API: Structs

The following is a full listing of the custom structs within the package.

Magnetic Anomaly Maps

MagNav.Map — Type

Map{T2 <: AbstractFloat}

Abstract type Map for a magnetic anomaly map.

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MagNav.MapS — Type

MapS{T2 <: AbstractFloat} <: Map{T2}

Scalar magnetic anomaly map struct. Subtype of Map.

Field	Type	Description
`info`	String	map information
`map`	Matrix{`T2`}	`ny` x `nx` scalar magnetic anomaly map [nT]
`xx`	Vector{`T2`}	`nx` map x-direction (longitude) coordinates [rad]
`yy`	Vector{`T2`}	`ny` map y-direction (latitude) coordinates [rad]
`alt`	`T2`	map altitude [m]
`mask`	BitMatrix	`ny` x `nx` mask for valid (not filled-in) map data

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MagNav.MapSd — Type

MapSd{T2 <: AbstractFloat} <: Map{T2}

Scalar magnetic anomaly map struct used to store an additional altitude map for drape maps. Subtype of Map.

Field	Type	Description
`info`	String	map information
`map`	Matrix{`T2`}	`ny` x `nx` scalar magnetic anomaly map [nT]
`xx`	Vector{`T2`}	`nx` map x-direction (longitude) coordinates [rad]
`yy`	Vector{`T2`}	`ny` map y-direction (latitude) coordinates [rad]
`alt`	Matrix{`T2`}	`ny` x `nx` altitude map [m]
`mask`	BitMatrix	`ny` x `nx` mask for valid (not filled-in) map data

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MagNav.MapS3D — Type

MapS3D{T2 <: AbstractFloat} <: Map{T2}

3D (multi-level) scalar magnetic anomaly map struct. Subtype of Map.

Field	Type	Description
`info`	String	map information
`map`	Array{`T2,3`}	`ny` x `nx` x `nz` 3D (multi-level) scalar magnetic anomaly map [nT]
`xx`	Vector{`T2`}	`nx` map x-direction (longitude) coordinates [rad]
`yy`	Vector{`T2`}	`ny` map y-direction (latitude) coordinates [rad]
`alt`	Vector{`T2`}	`nz` map altitude levels [m]
`mask`	BitArray{3}	`ny` x `nx` x `nz` mask for valid (not filled-in) map data

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MagNav.MapV — Type

MapV{T2 <: AbstractFloat} <: Map{T2}

Vector magnetic anomaly map struct. Subtype of Map.

Field	Type	Description
`info`	String	map information
`mapX`	Matrix{`T2`}	`ny` x `nx` x-direction magnetic anomaly map [nT]
`mapY`	Matrix{`T2`}	`ny` x `nx` y-direction magnetic anomaly map [nT]
`mapZ`	Matrix{`T2`}	`ny` x `nx` z-direction magnetic anomaly map [nT]
`xx`	Vector{`T2`}	`nx` map x-direction (longitude) coordinates [rad]
`yy`	Vector{`T2`}	`ny` map y-direction (latitude) coordinates [rad]
`alt`	`T2`	map altitude [m]
`mask`	BitMatrix	`ny` x `nx` mask for valid (not filled-in) map data

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MagNav.Map_Cache — Type

Map_Cache

Map cache struct, mutable.

Field	Type	Description
`maps`	Vector{`MapS`{`Float64`}}	vector of `MapS` scalar magnetic anomaly map structs
`map_sort_ind`	Vector{`Int64`}	`maps` indices sorted by altitude
`fallback`	`MapS`{`Float64`}	fallback `MapS` scalar magnetic anomaly map struct
`map_cache`	Dict	`maps` cache of scalar map interpolation functions (`f(lat,lon)`) at multiple altitudes
`fallback_cache`	Dict	`fallback` cache of scalar map interpolation functions (`f(lat,lon)`) at multiple altitudes
`dz`	Real	step size between map altitude levels [m]

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Vector Magnetometer

MagNav.MagV — Type

MagV{T2 <: AbstractFloat}

Vector magnetometer measurement struct.

Field	Type	Description
`x`	Vector{`T2`}	x-direction magnetic field [nT]
`y`	Vector{`T2`}	y-direction magnetic field [nT]
`z`	Vector{`T2`}	z-direction magnetic field [nT]
`t`	Vector{`T2`}	total magnetic field [nT]

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Flight Paths

MagNav.Path — Type

Path{T1 <: Signed, T2 <: AbstractFloat} <: Path{T1, T2}

Abstract type Path for a flight path.

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MagNav.Traj — Type

Traj{T1 <: Signed, T2 <: AbstractFloat}

Trajectory struct, i.e., GPS or other truth flight data. Subtype of Path.

Field	Type	Description
`N`	`T1`	number of samples (instances)
`dt`	`T2`	measurement time step [s]
`tt`	Vector{`T2`}	time [s]
`lat`	Vector{`T2`}	latitude [rad]
`lon`	Vector{`T2`}	longitude [rad]
`alt`	Vector{`T2`}	altitude [m]
`vn`	Vector{`T2`}	north velocity [m/s]
`ve`	Vector{`T2`}	east velocity [m/s]
`vd`	Vector{`T2`}	down velocity [m/s]
`fn`	Vector{`T2`}	north specific force [m/s]
`fe`	Vector{`T2`}	east specific force [m/s]
`fd`	Vector{`T2`}	down specific force [m/s]
`Cnb`	Array{`T2,3`}	`3` x `3` x `N` direction cosine matrix (body to navigation) [-]

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MagNav.INS — Type

INS{T1 <: Signed, T2 <: AbstractFloat} <: Path{T1, T2}

Inertial navigation system (INS) struct. Subtype of Path.

Field	Type	Description
`N`	`T1`	number of samples (instances)
`dt`	`T2`	measurement time step [s]
`tt`	Vector{`T2`}	time [s]
`lat`	Vector{`T2`}	latitude [rad]
`lon`	Vector{`T2`}	longitude [rad]
`alt`	Vector{`T2`}	altitude [m]
`vn`	Vector{`T2`}	north velocity [m/s]
`ve`	Vector{`T2`}	east velocity [m/s]
`vd`	Vector{`T2`}	down velocity [m/s]
`fn`	Vector{`T2`}	north specific force [m/s]
`fe`	Vector{`T2`}	east specific force [m/s]
`fd`	Vector{`T2`}	down specific force [m/s]
`Cnb`	Array{`T2,3`}	`3` x `3` x `N` direction cosine matrix (body to navigation) [-]
`P`	Array{`T2,3`}	`17` x `17` x `N` covariance matrix, only relevant for simulated data, otherwise zeros [-]

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Flight Data

MagNav.XYZ — Type

XYZ{T1 <: Signed, T2 <: AbstractFloat}

Abstract type XYZ for flight data. Simplest subtype is XYZ0.

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MagNav.XYZ0 — Type

XYZ0{T1 <: Signed, T2 <: AbstractFloat} <: XYZ{T1, T2}

Subtype of XYZ containing the minimum dataset required for MagNav.

Field	Type	Description
`info`	String	dataset information
`traj`	Traj{`T1`,`T2`}	trajectory struct
`ins`	INS{`T1`,`T2`}	inertial navigation system struct
`flux_a`	MagV{`T2`}	Flux A vector magnetometer measurement struct
`flight`	Vector{`T2`}	flight number(s)
`line`	Vector{`T2`}	line number(s), i.e., segments within `flight`
`year`	Vector{`T2`}	year
`doy`	Vector{`T2`}	day of year
`diurnal`	Vector{`T2`}	measured diurnal, i.e., temporal variations or space weather effects [nT]
`igrf`	Vector{`T2`}	International Geomagnetic Reference Field (IGRF), i.e., core field [nT]
`mag_1_c`	Vector{`T2`}	Mag 1 compensated (clean) scalar magnetometer measurements [nT]
`mag_1_uc`	Vector{`T2`}	Mag 1 uncompensated (corrupted) scalar magnetometer measurements [nT]

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MagNav.XYZ1 — Type

XYZ1{T1 <: Signed, T2 <: AbstractFloat} <: XYZ{T1, T2}

Subtype of XYZ containing a flexible dataset for future use. NaNs may be used in place of any unused fields (e.g., aux_3) when creating struct.

Field	Type	Description
`info`	String	dataset information
`traj`	Traj{`T1`,`T2`}	trajectory struct
`ins`	INS{`T1`,`T2`}	inertial navigation system struct
`flux_a`	MagV{`T2`}	Flux A vector magnetometer measurement struct
`flux_b`	MagV{`T2`}	Flux B vector magnetometer measurement struct
`flight`	Vector{`T2`}	flight number(s)
`line`	Vector{`T2`}	line number(s), i.e., segments within `flight`
`year`	Vector{`T2`}	year
`doy`	Vector{`T2`}	day of year
`diurnal`	Vector{`T2`}	measured diurnal, i.e., temporal variations or space weather effects [nT]
`igrf`	Vector{`T2`}	International Geomagnetic Reference Field (IGRF), i.e., core field [nT]
`mag_1_c`	Vector{`T2`}	Mag 1 compensated (clean) scalar magnetometer measurements [nT]
`mag_2_c`	Vector{`T2`}	Mag 2 compensated (clean) scalar magnetometer measurements [nT]
`mag_3_c`	Vector{`T2`}	Mag 3 compensated (clean) scalar magnetometer measurements [nT]
`mag_1_uc`	Vector{`T2`}	Mag 1 uncompensated (corrupted) scalar magnetometer measurements [nT]
`mag_2_uc`	Vector{`T2`}	Mag 2 uncompensated (corrupted) scalar magnetometer measurements [nT]
`mag_3_uc`	Vector{`T2`}	Mag 3 uncompensated (corrupted) scalar magnetometer measurements [nT]
`aux_1`	Vector{`T2`}	flexible-use auxiliary data 1
`aux_2`	Vector{`T2`}	flexible-use auxiliary data 2
`aux_3`	Vector{`T2`}	flexible-use auxiliary data 3

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MagNav.XYZ20 — Type

XYZ20{T1 <: Signed, T2 <: AbstractFloat} <: XYZ{T1, T2}

Subtype of XYZ for 2020 SGL datasets.

Field	Type	Description
`info`	String	dataset information
`traj`	Traj{`T1`,`T2`}	trajectory struct
`ins`	INS{`T1`,`T2`}	inertial navigation system struct
`flux_a`	MagV{`T2`}	Flux A vector magnetometer measurement struct
`flux_b`	MagV{`T2`}	Flux B vector magnetometer measurement struct
`flux_c`	MagV{`T2`}	Flux C vector magnetometer measurement struct
`flux_d`	MagV{`T2`}	Flux D vector magnetometer measurement struct
`flight`	Vector{`T2`}	flight number(s)
`line`	Vector{`T2`}	line number(s), i.e., segments within `flight`
`year`	Vector{`T2`}	year
`doy`	Vector{`T2`}	day of year
`utm_x`	Vector{`T2`}	x-coordinate, WGS-84 UTM zone 18N [m]
`utm_y`	Vector{`T2`}	y-coordinate, WGS-84 UTM zone 18N [m]
`utm_z`	Vector{`T2`}	z-coordinate, GPS altitude above WGS-84 ellipsoid [m]
`msl`	Vector{`T2`}	z-coordinate, GPS altitude above EGM2008 Geoid [m]
`baro`	Vector{`T2`}	barometric altimeter [m]
`diurnal`	Vector{`T2`}	measured diurnal, i.e., temporal variations or space weather effects [nT]
`igrf`	Vector{`T2`}	International Geomagnetic Reference Field (IGRF), i.e., core field [nT]
`mag_1_c`	Vector{`T2`}	Mag 1 compensated (clean) scalar magnetometer measurements [nT]
`mag_1_lag`	Vector{`T2`}	Mag 1 lag-corrected scalar magnetometer measurements [nT]
`mag_1_dc`	Vector{`T2`}	Mag 1 diurnal-corrected scalar magnetometer measurements [nT]
`mag_1_igrf`	Vector{`T2`}	Mag 1 IGRF & diurnal-corrected scalar magnetometer measurements [nT]
`mag_1_uc`	Vector{`T2`}	Mag 1 uncompensated (corrupted) scalar magnetometer measurements [nT]
`mag_2_uc`	Vector{`T2`}	Mag 2 uncompensated (corrupted) scalar magnetometer measurements [nT]
`mag_3_uc`	Vector{`T2`}	Mag 3 uncompensated (corrupted) scalar magnetometer measurements [nT]
`mag_4_uc`	Vector{`T2`}	Mag 4 uncompensated (corrupted) scalar magnetometer measurements [nT]
`mag_5_uc`	Vector{`T2`}	Mag 5 uncompensated (corrupted) scalar magnetometer measurements [nT]
`mag_6_uc`	Vector{`T2`}	Mag 6 uncompensated (corrupted) scalar magnetometer measurements [nT]
`ogs_mag`	Vector{`T2`}	OGS survey diurnal-corrected, levelled, magnetic field [nT]
`ogs_alt`	Vector{`T2`}	OGS survey, GPS altitude (WGS-84) [m]
`ins_wander`	Vector{`T2`}	INS-computed wander angle (ccw from north) [rad]
`ins_roll`	Vector{`T2`}	INS-computed aircraft roll [deg]
`ins_pitch`	Vector{`T2`}	INS-computed aircraft pitch [deg]
`ins_yaw`	Vector{`T2`}	INS-computed aircraft yaw [deg]
`roll_rate`	Vector{`T2`}	avionics-computed roll rate [deg/s]
`pitch_rate`	Vector{`T2`}	avionics-computed pitch rate [deg/s]
`yaw_rate`	Vector{`T2`}	avionics-computed yaw rate [deg/s]
`ins_acc_x`	Vector{`T2`}	INS x-acceleration [m/s^2]
`ins_acc_y`	Vector{`T2`}	INS y-acceleration [m/s^2]
`ins_acc_z`	Vector{`T2`}	INS z-acceleration [m/s^2]
`lgtl_acc`	Vector{`T2`}	avionics-computed longitudinal (forward) acceleration [g]
`ltrl_acc`	Vector{`T2`}	avionics-computed lateral (starboard) acceleration [g]
`nrml_acc`	Vector{`T2`}	avionics-computed normal (vertical) acceleration [g]
`pitot_p`	Vector{`T2`}	avionics-computed pitot pressure [kPa]
`static_p`	Vector{`T2`}	avionics-computed static pressure [kPa]
`total_p`	Vector{`T2`}	avionics-computed total pressure [kPa]
`cur_com_1`	Vector{`T2`}	current sensor: aircraft radio 1 [A]
`cur_ac_hi`	Vector{`T2`}	current sensor: air conditioner fan high [A]
`cur_ac_lo`	Vector{`T2`}	current sensor: air conditioner fan low [A]
`cur_tank`	Vector{`T2`}	current sensor: cabin fuel pump [A]
`cur_flap`	Vector{`T2`}	current sensor: flap motor [A]
`cur_strb`	Vector{`T2`}	current sensor: strobe lights [A]
`cur_srvo_o`	Vector{`T2`}	current sensor: INS outer servo [A]
`cur_srvo_m`	Vector{`T2`}	current sensor: INS middle servo [A]
`cur_srvo_i`	Vector{`T2`}	current sensor: INS inner servo [A]
`cur_heat`	Vector{`T2`}	current sensor: INS heater [A]
`cur_acpwr`	Vector{`T2`}	current sensor: aircraft power [A]
`cur_outpwr`	Vector{`T2`}	current sensor: system output power [A]
`cur_bat_1`	Vector{`T2`}	current sensor: battery 1 [A]
`cur_bat_2`	Vector{`T2`}	current sensor: battery 2 [A]
`vol_acpwr`	Vector{`T2`}	voltage sensor: aircraft power [V]
`vol_outpwr`	Vector{`T2`}	voltage sensor: system output power [V]
`vol_bat_1`	Vector{`T2`}	voltage sensor: battery 1 [V]
`vol_bat_2`	Vector{`T2`}	voltage sensor: battery 2 [V]
`vol_res_p`	Vector{`T2`}	voltage sensor: resolver board (+) [V]
`vol_res_n`	Vector{`T2`}	voltage sensor: resolver board (-) [V]
`vol_back_p`	Vector{`T2`}	voltage sensor: backplane (+) [V]
`vol_back_n`	Vector{`T2`}	voltage sensor: backplane (-) [V]
`vol_gyro_1`	Vector{`T2`}	voltage sensor: gyroscope 1 [V]
`vol_gyro_2`	Vector{`T2`}	voltage sensor: gyroscope 2 [V]
`vol_acc_p`	Vector{`T2`}	voltage sensor: INS accelerometers (+) [V]
`vol_acc_n`	Vector{`T2`}	voltage sensor: INS accelerometers (-) [V]
`vol_block`	Vector{`T2`}	voltage sensor: block [V]
`vol_back`	Vector{`T2`}	voltage sensor: backplane [V]
`vol_srvo`	Vector{`T2`}	voltage sensor: servos [V]
`vol_cabt`	Vector{`T2`}	voltage sensor: cabinet [V]
`vol_fan`	Vector{`T2`}	voltage sensor: cooling fan [V]
`aux_1`	Vector{`T2`}	flexible-use auxiliary data 1
`aux_2`	Vector{`T2`}	flexible-use auxiliary data 2
`aux_3`	Vector{`T2`}	flexible-use auxiliary data 3

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MagNav.XYZ21 — Type

XYZ21{T1 <: Signed, T2 <: AbstractFloat} <: XYZ{T1, T2}

Subtype of XYZ for 2021 SGL datasets.

Field	Type	Description
`info`	String	dataset information
`traj`	Traj{`T1`,`T2`}	trajectory struct
`ins`	INS{`T1`,`T2`}	inertial navigation system struct
`flux_a`	MagV{`T2`}	Flux A vector magnetometer measurement struct
`flux_b`	MagV{`T2`}	Flux B vector magnetometer measurement struct
`flux_c`	MagV{`T2`}	Flux C vector magnetometer measurement struct
`flux_d`	MagV{`T2`}	Flux D vector magnetometer measurement struct
`flight`	Vector{`T2`}	flight number(s)
`line`	Vector{`T2`}	line number(s), i.e., segments within `flight`
`year`	Vector{`T2`}	year
`doy`	Vector{`T2`}	day of year
`utm_x`	Vector{`T2`}	x-coordinate, WGS-84 UTM zone 18N [m]
`utm_y`	Vector{`T2`}	y-coordinate, WGS-84 UTM zone 18N [m]
`utm_z`	Vector{`T2`}	z-coordinate, GPS altitude above WGS-84 ellipsoid [m]
`msl`	Vector{`T2`}	z-coordinate, GPS altitude above EGM2008 Geoid [m]
`baro`	Vector{`T2`}	barometric altimeter [m]
`diurnal`	Vector{`T2`}	measured diurnal, i.e., temporal variations or space weather effects [nT]
`igrf`	Vector{`T2`}	International Geomagnetic Reference Field (IGRF), i.e., core field [nT]
`mag_1_c`	Vector{`T2`}	Mag 1 compensated (clean) scalar magnetometer measurements [nT]
`mag_1_uc`	Vector{`T2`}	Mag 1 uncompensated (corrupted) scalar magnetometer measurements [nT]
`mag_2_uc`	Vector{`T2`}	Mag 2 uncompensated (corrupted) scalar magnetometer measurements [nT]
`mag_3_uc`	Vector{`T2`}	Mag 3 uncompensated (corrupted) scalar magnetometer measurements [nT]
`mag_4_uc`	Vector{`T2`}	Mag 4 uncompensated (corrupted) scalar magnetometer measurements [nT]
`mag_5_uc`	Vector{`T2`}	Mag 5 uncompensated (corrupted) scalar magnetometer measurements [nT]
`cur_com_1`	Vector{`T2`}	current sensor: aircraft radio 1 [A]
`cur_ac_hi`	Vector{`T2`}	current sensor: air conditioner fan high [A]
`cur_ac_lo`	Vector{`T2`}	current sensor: air conditioner fan low [A]
`cur_tank`	Vector{`T2`}	current sensor: cabin fuel pump [A]
`cur_flap`	Vector{`T2`}	current sensor: flap motor [A]
`cur_strb`	Vector{`T2`}	current sensor: strobe lights [A]
`vol_block`	Vector{`T2`}	voltage sensor: block [V]
`vol_back`	Vector{`T2`}	voltage sensor: backplane [V]
`vol_cabt`	Vector{`T2`}	voltage sensor: cabinet [V]
`vol_fan`	Vector{`T2`}	voltage sensor: cooling fan [V]
`aux_1`	Vector{`T2`}	flexible-use auxiliary data 1
`aux_2`	Vector{`T2`}	flexible-use auxiliary data 2
`aux_3`	Vector{`T2`}	flexible-use auxiliary data 3

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Output Wrappers

MagNav.FILTres — Type

FILTres{T2 <: AbstractFloat}

Filter results struct.

Field	Type	Description
`x`	Matrix{`T2`}	filtered states, i.e., E(xt y1,..,y_t)
`P`	Array{`T2,3`}	non-linear covariance matrix
`r`	Matrix{`T2`}	measurement residuals [nT]
`c`	`Bool`	if true, filter converged

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MagNav.CRLBout — Type

CRLBout{T2 <: AbstractFloat}

Cramér–Rao lower bound extracted output struct.

Field	Type	Description
`lat_std`	Vector{`T2`}	latitude 1-σ [rad]
`lon_std`	Vector{`T2`}	longitude 1-σ [rad]
`alt_std`	Vector{`T2`}	altitude 1-σ [m]
`vn_std`	Vector{`T2`}	north velocity 1-σ [m/s]
`ve_std`	Vector{`T2`}	east velocity 1-σ [m/s]
`vd_std`	Vector{`T2`}	down velocity 1-σ [m/s]
`tn_std`	Vector{`T2`}	north tilt (attitude) 1-σ [rad]
`te_std`	Vector{`T2`}	east tilt (attitude) 1-σ [rad]
`td_std`	Vector{`T2`}	down tilt (attitude) 1-σ [rad]
`fogm_std`	Vector{`T2`}	FOGM 1-σ [nT]
`n_std`	Vector{`T2`}	northing 1-σ [m]
`e_std`	Vector{`T2`}	easting 1-σ [m]

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MagNav.INSout — Type

INSout{T2 <: AbstractFloat}

Inertial navigation system extracted output struct.

Field	Type	Description
`lat_std`	Vector{`T2`}	latitude 1-σ [rad]
`lon_std`	Vector{`T2`}	longitude 1-σ [rad]
`alt_std`	Vector{`T2`}	altitude 1-σ [m]
`n_std`	Vector{`T2`}	northing 1-σ [m]
`e_std`	Vector{`T2`}	easting 1-σ [m]
`lat_err`	Vector{`T2`}	latitude error [rad]
`lon_err`	Vector{`T2`}	longitude error [rad]
`alt_err`	Vector{`T2`}	altitude error [m]
`n_err`	Vector{`T2`}	northing error [m]
`e_err`	Vector{`T2`}	easting error [m]

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MagNav.FILTout — Type

FILTout{T1 <: Signed, T2 <: AbstractFloat} <: Path{T1, T2}

Filter extracted output struct. Subtype of Path.

Field	Type	Description
`N`	`T1`	number of samples (instances)
`dt`	`T2`	measurement time step [s]
`tt`	Vector{`T2`}	time [s]
`lat`	Vector{`T2`}	latitude [rad]
`lon`	Vector{`T2`}	longitude [rad]
`alt`	Vector{`T2`}	altitude [m]
`vn`	Vector{`T2`}	north velocity [m/s]
`ve`	Vector{`T2`}	east velocity [m/s]
`vd`	Vector{`T2`}	down velocity [m/s]
`tn`	Vector{`T2`}	north tilt (attitude) [rad]
`te`	Vector{`T2`}	east tilt (attitude) [rad]
`td`	Vector{`T2`}	down tilt (attitude) [rad]
`ha`	Vector{`T2`}	barometer aiding altitude [m]
`ah`	Vector{`T2`}	barometer aiding vertical acceleration [m/s^2]
`ax`	Vector{`T2`}	x accelerometer [m/s^2]
`ay`	Vector{`T2`}	y accelerometer [m/s^2]
`az`	Vector{`T2`}	z accelerometer [m/s^2]
`gx`	Vector{`T2`}	x gyroscope [rad/s]
`gy`	Vector{`T2`}	y gyroscope [rad/s]
`gz`	Vector{`T2`}	z gyroscope [rad/s]
`fogm`	Vector{`T2`}	FOGM catch-all [nT]
`lat_std`	Vector{`T2`}	latitude 1-σ [rad]
`lon_std`	Vector{`T2`}	longitude 1-σ [rad]
`alt_std`	Vector{`T2`}	altitude 1-σ [m]
`vn_std`	Vector{`T2`}	north velocity 1-σ [m/s]
`ve_std`	Vector{`T2`}	east velocity 1-σ [m/s]
`vd_std`	Vector{`T2`}	down velocity 1-σ [m/s]
`tn_std`	Vector{`T2`}	north tilt (attitude) 1-σ [rad]
`te_std`	Vector{`T2`}	east tilt (attitude) 1-σ [rad]
`td_std`	Vector{`T2`}	down tilt (attitude) 1-σ [rad]
`ha_std`	Vector{`T2`}	barometer aiding altitude 1-σ [m]
`ah_std`	Vector{`T2`}	barometer aiding vertical acceleration 1-σ [m/s^2]
`ax_std`	Vector{`T2`}	x accelerometer 1-σ [m/s^2]
`ay_std`	Vector{`T2`}	y accelerometer 1-σ [m/s^2]
`az_std`	Vector{`T2`}	z accelerometer 1-σ [m/s^2]
`gx_std`	Vector{`T2`}	x gyroscope 1-σ [rad/s]
`gy_std`	Vector{`T2`}	y gyroscope 1-σ [rad/s]
`gz_std`	Vector{`T2`}	z gyroscope 1-σ [rad/s]
`fogm_std`	Vector{`T2`}	FOGM catch-all 1-σ [nT]
`n_std`	Vector{`T2`}	northing 1-σ [m]
`e_std`	Vector{`T2`}	easting 1-σ [m]
`lat_err`	Vector{`T2`}	latitude error [rad]
`lon_err`	Vector{`T2`}	longitude error [rad]
`alt_err`	Vector{`T2`}	altitude error [m]
`vn_err`	Vector{`T2`}	north velocity error [m/s]
`ve_err`	Vector{`T2`}	east velocity error [m/s]
`vd_err`	Vector{`T2`}	down velocity error [m/s]
`tn_err`	Vector{`T2`}	north tilt (attitude) error [rad]
`te_err`	Vector{`T2`}	east tilt (attitude) error [rad]
`td_err`	Vector{`T2`}	down tilt (attitude) error [rad]
`n_err`	Vector{`T2`}	northing error [m]
`e_err`	Vector{`T2`}	easting error [m]

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Real-time EKF

MagNav.EKF_RT — Type

EKF_RT

Real-time (RT) extended Kalman filter (EKF) struct, mutable.

Field	Type	Description
`P`	Matrix{`Float64`}	non-linear covariance matrix
`Qd`	Matrix{`Float64`}	discrete time process/system noise matrix
`R`	Float64	measurement (white) noise variance
`baro_tau`	Float64	barometer time constant [s]
`acc_tau`	Float64	accelerometer time constant [s]
`gyro_tau`	Float64	gyroscope time constant [s]
`fogm_tau`	Float64	FOGM catch-all time constant [s]
`date`	Float64	measurement date (decimal year) for IGRF [yr]
`core`	Bool	if true, include core magnetic field in measurement
`nx`	Int64	total state dimension
`ny`	Int64	measurement dimension
`t`	Float64	time [s]
`x`	Vector{`Float64`}	filtered states, i.e., E(xt y1,..,y_t)
`r`	Vector{`Float64`}	measurement residual

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Compensation Parameters

MagNav.CompParams — Type

CompParams

Abstract type CompParams for aeromagnetic compensation parameters.

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MagNav.LinCompParams — Type

LinCompParams <: CompParams

Linear aeromagnetic compensation parameters struct. Subtype of CompParams.

To see default parameters, type LinCompParams().

General Parameters:

Parameter	Type	Description
`version`	VersionNumber	MagNav.jl version used to generate this struct
`features_setup`	Vector{`Symbol`}	vector of features to include, only used for `model_type = :elasticnet, :plsr`
`features_no_norm`	Vector{`Symbol`}	vector of features to not normalize, only used for `model_type = :elasticnet, :plsr`
`model_type`	Symbol	aeromagnetic compensation model type (`see below`)
`y_type`	Symbol	`y` target type (`see below`)
`use_mag`	Symbol	uncompensated scalar magnetometer to use for `y` target vector {`:mag_1_uc`, etc.}, only used for `y_type = :c, :d, :e`
`use_vec`	Symbol	vector magnetometer (fluxgate) to use for "external" Tolles-Lawson `A` matrix {`:flux_a`, etc.}, only used for `model_type = :TL, :mod_TL, :map_TL`
`data_norms`	Tuple	length-`4` tuple of data normalizations, `(A_bias,A_scale,y_bias,y_scale)` for `model_type = :TL, :mod_TL, :map_TL` or `(x_bias,x_scale,y_bias,y_scale)` for `model_type = :elasticnet, :plsr`
`model`	Tuple	linear model coefficients
`terms`	Vector{`Symbol`}	Tolles-Lawson terms to use for Tolles-Lawson `A` matrix (or matrices) within `x` data matrix {`:permanent`,`:induced`,`:eddy`}, only used for `model_type = :elasticnet, :plsr`
`terms_A`	Vector{`Symbol`}	Tolles-Lawson terms to use for "external" Tolles-Lawson `A` matrix {`:permanent`,`:induced`,`:eddy`,`:bias`}, only used for `model_type = :TL, :mod_TL, :map_TL`
`sub_diurnal`	Bool	if true, subtract diurnal from scalar magnetometer measurements
`sub_igrf`	Bool	if true, subtract IGRF from scalar magnetometer measurements
`bpf_mag`	Bool	if true, bpf scalar magnetometer measurements in `x` data matrix, only used for `model_type = :elasticnet, :plsr`
`reorient_vec`	Bool	if true, align vector magnetometers (fluxgates) with body frame
`norm_type_A`	Symbol	normalization for "external" Tolles-Lawson `A` matrix, only used for `model_type = :TL, :mod_TL, :map_TL` (`see below`)
`norm_type_x`	Symbol	normalization for `x` data matrix, only used for `model_type = :elasticnet, :plsr` (`see below`)
`norm_type_y`	Symbol	normalization for `y` target vector (`see below`)

model_type options:
- :TL = classical Tolles-Lawson
- :mod_TL = modified Tolles-Lawson
- :map_TL = map-based Tolles-Lawson
- :elasticnet = elastic net (ridge regression and/or Lasso)
- :plsr: = partial least squares regression (PLSR)
y_type options:
- :a = anomaly field #1, compensated tail stinger total field scalar magnetometer measurements
- :b = anomaly field #2, interpolated magnetic anomaly map values
- :c = aircraft field #1, difference between uncompensated cabin total field scalar magnetometer measurements and interpolated magnetic anomaly map values
- :d = aircraft field #2, difference between uncompensated cabin and compensated tail stinger total field scalar magnetometer measurements
- :e = BPF'd total field, bandpass filtered uncompensated cabin total field scalar magnetometer measurements
norm_type options:
- :standardize = Z-score normalization
- :normalize = min-max normalization
- :scale = scale by maximum absolute value, bias = 0
- :none = scale by 1, bias = 0

Linear Model-Specific Parameters:

Parameter	Type	Description
`k_plsr`	Int64	number of components, only used for `model_type = :plsr`
`λ_TL`	Float64	ridge parameter, only used for `model_type = :TL, :mod_TL, :map_TL`

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MagNav.NNCompParams — Type

NNCompParams <: CompParams

Neural network-based aeromagnetic compensation parameters struct. Subtype of CompParams.

To see default parameters, type NNCompParams().

General Parameters:

Parameter	Type	Description
`version`	VersionNumber	MagNav.jl version used to generate this struct
`features_setup`	Vector{`Symbol`}	vector of features to include
`features_no_norm`	Vector{`Symbol`}	vector of features to not normalize
`model_type`	Symbol	aeromagnetic compensation model type (`see below`)
`y_type`	Symbol	`y` target type (`see below`)
`use_mag`	Symbol	uncompensated scalar magnetometer to use for `y` target vector {`:mag_1_uc`, etc.}, only used for `y_type = :c, :d, :e`
`use_vec`	Symbol	vector magnetometer (fluxgate) to use for "external" Tolles-Lawson `A` matrix {`:flux_a`, etc.}, not used for `model_type = :m1`
`data_norms`	Tuple	length-`7` tuple of data normalizations, `(A_bias,A_scale,v_scale,x_bias,x_scale,y_bias,y_scale)`
`model`	Chain	neural network model
`terms`	Vector{`Symbol`}	Tolles-Lawson terms to use for Tolles-Lawson `A` matrix (or matrices) within `x` data matrix {`:permanent`,`:induced`,`:eddy`}
`terms_A`	Vector{`Symbol`}	Tolles-Lawson terms to use for "external" Tolles-Lawson `A` matrix {`:permanent`,`:induced`,`:eddy`,`:bias`}, not used for `model_type = :m1`
`sub_diurnal`	Bool	if true, subtract diurnal from scalar magnetometer measurements
`sub_igrf`	Bool	if true, subtract IGRF from scalar magnetometer measurements
`bpf_mag`	Bool	if true, bpf scalar magnetometer measurements in `x` data matrix
`reorient_vec`	Bool	if true, align vector magnetometers (fluxgates) with body frame
`norm_type_A`	Symbol	normalization for "external" Tolles-Lawson `A` matrix, only used for `model_type = :m2*` (`see below`)
`norm_type_x`	Symbol	normalization for `x` data matrix (`see below`)
`norm_type_y`	Symbol	normalization for `y` target vector (`see below`)

model_type options are broken into 3 architectures, with 1 being a standard feedforward neural network and 2 & 3 being used in conjunction with Tolles-Lawson
- :m1 = standard feedforward neural network (NN)
- :m2a = NN determines Tolles-Lawson (TL) coefficients
- :m2b = NN determines additive correction to classical TL
- :m2c = NN determines additive correction to classical TL, TL coefficients tuned as well
- :m2d = NN determines additive correction to each TL coefficient
- :m3tl = no NN, TL coefficients fine-tuned via SGD, without Taylor expansion for y_type = :b, :c (for testing)
- :m3s = NN determines scalar correction to TL, using expanded TL vector terms for explainability
- :m3v = NN determines vector correction to TL, using expanded TL vector terms for explainability
- :m3sc = :m3s with curriculum learning based on TL error
- :m3vc = :m3v with curriculum learning based on TL error
- :m3w = :m3s with window NN for temporal dataset
- :m3tf = :m3s with time series transformer for temporal dataset
y_type options:
- :a = anomaly field #1, compensated tail stinger total field scalar magnetometer measurements
- :b = anomaly field #2, interpolated magnetic anomaly map values
- :c = aircraft field #1, difference between uncompensated cabin total field scalar magnetometer measurements and interpolated magnetic anomaly map values
- :d = aircraft field #2, difference between uncompensated cabin and compensated tail stinger total field scalar magnetometer measurements
- :e = BPF'd total field, bandpass filtered uncompensated cabin total field scalar magnetometer measurements
norm_type options:
- :standardize = Z-score normalization
- :normalize = min-max normalization
- :scale = scale by maximum absolute value, bias = 0
- :none = scale by 1, bias = 0

Neural Network-Based Model-Specific Parameters:

Parameter	Type	Description
`TL_coef`	Vector{`Float64`}	Tolles-Lawson coefficients, not used for `model_type = :m1, :m2a`
`η_adam`	Float64	learning rate for Adam optimizer
`epoch_adam`	Int64	number of epochs for Adam optimizer
`epoch_lbfgs`	Int64	number of epochs for LBFGS optimizer
`hidden`	Vector{`Int64`}	hidden layers & nodes (e.g., `[8,8]` for 2 hidden layers, 8 nodes each)
`activation`	Function	activation function (`see below`)
`loss`	Function	loss function (`see below`)
`batchsize`	Int64	mini-batch size
`frac_train`	Float64	fraction of training data used for training (remainder for validation), only used for Adam optimizer
`α_sgl`	Float64	Lasso (`α_sgl=0`) vs group Lasso (`α_sgl=1`) balancing parameter {0:1}
`λ_sgl`	Float64	sparse group Lasso parameter, typically ~1e-5 (if non-zero)
`k_pca`	Int64	number of components for pre-processing with PCA + whitening, `-1` to ignore
`drop_fi`	Bool	if true, perform drop-column feature importance
`drop_fi_bson`	String	path/name of drop-column feature importance data BSON file to save/load (`.bson` extension optional)
`drop_fi_csv`	String	path/name of drop-column feature importance data CSV file to save (`.csv` extension optional)
`perm_fi`	Bool	if true, perform permutation feature importance
`perm_fi_csv`	String	path/name of permutation feature importance data CSV file to save (`.csv` extension optional)

activation options, which can be visualized by running plot_activation():
- relu = rectified linear unit
- σ = sigmoid (logistic function)
- swish = self-gated
- tanh = hyperbolic tan
loss options:
- mse = mean squared error
- mae = mean absolute error
- huber_loss = mean Huber loss

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