本文推导了激光多普勒测速 (LDA)频率估计的Cram r Rao下限。得到了不同于目前LDA工作者广为使用的基于纯频谐波信号的分析结果。得出以下结论 :对大多数LDA测量而言 ,其频率估计的Cram r Rao下限将是同样情况下纯频谐波信号频率估计的 ...本文推导了激光多普勒测速 (LDA)频率估计的Cram r Rao下限。得到了不同于目前LDA工作者广为使用的基于纯频谐波信号的分析结果。得出以下结论 :对大多数LDA测量而言 ,其频率估计的Cram r Rao下限将是同样情况下纯频谐波信号频率估计的 2到 6倍。展开更多
Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various applications.Therefore,a novel localisation algorithm is proposed for noisy range measurements in IIoT networks...Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various applications.Therefore,a novel localisation algorithm is proposed for noisy range measurements in IIoT networks.The position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position estimation.Therefore,the AMs selection algorithm for the localisation of BMs in the IIoT network is introduced.Only those AMs will participate in the localisation process,which increases the accuracy of the final location estimate.Then,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement noise.The results are compared with the state‐of‐the‐art and verified through numerous simulations.展开更多
文摘Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various applications.Therefore,a novel localisation algorithm is proposed for noisy range measurements in IIoT networks.The position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position estimation.Therefore,the AMs selection algorithm for the localisation of BMs in the IIoT network is introduced.Only those AMs will participate in the localisation process,which increases the accuracy of the final location estimate.Then,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement noise.The results are compared with the state‐of‐the‐art and verified through numerous simulations.
