For multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, a joint timing synchronization and frequency offset acquisition algorithm based on fractional Fourier transform ...For multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, a joint timing synchronization and frequency offset acquisition algorithm based on fractional Fourier transform (FRFT) is proposed. The linear frequency modulation signals superimposed on the data signals are used as the training signals. By performing FRFT on the received signals and searching the peak value of the FRFT results, the receiver can realize timing synchronization and frequency offset acquisition simultaneously. Compared with the existing methods, the proposed algorithm can provide better timing synchronization performance and larger frequency offset acquisition range even under multi-path channels with low signal to noise ratio. Theoretical analysis and simulation results prove this point.展开更多
The BeiDou software receiver uses the fast Fourier transform(FFT)to perform the acquisition.The Doppler shift estimation accuracy should be less than 500 Hz to ensure satellite signals to enter a locked state in the t...The BeiDou software receiver uses the fast Fourier transform(FFT)to perform the acquisition.The Doppler shift estimation accuracy should be less than 500 Hz to ensure satellite signals to enter a locked state in the tracking loop.Since the frequency step is usually 500 Hz or larger,the Doppler shift estimation accuracy cannot guarantee that satellite signals are brought into a stable tracking state.The straightforward solutions consist in increasing the sampling time and using zero-padding to improve the frequency resolution of the FFT.However,these solutions intensify the complexity and amount of computation.The contradiction between the acquisition accuracy and the computational load leads us to research for a more simple and effective algorithm,which achieves fine acquisition by a look-up table.After coarse acquisition using the parallel frequency acquisition(PFA)algorithm,the proposed algorithm optimizes the Doppler shift estimation through the look-up table method based on the FFT results to improve the acquisition accuracy of the Doppler shift with a minimal additional computing load.When the Doppler shift is within the queryable range of the table,the proposed algorithm can improve the Doppler shift estimation accuracy to 50 Hz for the BeiDou B1I signal.展开更多
Background:Accurate mapping of tree species is highly desired in the management and research of plantation forests,whose ecosystem services are currently under threats.Time-series multispectral satellite images,e.g.,f...Background:Accurate mapping of tree species is highly desired in the management and research of plantation forests,whose ecosystem services are currently under threats.Time-series multispectral satellite images,e.g.,from Landsat-8(L8)and Sentinel-2(S2),have been proven useful in mapping general forest types,yet we do not know quantitatively how their spectral features(e.g.,red-edge)and temporal frequency of data acquisitions(e.g.,16-day vs.5-day)contribute to plantation forest mapping to the species level.Moreover,it is unclear to what extent the fusion of L8 and S2 will result in improvements in tree species mapping of northern plantation forests in China.Methods:We designed three sets of classification experiments(i.e.,single-date,multi-date,and spectral-temporal)to evaluate the performances of L8 and S2 data for mapping keystone timber tree species in northern China.We first used seven pairs of L8 and S2 images to evaluate the performances of L8 and S2 key spectral features for separating these tree species across key growing stages.Then we extracted the spectral-temporal features from all available images of different temporal frequency of data acquisition(i.e.,L8 time series,S2 time series,and fusion of L8 and S2)to assess the contribution of image temporal frequency on the accuracy of tree species mapping in the study area.Results:1)S2 outperformed L8 images in all classification experiments,with or without the red edge bands(0.4%–3.4%and 0.2%–4.4%higher for overall accuracy and macro-F1,respectively);2)NDTI(the ratio of SWIR1 minus SWIR2 to SWIR1 plus SWIR2)and Tasseled Cap coefficients were most important features in all the classifications,and for time-series experiments,the spectral-temporal features of red band-related vegetation indices were most useful;3)increasing the temporal frequency of data acquisition can improve overall accuracy of tree species mapping for up to 3.2%(from 90.1%using single-date imagery to 93.3%using S2 time-series),yet similar overall accuracies were achieved using S2 time-series(93.3%)and the fusion of S2 and L8(93.2%).Conclusions:This study quantifies the contributions of L8 and S2 spectral and temporal features in mapping keystone tree species of northern plantation forests in China and suggests that for mapping tree species in China's northern plantation forests,the effects of increasing the temporal frequency of data acquisition could saturate quickly after using only two images from key phenological stages.展开更多
基金supported by the National Natural Science Foundation of China(60672047).
文摘For multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, a joint timing synchronization and frequency offset acquisition algorithm based on fractional Fourier transform (FRFT) is proposed. The linear frequency modulation signals superimposed on the data signals are used as the training signals. By performing FRFT on the received signals and searching the peak value of the FRFT results, the receiver can realize timing synchronization and frequency offset acquisition simultaneously. Compared with the existing methods, the proposed algorithm can provide better timing synchronization performance and larger frequency offset acquisition range even under multi-path channels with low signal to noise ratio. Theoretical analysis and simulation results prove this point.
基金the Open Project of State Key Laboratory of Automotive Simulation and Control,Jilin University(20161108)the National Natural Science Foundation of China(51505221)the Fundamental Research Funds for the Central Universities(NS2019022).
文摘The BeiDou software receiver uses the fast Fourier transform(FFT)to perform the acquisition.The Doppler shift estimation accuracy should be less than 500 Hz to ensure satellite signals to enter a locked state in the tracking loop.Since the frequency step is usually 500 Hz or larger,the Doppler shift estimation accuracy cannot guarantee that satellite signals are brought into a stable tracking state.The straightforward solutions consist in increasing the sampling time and using zero-padding to improve the frequency resolution of the FFT.However,these solutions intensify the complexity and amount of computation.The contradiction between the acquisition accuracy and the computational load leads us to research for a more simple and effective algorithm,which achieves fine acquisition by a look-up table.After coarse acquisition using the parallel frequency acquisition(PFA)algorithm,the proposed algorithm optimizes the Doppler shift estimation through the look-up table method based on the FFT results to improve the acquisition accuracy of the Doppler shift with a minimal additional computing load.When the Doppler shift is within the queryable range of the table,the proposed algorithm can improve the Doppler shift estimation accuracy to 50 Hz for the BeiDou B1I signal.
基金supported by National Natural Science Foundation of China(Grant No.41901382)Open Fund of State Key Laboratory of Remote Sensing Science(Grant No.OFSLRSS201917)the HZAU research startup fund(No.11041810340,No.11041810341).
文摘Background:Accurate mapping of tree species is highly desired in the management and research of plantation forests,whose ecosystem services are currently under threats.Time-series multispectral satellite images,e.g.,from Landsat-8(L8)and Sentinel-2(S2),have been proven useful in mapping general forest types,yet we do not know quantitatively how their spectral features(e.g.,red-edge)and temporal frequency of data acquisitions(e.g.,16-day vs.5-day)contribute to plantation forest mapping to the species level.Moreover,it is unclear to what extent the fusion of L8 and S2 will result in improvements in tree species mapping of northern plantation forests in China.Methods:We designed three sets of classification experiments(i.e.,single-date,multi-date,and spectral-temporal)to evaluate the performances of L8 and S2 data for mapping keystone timber tree species in northern China.We first used seven pairs of L8 and S2 images to evaluate the performances of L8 and S2 key spectral features for separating these tree species across key growing stages.Then we extracted the spectral-temporal features from all available images of different temporal frequency of data acquisition(i.e.,L8 time series,S2 time series,and fusion of L8 and S2)to assess the contribution of image temporal frequency on the accuracy of tree species mapping in the study area.Results:1)S2 outperformed L8 images in all classification experiments,with or without the red edge bands(0.4%–3.4%and 0.2%–4.4%higher for overall accuracy and macro-F1,respectively);2)NDTI(the ratio of SWIR1 minus SWIR2 to SWIR1 plus SWIR2)and Tasseled Cap coefficients were most important features in all the classifications,and for time-series experiments,the spectral-temporal features of red band-related vegetation indices were most useful;3)increasing the temporal frequency of data acquisition can improve overall accuracy of tree species mapping for up to 3.2%(from 90.1%using single-date imagery to 93.3%using S2 time-series),yet similar overall accuracies were achieved using S2 time-series(93.3%)and the fusion of S2 and L8(93.2%).Conclusions:This study quantifies the contributions of L8 and S2 spectral and temporal features in mapping keystone tree species of northern plantation forests in China and suggests that for mapping tree species in China's northern plantation forests,the effects of increasing the temporal frequency of data acquisition could saturate quickly after using only two images from key phenological stages.