JPH0663821B2 - Aircraft model identification method - Google Patents

Description

Detailed Description of the Invention

The present invention relates to an aircraft model identification method, and more particularly to an aircraft model identification method that utilizes sound pressure fluctuation of noise generated by an aircraft during flight or ground operation.

Aircraft noise accounts for a large proportion of noise pollution affecting the living environment. Since the establishment of the environmental standard for aircraft noise in 1973, the measurement and monitoring of aircraft noise have been frequently performed in line with its purpose. Among them, there are large-scale systems in many places around the airfield to monitor aircraft noise exposure over a long period of time, but in these, only noise caused by aircraft is specified. It has been recognized that it is necessary to classify and observe observations according to aircraft type and flight mode. However, although a method for automatically distinguishing between aircraft noise and other noise has been developed and put into practical use in recent years, there is still no established method for automatically identifying the type and flight form of an aircraft. The current situation.

The present invention has been made in view of the above circumstances. The type of an aircraft and the flight mode are identified using the noise itself generated by the aircraft, and acoustic measurement is performed at a noise observation point. It is an object of the present invention to provide a model identification method of an aircraft that can also identify the type and flight mode of the aircraft only from the above.

The present invention will be described in detail below with reference to embodiments. In the aircraft model identification method of the present invention,
First, a reference pattern is created for each group (hereinafter referred to as a model) that is basically classified into known types of aircraft and flight modes. Next, in the actual identification process for the unknown aircraft to be identified, the weighted distance in the vector space of the reference pattern and the detection pattern by each of the characteristic parameters of the discrimination target is calculated to determine which aircraft the aircraft belongs to. Is to provide a method for identifying the aircraft model.

In the following embodiments, in order to clarify the content of aircraft noise, the type of aircraft is, for example, "large four-engine turbojet transport aircraft and model number", "small twin-engine turbojet training aircraft and model number", " It is classified into "Large 4-engine turboprop transport aircraft and model number", "helicopter", "other jet aircraft" and "other propeller aircraft", and flight modes are classified into "takeoff" and "landing", for example.

Further, in this embodiment, the detection pattern is obtained by extracting the characteristic parameter after the frequency band analysis value of the aircraft noise is made relative to the peak value of the level fluctuation before analysis and the time position of the peak. To

FIG. 1 is a flow chart showing an embodiment of the present invention. In this embodiment, model discrimination is performed in two stages.

In the following description, VX indicates "vector X", VPi indicates "vector Pi", VY indicates "vector Y", VQi indicates "vector Qi", and VQp indicates "vector Qp". , And VQj indicates “vector Qj”.

In the first step, first, characteristic parameters are extracted from the noise sample of the aircraft to be identified observed in step 1, and the sample pattern VX = (X ₁ , X
₂ , ... X _N ), and in step 2, this sample pattern VX and the reference pattern VPi = (Pi = Pi of the characteristic parameter prepared in advance for each model (total number M))
₁ , Pi ₂ , ... Pi _N ), i = 1, ..., M, and calculates the distance D ₁ (VPi, VX) (the definition of the distance will be described later), and the smallest one is min. {D ₁ (VP
i, VX)}, min {D in step 3
_{If 1} (VPi, VX)} is also smaller than the preset discrimination threshold D ₀ , the sample is min {D ₁
It is determined that the model belongs to the model corresponding to (VPi, VX)} (step 4), and the process ends.

If D ₀ <min {D ₁ (VPi, V
X)}, it is determined that it does not belong to any of the M models, and the process proceeds to the second stage. First, in step 5, the sample pattern VY with a characteristic parameter different from that in the first stage is used.
Then, in step 6, the reference pattern VQ is calculated.
Using j (jet) and VQp (propeller), new distances D ₂ (VQj, VY) and D ₂ (VQp, V
Y) is calculated, and the magnitudes of the calculated values are compared in step 7, and it is determined whether another jet machine (step 8) or another propeller machine (step 9). If necessary, regardless of whether the machine is a jet machine or a propeller machine, the classification is performed according to the peak level of the loudness of the sound, and the determination procedure is ended.

FIG. 2 is a flow chart of another embodiment, and in this embodiment as well, the discrimination procedure is composed of two stages. In the first step, first, in step 11, the sample pattern VY is calculated from the noise sample of the aircraft to be identified, and in step 12, the distances D ₂ (VQj, VY) and D ₂ (VQp, VY) are calculated, In step 13, the magnitudes are compared to determine whether the sample is a jet machine (step 14) or a propeller machine (step 15).

Next, in the second stage, for each of the jet machine and the propeller machine (only the jet machine side is shown in the figure), the sample pattern VX is calculated in step 16 and the reference pattern prepared in step 17 is used. Of the distance D ₁ (VPi, VX), i = 1, ..., M is calculated, and in step 18, min {D ₁ (VPi, VX)} is the discrimination threshold D as in the case of the embodiment of FIG. If it is smaller than ₀ , it is judged that it belongs to the model corresponding to min {D ₁ (VPi, VX)} (step 19), and if not, it is judged to be another jet machine (step 20).

If it is determined to be other, classification is performed according to the peak level of the loudness, if necessary, and the process ends.

Now, the distance between the sample pattern and the reference pattern used in the above-mentioned two embodiments will be described. First, consider a vector space composed of N “feature parameters” extracted from the sound pressure fluctuation of aircraft noise.

When the total number of models to be identified is M and the "reference pattern" of the i-th model is Pi, the distance D to the sample pattern X of the sample to be identified is D.
₁ (VPi, VX),

[Equation 1] It is defined as Here, Win is a weight.

In the first and second embodiments described above, this weight W
The reciprocal of the variance in the n-th component of the i group is used for in.

The distance D ₁ (VPi, VX) defined here
Is similar to the Mahalanobis generalized distance used to measure distances between multivariate normal populations (with equal covariance matrices), or the weighted Euclidean distance in the simpler case. However, the difference is that the weight Win is given for each model and each feature pattern.

If weights are given to each model and characteristic parameter in this way, when the stability of a certain characteristic parameter of a certain model is not desirable, in other words, when the variance is very large, the weight that is the reciprocal of the weight. Is a very small value and has little effect on the calculated distance.

On the contrary, if the model to which the machine belongs can be specified only by looking at the values of some of the characteristic parameters, all the weights Win other than the model and the characteristic parameter are set to sufficiently small values and they are It can be said that it should have little effect.

In this way, the selection of the characteristic parameter that works effectively for each model has two advantages of shortening the calculation time and reducing the storage capacity of the reference pattern. It is considered to be useful when configuring the identification device used in the method of the present invention as a dedicated hardware device.

Thus, for each component of the characteristic parameter,
By using the distance weighted by the reciprocal Win of the variance for each model, the difference in the reference pattern between the models is emphasized, which leads to the improvement of the discrimination ability.

Distances D ₂ (VQj, VY) and D ₂ (VQ
The same definition is used for p, VY),
It shall be based on different feature parameters. Next, a method of selecting a characteristic parameter that is the basis of the identification process, and a method of creating a reference pattern and a detection pattern for each model, which includes the characteristic parameter, will be described.

FIG. 3 shows an example of the arrangement of measurement points in the field survey conducted for observing the sound pressure fluctuation of aircraft noise for this purpose. In this example, one measurement point was provided on each extension of the runway 31 and immediately below the flight path about 1 km away from the ends of the runway 31 to observe aircraft noise during takeoff and landing. Noise measurement microphone 3 placed at the measurement point
Although 2a and 32b were installed 1.2 m above the ground, the altitude to the aircraft was tens to hundreds of meters.

It is considered that the reference pattern need only be created once in principle, but in general, the place where noise is observed is often located far from the runway 31. From the microphones 32a and 32b for measuring noise to the aircraft. The altitude of will also change greatly. As will be described later, if the frequency characteristics of the noise from which the characteristic parameter is calculated change significantly, the aircraft noise will be acquired in advance at the observation point and the reference pattern will be calculated. Also,
The height of the microphones 32a and 32b from the ground is not limited to 1.2 m and may be appropriately selected.

The signal levels of the noise measurement signals of the aircraft obtained from the microphones 32a and 32b correspond to the noises of 5 aircrafts of the same model (for example, the noise at the time of take-off of a twin-engine small jet trainer). As shown in FIG. 4, the known or unknown aircraft gradually approaches from a distant place, passes through at or near the measurement point, and then moves to a distant place. Then, there is a transient change in which the signal level gradually changes from a low signal level to a high level, reaches a peak level at or near the measurement point position, and then decreases to a low signal level.

Here, the peak of the noise change curve differs depending on the aircraft model, mainly based on the difference of the noise source installed in the aircraft, and the noise change curve shows a predetermined signal level. The time during crossing (i.e., the transit time of the aircraft) varies primarily due to differences in the speed of the aircraft.

In the present invention, the characteristic pattern for discriminating the model by paying attention to the characteristics of the peak and the passage time of the noise change curve is first obtained from the noise change curve of the frequency band component included in the noise measurement signal. Extraction is performed from the obtained frequency component peak value level, its change amount, and the change at the peak time.

The reference pattern for the known aircraft and the detection pattern for the unknown aircraft are calculated by the following method.

That is, first, the microphone 32a,
The noise measurement signal obtained from 32b is taken in through a wideband bandpass filter, and in FIG.
As shown by the noise change curve in (A), it is obtained as noise change information that represents the overall level change of noise that occurs each time one aircraft passes, and the peak level value and the time when the peak level occurs are Desired.

In the waveform diagram of FIG. 4, the temporal change in the signal level of the sound of the aircraft of the same type of five aircraft is subjected to relative processing with respect to the time axis and the signal level as described later, and
The waveforms of the book are shown superimposed on one another.

Here, the noise change information is from the peak level over the range before and after the peak level.
The portion of the curve above the 10-15 dB lower level is captured as significant information.

In the case of this embodiment, a wideband bandpass filter having an A characteristic of JIS C1505-1988 is used, and a signal level suitable for human hearing characteristics when listening as noise is evaluated by this. It is designed to get the revised information.

It is assumed that the number of samples to be acquired is an appropriate number depending on the type of aircraft to be identified and the flight mode.
It is desirable that the number is sufficient to estimate the average and variance of the population of the feature parameters to be extracted, but for example, 5 to 10 is sufficient.

Second, the noise measurement signals obtained from the microphones 32a and 32b are separated into frequency component signals in real time by a plurality of narrow band pass filters, and in FIG. 4, f ₀ = 80Hz, 160Hz, 200Hz. , 400Hz, 500Hz
, As shown in the noise change curves of 1000Hz and 1250Hz, 1
The change in noise that occurs each time one aircraft passes is extracted as the change in the signal level of the multiple frequency components included in the noise measurement signal, and the type of noise source included in the change in noise ( That is, get information about "jet engine engine sound" and "turbo sound" of jet aircraft, "propeller sound" and "engine sound" of propeller aircraft, "rotor blade sound" and "engine sound" of helicopter). .

In the case of this embodiment, a 1/3 octave band filter of JIS standard C1513-1983 is applied as a narrow band pass filter, whereby the center frequency f ₀ is 80 Hz, 160 Hz, 200 Hz, as shown in FIG. 400H
The frequency components of the z, 500 Hz, 1000 Hz and 1250 Hz frequency bands can be extracted.

Thirdly, the peak level value of the noise level change of the noise change information of the broadband band pass filter and the peak occurrence time are held, and the level is adjusted so that the peak level value coincides with a predetermined reference level L _A. As a result, the measured noise change information is relativized with respect to the signal level.

By performing the relativization process for the peak level value, it is possible to avoid the variation of the pattern due to the change of the distance between the aircraft and the observation point (which occurs with the passage of time), and to make the noise on the ground or the like. Even if the light is reflected and enters the microphone, it is possible to avoid the influence on the pattern.

Along with this, fourthly, in the noise change information of the broadband band pass filter, the peak occurrence time of the change in the noise level is time-axis adjusted to a predetermined reference peak occurrence time T _A ,
As a result, the measured noise change information is relativized with respect to the time axis.

Fifth, the signal levels and time bases of the plurality of frequency components are adjusted by the level adjustment amount and the time base adjustment amount for the noise change information, and thereby the relativization processing is performed for the plurality of frequency components. .

Incidentally, the magnitude and time axis of the aircraft noise converted into the noise measurement signal by the microphones 32a and 32b are based on the flight position and the passage time of the aircraft when passing on or near the measurement point. However, the dispersion of the measurement information can be suppressed by performing such a relativization process, and as a result, a plurality of curves (in this case, 5 dB as shown by the curve “dB (A)” in FIG. 4) can be obtained. Curves) can be overlapped with each other so as to coincide with the reference peak level L _A and the peak generation time T _A at the peak level and the generation time.

When such relativization processing is performed, the curves f ₀ = 80 Hz, 160 Hz, 200 Hz, 400 Hz, 500 Hz, 1000 of the curve in FIG.
As shown in Hz and 1250 Hz, the distribution of the measurement information can be effectively suppressed by overlapping the five curves with respect to the peak level and the peak occurrence point for the frequency component as well.

Sixth, the peak level values of the plurality of frequency components thus relativized with respect to the signal level and time axis are held, and the plurality of peak level values are obtained as basic characteristic parameters. As a result, the reference pattern can be formed by the peak level values of the multiple frequency components of the known aircraft type, and the detection pattern can be formed by the peak level values of the multiple frequency components of the unknown aircraft type. Can be formed.

The peak level values for a plurality of frequency components forming the reference pattern and the detection pattern thus formed are expressed as a characteristic curve diagram with the horizontal axis as frequency. It can be represented as an array shape. Therefore, if the array shape of points as shown in FIG. 5 is obtained for each model, it is possible to represent the type of noise generation source possessed by each model aircraft by the difference in the shape.

At each point in FIG. 5, "white circles" indicate peak levels (dB) of frequency components of each frequency band, and "line segments that cross the white circles in the vertical direction" indicate average levels of observed values (center of white circles). The standard deviation (variation range) from the point position) is shown.

In the case of this embodiment, the aircraft noise identification system obtains the peak level values of a plurality of frequency components as basic characteristic parameters, and uses this to obtain the next characteristic parameter for model identification.

First, for each model, the peak level value Li (i = 1, 2 ... L) of the frequency component obtained from each of the i = 1, 2 ... Lth 1/3 octave band filters, deviation from the relative criterion level value L _a used when the relative treatment as described above _{ΔLi = Li-L a (i} = 1,2
... L) dB is obtained, and the model is identified using this deviation ΔLi as a characteristic parameter. Thus, a relatively small amount of data can be used to make the similarity determinations described above with respect to FIGS.

In the case of this embodiment, the fluctuation of the frequency component due to the Doppler effect occurs as a change within the 1/3 octave band.

Secondly, for each model, the signal level of the frequency component obtained from the i = 1, 2, ... Lth 1/3 octave band filter is a predetermined level Δ from the peak level value.
The time during which the threshold level lower by L (for example, ΔL = 5 to 10 dB) is exceeded is continued time Di (i = 1, 2, ...
... L), and the model is identified using this duration Di as a characteristic parameter.

Third, for each model, the time T _{A at} which the output signal of the wideband bandpass filter (ie, noise change information) reaches the peak level and the output signal of the 1/3 octave band filter (ie, each frequency component). Time difference Δ with time Ti (i = 1, 2 ... L) when reaches the peak level
Ti = Ti−T _A is obtained, and the model is identified using this peak level generation time difference as a characteristic parameter.

Fourthly, for the jet machine and the propeller machine (and the helicopter), i = 1, 2 ... The peak level value Li (i = 1, 2) of the frequency component obtained from the Lth 1/3 octave band filter. ... L), the difference between two peak level values Li and Li + 1 extracted from frequency bands adjacent to each other within a predetermined frequency range in the frequency axis direction is calculated, and the sum of squares of the absolute value of the difference is calculated. Is calculated and is used as a characteristic parameter to discriminate between a jet aircraft and a propeller aircraft (and helicopter).

For example, the first-order difference ΔL (1) is given by

[Equation 2]

[Equation 3] However,

[Equation 4]

[Equation 5] Can be obtained by

As described above, when the sum of squares of the differences between the peak values of the frequency components adjacent to each other in the frequency axis direction is obtained and used as the characteristic parameter, the jet machine and the propeller machine (and the helicopter) can be easily distinguished. You can do it.

Incidentally, in general, the noise spectrum of a jet machine makes a comparatively gentle spectrum change in the frequency axis direction, whereas the propeller machine (and helicopter)
Noise is mainly generated from the rotating part (that is, propeller, swirl blade, etc.), so that the spectrum changes drastically in the frequency axis direction (that is, only specific frequency components become large). Therefore, if the difference information representing the change in the peak value of the adjacent frequency components is used as the characteristic parameter,
The jet aircraft and the propeller aircraft (and helicopter) can be reliably identified.

According to the above configuration, the difference in the characteristics between the model groups can be emphasized and clarified by weighting the distance calculation for discrimination for each model and for each characteristic parameter. Parameter selection can be done very simply.

All the parameters are sorted by model, and the parameters for which the data is well organized and the variance is small for a specific model and the variance is large for other models may be appropriately extracted. .

On the other hand, conventionally, only the level value of the spectrum is simply used as the characteristic parameter,
Since the relativization process or the process for obtaining the peak value of the frequency component from the predetermined frequency band is not performed as in the above-described embodiment, the characteristic parameters are not well organized and the type and flight form of the aircraft It is difficult to extract the difference,
Moreover, since the number of feature parameters used is large, there is a problem that the configuration and processing time of the entire model identification system becomes large.

Another embodiment of the reference pattern of the characteristic parameters created for the example of the airfield shown in FIG.
Shown in the table. In this example, which uses a total of 10 patterns, all the flying aircraft are classified into 7 types shown in Table 2, other jet aircraft, and other propeller aircraft. A reference pattern and weighting factors were created. Then, based on this, the result of trying the model discrimination processing using the observation data for a total of 5 days is shown in Table 3 as an identification errata. In Table 3, Out is the identification result by the method of the present invention, and In represents the true model. Also, T / O indicates takeoff and L / D indicates landing. Despite the fact that the number of feature parameters used for discrimination is only 10 in this way, a very good discrimination ability can be obtained.

[Table 1]

[Table 2]

[Table 3]

In the embodiment of FIG. 4, when the peak level value of the frequency component is obtained, the center frequency f ₀ is f ₀ = 80 Hz to 125 H as a 1/3 octave band filter.
Although it has been explained that z is selected, the frequency range
A plurality of frequency bands may be set between 25 Hz and 8 kHz as needed.

If there is a possibility that the low frequency component has a characteristic, such as a transport aircraft having a large turbofan type engine, it is sufficient to include up to that band.

Further, in the above-mentioned embodiment, the 1/3 octave band filter is used to obtain the frequency component signal, but the present invention is not limited to this. For example, a 1/1 octave band filter is used. You may make it use the thing of a wide frequency band together.

Further, in the above embodiment, the reference relativity information is a wide band filter having the A characteristic.
The present invention is not limited to this, and C characteristics (JIS standard, C1505
-1988), which has a characteristic of drooping in a low frequency band and a high frequency band may be used. With this configuration, it is possible to suppress the disturbance generated in the relatively low frequency band and the high frequency band based on the weather condition or the like.

As is apparent from the above description, the present invention can automatically, quickly and accurately identify the type and flight form of an aircraft only from the acoustic measurement of noise generated by the aircraft. , Its effect is great.

[Brief description of drawings]

FIG. 1 is a flow chart of one embodiment of the present invention.

FIG. 2 is a flow chart of another embodiment.

FIG. 3 is a point arrangement diagram of an example of noise measurement in the same manner.

FIG. 4 is a diagram in which aircraft noise frequency analysis results are overlaid in order to explain the feature of feature parameter extraction.

FIG. 5 is a diagram similarly showing an example of relative levels of respective frequencies.

[Explanation of symbols]

31 ... Runway, 32a, 32b ... Microphones for noise measurement.

 ─────────────────────────────────────────────────── ─── Continuation of the front page (72) Inventor Susumu Shimizu 3-20-41 Higashimoto-cho, Kokubunji, Tokyo Inside Kobayashi Institute of Science (56) Reference JP-A-56-163425 (JP, A) JP-A-SHO 55-64300 (JP, A)

Claims (3)

[Claims] (A) A reference measurement signal representing a change in aircraft noise while an aircraft of a known type passes through a measurement point or its vicinity, and a frequency signal component included in the reference measurement signal is determined. By extracting for a plurality of frequency bands of, a plurality of reference frequency component signals representing changes in the frequency signal component while the aircraft of the known model passes through the measurement point or its vicinity, the plurality of reference frequencies The peak value of the component signal is held as the reference patterning information, the peak value of the reference measurement signal is held as the reference relativity information, and the peak value of the relativity information is calculated from the plurality of peak values of the reference patterning information. A plurality of peak values obtained by subtraction are obtained as a reference pattern, and (b) an unknown aircraft type to be measured passes the measurement point or its vicinity. Give the detection measurement signal of representing a change in aircraft noise, the frequency signal component contained in said detection measurement signal by extracting the said predetermined plurality of frequency bands,
Obtaining a plurality of detected frequency component signals representing changes in the frequency signal components while the aircraft of the unknown model passes the measurement point or its vicinity, and detects the peak values of the plurality of detected frequency component signals, respectively. Hold as a peak value of the detection measurement signal as detection relativity information, a plurality of peak values obtained by subtracting the peak value of the detection relativity information from the plurality of peak values of the detection patterning information (C) When the degree of similarity is within a predetermined range by comparing the detection pattern with the reference pattern, the unknown model is the known model corresponding to the reference pattern. A method for identifying a model of an aircraft, characterized by determining. 2. (a) Obtaining a reference measurement signal that represents changes in aircraft noise while an aircraft of a known type passes through a measurement point or its vicinity, and determine a frequency signal component contained in the reference measurement signal. By extracting for a plurality of frequency bands of, a plurality of reference frequency component signals representing changes in the frequency signal component while the aircraft of the known model passes through the measurement point or its vicinity, the plurality of reference frequencies The peak value of the component signal is held as the reference patterning information, the peak value of the reference measurement signal is held as the reference relativity information, and the peak value of the relativity information is calculated from the plurality of peak values of the reference patterning information. A plurality of peak values obtained by subtraction are obtained as a reference pattern, and (b) an unknown aircraft type to be measured passes the measurement point or its vicinity. Give the detection measurement signal of representing a change in aircraft noise, the frequency signal component contained in said detection measurement signal by extracting the said predetermined plurality of frequency bands,
Obtaining a plurality of detected frequency component signals representing changes in the frequency signal components while the aircraft of the unknown model passes the measurement point or its vicinity, and detects the peak values of the plurality of detected frequency component signals, respectively. Hold as a peak value of the detection measurement signal as detection relativity information, a plurality of peak values obtained by subtracting the peak value of the detection relativity information from the plurality of peak values of the detection patterning information (C) The square of the difference between the same frequency components among the plurality of peak values forming the detection pattern and the plurality of peak values forming the reference pattern is calculated, and the square of the difference is obtained. When the calculation result is multiplied by a predetermined weight and the whole is added, and when the addition result is less than or equal to a predetermined value, the reference pattern of the detection pattern is By degree of similarity is within a predetermined range for the model identification method of an aircraft, characterized in that the unknown model is determined to be the above known type corresponding to the reference pattern. 3. (a) Obtaining a reference measurement signal that represents changes in aircraft noise while an aircraft of a known type passes through a measurement point or its vicinity, and determine a frequency signal component included in the reference measurement signal. By extracting for a plurality of frequency bands of, a plurality of reference frequency component signals representing changes in the frequency signal component while the aircraft of the known model passes through the measurement point or its vicinity, the plurality of reference frequencies The peak value of the component signal is held as the reference patterning information, the peak value of the reference measurement signal is held as the reference relativity information, and the peak value of the relativity information is calculated from the plurality of peak values of the reference patterning information. The difference between the plurality of peak values obtained by subtraction is obtained as the first reference pattern, and the frequency axis among the plurality of peak values of the reference patterning information is For each of the two sets of peak values extracted from the frequency bands adjacent to each other in the predetermined frequency range, the square value of the difference between the two peak values is calculated, and the square value of the difference is calculated as follows. Add the pairs and obtain the added value as a second reference pattern, and (b) obtain a detected measurement signal that represents a change in aircraft noise while an unknown aircraft type to be measured passes at or near the measurement point. , By extracting the frequency signal component contained in the detection measurement signal for the predetermined plurality of frequency bands,
Obtaining a plurality of detected frequency component signals representing changes in the frequency signal components while the aircraft of the unknown model passes the measurement point or its vicinity, and detects the peak values of the plurality of detected frequency component signals, respectively. Hold as a peak value of the detection measurement signal as detection relativization information, of the plurality of peak values obtained by subtracting the peak value of the detection relativity information from the plurality of peak values of the detection patterning information The difference is obtained as the first detection pattern, and among the plurality of peak values of the above-mentioned detection patterning information, two peak values extracted from adjacent frequency bands in a frequency band within a predetermined frequency range in the frequency axis direction. The squared value of the difference between the two peak values is calculated for each of the pairs of, and the squared value of the difference is added for all the pairs. , The added value is obtained as a second detection pattern, and (c) the same frequency component of the first detection pattern and the difference between the plurality of peak values forming the first reference pattern for each of the known models. Squared, weighted and summed the difference between and the peak value difference of
The smallest calculation result is selected from the calculation results, and (d) when the value of the minimum calculation result is less than or equal to a predetermined threshold value, the model of the aircraft to be measured by the known model corresponding to the minimum calculation result. (E) When the value of the minimum calculation result exceeds the threshold value, the difference between the second detection pattern and the second reference pattern for the jet machine and the propeller machine is calculated. A method of identifying an aircraft model, which comprises performing a squared weighting operation and determining that the aircraft model to be measured is a jet aircraft or a propeller aircraft based on the smaller one of the arithmetic results.