Modeling and Frequency Estimation of Flight Cycle Signals in Complex Environments Based on the Fast Fourier Transform
Year: 2025
Type: article
Abstract: Airplanes have now become an important travel tool, and airspeed is the key data to be observed in the process of aircraft flight, therefore, in order to reduce the probability of aircraft stalls and other accidents due to abnormal airspeed data, it is of great significance to accurately measure the airspeed. This study focuses on the frequency estimation of the received signal in flight cycle, combines with the signal processing requirements of laser speed measurement system, constructs a variety of mathematical models and designs the corresponding solution algorithms, and systematically explores the noise characteristics, frequency estimation and intermittent signal processing and other key issues. In this paper, the fast Fourier transform (FFT) algorithm and descriptive statistical analysis of z(t) noise type belongs to Gaussian white noise. The reasonableness and estimation accuracy of the main frequency are verified by comparing the filtered signal with the fitted non-noise signal. This study proposes a frequency estimation method that combines Fast Fourier Transform (FFT) with descriptive statistical analysis. The method effectively suppresses environmental noise interference using an adaptive threshold adjustment based on the Constant False Alarm Rate (CFAR) algorithm. A band-pass filter is employed to eliminate noise, and residual analysis is used to validate the accuracy of frequency estimation. Additionally, an innovative signal reconstruction method under intermittent sampling is proposed, providing theoretical support for frequency estimation and signal processing in complex environments.
DOI: 10.61784/jcsee3043
Authors: QiuLin Yao, ShaoJie Guo, FanZhi Qu, QiSheng Liu, KuiSong Wang, JinZhu Cai, ChengCai Liang
Institution: Jiamusi University
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4408803151