Ionogram analysis using fuzzy segmentation and connectedness techniques

Abstract

Abstract. We present a new procedure for the analysis of ionograms that evolves from methods developed for image analysis and utilizes techniques based on the concepts of fuzzy segmentation and connectedness. Ionogram traces are often not "crisply" defined, and we demonstrate that it is possible to approximate them as fuzzy subsets within the two-dimensional space defined by the time-of-flight and the radio frequency. A real number between 0 and i is assigned to each pixel in an ionogram, thereby defining the membership of that pixel to each of the fuzzy subsets, effectively creating a "gray scale" ionogram. In this context, ionogram analysis becomes a problem in fuzzy geometry, and various geometrical properties, including the topological concepts of connectedness, adjacency, height, width, and major axis, can be defined. It is shown that not only does the fuzzy segmentation process separate signals from the chaotic noise background that often characterizes ionograms, but that it can also be applied to classify ionospheric echoes according to standard nomenclature, e.g., normal E, F, or E8 layers. Furthermore, in reference to the skeleton or thinning extraction procedures employed in imaging processing, the fuzzy connectedness between echoes in selected segments can be used to determine the primary layers that are characteristic of vertical incidence ionospheric reflection. This information can be provided as input to automatic scaling or true-height inversion routines, which can then be used to derive either the standard URSI set of ionospheric parameters or the electron density distribution in the overhead ionosphere, or both. This fuzzy algorithm approach has been successfully applied to midlatitude ionogram data from advanced digital ionospheric sounders operated by the National Central University and Utah State University.

Comparisons of GPS/MET retrieved ionospheric electron density and ground based ionosonde data

“…Synoptic ionospheric measurements derived from ionograms have provided valuable information both for high frequency (HF) propagation work and for studies related to the physics of the ionosphere. In this paper, an automatic ionogram scaling algorithm incorporating a fuzzy classification method (Tsai and Berkey, 2000) has been applied to obtain the virtual height-frequency trace and identify ionospheric parameters, including f min , f o E, f o F 1 , f o F 2 , h E, h E s , h F, f x I , and M(3000)F 2 . Even more information can be obtained from these fuzzy segmented records by inverting the virtual height-frequency trace to obtain an electron density profile, a process called true-height analysis.…”

Identificação do perfil da ionosfera utilizando lógica nebulosa: Parte I

“…A lógica nebulosa pode ser aplicada a esta etapa conforme trabalhos anteriores, [10,1] onde através da defuzificaçãoé possível extrair um valor de uma região.…”

Automatic Recognition Method of Layer Paths in the Ionogram Based on Mathematical Morphology Method and Inversion Technology

“…People extract parameters related to ionosphere from ionogram, such as group path and frequency, which truly reflect the state of ionosphere, and the process of extracting the feature parameters is called recognition [2][3][4][5]. Recognition of ionograms initially relies on the manual operation performed by experienced workers.…”