Statistical Analysis and Modeling of Low-Frequency Radio Noise and Improvement of Low-Frequency Communications

Chrissan, D. A.

doi:10.21236/ada360417

Cited by 27 publications

(25 citation statements)

References 40 publications

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“…An example of the difference between a data pdf and the Rayleigh distribution is shown in Figure 2, for July 1986 data from Thule, Greenland, in the 35.6-to 37.6-kHz range. Data pdf's are found to decay with an inverse power law for large values (out to some limit set by the dynamic range of the system, as discussed by Chrissan [1998] Note for a = 2 that this is the Rayleigh distribution. For a close to 2 it is essentially a Rayleigh distribution but with a heavier tail.…”

Section: Amplitude Probability Distributionsmentioning

confidence: 77%

A comparison of low‐frequency radio noise amplitude probability distribution models

Chrissan¹,

Fraser–Smith²

2000

Radio Science

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Abstract. One of the most commonly modeled statistics in atmospheric radio noise studies is the noise envelope voltage amplitude probability distribution (APD). Although a number of models have been introduced to characterize atmospheric noise envelope APDs, the quantity of real data that exist to verify their accuracy is somewhat limited, especially in the ELF and VLF bands. This paper presents the results of a statistical analysis in which thousands of hours of ELF/VLF noise are processed to derive APDs, which are then compared with various APD models to determine which of the models is most accurate. The error criterion used to find the optimal parameters of each APD model, as well as to compare the models against each other, is the expected value of the log error squared (where the log error is the difference in decibels between the data histogram and the model histogram). This criterion provides a means by which the models may be evaluated and compared numerically. The most accurate model is found to depend on geographic location, time of year and day, bandwidth, and center frequency, but two of the simplest models (i.e., each with only two parameters) are found to give extremely good performance in general. These are the Hall and alpha-stable (or a-stable) models, both of which approximate the Rayleigh distribution for low-amplitude values but decay with an inverse power law for high-amplitude values. This paper concludes that the Hall model is the optimal choice in terms of accuracy and simplicity for locations exposed to heavy sferic activity (e.g., lower latitudes) and the a-stable model is best for locations relatively distant from heavy sferic activity (e.g., the polar regions). IntroductionNaturally occurring radio noise above approximately 100 MHz is well modeled for most applications as a Gaussian random process; however, radio noise below 100 MHz (denoted atmospheric noise) is impulsive in nature and is not well modeled as Gaussian. Individual atmospheric events (mainly sferics, the electromagnetic emissions from lightning) produce large impulses in the noise waveform, so atmospheric noise consists of high-amplitude impulses superimposed on a background of low-level

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Section: Amplitude Probability Distributionsmentioning

confidence: 77%

A comparison of low‐frequency radio noise amplitude probability distribution models

Chrissan¹,

Fraser–Smith²

2000

Radio Science

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“…Atmospheric noise is mostly produced by lightning discharges and it propagates worldwide. The waveform received by typical receivers features strong and clustered impulses [15] with a Gaussian background noise, and the power spectrum is flat at low frequencies. It is non-stationary and varies based on seasons, days, geographic locations, and geology [16].…”

Section: Analysis Of the Emi And Designmentioning

confidence: 99%

“…The man-made EMI mainly consists of IFHs, which are generated by power lines and vary by days and geographic locations. The power spectrum comprises spikes at specific frequencies, which are positive integer multiples of the fundamental frequency [15]. Therefore, the total EMI appearing in the TEC downlink sites is directional and random in the spatial domain, impulsive and non-stationary in the time domain, and, in the frequency domain, flat for atmospheric noise while uneven for the EMI produced by IFHs.…”

Section: Analysis Of the Emi And Designmentioning

confidence: 99%

“…It is usually much more difficult to perform EMI cancelling with conventional fixed-parameter filters owing to inadequate priori knowledge, despite several statistical models [15][16][17][18] of the EMI. Adaptive noise cancelling (ANC), which adjusts its own weight parameters adaptively and thus, requires little or even no priori knowledge [19], seems appropriate for TEC EMI cancelling at downlink sites.…”

Section: Analysis Of the Emi And Designmentioning

confidence: 99%

“…Thus, the optimization problem becomes the optimization of d w and N that minimizes the bandwidth for the primary receiving SCM, which is subjected to a given volume and sensitivity threshold as (14) For reference SCMs, the optimization goal is to minimize the volume, which is subjected to a given bandwidth and sensitivity threshold as (15) Both r s and L s in (6), (7), and (8) are dependent variables of d w and N according to (11) and (12). This is the same as the case for…”

Section: Solution To the Scm Optimization Problemmentioning

confidence: 99%

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Design of the Magnetic Field Sensing System for Downlink Signal Reception and Interference Cancelling for Through-the-Earth Communication

Zhao¹,

Jiang²,

Zhang³

et al. 2016

Journal of Magnetics

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A magnetic field sensing system with a single primary sensor and multiple reference sensors deployed locally and orthogonally, was proposed for downlink signal reception and interference cancelling for Through-theEarth Communication (TEC). This paper mathematically analyzes a design optimization process for a search coil magnetometer (SCM), and applies that process to minimize the bandwidth of the primary SCM for TEC signal reception and the volume of reference SCMs for multiple distributions. The primary SCM achieves a 3-dB bandwidth of 7 Hz, a sensitivity threshold of 120 fT/√Hz, and a volume of 2.32 × 10 −4 m 3 . The entire sensing system volume is as small as 10 −2 m 3. Experiments with interference from industrial frequency harmonics demonstrated an average of 36 dB and 18 dB improvements in signal-to-interference ratio and signal-tointerference plus noise ratio, respectively, using multichannel recursive-least-squares algorithm. Thus, the proposed sensing system can reduce the interference effectively and allows reliable downlink signal reception.

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Historical and Technical Overview of SLF/ELF Electromagnetic Wave Propagation

Pan¹,

Li²

2013

Advanced Topics in Science and Technology in China

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In general, very low frequency (VLF) refers to electromagnetic waves with frequencies from 3 kHz to 30 kHz; ultra low frequency (ULF) refers to frequency range between 300 Hz to 3 kHz; the frequency range of 30 Hz to 300 Hz is defined as super low frequency (SLF); and the frequency below 30 Hz is referred to as extremely low frequency (ELF). In some early studies, alternative definitions may have been used with frequency range of 3 Hz to 3 kHz generally referred to ELF. In this book, the emphasis is on SLF (30-300 Hz) and ELF (below 30 Hz) ranges, using the MKS system of units and the time dependency e −iωt . Medium Characteristics of SLF/ELF Wave PropagationDue to the long wavelength, SLF/ELF waves, when excited and propagated on and near the Earth's surface, will cover lithosphere, atmosphere and ionosphere, whose electromagnetic characteristics differ significantly in their propagation paths. The permeability of the atmosphere and ionosphere is approximately μ 0 , the permeability in free space, as well as that of the lithosphere, except in the regions that are rich in iron, nickel, cobalt, etc. Therefore, the permeability of the lithosphere can be also treated as μ 0 , if neither the transmitter nor the receiver is located in mineral-rich areas. The atmosphere is a non-conductive medium with negligible conductivity, whose permittivity is close to ε 0 , the permittivity of free space. While the lithosphere is conductive, the conductivity of sea water σ sea is in the range of 2.5-5.5 S/m, and the conductivity σ g of rock and soil in the range of 10 −2 -10 −4 S/m. The dielectric constant ε r of the lithosphere does not have large effects on the propagation of SLF/ELF waves.The ionosphere is composed of partially ionized gas, which includes electrons, ions, and electrically neutral particles, above 70 km or so from the sea level. The electrons and ions make the ionosphere conductive, thus the majority of the energy of the incident VLF/SLF/ELF waves will be reflected by the ionosphere. When both the transmitter and the receiver are located in the space between the ground and the W. Pan, K. Li, Propagation of SLF/ELF Electromagnetic Waves,

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Statistical Analysis and Modeling of Low-Frequency Radio Noise and Improvement of Low-Frequency Communications

Cited by 27 publications

References 40 publications

A comparison of low‐frequency radio noise amplitude probability distribution models

A comparison of low‐frequency radio noise amplitude probability distribution models

Design of the Magnetic Field Sensing System for Downlink Signal Reception and Interference Cancelling for Through-the-Earth Communication

Historical and Technical Overview of SLF/ELF Electromagnetic Wave Propagation

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