CN107576387A - A kind of unmanned plane detection method based on the identification of vocal print multiple-harmonic - Google Patents
A kind of unmanned plane detection method based on the identification of vocal print multiple-harmonic Download PDFInfo
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- CN107576387A CN107576387A CN201710711117.4A CN201710711117A CN107576387A CN 107576387 A CN107576387 A CN 107576387A CN 201710711117 A CN201710711117 A CN 201710711117A CN 107576387 A CN107576387 A CN 107576387A
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Abstract
The invention discloses a kind of unmanned plane detection method based on the identification of vocal print multiple-harmonic.Traditional harmonic detecting method based on Fourier transformation is established to be analyzed on the basis of signal is smoothly assumed, frequency domain character is obtained using the time-domain information of signal, the frequency of each harmonic, amplitude and phase in stationary signal can be precisely determined, the deficiency of this method is that non-stationary signal can not be handled, be not suitable for analysis dynamic harmonic and jump signal, in actual harmonic detecting application, harmonic wave is likely to dynamic change.This method obtains characteristic frequency by doing time frequency analysis to the voice signal of target information source.According to position of the characteristic frequency in frequency spectrum, frequency band equal in width is divided into alternate characteristic frequency section successively and non-characteristic frequency section, that asks for each frequency separation can value.The relation to be risen and fallen on energy according to each frequency separation, according to each frequency separation can value magnitude relationship as discrimination standard, good detection performance can be realized.
Description
Technical field
The present invention relates to unmanned plane sound detection field, and in particular to a kind of unmanned machine examination based on the identification of vocal print multiple-harmonic
Survey method.
Background technology
The method of in general sound detection mainly includes：(1) harmonic detecting technique based on Fourier transformation；(2) it is based on
The harmonic detecting technique of wavelet transformation.Relative to latter technique, what the harmonic detecting technique based on Fourier transformation calculated answers
Miscellaneous degree is low, and the frequency of each harmonic, amplitude and phase in stationary signal is precisely determined, and is obtained in harmonic detecting extensively
Using.
But traditional harmonic detecting method based on Fourier transformation is built upon signal and smoothly assumes that basis is enterprising
Row analysis, the frequency domain character of signal is obtained using the time-domain information of signal, can be precisely determined in stationary signal each time it is humorous
Frequency, amplitude and the phase of ripple.This method is disadvantageous in that, it is impossible to non-stationary signal is handled, so being not suitable for analysis
Dynamic harmonic and jump signal, and in the application of actual harmonic detecting, harmonic wave is likely to dynamic change, and this method has
It is to be modified.
The content of the invention
In view of the above-mentioned deficiencies in the prior art, it is an object of the present invention to provide a kind of unmanned plane based on the identification of vocal print multiple-harmonic
Detection method.
The purpose of the present invention is achieved through the following technical solutions：A kind of unmanned plane based on the identification of vocal print multiple-harmonic
Detection method, comprise the following steps：
(1) voice signal of unmanned plane takeoff phase is handled, the front and rear time-frequency figure that takes off is contrasted, obtains characteristic frequency section
[f1L′,f1R′],[f2L′,f2R′],…,[fNL′,fNR′], [f1L′,f1R′] it is fundamental frequency, remaining is the harmonic frequency of integral multiple；
(2) position according to characteristic frequency in frequency spectrum, frequency band equal in width is divided into alternate characteristic frequency area successively
Between [f1L,f1R][f3L,f3R][f5L,f5R] ... with non-characteristic frequency section [f2L,f2R][f4L,f4R][f6L,f6R] ..., section sum
For m；
(3) voice signal to be identified is subjected to Fourier transformation, each frequency separation of step (2) division takes Fourier to become
Change the quadratic sum of k amplitude of highest in result as can value, obtain m frequency separation can value e1,
e2...em；
(4) if m frequency separation can value meet that height rises and falls successively, i.e. e1>E2, e2<E3, e3>E4, e4
<E5, e5>E6, e6<E7... this magnitude relationship, then it is determined as the sound of unmanned plane, is not the sound of unmanned plane otherwise.
Unmanned plane detection method proposed by the present invention based on the identification of vocal print multiple-harmonic, the detection effect to unmanned plane acoustical signal
Fruit is fine, and loss is low, and false-alarm is low.Compared with prior art, the present invention has following advantage：
1. traditional harmonic detecting technique based on Fourier transformation, only utilize stable each harmonic frequency and fundamental frequency
The relation of integral multiple, but the harmonic wave of actual signal is not absolute stability, there is frequency displacement in the range of certain frequency, it is impossible to meet mark
Accurate frequency multiplication relation.Frequency band is divided into the characteristic frequency section alternate successively of one fixed width and non-characteristic frequency area by the present invention
Between, it is allowed to harmonic wave fluctuates within the specific limits, and during using there is harmonic wave, the fluctuation characteristic of the highest energy in section is accurate as differentiating
Then, the dynamic harmonic of unmanned plane can be effectively monitored, Detection results are good.
2. the present invention utilizes alternate characteristic frequency section successively and a volt for the highest energy in non-characteristic frequency section
Property as criterion, calculate simple, can be good at characterizing the vocal print harmonic characteristic of unmanned plane.
Brief description of the drawings
Fig. 1 is the flow chart of the unmanned plane detection method based on the identification of vocal print multiple-harmonic of the present invention；
Fig. 2 is actually detected result figure of the detection method of the present invention in the campus environment of urban district.
Embodiment
The present invention is described in further detail below in conjunction with the drawings and specific embodiments.
As shown in figure 1, the unmanned plane detection method proposed by the present invention based on the identification of vocal print multiple-harmonic, including following step
Suddenly：
(1) voice signal of unmanned plane takeoff phase is handled, the front and rear time-frequency figure that takes off is contrasted, obtains characteristic frequency section
[f1L′,f1R′],[f2L′,f2R′],…,[fNL′,fNR′], [f1L′,f1R′] it is fundamental frequency, remaining is the harmonic frequency of integral multiple；
(2) position according to characteristic frequency in frequency spectrum, frequency band equal in width is divided into alternate characteristic frequency area successively
Between [f1L,f1R][f3L,f3R][f5L,f5R] ... with non-characteristic frequency section [f2L,f2R][f4L,f4R][f6L,f6R] ..., section sum
For m；By taking m=7 as an example, [f1L,f1R][f3L,f3R][f5L,f5R][f7L,f7R] it is characteristic frequency section, [f2L,f2R][f4L,f4R]
[f6L,f6R] it is non-characteristic frequency section, section bandwidth is identical, is 100HZ；
(3) voice signal to be identified is subjected to Fourier transformation, each frequency separation of step (2) division takes Fourier to become
Change the quadratic sum of k amplitude of highest in result as can value, obtain m frequency separation can value e1,
e2...em；K typically takes 1 to arrive 1/10th of bandwidth；
(4) relation to be risen and fallen on the energy according to each frequency separation, as discrimination standard, the sound for unmanned plane is discriminated whether
Sound.In the presence of unmanned plane, because characteristic frequency section has harmonic wave, non-characteristic frequency section is without harmonic wave, so characteristic frequency
Section can value should be greater than non-characteristic frequency section can value.If harmonic wave be present, e1 must be met>E2, e2<
E3, e3>E4, e4<E5, e5>E6, e6<The relation to be risen and fallen on this energy of e7, according to e1, e2, e3, e4, e5, e6's, e7 is this
Magnitude relationship discriminates whether the sound for unmanned plane as discrimination standard.
Embodiment
Control smart -4 four rotor wing unmanned aerial vehicles of big boundary to fly in cross acoustic array region overhead, run on computer
Labview programs, by the microphone collected sound signal of acoustic array, sample frequency 5120HZ, array element spacing is 0.17m,
Array number is 3, and target information source number is 1.Under current sampling frequency, characteristic frequency section can be obtained：[140,200],[300,
380],[470,550],[650,740].(unit：HZ).
First 7 successively alternate characteristic frequency section and non-characteristic frequency section [100,200] [200,300] [300,
400][400,500][500,600][600,700][700,800].(unit：HZ).Under this kind of dividing condition, k typically takes 1
To 10, preferably 5.
Good Detection results can be realized in the range of 30m.Fig. 2 is the detection method of the present invention in urban district campus environment
In actually detected result figure, wherein, false-alarm 2.2%.In more quiet environment, false-alarm can be lower.
Claims (1)
1. a kind of unmanned plane detection method based on the identification of vocal print multiple-harmonic, it is characterised in that comprise the following steps：
(1) voice signal of unmanned plane takeoff phase is handled, the front and rear time-frequency figure that takes off is contrasted, obtains characteristic frequency section
[f1L′,f1R′],[f2L′,f2R′],…,[fNL′,fNR′], [f1L′,f1R′] it is fundamental frequency, remaining is the harmonic frequency of integral multiple；
(2) position according to characteristic frequency in frequency spectrum, frequency band equal in width is divided into alternate characteristic frequency section successively
[f1L,f1R][f3L,f3R][f5L,f5R] ... with non-characteristic frequency section [f2L,f2R][f4L,f4R][f6L,f6R] ..., section sum is
m；
(3) voice signal to be identified is subjected to Fourier transformation, each frequency separation of step (2) division takes Fourier transformation knot
In fruit the quadratic sum of k amplitude of highest as can value, obtain m frequency separation can value e1, e2...em；
(4) if m frequency separation can value meet that height rises and falls successively, i.e. e1 ＞ e2, e2 ＜ e3, e3 ＞ e4, e4
This magnitude relationships of ＜ e5, e5 ＞ e6, e6 ＜ e7..., then be determined as the sound of unmanned plane, be not the sound of unmanned plane otherwise.
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| CN109490969A (en) * | 2018-09-21 | 2019-03-19 | 浙江大学 | One kind being based on the periodic unmanned plane detection method of unmanned plane downlink signal |
| CN111639595A (en) * | 2020-05-29 | 2020-09-08 | 桂林电子科技大学 | Unmanned aerial vehicle micro-motion characteristic signal detection method based on weight-agnostic neural network |
| CN111968671A (en) * | 2020-08-24 | 2020-11-20 | 中国电子科技集团公司第三研究所 | Low-altitude sound target comprehensive identification method and device based on multi-dimensional feature space |
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| CN104835498A (en) * | 2015-05-25 | 2015-08-12 | 重庆大学 | Voiceprint identification method based on multi-type combination characteristic parameters |
| CN106683665A (en) * | 2016-11-23 | 2017-05-17 | 新绎健康科技有限公司 | Audio scale analysis method and system |
| CN106772227A (en) * | 2017-01-12 | 2017-05-31 | 浙江大学 | A kind of unmanned plane direction determining method based on the identification of vocal print multiple-harmonic |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060107067A1 (en) * | 2004-11-15 | 2006-05-18 | Max Safal | Identification card with bio-sensor and user authentication method |
| CN104835498A (en) * | 2015-05-25 | 2015-08-12 | 重庆大学 | Voiceprint identification method based on multi-type combination characteristic parameters |
| CN106683665A (en) * | 2016-11-23 | 2017-05-17 | 新绎健康科技有限公司 | Audio scale analysis method and system |
| CN106772227A (en) * | 2017-01-12 | 2017-05-31 | 浙江大学 | A kind of unmanned plane direction determining method based on the identification of vocal print multiple-harmonic |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109490969A (en) * | 2018-09-21 | 2019-03-19 | 浙江大学 | One kind being based on the periodic unmanned plane detection method of unmanned plane downlink signal |
| CN111639595A (en) * | 2020-05-29 | 2020-09-08 | 桂林电子科技大学 | Unmanned aerial vehicle micro-motion characteristic signal detection method based on weight-agnostic neural network |
| CN111639595B (en) * | 2020-05-29 | 2022-03-18 | 桂林电子科技大学 | Unmanned aerial vehicle micro-motion characteristic signal detection method based on weight-agnostic neural network |
| CN111968671A (en) * | 2020-08-24 | 2020-11-20 | 中国电子科技集团公司第三研究所 | Low-altitude sound target comprehensive identification method and device based on multi-dimensional feature space |
| CN111968671B (en) * | 2020-08-24 | 2024-03-01 | 中国电子科技集团公司第三研究所 | Low-altitude sound target comprehensive identification method and device based on multidimensional feature space |
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