2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)最新文献_第3页

UAV Fast Signal Detection Algorithm with Convolutional Neural Network 基于卷积神经网络的无人机快速信号检测算法

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984389

Lejing Ma, B. Lian, Yangyang Liu, Haobo Li, Quanquan Wang, Jiaming Zhang

{"title":"UAV Fast Signal Detection Algorithm with Convolutional Neural Network","authors":"Lejing Ma, B. Lian, Yangyang Liu, Haobo Li, Quanquan Wang, Jiaming Zhang","doi":"10.1109/ICSPCC55723.2022.9984389","DOIUrl":"https://doi.org/10.1109/ICSPCC55723.2022.9984389","url":null,"abstract":"As UAVs tend to be miniaturized and invisible, the successful recognition rate of traditional UAV identification methods such as time-frequency analysis, video surveillance, and radio interference is getting lower and lower. Aiming at this problem, Firstly, the traditional time-frequency analysis method is used to preprocess the data, obtain the time-frequency spectrum of the data, and construct the training set of convolutional neural network. Then build VGG network and residual network model based on maximum pooling, and use sample training set to train the sample model. Finally, the time-frequency spectrum of the obtained remote control signal is input to the learning model, and the classification and recognition results can be output.Finally, based on the Y550 software radio platform, three UAVs including parrot, DJ-m100 and DJ-a3 were measured. The experimental results show that when the learning rate is 0.1, the recognition rate of the method proposed in this paper can reach more than 97%. Under different learning rates, the recognition rate is still above 95%, which is greatly improved compared to the traditional time-frequency analysis and recognition method, and has a strong application prospect.","PeriodicalId":346917,"journal":{"name":"2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123341116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Approximate Subgraph Mining Algorithm for Social Networks 社交网络的近似子图挖掘算法

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984437

Jian Feng, Yuwen Wang, Yajiao Wang

引用次数: 0

A LEO Spaceborne-Airborne Bistatic SAR Imaging Experiment 低轨星载-机载双基地SAR成像实验

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984341

Shuyao Zhang, Feifeng Liu, Zhanze Wang, Chenghao Wang, Ruihong Lv, Di Yao

引用次数: 0

Deep Learning Networks for Optimal Power Compensation in IR-UWB Channel IR-UWB信道中最优功率补偿的深度学习网络

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984297

Bo Lu, Mei-Chun Lin, Sai Ma, Shuai Song, Yuchao Wang

{"title":"Deep Learning Networks for Optimal Power Compensation in IR-UWB Channel","authors":"Bo Lu, Mei-Chun Lin, Sai Ma, Shuai Song, Yuchao Wang","doi":"10.1109/ICSPCC55723.2022.9984297","DOIUrl":"https://doi.org/10.1109/ICSPCC55723.2022.9984297","url":null,"abstract":"Considering the characteristics of wireless signal amplitude distribution in Impulse Radio Ultra-wideband (IR-IR-UWB) fading channel, the Automatic Gain Control (AGC) loop of IR-UWB communication system can obtain the optimal reference power to maximize the signal-to-noise ratio of the output signal of analog-to-digital converter (ADC) in AGC loop. In a multipath channel, the channel impulse response of IR- UWB signal arrives in clusters, which is different from OFDM signal amplitude obeying Rayleigh distribution, the arrival time of pulses in each cluster obeys Poisson distribution, and the amplitude obeys exponential distribution. Because of characteristics of IR-UWB signal distribution, under the condition of certain signal power, the different AGC reference power will make different ADC sampling noise power. An AGC optimal reference power is obtained by analyzing ADC sampling noise power with different reference power when the ADC sampling output SNR is maximum. According to IEEE 802.15.3a channel model, 4 different conditions IR-UWB channel impulse response amplitude distributions are simulated in this paper. Using normalization method, the average ADC sampling output SNR with different AGC reference power is simulated, and the maximum SNR result corresponding to reference power is the optimal one. An AGC optimal reference power with different ADC parameters is obtained by analysis method based on amplitude distribution. The IEEE 802.15.3a channel model is classified by a deep Convolutional Neural Network (CNN) so as to obtain channel model parameter under different channel impulse response. The simulation results show that the CNN has a high classification probability for 4 different channel model and an AGC loop stably outputs at the optimal reference power.","PeriodicalId":346917,"journal":{"name":"2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126550030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A MIMO Radar Imaging Method Based on Hankel Matrix Nuclear Norm Minimization Tensor Completion 基于Hankel矩阵核范数最小化张量补全的MIMO雷达成像方法

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984323

Pengcheng Wan, W. Feng, Yanzhong Hao, Hailong Wang

引用次数: 0

Variable-step self-adaptive filtering algorithm applied to active sonar self-interference suppression 应用于主动声纳自干扰抑制的变步长自适应滤波算法

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984452

Weijie Ning, Xiaomin Zhang, Yang Yu, Mingguang Li, Yi Zhang, Ping Dong

{"title":"Variable-step self-adaptive filtering algorithm applied to active sonar self-interference suppression","authors":"Weijie Ning, Xiaomin Zhang, Yang Yu, Mingguang Li, Yi Zhang, Ping Dong","doi":"10.1109/ICSPCC55723.2022.9984452","DOIUrl":"https://doi.org/10.1109/ICSPCC55723.2022.9984452","url":null,"abstract":"In order to reduce the conflict between the higher rate of convergence of the self-adaptive filtering algorithm and the lower misalignment rate, a variable step normalized self-adaptive filtering algorithm is proposed and applied to the active sonar emission leakage self-interference suppression system. This algorithm gets the expression of the optimal iterative variable step based on that the least mean square error exists between the optimal weight vector and the estimated value is the and then eliminates the impact of inputting noise estimation bias on the algorithm. And at last, we put the power of estimative residual SI signal into the expression of the optimal iterative variable-step and get the updated weight vector formula of the variable step normalized self-adaptive filtering algorithm. The filters can use different step length self-adaptively in different updated status. The results of the simulation experiments show that, compared to the traditional algorithm of the normalized minimum mean square error, the proposed algorithm has a lower average steady-state misalignment rate.","PeriodicalId":346917,"journal":{"name":"2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)","volume":"8 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133665896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Suppress False Alarms by Exploiting Ambiguity Features of Targets with Machine Learning 基于机器学习的目标模糊特征抑制虚警

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984300

Zhifei Wang, Junpeng Yu, Yuhao Yang, Lin Jin

引用次数: 1

Research on underground speech enhancement technology based on generative adversarial network 基于生成对抗网络的地下语音增强技术研究

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984342

Zhaozhao Zhang, Zhongqi Liu, Xiao Chen, Kangle Sun

引用次数: 0

Distributed Target Tracking Method Based on Variational Bayesian with Systematic Biases 基于系统偏差变分贝叶斯的分布式目标跟踪方法

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984349

Yong Jin, Qiancheng Zhang, L. Zhou, Yue Liu

{"title":"Distributed Target Tracking Method Based on Variational Bayesian with Systematic Biases","authors":"Yong Jin, Qiancheng Zhang, L. Zhou, Yue Liu","doi":"10.1109/ICSPCC55723.2022.9984349","DOIUrl":"https://doi.org/10.1109/ICSPCC55723.2022.9984349","url":null,"abstract":"The unknown and time-varying systematic biases and measurement noise and abnormal non-Gaussian process noise will result in low target tracking accuracy. Additionally, traditional Kalman filtering based on Gaussian noise is difficult to meet the requirements of the estimation accuracy. In this paper, a variational Bayesian-based adaptive Kalman filter algorithm under distributed fusion framework (DF-VBAKF) is proposed. It first a priori model of probability density is constructed based on the properties of multiple unknown parameters. And then, through variational Bayesian and adaptive Kalman filter, the posterior distribution probabilities of target state, system biases, and noise covariance are jointly updated by using standard variational method. Finally, collecting local state estimation and its covariance from different platform, the control center fuses them according to the covariance intersection fusion strategy, and feeds results back to platform. The simulation results show that the proposed algorithm has higher estimation accuracy for systematic biases, noise and target state, leading to improvement of target tracking accuracy, too.","PeriodicalId":346917,"journal":{"name":"2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134176356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

JYOLO: Joint Point Cloud for Autonomous Driving 3D Object Detection JYOLO:自动驾驶3D目标检测的联合点云

2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Pub Date : 2022-10-25 DOI: 10.1109/ICSPCC55723.2022.9984261

Hongpeng Tian, Lunlun Guo

引用次数: 1