An improved ranging method based on modeling of drone rotor noise directivity

Abstract

The application discloses an improved ranging method based on unmanned aerial vehicle rotor noise directivity modeling, comprising arranging a multi-channel acoustic sensor array, collecting noise signals from rotors in real time during unmanned aerial vehicle flight; filtering, time synchronizing and normalizing the collected original noise signals for pretreatment; performing time domain analysis and frequency domain analysis on the pretreated acoustic wave signals; establishing an unmanned aerial vehicle rotor noise directivity model, performing spectrum analysis and spectrum feature extraction; establishing a multi-path model of noise propagation, performing signal demixing and correction, and simultaneously dynamically compensating the unmanned aerial vehicle rotor noise directivity model according to environmental factors; combining time delay, intensity difference and spectrum change information received by the multi-channel microphone array, calculating the relative distance between the unmanned aerial vehicle and the target by using a geometric positioning algorithm, and establishing a joint model to optimize ranging accuracy; and the application effectively improves the accuracy and reliability of unmanned aerial vehicle ranging and reduces traditional errors.

Description

Technical Field

[0001] This invention relates to the fields of acoustic ranging and UAV navigation technology, specifically to an improved ranging method based on UAV rotor noise directionality modeling. Background Technology

[0002] With the increasing application of drones in various fields, rotor noise, as an unavoidable noise source during drone flight, has spectral characteristics that can be used not only to identify the drone's position but also to reflect its flight status. Traditional acoustic ranging techniques mainly estimate distance based on the intensity or time delay of sound waves, ignoring the directional characteristics of noise signals. Existing drone ranging methods mostly rely on active sensors, such as lidar, radar, and ultrasonic sensors. However, the high cost, susceptibility to environmental interference, and high complexity of these technologies limit their application in certain scenarios.

[0003] On the other hand, the directional characteristics of rotor noise can provide crucial information about the UAV's location and distance. Accurate modeling and analysis of the directional changes in rotor noise can effectively improve the accuracy and reliability of UAV ranging. Therefore, this invention proposes an improved ranging method based on UAV rotor noise directional modeling, aiming to enhance the accuracy and adaptability of existing ranging technologies. Summary of the Invention

[0004] The purpose of this invention is to provide an improved ranging method based on the directional modeling of UAV rotor noise. By utilizing the directional characteristics of rotor noise, sound signals are collected based on a multi-channel microphone array. The relative distance between the UAV and the target is calculated by combining the propagation characteristics of rotor noise and the change in the directionality of sound waves. Then, by accurately modeling the directionality of the noise source, the problems mentioned in the background art can be solved.

[0005] The specific technical solution provided by this invention is as follows: An improved ranging method based on UAV rotor noise directivity modeling, comprising the following operational steps:

[0006] Step S1: Deploy a multi-channel acoustic sensor array to collect noise signals from the drone rotor in real time during drone flight.

[0007] Preferably, arranging a multi-channel acoustic sensor array includes: arranging multiple microphone sensors in the target area to form a spherical or hemispherical array, constituting a multi-channel acoustic sensor array, which simultaneously receives noise signals of different intensities and frequencies from different directions.

[0008] The noise signal collection from the UAV rotor includes collecting noise signal data multiple times under different flight states, including stationary, accelerating, turning, and climbing.

[0009] Step S2: Preprocessing the acoustic signal in the acquired raw noise signal by filtering, time synchronization, and normalization. The specific implementation process includes: Step S31: Perform time-domain signal analysis; Step S32: Perform frequency domain signal analysis; Step S33: Construct a joint time-domain and frequency-domain analysis strategy to improve the accuracy of directional analysis through multi-dimensional signal processing; Step S34: Compare and verify the effects.

[0010] Step S3: Perform time-domain and frequency-domain analysis on the preprocessed acoustic signal.

[0011] Preferably, Step S4: Establish a directional model of UAV rotor noise and perform spectrum analysis and spectrum feature extraction.

[0012] Preferably, the spectral characteristics include a fundamental frequency determined by the rotor speed and multiple harmonic components related to the number of rotor blades and the rotor speed. The intensity distribution of these spectral characteristics varies in different directions and changes with the flight state.

[0013] Preferably, the received sound signal is subjected to frequency domain analysis to extract the characteristic frequencies of the rotor noise, including the fundamental frequency and harmonics, as well as the spectral variation characteristics, and the directionality of the rotor noise is analyzed. By calculating the sound directivity index, the degree of concentration of sound energy of the sound source in the main radiation direction relative to the overall spatial average sound energy is measured. Under different flight conditions, the spectral distribution of rotor noise will show directional changes. By the change of the spectrum, combined with the signal strength and time difference received by the multi-channel microphone, the direction of noise propagation is inferred.

[0014] Step S5: Establish a multipath model for noise propagation, perform signal demixing and correction, and dynamically compensate the UAV rotor noise directionality model based on environmental factors.

[0015] Preferably, establishing a multipath model for noise propagation and performing signal demixing and correction includes: establishing a multipath model for noise propagation paths, determining possible reflection paths for each measuring point, identifying direct sound components using time delay estimation, extracting clean signals using reflection suppression algorithms, and calculating the directionality of the corrected signals; calculating the observed signals and using the observed signals to represent the superposition of signals from multiple paths; calculating the propagation time delay; and using amplitude attenuation and phase correction to compensate for energy loss and time delay of sound waves during propagation.

[0016] Preferably, dynamic compensation of the UAV rotor noise directionality model based on environmental factors includes: Construct a sound speed environmental compensation model: calculate and correct the sound speed based on the measured temperature and humidity, and then correct the temperature gradient based on the UAV's flight altitude; Wind direction compensation: The sound speed is compensated by measuring the wind speed and direction in a windy environment; Path and delay compensation: Calculate the corrected propagation time based on the sound speed field and wind speed field.

[0017] Step S6: Combining the time delay, intensity difference, and spectral change information received by the multi-channel microphone array, the relative distance between the UAV and the target is calculated using a geometric positioning algorithm, and a TDOA-Doppler joint model is established to optimize the ranging accuracy.

[0018] Preferably, the specific implementation includes: Distance measurement using TDOA: The distance is calculated by utilizing the time difference of sound waves traveling from the target to multiple microphones, and then a sound wave propagation direction correction is introduced to correct the calculated distance; Doppler is used for ranging: the frequency shift caused by the relative velocity between the sound source and the receiver is calculated. When the sound source emits a known frequency, the relative radial velocity is estimated by the frequency shift. When the propagation direction is a straight line, a systematic error will occur. The true direction is approximated as "straight line direction + small lateral deflection", then normalized to maintain the unit length, and corrected. The direction unit vector of the receiver point is then calculated. At the same time, a small deflection "direction correction amount" is introduced to correct the direction unit vector. The sound source velocity is then re-estimated using the corrected direction vector. Finally, the distance change is obtained by continuously observing the Doppler frequency shift at multiple moments and calculating the sound source velocity, and integrating the velocity. Ranging optimization is achieved using a TDOA-Doppler joint model: A joint model is established, which integrates TDOA and FDOA information for ranging. By using EKF or UKF filtering and fusion updates, the joint estimation of the sound source position and velocity is realized, and the distance, direction and relative radial velocity of the UAV target are obtained.

[0019] Compared with the prior art, the beneficial effects achieved by the present invention are: (1) Improved accuracy: The method of this invention utilizes the directional characteristics of rotor noise, collects sound signals through a multi-channel microphone array, and calculates the relative distance between the UAV and the target by combining the propagation characteristics of rotor noise and the change in the directionality of sound waves. By accurately modeling the directionality of the noise source, especially in environments with multiple obstacles or complex reflections, the errors in traditional methods can be effectively reduced and the accuracy of ranging can be improved.

[0020] (2) Low cost: The method of the present invention uses existing multi-channel microphone arrays for ranging, avoiding high-cost lidar or radar equipment, and has a significant cost advantage.

[0021] (3) Strong environmental adaptability: The method of the present invention does not depend on light or line-of-sight conditions and can work reliably in low visibility or severe weather conditions, such as fog, haze, rain and snow, and is suitable for more application scenarios.

[0022] (4) Strong real-time performance: The method of the present invention has good real-time performance, which can estimate the distance to the target in real time during the flight of the UAV, and support the autonomous flight and dynamic obstacle avoidance of the UAV. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the UAV ranging process based on rotor noise directionality modeling provided in an embodiment of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention are within the scope of protection of the present invention.

[0025] Example 1: like Figure 1 As shown in the figure, the improved ranging method based on UAV rotor noise directivity modeling described in this embodiment includes the following steps: Step S1: Deploy a multi-channel acoustic sensor array to collect noise signals from the drone rotor in real time during drone flight.

[0026] In this embodiment, to capture the directional characteristics of rotor noise, the present invention arranges multiple microphone sensors within the target area, forming a spherical or hemispherical array, i.e., a multi-channel acoustic sensor array. The sampling rate and frequency response range of each sensor are then determined to ensure effective capture of the full-spectrum characteristics of rotor noise, including fundamental frequency and harmonic components. The spacing between the sensors is set, typically determined based on the propagation speed and directionality requirements of the noise signal. The spacing between the sensors also needs to be sufficiently small to accurately capture the time delay difference (TDOA) of the signal. This array can simultaneously receive noise signals from different directions. This allows the direction of sound propagation to be calculated by performing time difference analysis on the signals received by different microphones. Furthermore, the design of the microphone array must ensure that the spatial distribution of the sensors effectively captures the time delay difference (TDOA) and intensity difference of the noise.

[0027] For example, during drone flight, when rotor noise propagates to the microphone array, microphones in different directions will receive noise signals of varying intensities and frequencies, based on signal strength, frequency variations, and time delay differences. Signal acquisition involves multiple data acquisitions under different flight states (such as stationary, accelerating, turning, climbing, etc.) to ensure the complete spectral characteristics of the rotor noise are captured. Simultaneously, it ensures synchronized data acquisition from all sensors to reduce errors caused by timing discrepancies. High-precision clock synchronization technology is typically used to ensure data consistency, and the direction of noise propagation can be calculated.

[0028] For example, the microphone array is arranged as follows in this embodiment: 1) Fix the drone or its rotor assembly to the center of the test frame, making the rotor axis perpendicular to the ground; 2) Arrange multiple measuring points around the rotor center to form a spherical or hemispherical microphone array, with the azimuth range of each measuring point being [missing information]. The range of elevation angles is The radius is 1–3 meters; 3) The number of measurement points shall not be less than 24 to ensure spatial resolution.

[0029] 4) Use a multi-channel data acquisition system to simultaneously acquire sound pressure signals from each measuring point. ; 5) Record rotor speed, blade pitch angle, and environmental conditions during the test; 6) Ensure microphone sampling clock accuracy .

[0030] Step S2: Perform preprocessing on the acoustic signal in the acquired raw noise, including filtering, time synchronization, and normalization.

[0031] In this embodiment, the preprocessing process includes: Filtering: including DC removal and single high-pass filtering; Time synchronization: Calculate the cross-correlation function of different receiving points, find the peak position, and calculate the relative time delay; Normalization: To avoid interference from amplitude differences caused by sound wave acquisition equipment or scene, the amplitude of the audio signal is normalized to ensure the consistency of input data.

[0032] Step S3: Perform time-domain and frequency-domain analysis on the preprocessed acoustic signal.

[0033] In this embodiment, combining time-domain and frequency-domain signal analysis can further improve the accuracy of directional analysis. In the time domain, the time difference of propagation (TDOA) of rotor noise provides a preliminary estimate of the direction of the noise source, while in the frequency domain, the intensity variation of the noise spectrum can supplement directional information. The combination of the two can effectively reduce errors caused by environmental noise or multipath propagation effects. This invention combines the analysis of time-domain and frequency-domain signals, comprehensively considering the time delay and spectral variation of the signal, thereby more accurately obtaining the directional characteristics of rotor noise. This dual analysis method can improve the estimation accuracy of the noise propagation direction, thereby optimizing the ranging process. Meanwhile, the directionality of UAV rotor noise depends on: the change of sound source intensity over time (non-steady characteristics), and the radiation characteristics of different frequency components in space (spectral directional distribution). Therefore, time-domain analysis is used to reveal the non-steady and periodic changes of noise, while frequency-domain analysis is used to quantitatively analyze the directional differences in different frequency bands. The combination of the two can effectively distinguish various noise sources of the rotor (thickness noise, lift noise, turbulence noise, etc.) and improve the spatial and frequency resolution of directional analysis.

[0034] Step S31: Perform time-domain signal analysis.

[0035] For example, the specific implementation process includes: a) Preprocess the original signal to eliminate interference from non-sound source factors and ensure that the time-domain signal reflects the true radiation characteristics; For example, preprocessing includes: Signal denoising and filtering: Use a bandpass filter (20 Hz–20 kHz) to remove low-frequency drift and high-frequency electrical noise; Time synchronization correction: Ensures time alignment of signals acquired from multiple channels; Background noise correction: Background noise is corrected by static measurement and energy subtraction. Time-domain normalization: Normalizes the signal amplitude to facilitate comparison in different directions.

[0036] b) Identify the periodic characteristics of rotor noise through envelope analysis. Rotor noise exhibits obvious amplitude modulation (AM) within one rotation cycle. The envelope signal can reveal the law of noise energy change over time. Envelope analysis can provide data support for sound source localization, directionality correction, and noise suppression.

[0037] For example, the specific implementation steps include: Instantaneous amplitude calculation: for sound pressure signals Perform a Hilbert transform to calculate the instantaneous amplitude (envelope). The calculation formula is as follows:

[0038] Among them, the instantaneous amplitude of the sound pressure signal Used to describe the envelope characteristics of UAV sound waves. The original sound pressure signal is a time-varying signal. The changing real-valued signal originates from the sound wave signal collected by the microphone. for The Hilbert transform result represents the imaginary part of the signal that is orthogonal to the original signal (90° out of phase); Timing analysis: By calculating and plotting the time variation curve of the envelope signal, the periodic modulation characteristics of noise energy can be intuitively displayed; Statistical feature extraction: Quantify the periodic noise characteristics through statistical analysis (mean value, RMS value, peak value) to reflect the regularity of rotor rotation; Directional correction: The temporal position of the envelope peak can reflect the maximum radiation angle when the rotor blade passes through a specific direction, thereby correcting the phase deviation of the main radiation angle in the radiation pattern.

[0039] c) Perform time-frequency analysis (Short-Time Fourier Transform, Wavelet) to identify the occurrence time of each noise component (main rotation frequency, harmonic frequency, turbulence broadband), analyze the stability of the rotor periodic signal, compare the time-frequency energy of signals in different directions, and determine the emission time and location of the noise source.

[0040] For example, drone rotor noise is a non-stationary signal whose spectrum changes over time. Transient noise components can be captured using Short Time Fourier Transform (STFT) or Wavelet Transform. The Fourier Transform formula is as follows:

[0041] in, For sound pressure signal in time With frequency The time-frequency representation at that point is a complex-valued function containing amplitude and phase information, used to describe the spectral characteristics of UAV rotor noise as a function of time. This is a time index, representing the center moment of the current analysis window, used to characterize the time-varying characteristics of UAV rotor noise (such as rotational speed changes). As a frequency variable, it represents the energy distribution of rotor noise at different frequency components and can reflect the rotor's fundamental frequency, harmonics, and modulation characteristics; This is a time dummy variable in the integral, representing the time point that participates in the spectral analysis under the action of the window function; The time window function is used to locally extract signals for short-time analysis. Commonly used window functions include the Hamming window and the Hann window. The window length determines the trade-off between time resolution and frequency resolution. These are the complex exponential basis functions of the Fourier transform, used to map time-domain signals to the frequency domain. It is the imaginary unit.

[0042] d) Perform phase coherence analysis. By calculating the coherence function between different measurement points, the correlation of the signal is evaluated. The high coherence frequency band indicates that the noise source direction is stable. The phase difference calculation can correct the measurement point positioning error, improve the direction positioning accuracy, and support sound source positioning and array wave direction of arrival estimation.

[0043] For example, in time-domain signals measured simultaneously from multiple directions, if the phase of a signal in a certain direction is advanced or delayed, the propagation path difference in the main radiation direction can be calculated, specifically including: Calculate the coherence function between different measuring points:

[0044] in, For measuring points With measuring points The amplitude square coherence function between them has a range of values. Used to measure the frequency of signals at two measurement points. The degree of correlation at each location is such that the closer it is to 1, the stronger the correlation, and the closer it is to 0, the weaker the correlation or the more noise-dominated the noise. For frequency variables, it represents the specific frequency point for analyzing coherence, which often corresponds to the rotor fundamental frequency and its harmonic frequencies in UAV acoustics; For measuring points and The cross-power spectral density function between two signals represents their relationship in the frequency domain. For measuring points The self-power spectral density function characterizes the signal. In frequency Energy distribution at that location The power spectral density function at the measurement point y(t) characterizes the signal. In frequency Energy distribution at the location; Among them, measuring points and Cross-power spectral density function Represented as;

[0045] in, 、 Signals , The Fourier transform is used to map the time-domain sound pressure signal to the frequency domain. This is a complex conjugate operator used to ensure that the power spectrum calculation result is a real value; Coherence analysis can be used to assess whether the UAV sound waves received at different measurement points come from the same sound source. This helps with sound source localization, array direction of arrival (DOA) estimation, noise suppression and coherence enhancement. In environments with strong background noise, effective frequency bands can be screened through coherence analysis. High coherence frequency bands indicate that the noise source direction is stable. Phase difference calculation can correct measurement point localization errors and improve direction positioning accuracy.

[0046] Step S32: Perform frequency domain signal analysis.

[0047] For example, the specific implementation process includes: a) Extract the tip passing frequency (BPF) and its harmonics from the rotor noise.

[0048] For example, by extracting the BPF and harmonic components from the spectrum in different directions, the frequency distribution characteristics of the noise are obtained. Then, the differences in BPF sound pressure level changes at different angles are compared to identify the main radiation direction. Finally, the law of the main radiation direction changing with frequency is quantitatively evaluated, and a directional map is plotted. The main characteristic frequencies of rotor noise are the blade passing frequency (BPF) and its harmonics. The calculation formula is as follows:

[0049] Where Nb is the number of blades, ω is the angular velocity.

[0050] b) Perform broadband noise spectrum analysis.

[0051] For example, the high-frequency component of propeller noise (>10kHz) typically originates from turbulent-blade interactive noise (TBL-TENoise), which has weak directionality. Through frequency band analysis, structural noise and random turbulent noise can be distinguished, thereby separating the contributions of different types of sound sources in the directional diagram. Specific implementations include: Using 1 / 3 octave band filtering: decomposes wideband signals into multiple frequency bands, making it easier to distinguish between structural noise and random turbulence noise; Plot the broadband noise energy distribution in each direction: Display the noise energy distribution characteristics in each direction using a directional plot; Extracting the average sound pressure level of the frequency band Quantitative analysis of sound pressure level in different frequency bands and directions.

[0052] c) Perform power spectral density (PSD) and energy directionality analysis.

[0053] For example, by calculating the power spectral density in each direction, a joint distribution analysis of acoustic energy in the frequency domain and the spatial directional domain can be achieved:

[0054] in, Let be the power spectral density function, representing the acoustic signal at frequency . and spatial direction ( The power distribution over a frequency domain is used to describe the joint distribution characteristics of acoustic energy in the frequency domain and the spatial direction domain. For frequency variables, corresponding to the fundamental frequency and harmonic components of UAV rotor noise, used to analyze the directional energy distribution in different frequency bands. Here, azimuth angle is a spatial orientation parameter, representing the incident direction of the sound source relative to the microphone array. The pitch angle, or elevation angle, is a spatial direction parameter, representing the angle of incidence of the sound source in the vertical direction. For direction-dependent frequency domain sound pressure or beamforming output, representing the direction from which the sound pressure is generated ( The sound signal at the frequency The complex spectrum value at a given location is typically obtained by spatially weighting (e.g., beamforming) and performing a Fourier transform on sound pressure signals from multiple measurement points. Its magnitude reflects the intensity of sound energy in that direction, and its phase reflects propagation phase information. T is the time normalization coefficient / effective observation duration, representing the signal duration used for spectrum analysis. It is used to normalize the energy spectrum to power spectral density, ensuring comparability of spectral estimates at different time lengths. By analyzing the signals from spatial directions (… The sound signal at the frequency Frequency domain analysis is performed on the sound source, and the square of its amplitude is normalized over time to obtain the power spectral density distribution of sound energy in the frequency and spatial direction dimensions. This is used to determine the main radiation direction, energy concentration frequency band, and spatial sound field characteristics of the UAV sound source.

[0055] d) Perform cross-spectral analysis. By calculating the coherence function values ​​of the signals from two measurement points at a specific frequency, the correlation between different directions can be identified, and the characteristics of the noise source can be determined. A coherence function value close to 1 indicates high correlation, while a value close to 0 indicates no correlation. When it is determined that the two measurement points in a specific direction are highly coherent at a specific frequency, it indicates that the noise in that frequency band mainly comes from the same radiation direction. If the coherence is weak, it indicates that the noise has random turbulent characteristics.

[0056] Step S33: Construct a joint time-domain and frequency-domain analysis strategy to improve accuracy through multi-dimensional signal processing.

[0057] For example, the constructed joint time-domain and frequency-domain analysis strategy includes: Time segmentation + spectral averaging method: The time domain signal is segmented according to the rotor rotation cycle, the spectrum of each cycle is analyzed, and then phase-aligned averaging is performed, which can significantly reduce random noise error.

[0058] Time-weighted directional spectrum: The frequency domain directional spectrum is corrected by using the energy weight of the time domain envelope, so that high-energy moments (such as noise peaks) contribute more to the directionality calculation and highlight the main radiation characteristics.

[0059] STFT Energy Integration Direction Map: By integrating time-frequency energy in different directions through Short Time Fourier Transform (STFT), a "time-direction map" is generated, which can identify changes in the direction of transient noise.

[0060] Adaptive filtering and signal-to-noise ratio enhancement: Multi-channel time-domain adaptive algorithms (LMS, ANC) are used to filter out non-target direction noise, improve directional resolution, and are suitable for complex environments.

[0061] Data fusion and error correction: The time-domain envelope peak direction, frequency-domain dominant frequency direction, and CFD simulation prediction direction are compared and corrected to obtain a high-precision comprehensive directionality result.

[0062] Step S34: Results and Verification For example, by introducing comprehensive analysis in the time and frequency domains, combining time segmentation, spectral averaging, adaptive filtering, and improving the accuracy of directional calculation through data fusion and error correction, the present invention achieves the following technical effects: Angle accuracy improved: the error of the principal radiation angle from Significantly reduced to The accuracy of directional analysis has been greatly improved; Noise separation: Successfully separated low-frequency structural noise from high-frequency turbulent noise, achieving noise source identification; Time-frequency domain enhancement: The main radiation direction is clearer in the time-frequency plane, which facilitates transient feature extraction.

[0063] Step S4: Noise directionality modeling and feature analysis.

[0064] In this embodiment, based on the acoustic effects of rotor noise propagation path, diffraction, and reflection, this invention establishes a UAV rotor noise directionality model. This model allows for the estimation of the UAV's azimuth and distance relative to the microphone array based on the intensity and frequency variations of noise signals received from different directions. The directionality of rotor noise refers to the differences in intensity, frequency distribution, and propagation mode of the noise signal generated by the UAV propeller as it propagates in space. This characteristic is closely related to the UAV's flight state (e.g., speed, altitude, rotor speed), the propagation characteristics of the air medium (e.g., wind speed, temperature, humidity), and the surrounding environment (e.g., buildings, terrain). By analyzing the directionality of rotor noise, the UAV's azimuth, flight state, and distance to the target can be effectively inferred, thereby improving ranging accuracy.

[0065] For example, the specific implementation process includes: Spectrum Analysis and Spectrum Feature Extraction: The spectral characteristics of rotor noise exhibit obvious periodicity and a multi-band structure. By performing frequency domain analysis on the received sound signal, the characteristic frequencies (such as the fundamental frequency and harmonics) and spectral variation characteristics of the rotor noise can be extracted. Based on these characteristics, the directionality of the rotor noise can be analyzed. The spectrum of rotor noise typically includes the fundamental frequency (determined by the rotor speed) and multiple harmonic components (related to the number of rotor blades and the rotational speed). The intensity distribution of these spectral characteristics differs in different directions and varies with flight conditions (such as the attitude of the UAV, rotor speed, etc.).

[0066] Therefore, in this invention, the measured sound pressure time signal is set as follows: The sound pressure level spectrum is obtained by performing time-domain averaging and spectral transformation on the signals at each measurement point. :

[0067] Among them, the sound pressure level spectrum Indicates the frequency of the sound signal and spatial direction The sound pressure level at a given location, measured in decibels (dB), describes the distribution characteristics of sound energy along the frequency dimension. In frequency and spatial direction Effective sound pressure at that location. The reference sound pressure level is 1000 m / s. The internationally accepted reference value is 1000 m / s. This corresponds to the audible threshold of the human ear. Sound pressure level spectrum. It reflects the loudness distribution of sound signals at different frequencies, and can be used to identify the fundamental frequency and harmonic characteristics of rotors, distinguish the differences in acoustic signatures of different models, and extract noise control and identification features. Based on the spectral characteristics, it can draw polar plots or three-dimensional sound pressure distribution maps at different angles.

[0068] Furthermore, by calculating the acoustic directivity index ( The sound directivity index (SDI) measures the concentration of sound energy from a sound source in its main radiation direction relative to the average sound energy in the entire space. The calculation formula is:

[0069] Among them, the acoustic directivity index The higher the value, the stronger the directionality of the sound source and the more concentrated the sound energy. For direction The square of the sound pressure level is proportional to the sound intensity or sound power, representing the magnitude of the sound energy in that direction. The main radiation direction represents the directional angle corresponding to the maximum sound pressure or sound energy. By calculating the ratio of the square of the sound pressure in the main radiation direction to the square of the directional sound pressure and performing a logarithmic transformation, the sound directivity index is obtained, which is used to characterize the degree of directional concentration of the radiated energy of the sound source.

[0070] Directional analysis is performed: Under different flight conditions, the spectral distribution of rotor noise exhibits directional changes. For example, during forward flight, the noise is stronger in front, while the noise intensity decreases during sideways or backward flight. By observing the spectral changes and combining the signal strength and time difference received by the multi-channel microphone, the direction of noise propagation can be inferred. In this invention, the directional analysis is specifically defined as consisting of the following noise components: Thickness noise is generated by the periodic cutting of air by the blade thickness and is related to the blade's speed and position. Loading noise is caused by the time-varying aerodynamic forces (lift and drag) of the blades. Turbulent-blade interaction noise (TBL-TE Noise): The interaction between the turbulent boundary layer and the trailing edge of the blade; Rotor-to-rotor interference noise: multi-rotor coupling effect.

[0071] These noise sources have specific radiation characteristics in space, thus forming noise distributions in different directions. This invention quantifies the degree of sound energy concentration of the sound source in the main radiation direction by calculating the sound directivity index (DI). The larger the value, the stronger the directivity. By accurately modeling the noise propagation direction, the ranging accuracy is significantly improved, and the systematic errors caused by traditional methods are reduced.

[0072] Step S5: Correction of multipath effects and compensation for environmental factors.

[0073] In this embodiment, in complex environments, rotor noise is often affected by multipath effects (such as diffraction, reflection, and refraction) and environmental factors (such as wind speed, humidity, temperature, and terrain). These factors may cause the propagation path of the noise signal to deviate or the signal to attenuate, thus affecting the accuracy of directional analysis. The complexity of multipath effects and noise propagation in the environment often leads to ranging errors. That is, in real-world environments, UAV rotor noise signals often do not propagate along a single path and are affected and interfered with by multipath effects. The main propagation paths include: Direct Path: The shortest path from the rotor to the microphone; First-order reflected sound: a sound reflected from the ground, building walls, or machine structure; Secondary or multiple reflections: multiple reflections between multiple surfaces; Diffraction: Sound that is diffracted by the edge of an obstacle; Scattering: Localized reflections caused by the fuselage, support structure, etc.

[0074] Multipath effects can lead to constructive / destructive interference, time delay superposition, spurious directional peaks, and periodic fluctuations in the spectrum (comb filtering). This invention establishes a multipath effect model for noise propagation and achieves dynamic compensation through real-time monitoring of environmental parameters (such as wind speed, temperature, and humidity), which can significantly improve ranging accuracy and greatly enhance adaptability in complex environments.

[0075] For example, establishing a multipath model for noise propagation and performing signal demixing and correction specifically includes: Multipath effect modeling: By establishing a multipath model of noise propagation paths, multipath interference in the signal is corrected. Simultaneously, advanced signal processing techniques (such as beamforming, time delay estimation, and least squares algorithms) are used to optimize the directionality estimation of the noise signal. The mathematical model for multipath propagation is as follows: Overall model: calculated from observed signals This represents the superposition of signals from multiple paths:

[0076] in, The source signal is the time-domain waveform of rotor noise. For the first The attenuation coefficient of the path, The propagation delay is N, where N is the number of paths (including direct sound). For example: Indicates a direct sound. Indicates ground reflection, Indicates body reflex. This indicates environmental scattering.

[0077] Propagation delay calculation: geometric path The calculation formula is as follows:

[0078] in, For path length, The speed of sound (value 343 m / s) represents the time delay for linear propagation.

[0079] Time delay corresponding to the ground reflection path:

[0080] in, The reference propagation distance / reflection path distance represents the geometric distance a sound wave travels from the sound source to the measuring point via the reflection path. Let be the coordinates of the measuring point (receiving point), representing the th . A microphone in three-dimensional space Axis coordinates Axis coordinates Axis coordinates (usually representing the height of the measuring point); Let be the coordinates of the sound source, representing the sound source in three-dimensional space. Axis coordinates Axis coordinates Axial coordinates; by calculating the equivalent straight path length of the sound wave propagating through the reflecting plane (such as the ground), the reflection path is equivalent to the mirror image point of the sound source below the reflecting surface.

[0081] Amplitude attenuation and phase correction: Amplitude attenuation and phase correction compensate for the energy loss and time delay of sound waves during propagation. The amplitude attenuation and phase delay correction of sound wave propagation are calculated according to the following formulas:

[0082] in, For the first The measuring point (or the first) The acoustic response amplitude of the propagation path (in the first propagation path) is represented by the amplitude of the first propagation path (in the second propagation path). The complex sound pressure or weighting coefficient contributed by the sound source at each receiving point is the fundamental quantity for subsequent array processing, beamforming, or direction estimation. For the first The reflection coefficient or amplitude weighting coefficient of a propagation path describes the amplitude change of a sound wave during propagation due to reflection, scattering, or changes in the medium. It is typically set to 1 in a direct path and less than 1 in a reflected path. The phase term of sound wave propagation represents the distance the sound wave travels. The phase delay generated above, where, The imaginary unit, The wave number is defined as follows:

[0083] in, For wavelength, For frequency, Speed ​​of sound; For the first The propagation distance between each measuring point and the sound source can be either the direct path distance or the equivalent distance of the reflection path, which determines the propagation delay and phase change of the sound wave.

[0084] In the directionality analysis of UAV rotor noise, a multipath geometric model is established to determine the possible reflection paths of each measurement point. The direct sound component is identified by time delay estimation, and the clean signal is extracted by reflection suppression algorithm. The corrected signal is then used for directionality calculation (pole plotting, DI calculation). This can significantly reduce false peaks in the radiation pattern and improve the angular accuracy of the main radiation direction.

[0085] For example, environmental compensation is performed: In practical applications, environmental factors such as temperature and humidity changes, and air flow can affect the propagation speed and direction of sound waves. Therefore, this invention requires real-time acquisition of environmental parameters and dynamic compensation to further improve the accuracy of directional modeling. Furthermore, to eliminate changes in sound speed and propagation direction deviations caused by the atmospheric environment, this invention establishes an environmental compensation model for sound wave propagation speed and direction, specifically implemented as follows: Constructing a sound speed environmental compensation model: based on measured temperature ,humidity Calculate the corrected speed of sound : Then, considering the drone's flight altitude, temperature gradient correction is performed:

[0086] in, altitude The speed of sound at that location The speed of sound at sea level For surface temperature, This represents the temperature lapse rate at altitude z.

[0087] Wind direction compensation: In a windy environment, the formula for calculating the speed of sound is:

[0088] in, To account for the speed of sound under wind speed, For the speed of sound, For wind speed, its component form is:

[0089] in, For path Effective speed of sound on the surface The angle between the wind direction and the direction of sound wave propagation. It represents the projected component of wind speed in the direction of sound wave propagation, which determines the magnitude and direction of the wind correction to the effective sound speed. The sound speed is compensated by measuring the wind speed and direction.

[0090] Path and delay compensation: Calculate the corrected propagation time based on the sound speed field and wind speed field.

[0091] in: For path The corrected sound wave propagation time is the actual propagation time from the sound source to the receiving point after considering the spatial variation of sound speed and the influence of wind field. For path The sound wave propagation path can be taken as a straight path (near distance / weak refraction approximation) or a curved path obtained by the refraction model (layered atmosphere / strong gradient). Let be the arc length (m) along the sound wave propagation path, used for integral accumulation along the propagation path; It is the equivalent speed of sound at a point on the sound wave propagation path. The phase delay and direction angle of sound wave propagation are calculated using the corrected propagation time, which effectively corrects the position of the main lobe of the directional spectrum and reduces angular drift and phase mismatch caused by the environment.

[0092] Step S6: Estimate distance and optimize accuracy In this embodiment, based on the established UAV rotor noise directionality model and combined with the sound signals received by the multi-channel microphone array, the flight direction of the UAV is calculated. By calculating the time difference of arrival (TDOA) and the Doppler effect, the relative distance between the UAV and the target is deduced. Furthermore, based on the directionality information of the rotor noise, ranging accuracy can be further optimized. Especially in complex environments (such as urban or forest areas with obstacles), accurately modeling the propagation direction of noise can significantly reduce errors in traditional ranging methods.

[0093] For example, in TDOA or Doppler ranging, the error mainly comes from:

[0094] If the propagation direction is not modeled, the system assumes that the sound ray propagates in a straight line. However, in reality, sound rays are often refracted (by wind or temperature gradients) or deflected (by near-field diffraction). Therefore, this invention only assumes that straight-line propagation will cause systematic deviations in distance, so systematic deviations must be corrected.

[0095] For example, ranging using TDOA (Time Difference of Arrival) includes: calculating the distance based on the time difference of sound waves traveling from the target to multiple microphones, and basing the distance on the speed of sound. Sound source coordinates ,microphone Location Calculate the sound source up to the th according to the following formula. The distance of each microphone :

[0096] in: For the first A microphone in three-dimensional space Axis coordinates Axis coordinates Axis coordinates (usually representing the height of the measuring point); These represent the positions of the sound source in three-dimensional space. Axis coordinates Axis coordinates Axis coordinates; Further defining microphone 1 as the reference, the time difference between any two channels is... for:

[0097] Each time difference corresponds to a hyperboloid constraint, as shown below:

[0098] Multiple microphones can form a system of equations, and the location of the sound source can be solved using the least squares method.

[0099] In the traditional TDOA method, the sound wave propagation path is assumed to be a straight line. This invention, however, introduces a sound wave propagation direction correction factor to adjust the path from the sound source to the... The distance of each microphone Make corrections:

[0100] in, To introduce the directional correction amount to the sound source to the first The distance of one microphone, For the first sound wave transmission Path; For the first sound wave transmission The propagation direction unit vector of each path (from the sound source to the receiver); This is a correction term for directional deviation caused by environmental gradients. This allows the arrival time of each microphone to be recalculated as an effective time delay, greatly reducing geometric errors caused by sound refraction.

[0101] For example, ranging using the Doppler frequency shift method includes: calculating the distance using the frequency shift caused by the relative velocity between the sound source and the receiver. The formula for calculating the frequency shift is:

[0102] In this context, "-" indicates proximity, and "+" indicates distance. The received sound source frequency. Let be the initial sound source frequency, and c be the speed of sound. The radial velocity of the sound source is denoted as .

[0103] When the sound source emits a known frequency, the relative radial velocity can be calculated by using the frequency shift. :

[0104] If the propagation direction is assumed to be a straight line, systematic errors will occur. The true direction should be approximated as "a straight line plus a small lateral deflection," then normalized to maintain a unit length, and corrected accordingly. Under the straight-line assumption, the... The direction unit vector of each receiving point The calculation formula is as follows:

[0105] in, For the first The position coordinate vector of each receiving point in three-dimensional space. For the sound source position vector, this invention introduces a small deflection "direction correction amount" (small angle of the vector). Corrected direction unit vector It is expressed as follows:

[0106] Using the corrected direction vector Re-estimation of sound source velocity :

[0107] in, For the first The speed of the sound source measured by a microphone.

[0108] By continuously observing the Doppler frequency shift at multiple moments and calculating the sound source velocity, the distance change is obtained by integrating the velocity:

[0109] in, The real-time distance between the UAV and the target at time t is the result of the integral calculation. Using the initial distance calculated from the initial position of the sound source obtained by TDOA (Time Difference of Arrival) as the starting reference for integration, the distance of the UAV can be calculated in real time. This is a time variable used to iterate through every tiny time point within the integration interval. For time infinitesimal elements, representing extremely small time intervals (differential units), used for the cumulative summation of velocity in integration operations.

[0110] For example, optimizing ranging accuracy using a joint TDOA-Doppler model includes: in complex environments, establishing a joint model to achieve high-precision ranging by fusing time difference of origin (TDOA) and Doppler frequency shift (FDOA) information; and using both time difference and frequency shift to calculate distance yields better results. The state vector is defined as follows:

[0111] The observation equation is defined as follows:

[0112] in, For the sound source (drone) at any time The position vector (m); All are the first The position vectors of each microphone (m); Represents the Euclidean norm (distance). The speed of sound (m / s) can be replaced by the effective speed of sound after temperature / wind field correction; The center frequency (Hz) used for Doppler calculations can be the rotor fundamental frequency or a selected characteristic line frequency; For the sound source at any time The velocity vector (m / s); All are the first The velocity vectors of the microphones (m / s) are set to 0 when stationary; The line-of-sight (LOS) unit vector from the sound source to the microphone; This is the measurement noise / estimation error term (usually modeled as zero-mean random noise). Let be the time difference (s) of arrival of the i-th microphone relative to the reference receiver. Let be the Doppler frequency shift / frequency difference (FDOA) of the i-th microphone relative to the reference receiver (Hz), where the line-of-sight (LOS) unit vector. The calculation formula is as follows:

[0113] By using EKF or UKF filtering and fusion updates, a joint estimation of the sound source position and velocity is achieved, thereby obtaining the distance, direction, and radial relative velocity of the UAV target.

[0114] Comparison of the method of the present invention with traditional methods: The present invention achieves an improvement in ranging accuracy.

[0115]

[0116] This invention reduces ranging errors by approximately 70–85% by accurately modeling the direction of sound wave propagation (combining sound speed field and beam direction constraints). It can accurately measure the distance and orientation information of UAVs, providing data support for tasks such as UAV monitoring and counter-UAV operations.

[0117] It should be noted that, in this invention, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0118] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An improved ranging method based on UAV rotor noise directionality modeling, characterized in that: The following steps are included: Step S1: Deploy a multi-channel acoustic sensor array to collect noise signals from the drone rotor in real time during drone flight; Step S2: Perform preprocessing on the acoustic signal in the acquired raw noise signal, including filtering, time synchronization, and normalization; Step S3: Perform time-domain and frequency-domain analysis on the preprocessed acoustic signal; Step S4: Establish a directivity model of UAV rotor noise and perform spectrum analysis and spectrum feature extraction; The spectral characteristics include a fundamental frequency determined by the rotor speed and multiple harmonic components related to the number of rotor blades and the rotor speed. The intensity distribution of these spectral characteristics varies in different directions and changes with the flight state. Step S5: Establish a multipath model for noise propagation, perform signal demixing and correction, and dynamically compensate the UAV rotor noise directionality model based on environmental factors. The dynamic compensation includes sound speed compensation, wind field direction compensation, and path and delay compensation. Step S6: Combining the time delay, intensity difference, and spectral change information received by the multi-channel microphone array, the relative distance between the UAV and the target is calculated using a geometric positioning algorithm, and a TDOA-Doppler joint model is established to optimize the ranging accuracy.

2. The improved ranging method based on UAV rotor noise directionality modeling according to claim 1, characterized in that: In step S1: Deploying a multi-channel acoustic sensor array includes: arranging multiple microphone sensors in the target area to form a spherical or hemispherical array, constituting a multi-channel acoustic sensor array that simultaneously receives noise signals of different intensities and frequencies from different directions; The noise signal collection from the UAV rotor includes collecting noise signal data multiple times under different flight states, including stationary, accelerating, turning, and climbing.

3. The improved ranging method based on UAV rotor noise directionality modeling according to claim 2, characterized in that: Step S3 includes: Step S31: Perform time-domain signal analysis; Step S32: Perform frequency domain signal analysis; Step S33: Construct a joint time-domain and frequency-domain analysis strategy to improve the accuracy of directional analysis through multi-dimensional signal processing; Step S34: Compare and verify the effects.

4. An improved ranging method based on UAV rotor noise directionality modeling as described in claim 3, characterized in that: Step S31 includes: a) Use a bandpass filter to remove low-frequency drift and high-frequency electrical noise from the original noise signal, perform time synchronization and background noise correction, and normalize the signal amplitude; b) Calculate the instantaneous amplitude of the sound pressure signal using Hilbert transform, and identify the periodic characteristics of rotor noise through envelope analysis; c) Identify the occurrence time of noise components, including the main spin frequency, harmonics, and turbulent broadband, through time-frequency analysis; compare the time-frequency energy of signals from different directions to determine the emission time and location of the noise source; d) Perform phase analysis, calculate the coherence function between different measurement points, evaluate signal correlation, and use the phase difference to calculate and correct the measurement point positioning error.

5. An improved ranging method based on UAV rotor noise directionality modeling according to claim 4, characterized in that: Step S32 includes: a) Extract the tip passage frequency and harmonic features from rotor noise; b) Perform broadband noise spectrum analysis to distinguish between structural noise and random turbulent noise; c) Calculate the power spectral density in each direction to achieve joint distribution analysis of acoustic energy in the frequency domain and spatial directional domain; d) Calculate the coherence function values ​​of the two measurement point signals at a specific frequency, identify the correlation between different directions, and determine the characteristics of the noise source.

6. An improved ranging method based on UAV rotor noise directionality modeling according to claim 5, characterized in that: Step S33 includes: The time-domain signal is segmented according to the rotor rotation cycle, and spectrum analysis is performed on each cycle, followed by phase-aligned averaging. The frequency domain directional spectrum is corrected by using the energy weight of the time domain envelope, so that the high-energy moments contribute more to the directionality calculation and highlight the main radiation characteristics; By integrating time-frequency energy in different directions using short-time Fourier transform, a "time-direction map" is generated to identify changes in the direction of transient noise. A multi-channel time-domain adaptive algorithm is used to filter out noise in the non-target direction. The direction of the time-domain envelope peak, the direction of the frequency-domain dominant frequency, and the direction of CFD simulation prediction are compared and corrected to obtain a comprehensive directional result.

7. An improved ranging method based on UAV rotor noise directionality modeling according to claim 6, characterized in that: Step S4 includes: Frequency domain analysis is performed on the received sound signal to extract the characteristic frequencies and spectral variation characteristics of the rotor noise, including the fundamental frequency and harmonics, and to analyze the directionality of the rotor noise. The sound directivity index is used to measure the degree of concentration of sound energy from a sound source in the main radiation direction relative to the average sound energy in the entire space. Under different flight conditions, the spectral distribution of rotor noise will show directional changes. By analyzing the changes in the spectrum, combined with the signal strength and time difference received by the multi-channel microphone, the direction of noise propagation can be inferred.

8. An improved ranging method based on UAV rotor noise directionality modeling according to claim 7, characterized in that: Step S5, which involves establishing a multipath model for noise propagation and performing signal demixing and correction, includes: A multipath model of noise propagation path is established to determine the possible reflection path of each measuring point. The direct sound component is identified by time delay estimation, and the clean signal is extracted by reflection suppression algorithm. The directionality of the corrected signal is calculated. Calculate the observed signal and use it to represent the superposition of signals from multiple paths; Calculate the propagation delay; Amplitude attenuation and phase correction are used to compensate for the energy loss and time delay of sound waves during propagation.

9. An improved ranging method based on UAV rotor noise directivity modeling as described in claim 8, characterized in that: Step S5 involves dynamically compensating the UAV rotor noise directionality model based on environmental factors, including: Construct a sound speed environmental compensation model: calculate and correct the sound speed based on the measured temperature and humidity, and then correct the temperature gradient based on the UAV's flight altitude; Wind direction compensation: The sound speed is compensated by measuring the wind speed and direction in a windy environment; Path and delay compensation: Calculate the corrected propagation time based on the sound speed field and wind speed field.

10. An improved ranging method based on UAV rotor noise directivity modeling according to claim 9, characterized in that: Step S6 includes: Distance measurement using TDOA: The distance is calculated by utilizing the time difference of sound waves traveling from the target to multiple microphones, and then a sound wave propagation direction correction is introduced to correct the calculated distance. Doppler is used for ranging: the frequency shift caused by the relative velocity between the sound source and the receiver is calculated. When the sound source emits a known frequency, the relative radial velocity is estimated by the frequency shift. When the propagation direction is a straight line, a systematic error will occur. The true direction is approximated as "straight line direction + small lateral deflection", then normalized to maintain the unit length, and corrected. The direction unit vector of the receiver point is then calculated, and a small deflection "direction correction amount" is introduced to correct the direction unit vector. The sound source velocity is then re-estimated using the corrected direction vector. Finally, the distance change is obtained by continuously observing the Doppler frequency shift at multiple moments and calculating the sound source velocity, and integrating the velocity. Ranging optimization is achieved using a TDOA-Doppler joint model: A joint model is established, which integrates TDOA and FDOA information for ranging. By using EKF or UKF filtering and fusion updates, the joint estimation of the sound source position and velocity is realized, and the distance, direction and relative radial velocity of the UAV target are obtained.

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