Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning最新文献

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Co-Operative Team Control of Autonomous Vehicles by Improved Leader Follower Algorithm in WSN Environment 基于改进Leader Follower算法的WSN环境下自动驾驶车辆协同团队控制

Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning Pub Date : 2020-10-22 DOI: 10.1145/3432291.3432302

Shiwei Li

{"title":"Co-Operative Team Control of Autonomous Vehicles by Improved Leader Follower Algorithm in WSN Environment","authors":"Shiwei Li","doi":"10.1145/3432291.3432302","DOIUrl":"https://doi.org/10.1145/3432291.3432302","url":null,"abstract":"When the vehicle-mounted sensor is damaged or the external environment is strongly interfered with the working environment, the traditional Robot Leader-Follower control method will not work [1-9]. In view of the above problems, a network-based formation control algorithm is proposed. The research work of the algorithm mainly includes two parts: First, the establishment of a nonlinear incremental control kinematics model for mobile robot formation, research and implementation of formation algorithm Formation process; Second, rewrite the controller under the environment of wireless sensor network. The controller is optimized for the formation control algorithm in the network environment, and redistributes the computing tasks based on the traditional algorithm, a formation retainer is designed to enable the robot formation to adjust the formation in real time The calculation task is not only realized by the Leader, Followers also participate in part of the calculation, reducing the burden and calculation amount of the Leader, thereby making the formation control more coordinated and flexible, and realizes the formation process of the robot. This paper designs a multi robot formation control and tracking algorithm based on improved leader follower in wireless sensor environment. In this algorithm, the robot formation is composed of a leader and several followers. All robots are equipped with multiple sensors, but only the leader robot has the navigation function and enough computing power to implement the proposed algorithm. Experiment shows that under the same conditions, software simulation and physical formation experiments are performed on the standard Leader-Follower control algorithm and proposed algorithm respectively. By comparing and analyzing the effectiveness of the two formation methods, the proposed method has higher stability, smaller error, shorter formation time, and the formation algorithm is feasible.","PeriodicalId":126684,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131916244","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

Signal Detection and Modulation Classification for Satellite Communications 卫星通信信号检测与调制分类

Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning Pub Date : 2020-10-22 DOI: 10.1145/3432291.3432297

Verónica Toro-Betancur, Augusto Carmona Valencia, José Ignacio Marulanda-Bernal

{"title":"Signal Detection and Modulation Classification for Satellite Communications","authors":"Verónica Toro-Betancur, Augusto Carmona Valencia, José Ignacio Marulanda-Bernal","doi":"10.1145/3432291.3432297","DOIUrl":"https://doi.org/10.1145/3432291.3432297","url":null,"abstract":"Amateur ground stations are gaining increasing importance as both academic and hobby activities. However, due to the limited energy resources available in amateur satellites, ground stations need to be located in isolated places in order to establish a reliable communication. This usually implies limited Internet access. Hence, ground stations need to be able to recognize incoming signal without completely relying on an Internet connection. For this reason, we propose an algorithm to estimate parameters such as amplitude, center frequency, bandwidth and modulation type for amateur radio applications. For signal detection, we use an absolute-valued sinc approximation which estimates the center frequency and bandwidth of signals with signal-to-noise ratios over -6 dB with a precision of 5% and 2% respectively. In addition, Support Vector Machines (SVM) binary classifiers are used in series to classify the four most common modulation types used in amateur satellites. With accuracies over 90%, SVM outperforms solutions based on Artificial Neural Networks.","PeriodicalId":126684,"journal":{"name":"Proceedings of the 2020 3rd International Conference on Signal Processing and Machine Learning","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125155901","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

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