AbstractSince the distributed MIMO radar (DMR) has widely spread transmitters and receivers, it can provide higher target detection probability, as well as superior target tracking and localization performance than the monostatic/bistatic radar systems. An effective radar resource allocation scheme can optimise the DMR system parameter and obtain better system performance. In this paper, a critical but limited system resource is optimised, that is, select a subset of the active radar antennas. In this scenario, the convolutional neural network of the antenna subset selection for target localization (LCNN‐ASS) algorithm in the DMR is proposed based on two free switching policies. The proposed algorithm is immune to the failure of a single policy and selects antenna subsets from the entire sets with a remarkable computational speed. Therefore, the proposed algorithm increases the flexibility of resource scheduling over traditional algorithms. Simulation experiments and performance analysis demonstrate the localization performance and flexibility of the LCNN‐ASS algorithm.

More from our Archive

• DOI: 10.3390/en16155682 2023

  Summary of Health-State Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy

  Xinwei Sun, Yang Zhang, Yongcheng Zhang, Licheng Wang, Kai Wang

• DOI: 10.3390/en16155809 2023

  Review of Family-Level Short-Term Load Forecasting and Its Application in Household Energy Management System

  Ping Ma, Shuhui Cui, Mingshuai Chen, Shengzhe Zhou, Kai Wang

• DOI: 10.3390/s23156949 2023

  Visual Intelligence in Precision Agriculture: Exploring Plant Disease Detection via Efficient Vision Transformers

  Sana Parez, Naqqash Dilshad, Norah Saleh Alghamdi, Turki M. Alanazi, Jong Weon Lee

• DOI: 10.3390/s23156762 2023

  HealthLock: Blockchain-Based Privacy Preservation Using Homomorphic Encryption in Internet of Things Healthcare Applications

  Aitizaz Ali, Bander Ali Saleh Al-rimy, Faisal S. Alsubaei, Abdulwahab Ali Almazroi, Abdulaleem Ali Almazroi

• DOI: 10.3390/machines11080774 2023

  Sustainable Crop Protection via Robotics and Artificial Intelligence Solutions

  Vasiliki Balaska, Zoe Adamidou, Zisis Vryzas, Antonios Gasteratos

• DOI: 10.3390/mi14071480 2023

  A Review of the Residual Stress Generation in Metal Additive Manufacturing: Analysis of Cause, Measurement, Effects, and Prevention

  Nabin Bastola, Muhammad P. Jahan, Nithin Rangasamy, Chandra Sekhar Rakurty

• DOI: 10.3390/electronics12143155 2023

  In-Memory Computing Integrated Structure Circuit Based on Nonvolatile Flash Memory Unit

  Peilong Xu, Dan Lan, Fengyun Wang, Incheol Shin

• DOI: 10.3390/s23146507 2023

  CNN-CNN: Dual Convolutional Neural Network Approach for Feature Selection and Attack Detection on Internet of Things Networks

  Basim Alabsi, Mohammed Anbar, Shaza Rihan

• DOI: 10.1039/d3mh00882g 2023

  Defect-stabilized and oxygen-coordinated iron single-atom sites facilitate hydrogen peroxide electrosynthesis

  Taotao Gao, Lu Qiu, Minghao Xie, Zhaoyu Jin, Panpan Li, Guihua Yu

• DOI: 10.3390/electronics12153300 2023

  Anomaly Detection in 6G Networks Using Machine Learning Methods

  Mamoon M. Saeed, Rashid A. Saeed, Maha Abdelhaq, Raed Alsaqour, Mohammad Kamrul Hasan, Rania A. Mokhtar