scholarly journals The magnitude image from the 50% compressed PPH (R) shows an increased noise floor visible in the shadows.

2015 ◽  
Author(s):  
John Topolski
Keyword(s):  
Noise Floor ◽  
Magnitude Image

3D localization for subcentimeter-sized devices

2021 ◽  
Vol 64 (3) ◽  
pp. 117-125
Author(s):  
Rajalakshmi Nandakumar ◽  
Vikram Iyer ◽  
Shyamnath Gollakota
Keyword(s):  
Metropolitan Area ◽  
Ultra Wideband ◽  
Phase Information ◽  
Noise Floor ◽  
Localization System ◽  
Coin Cell ◽  
3D Localization ◽  
Button Cell ◽  
Iot Devices ◽  
5 Ghz

The vision of tracking small IoT devices runs into the reality of localization technologies---today it is difficult to continuously track objects through walls in homes and warehouses on a coin cell battery. Although Wi-Fi and ultra-wideband radios can provide tracking through walls, they do not last more than a month on small coin and button cell batteries because they consume tens of milliwatts of power. We present the first localization system that consumes microwatts of power at a mobile device and can be localized across multiple rooms in settings such as homes and hospitals. To this end, we introduce a multiband backscatter prototype that operates across 900 MHz, 2.4 GHz, and 5 GHz and can extract the backscatter phase information from signals that are below the noise floor. We build subcentimeter-sized prototypes that consume 93 μW and could last five to ten years on button cell batteries. We achieved ranges of up to 60 m away from the AP and accuracies of 2, 12, 50, and 145 cm at 1, 5, 30, and 60 m, respectively. To demonstrate the potential of our design, we deploy it in two real-world scenarios: five homes in a metropolitan area and the surgery wing of a hospital in patient pre-op and post-op rooms as well as storage facilities.

Enhanced Sensitivity and Reduced Noise Floor in Magnetoelectric Laminate Sensors by an Improved Lamination Process

2011 ◽  
Vol 94 (11) ◽  
pp. 3738-3741 ◽  
Author(s):  
Menghui Li ◽  
David Berry ◽  
Jaydip Das ◽  
David Gray ◽  
Jiefang Li ◽  
...  
Keyword(s):  
Noise Floor ◽  
Enhanced Sensitivity ◽  
Lamination Process

Techniques for Schumann Resonance Measurements: A Comparison of Four Amplifiers With a Noise Floor Estimate

2015 ◽  
Vol 64 (10) ◽  
pp. 2759-2768 ◽  
Author(s):  
Jose A. Gazquez Parra ◽  
Manuel Fernandez Ros ◽  
Nuria Novas Castellano ◽  
Rosa M. Garcia Salvador
Keyword(s):  
Schumann Resonance ◽  
Noise Floor

Digital resampling diversity sparsity constrained-wavefield reconstruction using single-magnitude image

2015 ◽  
Vol 40 (8) ◽  
pp. 1842 ◽  
Author(s):  
Yair Rivenson ◽  
Maya Aviv (Shalev) ◽  
Aryeh Weiss ◽  
Hana Panet ◽  
Zeev Zalevsky
Keyword(s):  
Magnitude Image

scholarly journals Signature of Arctic first-year ice melt pond fraction in X-band SAR imagery

2017 ◽  
Vol 11 (2) ◽  
pp. 755-771 ◽  
Author(s):  
Ane S. Fors ◽  
Dmitry V. Divine ◽  
Anthony P. Doulgeris ◽  
Angelika H. H. Renner ◽  
Sebastian Gerland
Keyword(s):  
Wind Speed ◽  
Correlation Coefficient ◽  
First Year ◽  
Backscatter Signal ◽  
Noise Floor ◽  
Ice Melt ◽  
Wind Speeds ◽  
Melt Pond ◽  
Melt Ponds ◽  
X Band

Abstract. In this paper we investigate the potential of melt pond fraction retrieval from X-band polarimetric synthetic aperture radar (SAR) on drifting first-year sea ice. Melt pond fractions retrieved from a helicopter-borne camera system were compared to polarimetric features extracted from four dual-polarimetric X-band SAR scenes, revealing significant relationships. The correlations were strongly dependent on wind speed and SAR incidence angle. Co-polarisation ratio was found to be the most promising SAR feature for melt pond fraction estimation at intermediate wind speeds (6. 2 m s−1), with a Spearman's correlation coefficient of 0. 46. At low wind speeds (0. 6 m s−1), this relation disappeared due to low backscatter from the melt ponds, and backscatter VV-polarisation intensity had the strongest relationship to melt pond fraction with a correlation coefficient of −0. 53. To further investigate these relations, regression fits were made both for the intermediate (R2fit = 0. 21) and low (R2fit = 0. 26) wind case, and the fits were tested on the satellite scenes in the study. The regression fits gave good estimates of mean melt pond fraction for the full satellite scenes, with less than 4 % from a similar statistics derived from analysis of low-altitude imagery captured during helicopter ice-survey flights in the study area. A smoothing window of 51 × 51 pixels gave the best reproduction of the width of the melt pond fraction distribution. A considerable part of the backscatter signal was below the noise floor at SAR incidence angles above  ∼  40°, restricting the information gain from polarimetric features above this threshold. Compared to previous studies in C-band, limitations concerning wind speed and noise floor set stricter constraints on melt pond fraction retrieval in X-band. Despite this, our findings suggest new possibilities in melt pond fraction estimation from X-band SAR, opening for expanded monitoring of melt ponds during melt season in the future.

scholarly journals Comparison of Buoy-Mounted and Bottom-Moored ADCP Performance at Gray’s Reef

2007 ◽  
Vol 24 (2) ◽  
pp. 270-284 ◽  
Author(s):  
Harvey E. Seim ◽  
Catherine R. Edwards
Keyword(s):  
High Frequency ◽  
Major Axis ◽  
National Data ◽  
Noise Floor ◽  
The North ◽  
Experimental Configuration ◽  
Adcp Measurements ◽  
Adcp Data ◽  
Adcp Observations ◽  
East West

Abstract Simultaneous ADCP profile measurements are compared over a 2-month period in late 2003. One set of measurements comes from a National Data Buoy Center (NDBC) buoy-mounted ADCP, the other from a bottom-mounted, upward-looking ADCP moored roughly 500 m from the buoy. The study was undertaken to evaluate the proficiency of an experimental configuration by NDBC; unfortunately, the ADCP was not optimally configured. The higher temporally and vertically resolved bottom-mounted ADCP data are interpolated in time and depth to match the buoy-mounted ADCP measurements. It is found that the two ADCP measurements are significantly different. The buoy-mounted measurements are affected by high-frequency (<10 h period) noise that is vertically coherent throughout the profiles. This noise results in autospectra that are essentially white, unlike the classic red spectra formed from the bottom-mounted ADCP observations. The spectra imply a practical noise floor of 0.045 m s−1 for the buoy-mounted system. Contamination by surface waves is the likely cause of this problem. At tidal frequencies the buoy-mounted system underestimates major axis tidal current magnitude by 10%–40%; interference from the buoy chain and/or fish or plankton are considered the most likely cause of the bias. The subtidal velocity field (periods greater than 40 h) is only partially captured; the correlation coefficient for the east–west current is 0.49 and for the north–south current is 0.64.

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