A Precise Calibration Method on Phase Center of Uplink Antenna Array Considering Its Actual Pointing

With regard to the inferior techniques and low accuracy of phase center calibration of an antenna array, this paper proposes a new calibration method considering the actual antenna pointing by introducing a precise engineering surveying technique to measure the real state of antennas. First, an industrial photogrammetric system is utilized to obtain the coordinates of points on antenna panels in different postures, and the actual pointing of the mechanical axis is obtained via least-squares fitting. Then, based on this, the coordinates of antenna rotation center are obtained by seeking the intersection of mechanical axes via using the matrix method. Finally, the mechanical axis in arbitrary postures is estimated based on the inverse-angle weighting interpolation method, and the reliable phase center is obtained by moving a fixed length from the projective center along the mechanical axis. An uplink antenna array including three ? 3 m antennas is taken as experimental object, and all photogrammetric coordinate systems are unified by the engineering control network, with each antenna phase center precisely calibrated via the proposed method. The results of electrical signal synthesis indicate that this method can effectively overcome the influence of gravity deformation and mechanical installation error, and enhance the synthetic signal magnitude of the uplink antenna array.

Metric-based Few-shot Classification in Remote Sensing Image

Target recognition based on deep learning relies on a large quantity of samples,but in some specific remote sensing scenes,the samples are very rare.Currently,few-shot learning can obtain high-performance target classification models using only a few samples,but most researches are based on the natural scene.Therefore,this paper proposes a metric-based few-shot classification technology in remote sensing.First,we constructed a dataset(RSD-FSC)for few-shot classification in remote sensing,which contained 21 classes typical target sample slices of remote sensing images.Second,based on metric learning,a k-nearest neighbor classification network is proposed,to find multiple training samples similar to the testing target,and then the similarity between the testing target and multiple similar samples is calculated to classify the testing target.Finally,the 5-way 1-shot,5-way 5-shot and 5-way 10-shot experiments are conducted to improve the generalization of the model on few-shot classification tasks.The experimental results show that for the newly emerged classes few-shot samples,when the number of training samples is 1,5 and 10,the average accuracy of target recognition can reach 59.134%,82.553%and 87.796%,respectively.It demonstrates that our proposed method can resolve few-shot classification in remote sensing image and perform better than other few-shot classification methods.

The developing condition analysis of semiconductor laser frequency stabilization technology

The frequency stability of free-running semiconductor lasers is influenced by several factors, such as driving current and external operating environment. The frequency stabilization of laser has become an international research hotspot in recent years. This paper reviews active frequency stabilization technologies of laser diodes and elaborates their principles. Based on differences of frequency discrimination curves, these active frequency stabilization technologies are classified into three major types, which are harmonic frequency stabilization,Pound-Drever-Hall(PDH) technology and curve subtraction frequency stabilization. Further, merits and demerits of each technology are compared from aspects of frequency stability and structure complexity. Finally, prospects of frequency stabilization technologies of semiconductor lasers are discussed in detail. Combining several of these methods are future trends, especially the combination of frequency stabilization of F–P cavity. And PID electronic control for optimizing the servo system is generally added in the methods mentioned above.

Future Internet: trends and challenges

Traditional networks face many challenges due to the diversity of applications,such as cloud computing,Internet of Things,and the industrial Internet.Future Internet needs to address these challenges to improve network scalability,security,mobility,and quality of service.In this work,we survey the recently proposed architectures and the emerging technologies that meet these new demands.Some cases for these architectures and technologies are also presented.We propose an integrated framework called the service customized network which combines the strength of current architectures,and discuss some of the open challenges and opportunities for future Internet.We hope that this work can help readers quickly understand the problems and challenges in the current research and serves as a guide and motivation for future network research.

Multi-UAV cooperative target tracking with bounded noise for connectivity preservation

We investigate cooperative target tracking of multiple unmanned aerial vehicles(UAVs)with a limited communication range.This is an integration of UAV motion control,target state estimation,and network topology control.We first present the communication topology and basic notations for network connectivity,and introduce the distributed Kalman consensus filter.Then,convergence and boundedness of the estimation errors using the filter are analyzed,and potential functions are proposed for communication link maintenance and collision avoidance.By taking stable target tracking into account,a distributed potential function based UAV motion controller is discussed.Since only the estimation of the target state rather than the state itself is available for UAV motion control and UAV motion can also affect the accuracy of state estimation,it is clear that the UAV motion control and target state estimation are coupled.Finally,the stability and convergence properties of the coupled system under bounded noise are analyzed in detail and demonstrated by simulations.

High resolution remote sensing image ship target detection technology based on deep learning

With the development of China’s high-resolution special projects and the rapid development of commercial satellite, the resolution of the mainstream satellite remote sensing images has reached the sub-meter level. Ship target detection in high-resolution remote sensing images has always been the focus and hotspot in image understanding. Real-time and effective detection of ships play an extremely important role in marine transportation, military operations and so on. Firstly, the full-factor ship target sample library of high-resolution image is synthetically prepared. Then, based on the Faster R-CNN framework and Resnet model, optimize the parameters of the model to achieve accurate results. The simulation results show that the detection model trained in this paper has the highest recall rate of 98.01% and false alarm rate of 0.83%. It can be applied to the practical application of ship detection in remote sensing images.

Full-band direct-conversion receiver with enhanced port isolation and I/Q phase balance using microwave photonic I/Q mixer

A full-band direct-conversion receiver using a microwave photonic in-phase and quadrature （I/Q） mixer is proposed and experimentally evaluated in terms of radio frequency （RF） range, port isolation, phase imbalance, conversion gain, noise figure, spurious-free dynamic range, and error vector magnitude. The proposed microwave photonic I/Q mixer shows significant advantages in locM oscillator leakage and I/Q phase imbalance over entire RF bands, which are recognized as major drawbacks of conventional direct-conversion receivers.

Satellite group autonomous operation mechanism and planning algorithm for marine target surveillance

In terms of fast response problem of unanticipated marine target, it is necessary to design the satellite-ground-combined operation mechanism and planning algorithm for autonomous task planning. Firstly, based on the autonomous operation and task planning of remote sensing satellite group, it is divided into two parts: ground planning and satellite autonomous planning. Secondly,the satellite-ground-combined operation mechanism and operation flow for task planning are proposed after fully considering the resource characteristics and task demand characteristics of the ground and satellite. The satellite autonomous task planning algorithm based on extended contract net is designed. Through the simulation operation of the self-developed distributed simulation demonstration software, it shows that the operation mechanism can coordinate and cooperate effectively between the satellite autonomous task planning and ground planning. It can give full play to the advantages of the ground computing resources, reflect the control intention, make full use of the real-time feature and flexibility of the satellite calculation, and respond fast to the unanticipated task. Besides, it has solved problems of the untimely response of ground control on unanticipated observation task, the limitation of satellite computing resources and satellite-ground planning and coordination, which can effectively improve the responsiveness of remote sensing satellite to the observation task of maritime unanticipated target.

Modified min-sum SCAN decoding algorithm for polar codes

The state-of-the-art soft-output decoder of polar codes is the soft cancellation(SCAN) decoding algorithm, which performs well at the cost of plentiful computations. Based on the SCAN decoding algorithm, a modified method with revised iterative formula is proposed, marked modified min-sum SCAN(MMS-SCAN). The proposed algorithm simplifies the update formula of nodes and reduces the complexity of iterative decoding process by the piecewise approximation function. Meanwhile, the bit error rate(BER) of the proposed method can approach the performance of original SCAN decoding method without performance loss. The simulation reveals that the MMS-SCAN decoding algorithm can achieve the effect that the BER curve almost coincides with the original SCAN decoding curve.

Space moving target detection using time domain feature

The traditional space target detection methods mainly use the spatial characteristics of the star map to detect the targets, which can not make full use of the time domain information. This paper presents a new space moving target detection method based on time domain features. We firstly construct the time spectral data of star map, then analyze the time domain features of the main objects(target, stars and the background) in star maps, finally detect the moving targets using single pulse feature of the time domain signal. The real star map target detection experimental results show that the proposed method can effectively detect the trajectory of moving targets in the star map sequence, and the detection probability achieves 99% when the false alarm rate is about 8×10^(-5), which outperforms those of compared algorithms.