New Observations by Wind Profiling Radars

Yamamoto, Masayuki

doi:10.5772/37140

Cited by 3 publications

(3 citation statements)

References 86 publications

(101 reference statements)

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“…For adaptive signal processing of RIM, we used the Capon method (Capon, 1969). In order to achieve the best range resolution with minimal range sidelobe effects, the Capon method solves the constrained optimization problem in which the brightness (i.e., received signal intensity produced using RIM) is minimized with the constraint of constant gain at the desired subrange (Palmer et al, 1999;Yamamoto et al, 2012). Because the Capon method satisfies both high range resolution and simple calculation, it is widely used for adaptive signal processing of RIM.…”

Section: Measurement Resultsmentioning

confidence: 99%

“…The limitation in the range resolution is a major obstacle to improving turbulence parameter retrieval. Range imaging (RIM), which is also referred to as frequency domain interferometric imaging (FII), is a technique that improves the range resolution down to several tens of meters by using frequency diversity and adaptive signal processing (Luce et al, 2001;Palmer et al, 1999;Yamamoto, 2012). Measurements using 50-MHz atmospheric radars have demonstrated that RIM is useful for resolving turbulence and wind perturbations triggered by Kelvin-Helmholtz instability (Mega et al 2010;Luce et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Though fences are used for mitigating clutter, they do not always suppress clutter sufficiently. Adaptive clutter rejection, which controls side lobes using multiple receivers and adaptive signal processing, is a promising technique that suppresses clutter effectively (Cheong et al, 2006;Kamio et al, 2004;Yamamoto, 2012). Yu et al (2010) proposed RIM with adaptive clutter suppression by using both multiple receivers and multiple frequencies.…”

Section: Introductionmentioning

confidence: 99%

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Development of a digital receiver for range imaging atmospheric radar

Yamamoto

Fujita

Aziz

et al. 2014

Journal of Atmospheric and Solar-Terrestrial Physics

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In this paper, we describe a new digital receiver developed for a 1.3-GHz range imaging atmospheric radar. The digital receiver comprises a generalpurpose software-defined radio receiver referred to as the Universal Software Radio Peripheral 2 (USRP2) and a commercial personal computer (PC). The receiver is designed to collect received signals at an intermediate frequency (IF) of 130 MHz with a sample rate of 10 MS s −1. The USRP2 digitizes IF received signals, produces IQ time series, and then transfers the IQ time series to the PC through Gigabit Ethernet. The PC receives the IQ time series, performs range sampling, carries out filtering in the range direction, decodes the phase-modulated received signals, integrates the received signals in time, and finally saves the processed data to the hard disk drive (HDD).

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Section: Measurement Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of a digital receiver for range imaging atmospheric radar

Yamamoto

Fujita

Aziz

et al. 2014

Journal of Atmospheric and Solar-Terrestrial Physics

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Error estimation of spectral parameters for high-resolution wind and turbulence measurements by wind profiler radars

et al. 2014

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Using numerical simulations, we investigated a method for calculating the spectral parameters from Doppler spectra collected by high-resolution wind profiler radars (WPRs). Because high-resolution WPRs collect a huge amount of Doppler spectra, calculations must be simple and fast. The proposed method has two steps. In the first step, the echo range (R echo ), in which the Doppler spectrum point with peak intensity is contained and all the smoothed Doppler spectrum points have intensities that are greater than the noise intensity, was determined. For producing the smoothed Doppler spectrum, a running average with equal weight (RA) or multitaper method (MTM) was used. In the second step, the spectral parameters were calculated using the Doppler spectrum points within R echo . By comparing the performance of the computation methods using RA and MTM, we concluded that the computation method using RA is more suitable because it has better estimation performance for small spectrum widths and the calculations are faster. Estimation error of the spectral parameters depends on the determination accuracy of the Doppler spectrum peak and R echo . Furthermore, for the case of a 512-point Doppler spectrum and 13-point RA, the estimation errors tend to be independent of the signal-to-noise ratio (SNR) when the peak level of the Doppler spectrum (p est ) is ∼8 dB or more greater than the noise intensity. For p est of < ∼8 dB, the estimation errors are well correlated to p est and the SNR. Therefore, the number of incoherent integration times should be determined by considering the SNR and p est .

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