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Ordered Sequence Detection and Barrier Signal Design for Digital Pulse Interval Modulation in Optical Wireless Communications
摘要： This paper proposes an ordered sequence detection (OSD) for digital pulse interval modulation (DPIM) in optical wireless communications. Leveraging the sparsity of DPIM sequences, OSD shows comparable performance to the optimal maximum likelihood sequence detection (MLSD) with much lower complexity. Compared with the widely adopted sample-by-sample optimal threshold detection (OTD), it considerably improves the bit error rate (BER) performance by mitigating error propagation. Moreover, this paper proposes a barrier signal-aided digital pulse interval modulation (BDPIM), where the last of every K symbols is allocated with more power as an inserted barrier signal. BDPIM with OSD (BDPIM-OSD) can limit the error propagation between two adjacent barriers. To reduce the storing delay when using OSD to detect extremely large packets, we propose BDPIM with a combination of OTD and OSD (BDPIM-OTD-OSD), within which long sequences are cut into pieces and separately detected. Approximate upper bounds of the average BER performance of DPIM-OTD, DPIM-OSD, BDPIM-OSD and BDPIM-OTD-OSD are analyzed. Simulations are conducted to corroborate our analysis. Optimal parameter settings are also investigated in uncoded and coded systems by simulations. Simulation results show that the proposed OSD and BDPIM bring significant improvement in uncoded and coded systems over various channels.
关键词： signal design,error propagation,bit error rate,ordered sequence detection,Digital pulse interval modulation
更新于2025-09-23 15:23:52
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Error Budget for Geolocation of Spectroradiometer Point Observations from an Unmanned Aircraft System
摘要： We investigate footprint geolocation uncertainties of a spectroradiometer mounted on an unmanned aircraft system (UAS). Two microelectromechanical systems-based inertial measurement units (IMUs) and global navigation satellite system (GNSS) receivers were used to determine the footprint location and extent of the spectroradiometer. Errors originating from the on-board GNSS/IMU sensors were propagated through an aerial data georeferencing model, taking into account a range of values for the spectroradiometer field of view (FOV), integration time, UAS flight speed, above ground level (AGL) flying height, and IMU grade. The spectroradiometer under nominal operating conditions (8° FOV, 10 m AGL height, 0.6 s integration time, and 3 m/s flying speed) resulted in footprint extent of 140 cm across-track and 320 cm along-track, and a geolocation uncertainty of 11 cm. Flying height and orientation measurement accuracy had the largest influence on the geolocation uncertainty, whereas the FOV, integration time, and flying speed had the biggest impact on the size of the footprint. Furthermore, with an increase in flying height, the rate of increase in geolocation uncertainty was found highest for a low-grade IMU. To increase the footprint geolocation accuracy, we recommend reducing flying height while increasing the FOV which compensates the footprint area loss and increases the signal strength. The disadvantage of a lower flying height and a larger FOV is a higher sensitivity of the footprint size to changing distance from the target. To assist in matching the footprint size to uncertainty ratio with an appropriate spatial scale, we list the expected ratio for a range of IMU grades, FOVs and AGL heights.
关键词： geolocation,error propagation,UAV,spectroradiometer,footprint,UAS,aerial spectroscopy
更新于2025-09-19 17:15:36
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Total soil carbon assessment: linking field, lab, and landscape through VNIR modelling
摘要： Context Point based measurements provide only a limited overview of landscape variation in measured properties. Upscaling of measurements from point to landscape comes with challenges particularly considering error propagation. Objectives We investigated the impact of using proximal derived measurements of soil total carbon taken at point locations on upscaling to landscape levels. Methods 1087 soil samples across Florida, USA were collected, laboratory (LAB) analysed for total carbon (TC), and then measured using visible-/near-infrared (VNIR) spectroscopy. Proximal TC values were generated through chemometric modelling using random forest (RF) and partial least squares (PLS) regression. These three datasets were then upscaled to the State of Florida, USA using ordinary kriging and compared. Results R2 (RPD) values for the PLS and RF chemometric models were 88% (2.96) and 91% (3.23), respectively. All 3 spatial models had an accuracy of 54% on an independent validation dataset, with greater than 70% accuracy if predicted values were considered within the interpolation variance range. When comparing spatial interpolations derived from the proximally measured samples, only 18% of the PLS versus 51% of the RF fell within a range of 0.05 logTC (g kg-1) of the LAB measured interpolations. Conclusions Using proximal sampling and modelling provides comparable output to laboratory measured soil TC measurements at point level, but when upscaled to landscape level the selection of proximal modelling method will impact the spatial interpolations derived. The error propagation within sequential modelling must be considered particularly when one wishes to use sequential modelling to analyse change in environmental properties.
关键词： Geostatistics,Proximal sampling,Error propagation,Total carbon (TC),Spectroscopy,Upscaling
更新于2025-09-04 15:30:14