An Initial Alignment Technology of Shearer Inertial Navigation Positioning Based on a Fruit Fly-Optimized Kalman Filter Algorithm

Algorithm 1

	Inputs: sizepop, maxgen, d, fmin, fmax. etc.
	Outputs: X
	% Initialization
	Set the parameters of the improved Kalman filter algorithm: population size sizepop, maximum iteration number maxgen, dimension d, frequency variables fmin and fmax, etc.
	% Obtain the current population optimal concentration
	For i = 1, 2, …, sizepop
	Kalman (S(i))
	% Obtain smell concentration
	Smell (i) = MSE (Kalman (S(i)))
	End
	% Obtain the best individual concentration
	Sbest, X ⟵
	% Obtain the optimal system noise covariance
	Q ⟵ Sbest
	% Iterative optimization
	For  = 1: maxgen
	For i = 1, 2, ..., sizepop
	If Number of optimum values not updated >2.
	Else
	End
	Kalman (S(i))
	% Obtain smell concentration
	Smell(i) = MSE (Kalman (S(i)))
	End
	% Obtain the best individual concentration
Sbest, X ⟵
	% Obtain the optimal system noise covariance
	Q ⟵ Sbest
	End