Achieving fast and accurate estimation of vehicle attitude is an important guarantee for successful mission execution. In order to improve the accuracy and speed of attitude estimation, an improved particle swarm algorithm is proposed to be applied in the spectral peak search, considering that the multiple signal classific-ation algorithm (MUSIC) algorithm is computationally intensive and slow in the spectral peak search. Firstly, the variation pattern between the attitude position of each electromagnetic vector sensor on the aircraft fuselage and the signal information transmitted from the ground base station is relied upon to form the steering vector required in the MUSIC algorithm, to establish the mathematical model expression of the electromagnetic wave signal of the signal receiving array composed of electromagnetic vector sensors, to find the covariance matrix, to decompose the eigenvalues of the matrix to obtain the noise subspace, and to construct the The attitude space spectral function is constructed to complete the establishment of the signal space spectrum and the search of the spectral peak, so as to obtain the unique spectral peak characterizing the attitude. Finally, the simulation shows that the improved particle swarm algorithm can effectively improve the estimation accuracy and search speed of the flight attitude.