Table of Content

20 June 2023 Volume 50 Issue 3

  

Special Issue on 6G Key Technologies for IT3.0 Based on the Integration of Communication,Sensing and Computing

Deterministic service of space-air-ground integrated networks: architecture,challenges and key technologies

CAO Huan,CHEN Yan,ZHOU Yiqing,SU Yongtao,LIU Zifan,CHEN Daojin,DING Yashuai

Journal of Xidian University. 2023, 50(3):  1-18.  doi:10.19665/j.issn1001-2400.2023.03.001

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It is one of the important development directions of future 6G communication to meet the extreme communication needs of users in the global vertical industry,cooperate with the terrestrial mobile communication network and the rapidly developing non-terrestrial network (NTN),and break the traditional rigid service mode of "doing your best" to provide users with global deterministic services.First,this paper summarizes the future space-air-ground integrated network architecture and the deterministic service connotation and scenario requirements under this architecture.In addition,it proposes a global network-oriented deterministic service management and a control technology framework.Then,it analyzes the three major challenges faced in the process of global deterministic service,including the difficulty in ensuring the business awareness of users in the global whole scene,orchestrating the end-to-end slicing network space-air-ground integration,and quickly coordinating and scheduling the global multidimensional resources in the slicing subnet.In response to the above challenges,three solutions are introduced respectively,namely,intelligent cloud-based full-domain,full-scene service sensing technology,satellite earth end-to-end intelligent slicing orchestration based on network topology prediction,and data and model-driven satellite earth resource intelligent allocation technology,which provide a reference for the development of space-air-ground integrated network extreme service technology.

ResNet enabled joint channel estimation and signal detection for OTFS

ZHOU Shuo,ZHOU Yiqing,ZHANG Chong,XING Wang

Journal of Xidian University. 2023, 50(3):  19-30.  doi:10.19665/j.issn1001-2400.2023.03.002

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Orthogonal time frequency space (OTFS) modulation can realize reliable broadband communication at a high doppler frequency offset,which is one of the potential application technologies in the 6G communication-sensing-computing scenario.In order to solve the problems of high complexity and limited performance of the receiver in this system,a joint channel estimation and signal detection algorithm based on modified ResNet is proposed,with the transmission symbol information recovered directly without obtaining explicit channel information.According to the stability of the delay doppler domain channel,deep learning technology is introduced into the receiver design,and a lightweight residual neural network model that can fully extract the signal features is designed by using the embedded pilot data frame structure.It can directly fit the input-output relationship of delay doppler domain signals to achieve implicit channel estimation and complete signal detection.In the joint design,the optimal network model is obtained by off-line training with the data collected in the actual communication link,which can be used for on-line detection.Meanwhile,the joint optimization of channel estimation and signal detection is realized with the help of an error back propagation mechanism and gradient descent criterion,which effectively improves the communication performance.Simulation results show that the proposed scheme has better robustness and good generalization compared with the traditional receiver algorithm,which not only reduces the algorithm complexity,but also improves the BER performance by about 2dB.

Integrated sensing,communication,and computation via the over-the-air computing architecture

HAN Kaifeng,ZHOU Ziqin,WANG Zhiqin,GONG Yi,LI Xiaoyang

Journal of Xidian University. 2023, 50(3):  31-39.  doi:10.19665/j.issn1001-2400.2023.03.003

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Limited by equipment conditions and computer network hierarchy,traditional researches treat data sensing,communication,and computation as independent processes,which lack global consideration and seriously hinder the efficiency of data processing.To improve the efficiencies of data sensing and transmission,the sensing-communication fusion is proposed to design dual-functional signals for supporting both radar sensing and data communication.To improve the efficiencies of data communication and computation,over-the-air computing aims to exploit the waveform superposition property of signals in the multiple access channel to enable data computation during transmission.In order to achieve efficient data processing,the dual-functional signals in sensing-communication fusion and the waveform superposition property in over-the-air computing are utilized to realize the integrated sensing,communication,and computation over the air.The corresponding beamformers are designed to reduce channel noise and signal interference,so as to improve the accuracies of sensing,communication,and computation.This technology can be applied in various fields such as target detection,vehicle networking,and edge intelligence.Experimental results show that the technology can significantly improve the efficiency and accuracy of data processing in comparison with the traditional schemes.

Algorithm for recognition of lightweight intelligent modulation based on the CNN-transformer networks

YANG Jingya,QI Yanli,ZHOU Yiqing,ZHAO Dengpan,WANG Shangquan,SHI Jinglin

Journal of Xidian University. 2023, 50(3):  40-49.  doi:10.19665/j.issn1001-2400.2023.03.004

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Existing modulation recognition methods based on deep learning have the problems of low recognition accuracy under the influence of noise and uncertain channel interference,and are difficult to apply to mobile terminals due to a large number of parameters.This paper proposes a lightweight modulation recognition method based on the Convolutional Neural Network (CNN) and Transformer to solve the above problems.In order to improve the accuracy,the CNN is first used to extract the local features of the signal.Then,the CNN-based channel attention and Transformer-based temporal attention modules are used to focus on the features that are most conducive to recognition from the two dimensions of the signal channel and time domain,respectively,while reducing the impact of the channel,noise,etc.The proposed method can be applied to a variety of signal representations,such as raw IQ signals,amplitude-phase signals,and transform domain features.Simulation shows that on the RadioML2016.10b dataset,compared with the existing convolutional network methods,the average recognition accuracy of the proposed method is increased by 8%~12%.Compared with the methods based on the residual neural network and long-term memory network,the number of parameters is reduced by 90%~92%,and the amount of calculation is reduced by about 83%~93%.Experimental results show that the proposed method can improve the accuracy of model classification while effectively reducing the number of parameters and the amount of calculation.

Algorithm for prediction of the 6G vehicle trajectory based on the GNN-LSTM-CNN network

CAI Gouqing,LIU Ling,ZHANG Chong,ZHOU Yiqing

Journal of Xidian University. 2023, 50(3):  50-60.  doi:10.19665/j.issn1001-2400.2023.03.005

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The 6G era will realize the interconnection of all things and establish a multi-layer and full-coverage seamless connection.The Internet of Vehicles will be developed and deployed with the help of the 6G technology as a key area for the integration and intersection of communication,transportation,automobile and other industries.Aiming at the insufficient accuracy of the prediction of vehicle trajectories in the 6G Internet of Vehicles,this paper proposes a three-channel neural network model with the method of deep learning.This model takes the impacts of vehicle interaction information,target vehicle trajectories and lane structure information on trajectories into consideration.The long short-term memory network (LSTM) is used to extract the vehicle track information features,graph neural network (GNN) to extract interaction features between different vehicles,and the convolution neural network (CNN) is used to extract lane structure features.The predicted trajectory of the target vehicle is obtained by calculating the weight of the three-channel feature vector and the model is trained and tested by the NGSIM data set.The test results show that the three-channel network prediction method considering multi-dimension information has advantages in prediction accuracy and long time domain prediction compared with other prediction models,and the prediction accuracy is improved by more than 20%.Reducing the data transmission volume of the 6G Internet of Vehicles system can improve the user’s privacy security of the Internet of Vehicles system.

UAVs trajectory planning and power allocation based on the convergence of communication,sensing and computing

WU Yihao,QI Yanli,ZHOU Yiqing,CAI Qing,LIU Ling,SHI Jinglin

Journal of Xidian University. 2023, 50(3):  61-74.  doi:10.19665/j.issn1001-2400.2023.03.006

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Regional natural disasters often cause damage to ground-based communication facilities,and UAVs networks can act as aerial base stations to restore communications.Existing research has focused on how to provide efficient communication services to rescuers in static scenarios with a limited UAV spectrum and battery capacity.However,the location movement and service changes of communication rescuers in real scenarios lead to the failure of static schemes.To solve this problem,this paper proposes a collaborative UAVs scheduling algorithm through the convergence of communication,sensing and computing.First,we perform sensing the environmental information,i.e.,the rescuers' historical location information and service demand,in real-time to realize the prediction of the rescuers' future location and service demand and provide a priori information for the scheduling of UAVs.Second,an improved k-sums algorithm is proposed to deploy the UAVs' location concerning the UAV load constraint to achieve UAVs' load balancing.Furthermore,a reinforcement learning algorithm is used to optimize the UAVs' transmit power to ensure rescuers' communication service quality under a limited bandwidth.Compared to static scenarios where rescuer-UAV associations are established based on signal-to-noise ratios,the proposed UAV scheduling algorithm through the convergence of communication,sensing and computing in this paper can effectively improve network utility (network communication benefits minus communication costs) by 20%.The algorithm provides a guaranteed business experience for rescuers in emergency disaster relief scenarios.

Design of a sensing assisted robust transceiver in millimeter-wave systems

LI Mingrui,CHEN Li,WANG Weidong

Journal of Xidian University. 2023, 50(3):  75-82.  doi:10.19665/j.issn1001-2400.2023.03.007

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As an important branch of integrated sensing and communications technology,sensing-assisted communication provides a great potential to help estimate the millimeter-wave channel by utilizing massive sensor data.However,due to the limitation of sensor accuracy,the path angle information obtained by sensors such as a gyroscope is imperfect,which causes the deviation of beam direction and performance loss.The design of a robust transceiver is proposed to cope with the measurement error of sensors.In this design,the relationship between the geometric channel model and angular measurement error is established for the static user and mobile user respectively.Based on the error model,the analog beamforming design is obtained by maximizing the spectral efficiency.The closed-form solution to a statistics modified robust transceiver is further derived with the help of second-order Tylor expansion.Simulation results show that the proposed design could efficiently reduce the spectral efficiency performance loss compared with the transceiver which directly uses the observed angle values.The robust transceiver could achieve a better performance with a large measurement error.

BiGRU-LGB cloud load prediction model incorporating stacking framework

LIU Hui,DONG Xiyao,YANG Zhihan

Journal of Xidian University. 2023, 50(3):  83-94.  doi:10.19665/j.issn1001-2400.2023.03.008

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With the rapid development of cloud computing technology,more and more users deploy applications on cloud platforms.The scheduling of cluster resources within the platform can improve the actual utilization of the cloud platform data center,and efficient cloud platform load prediction is a key technology for solving the cluster resource scheduling problem,so this paper establishes a cloud load prediction model that incorporates the Stacking framework,multilayer bidirectional gated recurrent unit (BiGRU) network and light gradient boosting machine (LightGBM) algorithm.The structure of the model consists mainly of two kinds of learners:one is the primary learner,which uses a temporal encoding layer to process the original load sequence and reduces the training time and the number of hidden layers by taking advantage of the BiGRU network with fewer parameters and complete information learning,to learn the temporal dimension information in the load sequence with the original load sequence processed by feature engineering used to efficiently train the LightGBM algorithm to learn the load feature dimension information in the sequence.Then comes the other learner,which integrates the load information in temporal and feature dimensions using the GRU network to complete the training of the whole load prediction model.The prediction accuracy of the overall load prediction model is improved by joint learning of the two layers of learners.Experiments are conducted on Huawei Cloud Cluster dataset with the results showing that the prediction accuracy of the BiGRU-LGB model incorporating the Stacking is improved by about 13% and the training time overhead is reduced to some extent compared with the traditional single prediction models,such as BiGRU and LightGBM,and the existing combined prediction model GRU-LSTM.

Computer Science and Technology & Cyberspace Security

Algorithm for H.264/AVC adaptive watermarking

WANG Yong,HUANG Junyu,CHEN Yifang,ZHANG Jun,CHEN Xiaozong

Journal of Xidian University. 2023, 50(3):  95-104.  doi:10.19665/j.issn1001-2400.2023.03.009

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H.264/AVC video watermarking plays an important role in video copyright protection and information hiding.At present,the existing watermarking researches lack the consideration of the variable complexity of the video frame in the process of watermark embedding,which can easily lead to two problems:first,these watermark algorithms may not be able to fully embed the required watermark in frames with low picture complexity,and second,the redundancy of frames with high picture complexity cannot be fully utilized.Therefore,in this paper we analyze the factors of frame complexity in the process of video coding,and propose an adaptive watermarking algorithm based on the complexity of the video frame.In the proposed algorithm,the complexity degree of the current key frame is predicted using the total number of Intra_4×4 sub macroblocks of the previous key frame that satisfy the embedding conditions.The repetition time of embedding for each watermark bit is adjusted automatically according to the predicted complexity degree to achieve adaptiveness.A key frame with a prediction error is labelled an invalid frame.The watermark extraction is conducted by majority voting.Experimental results show that the proposed algorithm achieves watermark invisibility,and code stream stability as well,which outperforms the other recent algorithms.The algorithm is instructive to the research based on repeatedly embedding watermark bits.

Construction and encoding/decoding methods for the permutation codes correcting a single adjacent transposition error

HAN Hui,MU Jianjun,JIAO Xiaopeng,ZHAO Zhanzhan

Journal of Xidian University. 2023, 50(3):  105-111.  doi:10.19665/j.issn1001-2400.2023.03.010

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The rank modulation scheme is an encoding method that uses permutations to represent information for efficiently writing and storing data in flash memory storage.In this setup,the information is represented by the relative ranking but not the absolute value of the cell charge levels.Therefore,rank modulation codes can be constructed on permutations for alleviating the problems of cell over-injection and the adjacent transposition errors caused by charge leakage.Thus,permutation codes in the rank modulation scheme can improve the reliability of flash memory systems.However,for the multi-level flash memory systems,the existing rank-modulated permutation codes that correct a single adjacent transposition error lack effective coding and decoding algorithms.To solve this problem,a construction method for permutation codes correcting a single adjacent transposition error is proposed under the Kendall τ-distance metric by interleaving two kinds of permutation codes on the partitioned block set of the permutation symbol set.Then,an encoding algorithm for this kind of permutation codes is proposed by using unranking mapping and the interleaving technology of permutation codes.At the same time,an effective decoding algorithm for this kind of permutation codes is proposed by using ranking mapping of permutations.The proposed permutation codes have a simple structure.In addition,the effectiveness of the proposed construction method and its encoding/decoding algorithm is verified by some computational examples.

Binary sparse convolutional erasure correction coding

GUO Wangmei,LIU Dandan,CHEN Qi,GAO Jingliang

Journal of Xidian University. 2023, 50(3):  112-121.  doi:10.19665/j.issn1001-2400.2023.03.011

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Due to the requirement of low latency and high reliability in 6G wireless communication,we propose a coding scheme called binary sparse convolutional erasure correction coding (BSCECC),which can be utilized to transmit information in a binary erasure channel.The coding scheme is a combination of convolutional coding and low-density parity-check (LDPC) coding.Data packages are uniformly grouped and convolutionally coded by a matrix with blocks the generating matrix of an LDPC code,binary random matrices and zero matrices.Under the coding scheme,the sink node can decode the information while it is receiving the data packages.Hence,the latency of the whole system can be largely shortened.We analyze the average package delay and average maximum package delay of the scheme with the result verified by simulation.Simulations also show that our scheme performs 30.8% better in transmission rate than the systematic LDPC under the same reliability,and better in reliability than the Raptor10 code under the same code rate.Thus,our coding scheme can be applied to the scenarios with the requirements of low latency and high reliability.

Multi-scale object detection algorithm combined with super-resolution reconstruction technology

WANG Juan,LIU Zishan,WU Minghu,CHEN Guanhai,GUO Liquan

Journal of Xidian University. 2023, 50(3):  122-131.  doi:10.19665/j.issn1001-2400.2023.03.012

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At present,most object detection algorithms have poor performance because of the large span of scales,leading to errors and omissions.To address the above issues,a multi-scale object detection algorithm combined with the super-resolution technology is proposed in this paper.First,based on the one-stage YOLO framework,the super-resolution module is employed to the neck network during the process of multi-scale feature fusion,which avoids further loss of detailed features in deeper layers.Second,the attention module is integrated in the shallower layers to focus on the channel information on object contour features and to suppress irrelevant features,thus improving the superficial representational capacity.Finally,ablation and comparative experiments are carried out on PASCAL VOC 2007 and MS COCO 2017 public datasets.Experimental results show that the proposed module can improve the detection performance.Compared with the current contrast algorithms,not only can the average accuracy rate of small,medium and large objects be increased by 1.20%,1.20% and 1.30%,but also the average recall rate can be improved by 4.20%,3.50% and 4.20%,respectively.

New grouped piggybacking framework for distributed storage

WANG Yubo,SUN Rong,LIU Jingwei

Journal of Xidian University. 2023, 50(3):  132-141.  doi:10.19665/j.issn1001-2400.2023.03.013

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With the rapid development of Internet technology,the explosive growth of the global data volume has led to a serious challenge to the reliability and availability of distributed storage systems.As an efficient data fault-tolerant technology,the piggybacking framework has become a research hotspot in recent years.Most piggybacking frameworks reduce the repair bandwidth by sacrificing the number of substripes and the repair degree,in which the amount of data saved in the process of repairing ineffective nodes usually cannot effectively improve the disk reading efficiency.For the above situation,a novel grouped piggybacking framework is proposed,which can reduce both the number of substripes and the repair degree of information nodes while ensuring a low repair bandwidth,thereby improving the Input/Output(I/O) performance of distributed storage systems.In the novel framework,the parity nodes are divided into two parts,and information symbols and parity symbols are grouped according to certain rules so as to be piggybacked into the corresponding parity nodes.The process is simple to implement.Using the proposed framework,information nodes and parity nodes can be effectively repaired at the same time,which not only reduces the number of substripes,but also makes it to have a strong comprehensive repair ability when the number of parity nodes is large.Compared with other piggybacking frameworks,the new grouped piggybacking framework can better balance the repair bandwidth,the repair degree and the number of substripes,and is suitable for application in actual systems.

RELIC-GNN:an efficient state register identification algorithm

DONG Meng,GAO Yiming,PAN Weitao,QIU Zhiliang,YANG Jianlei,DI Zhixiong,ZHENG Ling

Journal of Xidian University. 2023, 50(3):  142-150.  doi:10.19665/j.issn1001-2400.2023.03.014

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With the horizontalization of integrated circuit (IC) design and globalization of manufacturing,a large number of hardware ICs produced by third-party vendors are used in the chip design,which raises concerns about design backdoors/hardware Trojan horses being inserted into chips.Reverse engineering can recover the design netlist of IC chips,and designers can determine whether the design functions have been tampered with by extracting high-level descriptions and analyzing the key logic.However,the poor readability of the reverse netlist with its data paths and control logic mixed makes it difficult to abstract the high-level descriptions quickly and accurately.In this paper,the problem is equivalently defined as the classification problem of the netlist path structure,and an efficient state register identification algorithm based on the graph neural network is proposed.First,pre-processing of the netlist is conducted to eliminate the differences of the process library and to reduce the modeling complexity.Second,the netlist is modeled as the directed graph and the path structure of each register is extracted.Then the graph neural network model is used to map corresponding features of each register with the path structure inputted.Finally,the features are clustered so as to classify the registers into status registers and control registers.Experimental results prove that the algorithm can run correctly on a million-gate netlist with the average recognition accuracy reaching 88.37%,which is improved in recognition accuracy,operation speed and migratability compared with the existing algorithms.

Information and Communications Engineering & Electronic Science and Technology

Review on polarimetric SAR terrain classification methods using deep learning

XIE Wen,HUA Wenqiang,JIAO Licheng,WANG Ruonan

Journal of Xidian University. 2023, 50(3):  151-170.  doi:10.19665/j.issn1001-2400.2023.03.015

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Polarimetric synthetic aperture radar (PolSAR) is one of the main sources of remote sensing data,because it can realize all-day and all-weather imaging.Terrain classification is an important research in the field of PolSAR data interpretation,which has become one of the hotspots in the research field and has been widely used in both military and civilian applications.In recent years,deep learning has achieved remarkable results in many research fields,some of which have been made in the field of PolSAR image processing.Compared with traditional image classification methods,the deep learning method has the advantages of automatic extracting deep features,strong generalization and high accuracy.In this paper,the existing terrain classification methods for the PolSAR image based on deep learning are reviewed.According to the different network models in deep learning,the research on PolSAR terrain classification is described in detail from three aspects,that is,deep belief network,sparse autoencoder network and convolutional neural network.Finally,the advantages and disadvantages of PolSAR terrain classification based deep learning are summarized in comparison with classical classification methods.Meanwhile,the development trend of PolSAR terrain classification is analyzed and discussed.

Parallel UTD method for calculation of electromagnetic problems in city environments

WANG Nan,CHEN Guiqi,ZHAO Yanan,ZHANG Yu

Journal of Xidian University. 2023, 50(3):  171-181.  doi:10.19665/j.issn1001-2400.2023.03.016

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Electromagnetic situation prediction in complex city environments is the basic problem in wireless communication spectrum utilization,urban system engineering planning and electromagnetic compatibility design,which is of both engineering practical value and theoretical exploration value.The uniform geometrical theory of diffraction (UTD) in computational electromagnetics is an effective method to analyze electromagnetic problems in electrically large environments,and the electromagnetic situation in urban environment can be simulated and analyzed effectively after building a parallel strategy.By introducing the analytical hexahedron model and using reflection,edge diffraction and its related ray types like double diffraction and reflection-diffraction to describe the propagation of electromagnetic waves in the non-sighted regions,an analyzing and tracing method for double edge diffraction is presented with practical examples given and the validity and accuracy verified by comparison with the results from Moment Methods and from actual measurement,respectively.In order to shorten the calculation time of the ray tracing module,to improve the calculation efficiency and to further expand the calculating scale of an engineering problem that can be applied,a parallel calculation strategy with a better scalability is designed,with which calculation tasks are distributed through the similarity of the ray types to ensure a better balance of parallel calculations.The scalability of this parallel strategy is presented by discussing the parallel efficiency and parallel speedup ratio.The engineering practicality of the parallel UTD method in this paper is shown through comparison with the actual measurement results.

Design and experimental verification of self-interference suppression for full-duplex measurement and control links

YU Wei,ZHANG Yi,ZHANG Zhiya,SHEN Ying,PAN Wensheng,SHAO Shihai

Journal of Xidian University. 2023, 50(3):  182-191.  doi:10.19665/j.issn1001-2400.2023.03.017

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In order to solve the contradiction between the increasing aerospace measurement and control services and the shortage of spectrum resources for aerospace measurement and control links,an implementation architecture for co-time co-frequency full-duplex measurement and control links is proposed,designed,and experimentally verified.The links adopt a multi-domain self-interference cancellation mechanism that combines the spatial domain,radio frequency domain and digital domain,which improves the spectral efficiency under the condition that the ranging accuracy should not be reduced.For spatial self-interference cancellation,an integrated parabolic antenna with high isolation in a common surface feed is designed,and structured choke measures are adopted between the transceiver elements.The coupling effect between the transmitting and receiving elements is reduced.The isolation of the antenna system is improved.The self-interference cancellation in the radio frequency domain adopts the multi-tap self-interference cancellation method.A multi-tap reconstruction radio frequency signal model is established,and the minimum norm solution of the multi-tap coefficients is given aiming at minimizing residual interference power.In the digital domain,a nonlinear self-interference signal model is established and the least squares estimation is adopted to obtain nonlinear coefficients.In addition,this paper verifies the feasibility and effectiveness of the multi-domain self-interference cancellation architecture of the full-duplex measurement and control system through simulation and experiments.The results show that the total self-interference suppression reaches 156.5 dB with accurate distance measurement.

Estimation of robust parameter in the presence of conformal polarization sensitive array element failure

LAN Xiaoyu,JIANG Lai,GENG Manghe,WANG Yupeng

Journal of Xidian University. 2023, 50(3):  192-201.  doi:10.19665/j.issn1001-2400.2023.03.018

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The estimation performance of traditional polarization-direction of arrival (DOA) parameter estimation methods will deteriorate seriously or even fail when some array elements fail.Meanwhile,error data from some failed array elements are introduced in the face of an increasingly complex electromagnetic environment.It is also a significant challenge to the robustness of the methods.For the above issues,to fully explore the impact of some array element failure and error data on the parameter estimation performance of the methods,two cases of partial elements complete failure and partial elements error probability are considered in the conformal polarization sensitive array (CPSA),and a spatially two-dimensional joint sparse polarization-DOA parameter estimation method based on variational sparse Bayesian learning (VSBL) is proposed.First,a two-dimensional sparse received array signal model based on the CPSA is established by using spatial sparse characteristics of the source.Second,the singular value decomposition method is used to reduce the dimension of the array output matrix,so as to reduce the computation load of the method.Subsequently,a robust DOA estimation is obtained by using the VSBL.Finally,the polarization parameter estimation of the source is obtained by the modulus constraint method.Simulation results validate that the proposed method has a relatively more robust parameter estimation performance and a higher estimation accuracy and a higher angle resolution in the case of array element failure.

Improved arithmetic optimization algorithm for sparse planar arrays synthesis

GUO Qiang,LIU Congye,WANG Yani,WANG Yong,CHERNOGOR Leonid

Journal of Xidian University. 2023, 50(3):  202-212.  doi:10.19665/j.issn1001-2400.2023.03.019

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An array antenna synthesis algorithm based on an improved arithmetic optimization algorithm is proposed to address the problems of sidelobe level suppression and null steering synthesis of sparse planar array radiation pattern.First,the math optimizer accelerated in the arithmetic optimization algorithm is reconstructed using a nonlinear function to balance the exploitation and exploration process weights.Second,top three best individuals are used instead of the current best optimal individuals for exploration and exploitation and an elite variation strategy is introduced to enhance the ability of the algorithm to escape from the local optimum and improve the convergence accuracy of the algorithm.Finally,an adaptive matrix mapping law is proposed to judge the current array element distribution,and if it does not satisfy the minimum array element spacing constraint,it is adjusted by an adjustment strategy to avoid infeasible solutions while ensuring the degrees of freedom of the array element.Compared with the existing algorithms in the literature,the improved arithmetic optimization algorithm has improved the optimization accuracy and stability of both single-peak and multi-peak standard test functions; In the experiments of sparse planar array sidelobe level suppression and null synthesis,the proposed algorithm can synthesize a better peak sidelobe level and null depth,which proves the effectiveness of the proposed algorithm.