阅读说明：本技术 基于级联滤波器的旋翼和尾桨气动噪声分离方法 (Rotor wing and tail rotor aerodynamic noise separation method based on cascade filter ) 是由 魏春华 张卫国 王勇 马帅 郑谢 于 2021-10-14 设计创作，主要内容包括：本发明公开了一种基于级联滤波器的旋翼和尾桨气动噪声分离方法,其包括：使用Vold-Kalman滤波器来跟踪叶片通过频率谐波的特定级数,以分别提取主旋翼噪声和尾桨谐波噪声；利用宽带噪声的二阶循环平稳特征,利用循环维纳滤波器对剩余的宽带噪声进行过滤,以实现对主旋翼宽带噪声和尾桨宽带噪声的过滤。本发明具有原理简单、操作简便、精度高等优点。(The invention discloses a rotor wing and tail rotor aerodynamic noise separation method based on a cascade filter, which comprises the following steps: tracking a specific number of stages of blade passing frequency harmonics using a Vold-Kalman filter to extract main rotor noise and tail rotor harmonic noise, respectively; and the second-order cyclostationarity characteristic of the broadband noise is utilized, and the circulating wiener filter is utilized to filter the residual broadband noise so as to filter the broadband noise of the main rotor and the broadband noise of the tail rotor. The invention has the advantages of simple principle, simple and convenient operation, high precision and the like.)

1. A rotor and tail rotor aerodynamic noise separation method based on a cascade filter is characterized by comprising the following steps:

tracking a specific number of stages of blade passing frequency harmonics using a Vold-Kalman filter to extract main rotor noise and tail rotor harmonic noise, respectively;

and the second-order cyclostationarity characteristic of the broadband noise is utilized, and the circulating wiener filter is utilized to filter the residual broadband noise so as to filter the broadband noise of the main rotor and the broadband noise of the tail rotor.

2. The cascaded-filter-based rotor and tail rotor aerodynamic noise separation method according to claim 1, characterized in that for the main rotor and tail rotor broadband noise, the extracted main rotor broadband noise time domain signal and noise power spectral density PSD are respectively used as the frequency shift input of the cyclic wiener filter with the blade pass frequency and multiple harmonics.

3. The method according to claim 2, wherein the broadband noise of the main rotor and the tail rotor is analyzed by a cyclic spectrum analysis to obtain a cyclostationary feature, and an enhanced envelope spectrum EES of the broadband noise of the main rotor is further separated, and the blade passing frequency BPF and its higher harmonics of the main rotor and the tail rotor are obtained from a graph of the enhanced envelope spectrum EES.

4. The method of claim 2, wherein the filtering with the cyclic wiener filter comprises:

modulating broadband noise of main rotor and tail rotorAndresidual wideband noise after extraction of tonal noise from a Vold-Kalman filterAnd (3) medium separation:

wherein the subscript mr represents the main rotor, tr represents the tail rotor, b represents the wideband, representing the ambient wideband noise; the modulated broadband noise of the main rotor and the tail rotor is a cyclostationary signal; spectral redundancy is used to estimate from the residual wideband noise, in the frequency domainPer cycle frequency ofAn up-shift signal, wherein，A set of cyclic frequencies representing blade passing frequencies and higher harmonics of the main rotor or tail rotor;

the estimationIs represented as a regression problem:

where the subscript S denotes the signal to be separated, K denotes the number of cyclic frequencies, f denotes the signal frequency, denotes the estimated broadband noise of the main or tail rotor in the frequency domain, and the filter is designed to operate at frequencyThe frequency response of the time; the expression of the corresponding reduction of the frequency is as follows:

suppose thatIs a frequency shift observation vector of the residual wideband noise vector, denoted asWherein Is the cyclic frequency of the main or tail rotor, then the formula of the reduced cyclic regression is expressed in vector form; the goal is to find a branchMatrix arrayAnd making the estimation result:

the following solutions were found:

in rankUnder the equivalent constraint of (2), pedigreeIs defined asWherein V isThe discretized vector.

5. The cascaded-filter based rotor and tail rotor aerodynamic noise separation method of claim 4, wherein the equivalence constraint is imposed to force the accuracy of only one cyclostationary source, which number changes accordingly when more cyclostationary sources are observed:

wherein the content of the first and second substances,is first ofA feature vector associated with the feature value;

wherein the sum of the spectrum matrices is defined asAndthe modulated broadband noise sum of the main rotor and the tail rotor is extracted by estimation of the Welch method.

6. The cascaded-filter based rotor and tail rotor aerodynamic noise separation method according to any one of claims 1-5, wherein said Vold-Kalman filter comprises a structural equation and a measurement equation.

7. The cascaded-filter based rotor and tail rotor aerodynamic noise separation method of claim 6, wherein harmonic aerodynamic noise of a helicopter main rotor and a tail rotor is expressed as:

Wherein a slowly varying complex envelope on the shaft corresponding to order k, representing the instantaneous rotational speed of the shaft,the complex phase quantities, representing the k-th order, are a collection of all independent rotation axes,is a discrete set of correlation orders generated by an axis; broadband noise generated by flow noise, turbulence and transient events in actual helicopter rotor aerodynamic noise measurements, the overall measurement signal will be in the form of:

whereinIs a cause and effect relationship withIs not relevant.

8. The cascaded-filter based rotor and tail rotor aerodynamic noise separation method of claim 7, wherein the order tracking is converted to a complex envelope from the recorded response and the axis rotation speed of the orderEstimating a problem; structural equation specification, complex envelopeIs smooth and slowly varying compared to the carrier signal, for complex envelopeProvision is made for the repetition difference to satisfy one of the following equations:

whereinRepresenting a difference order ofThe difference operator of (a) is calculated,is a small heterogeneous term; the estimated complex envelope function is correlated with the measured data byThe measurement equation is implemented:

unknown complex envelope functionIn the method, a structural equation and a measurement equation appear in the form of measurement coefficients, a weighting function is selected, and the sum of unmeasured functions as redundant parameters is discarded to obtain a linear overdetermined equation set, so that a weighted linear least square method problem is constructed:

Of which the value is largeMandatory requirement of time pointsThe stationarity of the surroundings, while small values allow the observed data to dominate the estimation result at this point in time.

9. The cascaded-filter based rotor and tail rotor aerodynamic noise separation method of claim 8, wherein the complex envelope isWhen estimated, the tonal noise of the main and tail rotors is extracted:

whereinAndare discrete order sets corresponding to the blade frequency harmonics of the main and tail rotors respectively,

to aMain rotor with blades and oneTail rotor of blade, BPF order setAndcomprises the following steps:

。

Technical Field

The invention mainly relates to the technical field of noise analysis of aircrafts, in particular to a method for separating aerodynamic noise of a main rotor and a tail rotor based on a cascade filter, which is suitable for aircrafts such as helicopters with the combination of the main rotor and the tail rotor.

Background

With the ever-increasing demands on aircraft, such as helicopters, for reliability and comfort, noise problems have become an important factor limiting the large-scale use of helicopters. Analysis and control of helicopter noise is challenging because the noise is highly directional, highly dependent on flight conditions, unstable, and consists of several different noise sources. The main sources of noise in helicopters are the main rotor, tail rotor, gearbox and turbine. Acoustic measurements will record the sum of all these noise sources, and it is not trivial to determine the contribution of each source from the measured sound pressure time history. However, to understand, design, and evaluate noise reduction techniques, it is helpful to identify a single noise source.

The main rotor wing and the tail rotor aerodynamic noise are used as main noise sources of the helicopter, and independent noise reduction research needs to be carried out separately. Due to the coupling of the rotor, high levels of aerodynamic noise blades and air are generated during operation, accounting for a major portion of helicopter noise. Rotor aerodynamic noise can be classified into tonal noise and broadband noise according to frequency domain characteristics. Tonal noise is mainly composed of thickness noise, loading noise, Blade-Vortex Interaction (BVI) noise, and high-speed impulse noise, which is mainly discrete tones of the Blade Passing Frequency (BPF) and its higher harmonics. The load noise and the thickness noise are also collectively referred to as "rotational noise". The low-frequency part of the rotor noise is formed by the rotation process of the aerodynamic force of the blades and the air quantity rising in pulsation, and is the most serious part of the noise of the modern helicopter. The overall noise level of the helicopter plays a crucial role in the impact. Broadband noise is caused by turbulence near the blade surface and is continuously distributed in the frequency spectrum.

In the prior art, the traditional classical method only considers the periodic characteristics of the aerodynamic noise of a main rotor and a tail rotor to carry out harmonic separation. Due to the randomness of the broadband noise, the separation is usually not considered, which results in inaccuracy of the conventional main rotor and tail rotor aerodynamic noise separation method.

The classical approach to rotor aerodynamic noise analysis focuses primarily on noise prediction and numerical simulation based on the Ffowcs Williams-Hawkings equation. Overseas, Lowson and Wright developed some of the earliest applications of noise prediction theory to helicopter rotors, and conducted many studies on tonal noise and wideband noise prediction. However, formula-based noise prediction and numerical simulation have large calculation amount and low robustness, and cannot be well applied to the aerodynamic noise separation of the actual main rotor and the tail rotor. Thus, the noise separation problem can be solved by using a signal processing based approach.

Separation of rotor aerodynamic noise can be seen as a signal decomposition problem. To date, there are some common signal processing techniques used for signal decomposition in acoustic analysis. Empirical Mode Decomposition (EMD) is one of the powerful and widely used methods, and is widely used for blind source separation of rotating mechanical sound sources. Variations of the EMD method, such as integrated Empirical Mode Decomposition (EEMD) and Variational Mode Decomposition (VMD), have been developed to improve this technique. The resolved signals using such methods do not necessarily represent components associated with a particular mechanical source. Therefore, these methods are not suitable for separating the main rotor and the tail rotor aerodynamic noise.

The noise of the main rotor and the tail rotor mainly includes discrete tones of the Blade Passing Frequency (BPF) and its higher harmonics. Therefore, most methods of separating the main rotor and tail rotor noise rely on different rotational speeds of these rotors and their corresponding different BPFs.

For example, practitioners have proposed a separation method based on multi-leaf channel time averaging. The method divides the time signal into time windows of a length equal to the main or tail rotor blade pass period, then averages these windows using a conditional averaging technique and leaves an averaged main or tail rotor signal.

Another practitioner, Olsman et al, has proposed a method to generalize the classical fourier series method to non-periodic non-stationary data. The technique is directed to separating the main rotor and tail rotor noise using cubic Hermite-B spline interpolation of time-varying Fourier series coefficients. These methods separate rotor aerodynamic noise, i.e. tonal noise, only according to its harmonic characteristics and do not consider separating broadband noise. However, considering only tonal noise separation may result in separation that is not accurate enough.

The current separation method only considers the harmonic characteristics of rotor aerodynamic noise, and adopts a harmonic extraction method to separate the tonal noise of the main rotor and the tail rotor, so that the separation result is inaccurate. The invention provides a cascade filtering method by utilizing a Vold-Kalman filter and a circular wiener filter according to the periodic characteristics of tone noise and the second-order cyclic stationarity of modulated broadband noise, and the tone of a main rotor and the tone of a tail rotor are separated from the modulated broadband noise. The cascaded filtering method considers the separation of the rotor modulated broadband noise, so that the pneumatic noise separation of the main rotor and the tail rotor is more accurate, and the gap of the previous research on the noise separation of the main rotor and the tail rotor is filled.

Disclosure of Invention

The technical problem to be solved by the invention is as follows: aiming at the technical problems in the prior art, the invention provides the rotor and tail rotor pneumatic noise separation method based on the cascade filter, which has the advantages of simple principle, simplicity and convenience in operation and high precision.

In order to solve the technical problems, the invention adopts the following technical scheme:

a rotor and tail rotor aerodynamic noise separation method based on a cascade filter comprises the following steps:

tracking a specific number of stages of blade passing frequency harmonics using a Vold-Kalman filter to extract main rotor noise and tail rotor harmonic noise, respectively;

and the second-order cyclostationarity characteristic of the broadband noise is utilized, and the circulating wiener filter is utilized to filter the residual broadband noise so as to filter the broadband noise of the main rotor and the broadband noise of the tail rotor.

As a further improvement of the process of the invention: for main rotor and tail rotor broadband noise, the blade pass frequency and multiple harmonics are used as the frequency shift input of the cyclic wiener filter, respectively, and the main rotor broadband noise time domain signal and noise Power Spectral Density (PSD) are extracted.

As a further improvement of the process of the invention: and performing cyclic Spectrum analysis on the broadband noise of the main rotor and the tail rotor to obtain a cyclostationary feature, further separating an Enhanced Envelope Spectrum (EES) of the broadband noise of the main rotor, and obtaining a BPF (blast paging Frequency) and higher harmonics of the BPF of the main rotor and the tail rotor from a graph of the EES.

As a further improvement of the process of the invention: the process of filtering by using the circular wiener filter comprises the following steps:

modulating broadband noise of main rotor and tail rotorAndresidual wideband noise after extraction of tonal noise from a Vold-Kalman filterAnd (3) medium separation:

where the subscript mr denotes the main rotor, tr denotes the tail rotor, b denotes the wide band,representing ambient broadband noise. The modulated broadband noise of the main rotor and the tail rotor is a cyclostationary signal; spectral redundancy to recover residual wideband noiseMiddle estimationFrequency of each cycle in the frequency domainUp shift signalWherein，A set of cyclic frequencies representing blade passing frequencies and higher harmonics of the main rotor or tail rotor;

the estimationIs represented as a regression problem:

wherein the subscript S denotes the signal to be separated, K denotes the number of cycle frequencies, f denotes the signal frequency, whereinRepresenting the estimated broadband noise of the main or tail rotor in the frequency domain,is designed with filters at frequencyThe frequency response of time, K being the number of cycle frequencies; the expression of the corresponding reduction of the frequency is as follows:

suppose thatIs a frequency shift observation vector of the residual wideband noise vector, denoted asWhereinIs the cyclic frequency of the main or tail rotor, then the formula of the reduced cyclic regression is expressed in vector form; the goal is to find a transition matrix And making the estimation result:

the following solutions were found:

in rankUnder the equivalent constraint of (2), pedigreeIs defined as。

As a further improvement of the process of the invention: the constraint is imposed to force the accuracy of only one cyclostationary source, and when more cyclostationary sources are observed, this number changes accordingly:

wherein the content of the first and second substances,is prepared by reacting withThe first eigenvalue related eigenvector of (a);

wherein the frequency spectrum matrixAndis defined asAndextracting modulated broadband noise of main rotor and tail rotor by Welch method estimationAnd。

as a further improvement of the process of the invention: the Vold-Kalman filter comprises a structural equation and a measurement equation.

As a further improvement of the process of the invention: the harmonic aerodynamic noise of the main and tail rotors of a helicopter is expressed as:

whereinIs a collection of all the individual axes of rotation,is composed of a shaftA discrete set of generated correlation orders; broadband sound generated by flow noise, turbulence and transient eventsIn the actual measurement of the aerodynamic noise of a helicopter rotor, the total measurement signalWill be of the form:

whereinIs a cause and effect relationship withIs not relevant.

As a further improvement of the process of the invention: response and order from recording The axis rotation speed of (1), converting the order tracking into a complex envelopeEstimating a problem; structural equation specification, complex envelopeIs smooth and slowly varying compared to the carrier signal, for the envelopeProvision is made for the repetition difference to satisfy one of the following equations:

whereinRepresenting a difference order ofThe difference operator of (a) is calculated,is a small heterogeneous term; the estimated complex envelope function is related to the measurement data and is implemented by the measurement equation:

unknown complex envelope functionIn the case of structural and measurement equations in the form of measurement coefficients, by selecting a weighting functionAnd discarding unmeasured functions as redundant parametersAndand obtaining a linear overdetermined equation set, and constructing a weighted linear least square method problem:

of which the value is largeMandatory requirement of time pointsThe stationarity of the surroundings, while small values allow the observed data to dominate the estimation result at this point in time.

As a further improvement of the process of the invention: when envelope of complex numberWhen estimated, the tone of the main and tail rotorsTonal noise is extracted:

whereinAndare discrete order sets corresponding to the blade frequency harmonics of the main and tail rotors, respectively.

To aMain rotor with blades and one Tail rotor of blade, BPF order setAndcomprises the following steps:

。

compared with the prior art, the invention has the advantages that:

1. the rotor wing and tail rotor pneumatic noise separation method based on the cascade filter is simple in principle, simple and convenient to operate and high in precision, and provides a cascade filtering method for separating the tones of the main rotor wing and the tail rotor from the modulated broadband noise by using a Vold-Kalman filter and a circular wiener filter according to the periodic characteristics of the tones and the second-order cycle stationarity of the modulated broadband noise. The cascaded filtering method provided by the invention considers the separation of the rotor modulated broadband noise, so that the pneumatic noise separation of the main rotor and the tail rotor is more accurate, and the gap of the previous research on the separation of the main rotor and the tail rotor noise is filled. The invention solves the problem that the existing noise separation method only considers the harmonic characteristic of the rotor aerodynamic noise, and the harmonic extraction method is adopted to separate the tonal noise of the main rotor and the tail rotor, so that the separation result is inaccurate.

2. The invention discloses a rotor wing and tail rotor aerodynamic noise separation method based on a cascade filter, which is a cascade filtering method using a Vold-Kalman filter and a circulating wiener filter to separate the tones of a main rotor wing and a tail rotor wing and modulate broadband noise respectively. Broadband noise exhibits second order cyclostationarity due to the periodic modulation of the rotor rotational motion. According to the cyclostationarity, the method provided by the invention also separates the broadband noise while considering the pitch noise periodicity of the harmonic separation. In this way a more accurate separation of the main and tail rotors can be obtained.

Drawings

FIG. 1 is a schematic flow diagram of the process of the present invention.

FIG. 2 is a schematic diagram of the Vold-Kalman filter in a specific application example.

FIG. 3 is a schematic diagram of an implementation process of the cyclic wiener filtering based on Welch method estimation in a specific application example.

Fig. 4 is a schematic diagram of a helicopter noise time domain signal in a specific application example of the invention.

FIG. 5 is a schematic power spectral density plot of helicopter noise measurements in a specific application example of the present invention.

FIG. 6 is a schematic time domain signal diagram of the main and tail rotor tonal noise extracted in an example embodiment of the present invention; wherein (a) is the extracted main rotor noise time domain signal; (b) is a tail rotor noise time domain signal.

Fig. 7 is a schematic diagram of the power spectral density of the extracted tonal noise in an example embodiment of the present invention.

FIG. 8 is a schematic diagram of a Vold-Kalman filtered time domain signal in a specific application example of the present invention.

FIG. 9 is a diagram showing the PSD results of the main rotor broadband noise time domain signal and noise extracted in a specific application example of the invention; wherein (a) is the extracted main rotor broadband noise time domain signal; (b) is the main rotor broadband noise PSD.

Fig. 10 is a schematic diagram of the spectrum of the main rotor broadband noise emphasis envelope in a specific application example of the present invention.

FIG. 11 is a detailed flow chart of the present invention in a specific application example.

Detailed Description

The invention will be described in further detail below with reference to the drawings and specific examples.

As shown in fig. 1 and 11, the rotor and tail rotor aerodynamic noise separation method based on the cascade filter of the present invention includes:

a Vold-Kalman filter is used to track the specific number of stages of blade pass frequency harmonics to extract the main-rotor noise (main rotor noise) and the tail-rotor harmonic noise, respectively.

And filtering the residual broadband noise by using a circular wiener filter by using the second-order cyclostationarity characteristic of the broadband noise so as to realize the optimal filtering of the broadband noise of the main rotor (the broadband noise of the main rotor) and the broadband noise of the tail rotor.

In a specific application example, the Vold-Kalman filter of the invention is mainly used for tone noise separation. The Vold-Kalman filter is a time-domain tracking filter and is suitable for separating the rotation frequency harmonic from the rotation mechanical noise signal.

The helicopter rotor tonal noise mainly comprises the shaft frequency harmonic waves of a main rotor and a tail rotor and the blade passing frequency harmonic waves thereof. All these frequency components are essentially harmonics of the shaft frequency of the main rotor (main rotor) or tail rotor.

In a specific application example, as shown in fig. 2, the Vold-Kalman filter includes a structural equation and a measurement equation.

Referring to FIG. 3, taking a shaft as an example, the instantaneous rotational speed (in revolutions per second) of the shaft is shown as. The instantaneous angle of rotation is the time integral of the rotational speed, belonging tokComplex Phase Quantity of order (Complex Phase Quantity)Is defined as:

（1）

then a complex sequence time relationshipCan be defined as:

（2）

whereinIs a slowly varying complex envelope. The same format applies to the tail shaft.

The general case of harmonic aerodynamic noise of the main and tail rotors of a helicopter can then be expressed as:

（3）

whereinIs a collection of all the individual axes of rotation,is composed of a shaftA discrete set of generated correlation orders. Generated by flow noise, turbulence and transient eventsBroadband sound ofIn actual helicopter rotor aerodynamic noise measurements, the periodic tonal noise is also recorded in equation (10)The sum of (1). Total measurement signalWill be of the form:

（4）

whereinIs a cause and effect relationship withIs not relevant.

A limited non-aliased response time relationshipHas been discretized where the sampling rate is set to 1 sample per second without any loss of generality. Shaft Shaft rotation speed ofAlso assumed to be obtained by observing the encoder or tachometer.

Then, from the recorded response and orderThe axis rotation speed of (1), converting the order tracking into a complex envelopeA problem is estimated.

In the present invention, the Vold-Kalman filter is related to the classical Kalman filter by making a compromise between structural equations and measurement equations, in which only the ratio between the two sets of equations is used.

The structural equation specifies that, due to the inertia of the rotor, the complex envelopeShould be smooth and slowly varying compared to the carrier signal. For envelopeOne way to specify this is to require that the repetition difference should be small, for example, one of the following equations is satisfied:

whereinRepresenting a difference order ofThe difference operator of (a) is calculated,is a small heterogeneous term. In addition to the stationary condition of the structural equation, the estimated complex envelope function must be related to the measurement data in some way, which can be achieved by the measurement equation:

（8）

unknown complex envelope functionIn the form of measurement coefficients on the left-hand side of the structural equation and the measurement equation, it is thus possible to select a weighting functionAnd discarding unmeasured functions as redundant parameters Andand obtaining a linear overdetermined equation set and constructing a weighted linear least square method problem.

Of which the value is largeMandatory requirement of time pointsThe stationarity of the surroundings, while small values allow the observed data to dominate the estimation result at this point in time.

The basic formula (9) and formula (10) are usually solved as a linear least squares problem.

Then, when the complex envelopeWhen estimated, tonal noise of the main and tail rotors can be easily extracted.

WhereinAndare discrete order sets corresponding to the blade frequency harmonics of the main and tail rotors, respectively.

To aMain rotor with blades and oneTail rotor of blade, BPF order setAndcomprises the following steps:

in a specific application example, the invention utilizes a circulating wiener filter to separate broadband noise, and the second stage of the cascade filter is to modulate the broadband noise of a main rotor and a tail rotorAndresidual wideband noise after extraction of tonal noise from a Vold-Kalman filterSeparating.

The modulated broadband noise of the main rotor and the tail rotor is a cyclostationary signal. Separation from noise measurementsThe cyclostationary source method is to use a circular wiener filter. Spectral redundancy can be used to recover residual wideband noise Middle estimationThe method is to design an optimal filter for each cycle frequency in the frequency domainUp shift signalWherein，A set of cyclic frequencies representing the blade pass frequency and higher harmonics of the main rotor or tail rotor. Filtering the measurements over the shifted cyclic frequency is destructive to noise, and constructive to the desired source due to averaging effects. Thus, the estimate can be expressed as a regression problem.

WhereinRepresenting the estimated wideband noise (CS 2 signal) of the main or tail rotor in the frequency domain,is designed with filters at frequencyFrequency response of time, K is the cycle frequencyThe number of the cells. The frequency versus step down of equation (16) (assuming a limited number of cyclostationary sources) is expressed as follows.

Suppose thatIs a frequency shift observation vector of the residual wideband noise vector, denoted asWhereinIs the cycle frequency of the main or tail rotor. Then, equation (16) of the regression of the reduction cycle can be expressed in a vector form. Thus, the goal is to find a transition matrixAnd making the estimation result:

this problem can be solved by finding the following solutions:

in rankUnder the equivalent constraint of (2), pedigreeIs defined as. It should be noted that the rank-one constraint is imposed to force the accuracy of only one cyclostationary source, and when more cyclostationary sources are observed, this number should be changed accordingly. This excellence The solution of the problem is given

（19）

Wherein the content of the first and second substances,is the feature vector associated with the first feature value.

（20）

Wherein the frequency spectrum matrixAndis defined asAndit can be estimated by the Welch method. The implementation of the cyclic wiener filtering based on the Welch method estimation is shown in fig. 5. Thus, modulated broadband noise of the main rotor and tail rotorAndcan be extracted by equation (16).

The effectiveness of the cascaded filtering main tail rotor noise separation method provided by the invention is verified by taking a helicopter whole-aircraft noise wind tunnel test as an example. In the test, the helicopter scaling model is in a hovering state. The main rotor and tail rotor rotate together at speeds of 1428 rpm and 7230 rpm, respectively.

According to theoretical calculationObtaining, main rotor shaft frequencyShaft frequency of tail rotorMain rotor through frequencyPassing frequency of tail rotor. The noise is collected and picked up through a microphone, the sampling frequency is 102.4KHz, and the collection time is 20 seconds. The time domain signal collected by the microphone is shown in fig. 4.

The Power Spectral Density (PSD) of the noise measurement is shown in fig. 5. The tonal noise blades of the main and tail rotors are clearly visible both by frequency components and higher harmonics, marked by triangles and circles in the figure, respectively.

The cascaded filtering method proposed by the present invention is applied to the measurement signal to separate the main rotor and tail rotor tones and broadband noise.

Step S1: the tonal noise of the main rotor and the tail rotor is extracted by using the Vold-Kalman filter (through formulas 1-14) provided by the invention.

The time domain signals of the main and tail rotor tone noise are extracted as shown in fig. 6(a) and fig. 6(b), respectively. The Power Spectral Density (PSD) of the extracted tonal noise is shown in fig. 7. The raw measured PSD, extracted main rotor tone noise, and extracted tail rotor tone noise are plotted in a graphical window with a solid black line, a dashed black line, and a solid gray line, respectively. It can be seen from the figure that the extracted tonal noise of the main rotor and the tail rotor is matched with the original blade passing frequency component, thereby verifying the effectiveness of the method for extracting the tonal noise of the main rotor and the tail rotor in the first step (Vold-Kalman filter) of the method.

After the tonal noise is extracted through the Vold-Kalman filtering, the residual signal is a broadband noise signal. The filtered time domain signal is shown in fig. 8.

Step S2: the broadband noise of the main rotor and the tail rotor is separated from the residual broadband noise by using the circulating wiener filter provided by the invention through formulas 15-20.

For main rotor and tail rotor broadband noise, the blade pass frequency and multiple harmonics are used as the frequency shift inputs to the cyclic wiener filter, respectively. The extracted main rotor broadband noise time domain signal and noise PSD results are shown in fig. 9(a), (b).

To verify the effectiveness of the algorithm, the main rotor broadband noise is further subjected to cyclic spectrum analysis to obtain more cyclostationary features, and an Enhanced Envelope Spectrum (EES) of the main rotor broadband noise is isolated, as shown in fig. 10. The BPF of the main rotor and its higher harmonics are clearly seen in the EES diagram, which is circled with a triangle in the EES, illustrating the good separation of the main rotor broadband noise by the method of the present invention.

Similarly, the tail rotor broadband noise separation is similar to the analysis method for the main rotor, and is not described here again.

The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.