A Rasr Analysis Method Based on F-Scan Sar

Liu, Yuqing; Cui, Lei; Wang, Pengbo; Guo, Yanan

doi:10.1109/igarss46834.2022.9883288

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Raw data from the Axivity sensor were downloaded through the OpenMovement software (OMGui) and saved in a binary CWA file. An open-source MATLAB (MathWorks, Natick, MA, USA) script by Felix Liu [ 29 ], which uses methods published by Brønd, Andersen, and Arvidsson [ 30 ] was used to generate ActiGraph equivalent activity counts per minute from Axivity accelerometers. It should be noted this algorithm also down-samples the Axivity data to 30Hz to make the data comparable to the ActiGraph results.…”

Section: Methodsmentioning

confidence: 99%

Validation of physical activity levels from shank-placed Axivity AX6 accelerometers in older adults

Gafoor,

Ruder,

Kobsar

2024

PLoS ONE

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This cross-sectional study aimed to identify and validate cut-points for measuring physical activity using Axivity AX6 accelerometers positioned at the shank in older adults. Free-living physical activity was assessed in 35 adults aged 55 and older, where each participant wore a shank-mounted Axivity and a waist-mounted ActiGraph simultaneously for 72 hours. Optimized cut-points for each participant’s Axivity data were determined using an optimization algorithm to align with ActiGraph results. To assess the validity between the physical activity assessments from the optimized Axivity cut-points, a leave-one-out cross-validation was conducted. Bland-Altman plots with 95% limits of agreement, intraclass correlation coefficients (ICC), and mean differences were used for comparing the systems. The results indicated good agreement between the two accelerometers when classifying sedentary behaviour (ICC = 0.85) and light physical activity (ICC = 0.80), and moderate agreement when classifying moderate physical activity (ICC = 0.67) and vigorous physical activity (ICC = 0.70). Upon removal of a significant outlier, the agreement was slightly improved for sedentary behaviour (ICC = 0.86) and light physical activity (ICC = 0.82), but substantially improved for moderate physical activity (ICC = 0.81) and vigorous physical activity (ICC = 0.96). Overall, the study successfully demonstrated the capability of the resultant cut-point model to accurately classify physical activity using Axivity AX6 sensors placed at the shank.

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Section: Methodsmentioning

confidence: 99%

Validation of physical activity levels from shank-placed Axivity AX6 accelerometers in older adults

Gafoor,

Ruder,

Kobsar

2024

PLoS ONE

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“…Compared to MEB or SCORE, which also transmit a wide beam, a higher signal-to-noise ratio (SNR) can be obtained and the peak power requirement can be alleviated. Furthermore, the range ambiguity-to-signal ratio (RASR) can be effectively reduced [15]. Liu et al propose that range-frequency bandpass filters can effectively isolate range ambiguities introduced by pencil beam scanning, and they provide well-considered waveform design criteria [16].…”

Section: Introductionmentioning

confidence: 99%

A Signal Model Based on the Space–Time Coding Array and a Novel Imaging Method Based on the Hybrid Correlation Algorithm for F-SCAN SAR

Liu,

Wang,

Men

et al. 2023

Remote Sensing

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The F-SCAN principle is a better alternative to the scan-on-receive technique (SCORE) based on digital beamforming (DBF), which can avoid low gain caused by a conventional broad beam in the case of a wide swath. In F-SCAN SAR, a pencil beam scans the entire target area from far to near, providing high energy independent of the position and ensuring a low range ambiguity-to-signal ratio (RASR). Moreover, echo compression can be achieved via appropriate system parameter configuration, significantly shortening the receive window and reducing the amount of data. A wider range swath can, therefore, be achieved. However, for this novel F-SCAN SAR working mode, signal modeling and imaging processing are key issues that needed to be addressed. In this paper, the far-field synthetic antenna pattern of the space–time coding array (STCA) is first derived and analyzed, based on which the signal modeling of the F-SCAN SAR is carried out. Then, according to the signal model and echo characteristics, a novel imaging processing method based on the hybrid correlation algorithm is presented for the F-SCAN SAR. First, the dechirp operation is performed to compensate for the quadratic phase of the range time. The range compressed result is obtained after a range Fourier transform, where different range targets are successfully separated and range aliasing is avoided. Then, the modified azimuth reference function is correlated with the echo at each range cell to complete range cell migration correction (RCMC) and azimuth compensation. The received signal parameters and the Doppler parameters of each range cell are derived to update the azimuth reference function. Finally, accurate focused results are obtained in the range-frequency, azimuth-time domain. The simulation results indicate that the signal model based on the STCA can satisfy the requirements of the F-SCAN principle, and the proposed imaging algorithm can complete the precise focusing processing of the F-SCAN SAR echo.

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