Electronics Letters最新文献

With the accelerated deployment of 5G massive multi-input multi-output (MIMO)-structured base stations (BSs), accurate and efficient testing of massive MIMO arrays is essential to ensure that the 5G massive MIMO antenna array can perform as expected. The increasing trend of both antenna element and array physical size makes massive MIMO antenna pattern measurement in the direct-far-field OTA environment unrealistic due to the signal attenuation, long measurement time, and ultra-high test system construction cost. Therefore, the necessity of a compact, efficient, low-complex, yet accurate OTA test method is evident, especially for 5G massive MIMO BS arrays. This paper proposes a novel multi-probe-enabled midfield (MF) OTA test method for 5G massive MIMO devices in compact measurement settings where antenna pattern measurement can be efficiently performed. The detailed theoretical analysis for the 5G massive MIMO array MF antenna pattern reconstruction method is presented, and the simulated validation results based on the typical 5G BS antenna arrays are provided and analysed, which demonstrate the effectiveness of the proposed MF OTA test method and can provide insights into the radio frequency parametric measurement for 5G massive MIMO devices.

This study introduces a model-free sliding mode control scheme based on a complementary extended state observer (CESO) for a permanent magnet synchronous motor (PMSM) in cathode copper production. First, an ultra-local model of the PMSM is proposed. Compared with the existing ultra-local model, the ultra-local model proposed in this study divides the disturbances into periodic and aperiodic perturbations. Furthermore, based on the proposed ultra-local model, a CESO is proposed. Compared to the linear extended state observer (LESO) and the non-linear extended state observer (NLESO), the proposed CESO combines the advantages of the LESO and the NLESO. The proposed CESO exhibits reduced disturbance estimation errors and greater resilience to significant periodic and minor aperiodic disturbances. By leveraging the ultra-local model, a novel combined control strategy is devised, utilizing the CESO and a model-free sliding mode control. The stability of the proposed control scheme has been verified through the Lyapunov stability theorem and the Hurwitz stability criterion. Subsequently, comparative simulations on disturbance rejection are conducted under periodic and aperiodic disturbances, illustrating the efficacy of the proposed control approach.

This brief proposes a calibration method for offset, gain, and timing-skew mismatches in time-interleaved analog-to-digital converters. The calibration method includes the estimation method and the correction method. The estimation method is based on the difference between the spectral distribution of the error harmonics and the input signal. The correction method adopts sequential correction. Simulation results demonstrate that the proposed method calibrated all three mismatches simultaneously and eased the limit for input signals compared with the traditional method.

This article introduces a high-precision SAR echo simulation method aimed at providing a large-scale SAR echo simulation algorithm that includes complex target information. This method independently simulates targets and ground clutter, calculating the shadow areas of targets and the dihedral effects between targets and the ground by constructing virtual rough surfaces and using ray tracing. Furthermore, this method can provide data sources for algorithms that improve detector performance using shadow or specular reflection characteristics. Finally, after comparing the simulation results with the publicly available MSTAR dataset, the experimental results show that this method ensures the accuracy of the results. This research provides a rich data source for the study of SAR detection and tracking algorithms and offers strong support for practical applications.

The data-driven machine learning technology used for neural decoding emphasizes the requirements of in vivo and in vitro neural signal acquisition with high spatial and temporal resolution. However, micro-electrode arrays (MEAs) that simultaneously achieve high spatial and temporal resolution neural signal acquisition are not yet available. Meanwhile, the high data bandwidth of large-scale MEA brings challenges in power consumption, data transmission, storage, and neural signal processing. This research aims to improve the spatial and temporal resolution of MEA, reduce the cost and time of large-scale MEA customization through cascading, and reduce the bandwidth of large-scale MEA through spike compression. Firstly, based on in-pixel spike detection, a row-based neural spike readout mechanism and related array circuit to improve the neural spike readout performance and temporal resolution of neural signal acquisition is proposed. To further enhance the spatial resolution and reduce the risk of large-scale MEA fabrication, the cascading capability of the readout circuit is explored. Lastly, spatial correlation-based neural spike encoding is proposed to reduce the data bandwidth, achieving a 5.2× compression rate. This is a study on implementing large-scale MEA through cascaded readout circuits and novel study to utilize the spatial correlation between detected neural spikes for further compression.

One of the most important challenges in an autonomous and robotics system is the path planning in which the system finds the optimal path from start point to goal point. The traditional path planning algorithms may have large memory requirements which scale with the size and resolution of the configuration space. To address these challenges, this paper introduces a novel path planning algorithm that combines Particle Swarm Optimization and Artificial Potential Field in the form of a path planning algorithm for mobile robots. The biological and physical concepts from Particle Swarm Optimization and Artificial Potential Field algorithms are combined to yield an algorithm which minimizes instances of getting stuck in local minima and generates a smooth but feasible path. The developed method requires memory which scales only with the number of particles and the time taken to reach the goal. This results in a memory-efficient solution that generates smooth and feasible paths for mobile robots navigating in a 2D space.

Accurate jamming detection is a prerequisite and important support for efficient anti-jamming communication in integrated sensing and communications. In recent years, the reactive follower jamming, characterized by the high jamming efficiency and the difficulty of detection, is seen as the main opponent for frequency hopping spread spectrum communication, which poses a threat to the reliability of wireless communication. Based on the background, a multiple-nodes based cooperative jamming detection scheme is proposed for accurately detecting the reactive follower jamming. Specifically, the secondary nodes send the average energy of the suspected attacked symbols to the primary user as the uplink data in each detection period, and then the detection result is provided by the detection threshold. The optimal detection threshold is derived for the primary user according to the uplink data and channel conditions. In addition, the theoretical probability of detection under the optimal threshold is derived, and the complexity of the scheme is analysed. Simulation results show that the proposed multiple-nodes based cooperative jamming detection scheme for reactive follower jamming can effectively improve the probability of detection.

Energy information is vulnerable to malicious denial of service (DoS) attacks due to the diversity and openness of the smart grid environment. In order to cope with the above challenges, this paper first proposes to adopt Markov hopping model to describe the random packet loss of measurement due to DoS attacks. Then, based on Holt two-parameter exponential smoothing and untraced Kalman filtering techniques, a one-step predictive value compensation method for measurement data loss is proposed, and an improved dynamic untraced particle filtering algorithm based on data fusion compensation strategy is designed. Finally, an IEEE-30 bus system is used to simulate the proposed dynamic state estimation method, which proves that the proposed method can effectively resist DoS attack.

In this article, a modified scheme of the fast dipole method (FDM) is proposed based on the Cartesian tensor. To achieve separation between the field and the source, the Taylor series used in the FDM is an incomplete second-order expansion, which limits computational accuracy and flexibility. To address this issue, a Cartesian tensor is employed to expand the interactions between the far-group pairs. This approach allows for a complete expansion of any order and offers flexibility in precision control, enabling a balance between computational efficiency and accuracy requirements across different application scenarios. Moreover, the computational accuracy of the proposed method can be improved without a substantial increase in time and memory requirements compared to the FDM. The validity and accuracy of the proposed method are demonstrated with numerical examples.

This letter considers the posterior Cramér–Rao lower bounds (PCRLB) problem for extended target tracking from a stack of measurement data that are modelled as random variables in the random finite sets framework. The scalars in the traditional PCRLB are converted into vectors based on random finite sets to derive a theoretical lower bound. In this way, the proposed method can be applied to the multi-target tracking problem and accommodates scenarios with targets of varying. Moreover, solving the data association problem from four parts caused by the conjugate update of the Poisson multi-Bernoulli mixture filter is considered. Simulation results are presented to verify the effectiveness of the derived PCRLB.