From the MPD file each meteor trail event is given a identifier, like "0000X". When we write [meteor_id] we are referring to one of these identifiers.
All files in the analysis output were generated with MATLAB 2019b and is in "mat" format. They can be opened using the freely available Python package "scipy".
An example script that opens a "mat" file is:
import scipy.io as sio
mat = sio.loadmat('ME20181213.0000X_meteor.mat')
After this line all the data available in MATLAB is also accessible in Python as numpy arrays arranged in dictionaries according to the fields of the MATLAB struct.
There are three types of files in the analysis output:
- [meteor_id]_ipp_select.mat
This file contains one variable called "ipp". It is a vector of integers that describe which radar pulses from the SKiYMET ME files (i.e. ME20181213.[meteor_id]) were considered a part of the meteor event.
- ME20181213.[meteor_id]_meteor.mat
This file contains a structure called "meteor". This is the output from the MUSIC analysis of the meteor trail SKiYMET ME files (i.e. ME20181213.[meteor_id]). The "meteor" struct has the following fields:
used_indecies: A list of Boolean values (0,1) that indicate if the radar pulse in the ipp field were above the noise floor and used in subsequent analysis. Same length as "ipp".
ipp: Same list of radar pulses as in [meteor_id]_ipp_select.mat
power: Received power in non physical units.
k_vector: MUSIC output DOA for each radar pulse as normalized wave vectors.
azimuth: Azimuth measured as degrees East from North of the MUSIC DOA output.
elevation: Elevation measured as degrees East from North of the MUSIC DOA output.
MUSIC_peaks: MUSIC peak value for each radar pulse.
coherent_azimuth: Azimuth measured as degrees East from North of the MUSIC DOA output when applied on the coherently integrated spatial correlation matrix.
coherent_MUSIC_peak: MUSIC peak of the coherently integrated spatial correlation matrix.
coherent_elevation: Elevation measured as degrees East from North of the MUSIC DOA output when applied on the coherently integrated spatial correlation matrix.
coherent_k_vector: MUSIC output DOA coherently integrated spatial correlation matrix as an normalized wave vector.
- ME20181213.[meteor_id]_sim.mat
This file contains a structure called "results". This is the output from the DOA ambiguity analysis and Monte-Carlo simulations of DOA determination. The "result" struct has the following fields:
inclusion_radius: The radius used to include a DOA determination output to the corresponding matrix element in discretization.
output_points: Compiled list of first and second order ambiguities to the chosen k0 (picked from the coherently integrated spatial correlation matrix results).
output_distances: List of all distances between first and second order ambiguities and the chosen k0
all_distances: Ambiguity distances of all_points.
all_points: Complete list of ambiguities (i.e. first and second order). Low probability ambiguities are filtered away and the list is sorted to create the output_points fields.
seeds: Chosen ambiguities to k0.
seeds_d: Distances between chosen ambiguities and k0.
ambiguity_list_points: List of ambiguities to each of the seeds.
ambiguity_list_distances: List of distances between the seeds ambiguities and the seeds.
k0: The chosen k0.
SNRdB: The SNRs used in dB for each Monte-Carlo iteration. The SNR was set to a distribution to emulate the measurements so for each "seed" there is a vector of SNRs that describe the injected noise in that DOA determination.
seed_n: Number of chosen ambiguities to k0
dists: DOA determination Monte-Carlo iteration data for each seed as input DOA and for each SNR. This is the "raw" output of the simulation.
P: Calculated probability matrices as a function of SNR using the inclusion_radius, output_points and the dists variables.
peaks: Distribution of MUSIC peak value for each Monte-Carlo iteration.