Electronics Letters最新文献

Stepped frequency waveform (SFW) has been widely utilized in millimetre-wave radars, achieving high-resolution range profiles without expanding the radar's instantaneous bandwidth. Nevertheless, the inherent large time-bandwidth product associated with SFW results in significant ranging errors and energy dispersion, impeding its effectiveness in detecting high-speed targets. Spurred by this limitation, this study introduces an innovative velocity estimation method that employs the fractional Fourier transform (FrFT) to overcome these drawbacks. Specifically, by harnessing the Doppler signature of a moving target, which appears as a chirp signal with a frequency rate directly proportional to the target's velocity, FrFT provides precise velocity measurements. Following the velocity estimation, a compensation process is implemented using the derived metrics, after which the inverse fast Fourier transform locates the target accurately. Furthermore, an iterative algorithm based on the golden section search technique has been developed to enhance the computational efficiency of determining the optimal order for the FrFT. The validity of the proposed method is confirmed through simulation data, demonstrating that the developed approach can accurately estimate the velocity of high-speed targets with a notably reduced computational complexity.

In this letter, an algorithm applying an adaptive wavelet neural network (AWNN) and sliding-mode control (SMC) is proposed, investigated and exploited for tracking control of micro-electromechanical system (MEMS) gyroscope. Such an AWNN model can be regarded as a special radius basis function neural network and utilizes Mexican hat function as activation function. Besides, Taylor expansion is used for analyzing activation radius, which is considered as an adaptive variable. The parameters of the MEMS gyroscope model are hard to obtain in engineering applications; thus, AWNN is designed to approximate the uncertain function of MEMS gyroscope and the unknown asymmetrical dead zone in the control scheme. The weights updating laws and the activation radius adaptive laws in AWNN are derived from the Lyapunov stability analysis, which results in the control error converging to the desired value and the weights and activation radius converging to its real value. To achieve the effect of error acceleration, a power function is used to design a sliding mode function. Computer simulation results substantiate the theoretical analysis and further demonstrate the efficacy of such an algorithm combined with AWNN and SMC for MEMS gyroscope control.

The conventional frequency diverse array (FDA) system, using linear frequency offsets, generates periodic grating lobes and coupling effects, which may increase interference for potential users and difficulty in controlling parameters. To mitigate these issues and generate a dot-shaped beampattern, nonlinear frequency offsets are introduced to break periodicity and decouple the interdependence of parameters in the range and angle dimensions. Furthermore, to obtain the optimal nonlinear frequency offsets, we propose an algorithm based on the FDA multiple-input multiple-output (FDA-MIMO) structure that integrates the revised dingo optimization algorithm (RDOA) with a Kaiser window function (referred to as the RDOAK algorithm). Specifically, the RDOA is used to optimize the nonlinear frequency offset coefficients, while the Kaiser window function is applied to adjust the waveform. Simulation results demonstrate the superior performance of our proposed RDOAK approach in preventing the mainlobe shift of the beampattern, eliminating grating lobes, suppressing jammings, and achieving a narrower mainlobe width in the range dimension compared to other widely used algorithms.

To address the issues of short flight duration and the inability to carry high-computation resources in small observation unmanned aerial vehicles (UAVs) due to limited energy and payload capacities, this paper proposes a deployment framework for an air–water surface collaborative observation system based on energy-replenishment and computation offloading. In this framework, UAVs serve as platforms for observation tools, while unmanned surface vehicles (USVs) function as platforms for energy replenishment and edge computing nodes. The edge computing nodes are capable of processing, analyzing, and distributing observation data received from the UAVs. UAVs can perform coordinated landing and recharging on the USVs using high-precision BeiDou positioning. Experimental results indicate that the application of this framework allows small observation UAVs to avoid the burden of carrying heavy computational loads during flight and enables cyclic recharging and operation using the USV platform. The findings of this study have broad applicability in various scenarios, including environmental monitoring, disaster patrol, marine mapping, and marine aquaculture.

Wildfires spread quickly and require frequent satellite observations for early detection and effective management. Large Earth observation satellite constellations (EOSC) can meet this need with their broad coverage and diverse spectral capabilities. However, designing an efficient scheduling strategy for revisit tasks remains challenging due to the complex time-coupled requirements and the multi-objective nature of large-scale EOSC operations. To address this, we introduce a time-driven multi-objective (TDMO) scheduling method. The key innovation of TDMO lies in its explicit integration of revisit intervals and a time-driven mechanism, ensuring consistent observation frequencies and improved coordination of resources. Experiments across different wildfire scenarios show that TDMO effectively enhances scheduling efficiency and monitoring performance, offering a novel solution for dynamic and complex revisit scheduling in wildfire management.

This article examines the use of a high Al mole fraction (x = 0.34) and a thin barrier layer (t = 11.5 nm) in AlGaN/GaN high electron mobility transistors (HEMTs) with both single-gate and dual-gate configurations. Single-gate HEMTs with the current epitaxial structure demonstrated an improved maximum drain current and transconductance, as well as reduced current collapse, compared to similar devices with the reference structure (x = 0.22 and t = 17.5 nm). Furthermore, a dual-gate HEMT with the current epitaxial structure and a gate length of 80 nm showed an extrapolated unity current gain cut-off frequency of 74 GHz and an extrapolated maximum oscillation frequency of 248 GHz, whereas a similar device using the reference structure exhibited values of 63 and 231 GHz, respectively.

Voltage sags are one of the primary factors in power quality issues that lead to losses for sensitive users and reduce the operation resilience of distribution networks. However, due to the lack of accessibility in sensitive users’ production information, accurately quantifying the resilience of distribution networks under the impact of voltage sags is challenging. In this letter, first an operation resilience index using a trapezoidal curve is defined. Considering the varying tolerance levels of sensitive users to voltage sags, a feature indices system is established using the adaptive S-transform, and a sample dataset is generated through the Monte Carlo method. Finally, a mapping relationship between sag characteristics and operation resilience indices is established using the XGBoost-stacking algorithm. Simulations based on voltage sag recorded data validate the effectiveness and practicality of the proposed method. This data-physics hybrid-driven model offers a quantitative approach for developing resilience enhancement strategies.

This letter presents a subthreshold second-order transconductor–capacitor (G_m-C) low-pass filter (LPF) for portable biomedical applications. To broaden the range of linear signal processed by the filter, we modified the source–follower biquad and proposed an advanced g_m cell based on source-coupled pair (SCP). The implementation of self-cascode composite transistors (SC) in the g_m cell resulted in a notable enhancement of the filter's linearity and dynamic range (DR) while maintaining a low power consumption level. The circuit simulations using a 55-nm CMOS process verified that the proposed filter consumes 4.6 nW and contributes input-referred noise (IRN) of 104 µV_rms at a cut-off frequency of 100 Hz. Furthermore, the LPF attains DR and figure of merit (FoM) values of 63.88 dB and 14.71 fJ, respectively, which is a potential candidate for biomedical applications.

Nonreciprocal transmission is essential in a wide range of applications due to its unique ability to control the flow of signals or energy in a specific direction without allowing for its reversal. This letter proposes a novel ultra-wideband magnetless transmission line using pseudorandom noise (PN) sequence modulation. By applying the properties of strong autocorrelation for PN sequences, two PN sequences with the same elements but different delays are utilised to achieve synchronised forward transmitting propagation and unsynchronised backward isolating propagation. A pair of double-sideband mixers are applied for the modulation. An experiment is performed to demonstrate the proposed nonreciprocal transmission line. The experimental results show that a ultra-wideband from 0.5 to 3.5 GHz forward propagation and more than 13 dB backward isolation are achieved. The proposed nonreciprocal transmission line is compatible with silicon-based integration.

This letter proposes a novel approach to suppress forward jamming from mainlobe using partially occupied Doppler division multiple access (DDMA) waveforms. The partially occupied DDMA mode indicates that the radar only transmits waveforms through part of the orthogonal Doppler channels. It is proved that the forward jamming yields a fake Doppler component in the blank Doppler channel, if the jammer works in saturation. Leveraging this feature, a jamming mitigation method in slow time is proposed and conduct numerical experiments to validate our method.