methodology using a mixture of Ar and C ₄ F ₈ is first introduced, which is more effective in etching and helps to sharpen the lateral angle of the LN ridge waveguide. The LF-shaped electrode is designed to improve the performance of the sensor with a bandwidth of 1 MHz to 20 GHz and a sensitivity of 1 mV/m. The volume of the sensor with the package is only 20×5×5 mm, which can be utilized in narrow spaces of the weak electromagnetic signal in the wideband frequency range.

In this paper, a weighted l₁-norm is proposed in a l₁-norm-based singular value decomposition (L1-SVD) algorithm, which can suppress spurious peaks and improve accuracy of direction of arrival (DOA) estimation for the low signal-to-noise (SNR) scenarios. The weighted matrix is determined by optimizing the orthogonality of subspace, and the weighted l₁-norm is used as the minimum objective function to increase the signal sparsity. Thereby, the weighted matrix makes the l₁-norm approximate the original l₀-norm. Simulated results of orthogonal frequency division multiplexing (OFDM) signal demonstrate that the proposed algorithm has s narrower main lobe and lower side lobe with the characteristics of fewer snapshots and low sensitivity of misestimated signals, which can improve the resolution and accuracy of DOA estimation. Specifically, the proposed method exhibits a better performance than other works for the low SNR scenarios. Outdoor experimental results of OFDM signals show that the proposed algorithm is superior to other methods with a narrower main lobe and lower side lobe, which can be used for DOA estimation of UAV and pseudo base station.