In the real environment, the unstable radar measurement noise can degrade the tracking performance of the maneuvering target. In this Letter, the iterative formulation of the noise is simplified, and the noise-adaptive matrix is introduced to calculate the fading factor of the strong tracking algorithm, so that the effect of measurement noise on the fading factor can be corrected in real time. The superior performance of the proposed method is verified by comparison with three existing improved methods on a typical example.

An ultra-low profile and high-performance UHF RFID reader antenna is proposed in this letter, which can be switched between far-field (FF) and near-field (NF) operative mode. This antenna is composed of four dipoles and a reconfigurable feed network. The four dipoles form a square, and the feed network is located in the center. The feed network is a four-way power divider, and the phase of the output signal can be controlled by switching diodes. As a balanced structure, double-sided parallel-strip lines (DSPSLs) are used to feed four dipoles. Compared to the balun structure, the application of DSPSLs can reduce the area of the feed network, thereby reducing the influence of the feed network on the central magnetic field. Experimental results show that the proposed antenna can provide a NF reading area of 180×180 mm2, and the identification rate can reach 100% within 30-60 mm height. The FF gain of the antenna is 4.7 dBic, and the overlapping range of 3 dB AR bandwidth and -10 dB impedance bandwidth is 800-960 MHz. The good FF and NF performance of this antenna is conducive to its application in RFID systems.

A novel frequency-to-voltage converter (FVC) based phase-locked loop (PLL) is proposed to overcome the inability of an FVC-based frequency-locked loop (FLL) to lock phase. The proposed dual-loop PLL adds variable phase-locking capability, such that the phase locking angle can vary from 0o – 360o. The additional variable phase-locking can be applied in data communication in the form of phase modulation. The design is targeted for a 0.5-µm CMOS process. The proposed design generates a 480MHz clock from a reference clock of 15MHz. In simulation, the proposed PLL locks within 3.56 µs while consuming 1.61 mW of power.

Automatic dependent surveillance-broadcast (ADS-B) has been widely used due to its low cost and high precision. The deep learning methods for ADS-B signal classification have achieved a high performance. However, recent studies have shown that deep learning networks are very sensitive and vulnerable to small noise. We propose an ADS-B signal poisoning method based on U-Net. This method can generate poisoned signals. We assign one of ADS-B signal classification networks as the attacked network and another one as the protected network. When poisoned signals are fed into these two well-performed classification networks, the poisoned signal will recognized incorrectly by the attacked network while classified correctly by the protected network. We further propose an Attack-Protect-Similar loss to achieve “triple-win” in leading attacked network poor performance, protected network well performance and the poisoned signals similar to unpoisoned signals. Experimental results show attacked network classifies poisoned signals with a 1.55% classification accuracy, while the protected network classifies rate is still maintained at 99.38%.

In this letter, we address the challenge in forecasting non-stationary financial time series by proposing a meta-learning based forecasting model equipped with a CNN predictor and a LSTM meta-learner. The model is applied to a set of short subseries which are the result of dividing a long non-stationary financial time series. As a result, a promising performance can be achieved by the proposed model in terms of making more accurate prediction than the traditional CNN predictor and AR based forecasting models in non-stationary conditions.

Radio frequency (RF) fingerprinting is a challenging and important technique in individual identification of wireless devices. Recent work has used deep learning-based classifiers on ADS-B signal without missing aircraft ID information. However, traditional methods are difficult to obtain well performance accuracy for classical deep learning methods to recognize RF signals. This letter proposes a Gaussian Low-pass Channel Attention Convolution Network (GLCA-Net), where a Gaussian Low-pass Channel Attention module (GLCAM) is designed to extract fingerprint features with low frequency. Particularly, in GLCAM, we design a Frequency-Convolutional Global Average Pooling (F-ConvGAP) module to help channel attention mechanism learn channel weights in frequency domain. Experimental results on the datasets of large-scale real-world ADS-B signals show that our method can achieve an accuracy of 92.08%, which is 6.21% higher than Convolutional Neural Networks.

The fusion of multiple monitoring sensors is crucial to improve the accuracy and robustness of machinery fault diagnosis. However, existing fault diagnosis methods may underestimate the interference of noise in the multi-sensor fusion process, leading to unsatisfied performance. To handle this problem, this paper proposes a deep model based on the frequency adaptive wavelet pyramid. First, an adaptive frequency selection strategy is designed to prune the seriously polluted frequencies and only retain some key frequencies. Then, the self-attention mechanism is used to perform information fusion on the selected frequency bands of different sensors. Finally, a wavelet fusion pyramid is adopted by repeating the fusion process at multiple wavelet decomposition levels. In this way, different sensors can be fused in a more fine-grained manner. The experimental results on two multi-sensor-based fault diagnosis datasets demonstrate the anti-noise capability of our proposed method.

In this letter, we propose a velocity measurement algorithm based on auto-correlation in a single frame for IEEE 802.11ad. The periodicity of the Golay sequences in the preamble is exploited in the algorithm to estimate the time delay and achieve the velocity measurement at the same time. The improved measuring accuracy is obtained as the effection of noise is suppressed by designing and utilizing the auto-correlation function.

Terahertz (THz) imaging has an outstanding advantage of high resolution due to the high frequency and has promising potential in VideoSAR. However, limited to the THz source power and the air absorption, the THz image usually has a low SNR and is susceptible to noise in remote sensing and imaging. In order to improve the quality of THz images, a THz image enhancement method is proposed based on the noise2noise idea. The THz images are reconstructed with the AFBP algorithm. They are organized as noisy image pairs and filtered with a mask to remove the influence of moving targets. Then, the Noise2Noise network is constructed based on the CNN network and takes the noisy image pair as input and reference. In the training stage, 1000 noisy image pairs are used as the training set and 100 noisy images are used as the test set to verify the performance of the proposed method. The experimental results based on real VideoSAR data demonstrate that the proposed method is capable of suppressing noise and enhancing the THz image.

Self-rectifying memristor has no need for selector devices, but possess the one-way transmission behavior and multi-level non-volatile memory characteristics, which makes it promising candidate for electronic synapse. In this letter, we propose a novel self-rectifying memristor based on Pt/Hf0.5Zr0.5O2/TiN structure. The devices show large memory window (104) and high rectifying ratio (104), which can block the sneak current in passive crossbar array without any additional hardware overhead. Moreover, the devices demonstrate excellent multi-level states modulation capability, low power consumption, high endurance and long retention. The final benchmark demonstrates that the proposed Pt/Hf0.5Zr0.5O2/TiN self-rectifying memristor is a promising candidate for electronic synapse application.

Genomics data analysis requires efficient tools to address the vast amount of data generated by current next-generation sequencing technologies. K-mer counting works face difficulties in balancing high memory overhead with statistical precision. We designed a high-frequency k-mer statistical computation based on the Space Saving algorithm and a novel hash table structure, which reduces the memory overhead by 46\% while ensuring high computational efficiency.

In this letter, the angular jamming effect of electronically controlled corner reflectors after a maneuver aircraft is analyzed. Motion characteristics of the corner reflectors towed by a high-speed aircraft are calculated through the Lagrange theory, and the angle measurement error of the jamming method for the monopulse angle measurement algorithm is analyzed based on the motion states and the electromagnetic states (scattering or penetration) of the corner reflectors. Results show that the measured angle by a monopulse radar seeker can be effectively interfered by this method under complex maneuver motion while the automatic tracking is employed.

This letter studied the phase error in the frequency-interleaving digital-to-analog converter (FI-DAC) and proposed a comprehensive phase estimation method. Firstly, the model of FI-DAC system was established, and the phase error were considered that consists of three parts: time delay error, initial phase error and nonlinear phase error. By analyzing the phase function characteristics of the system, the least squares (LS) was used to linearly fit the phase functions of the non-overlapping bands to estimate both the time delay and initial phase of each sub-band channel. Then, the nonlinear phase error of the system was estimated by the difference between the system phase function and the estimated linear phase function. Finally, the effectiveness of the proposed estimation method was verified by the built FI-DAC test bench.

With the increasing penetration of renewable energy, virtual power plants reduce the impact on the power grid by integrating massive distributed resources for unified management. However, the optimal scheduling of a large number of distributed resources in virtual power plants has become a new problem in recent years. Therefore, aiming at the real-time optimal scheduling problem in the optimal scheduling of virtual power plant, this letter regards the virtual power plant as a multi-agent system, and proposes a novel real-time active power dispatch scheme of virtual power plant based on distributed model predictive control, so that each agent can not only calculate its own optimization function relatively independently, but also fully refer to the neighbor information. Simulation results show the feasibility and effectiveness of the proposed method.

The power consumption of chips has emerged as a major concern with the increased integration of analog circuitry. This work focuses on a two-stage comparator based on a preamplifier with latch for successive approximation analog-to-digital converter. In order to minimize power loss and delay time, the charge steering approach was used in the design of latch as well as preamplifier. The suggested comparator is simulated in SMIC 0.18um process in comparison to the comparator without charge steering mode. The results reveal that the average power consumption is only around 22uW for varied input voltage at a supply voltage of 1.2V, which is relatively lowered by approximately 30%. Meanwhile, delay time is also reduced by about 25%.

The motion of targets is well known to result in their defocussing and displacement in SAR imagery, but detection of motion and re-focussing of targets under generic conditions remains of ongoing interest. One class of methods involves forming images of sub-apertures in which motion defocussing will be reduced. In this paper, we use dynamic tomographic image formation methods utilising an optical flow constraint to form a video of SAR sub-aperture images. These retain fine resolution of the full aperture, focussing along-track motion.

The accurate prediction of radio wave propagation is extremely important for wireless network planning and optimization. However, inexact matching between the traditional empirical model and actual propagation environments, as well as the insufficiency of the sample data required for training a deep learning model, lead to unsatisfactory prediction results. Our paper proposes a field strength prediction model based on a deep neural network that is aimed at a tiny dataset composed of the geographic information and corresponding satellite images of a target area. This model connects two pretrained networks to minimize the parameters to be learned. Simultaneously, we construct a convolutional neural network (CNN) model for comparison based on a previous advanced study in this field. Experimental results show that the proposed model can obtain the same accuracy as that of previously developed CNN models while requiring less data.

This paper proposes a joint optimization method for the imaging algorithm and sampling scheme of sparse spotlight syhthetic aperture radar (SAR) imaging based on deep convolutional neural networks. Traditional compressed sensing (CS) based sparse SAR imaging has been widely studied. Deep learning and sparse unfolding networks have been introduced into sparse SAR imaging, but most current works focus only on the imaging stage and simply adopt the conventional uniform or random down-sampling scheme. Considering that the imaging quality also depends on the sampling pattern besides the imaging algorithm, this paper introduces a learning-based strategy to jointly optimize the sampling scheme and the imaging network parameters of the reconstruction module. In a deep learning-based image reconstruction scheme, joint and continuous optimization of the sampling patterns and convolutional neural network parameters is achieved to improve the image quality. Simulation results based on real SAR image dataset illustrate the effectiveness and superiority of the proposed framework.