CN107576387B - Unmanned aerial vehicle detection method based on voiceprint multi-harmonic recognition - Google Patents
Unmanned aerial vehicle detection method based on voiceprint multi-harmonic recognition Download PDFInfo
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Abstract
The invention discloses an unmanned aerial vehicle detection method based on voiceprint multi-harmonic recognition. The traditional harmonic detection method based on Fourier transform is established on the assumption of stable signals for analysis, frequency domain characteristics are obtained by using time domain information of the signals, and the frequency, amplitude and phase of each harmonic in the stable signals can be accurately determined. The method obtains the characteristic frequency by performing time-frequency analysis on the sound signal of the target information source. According to the position of the characteristic frequency in the frequency spectrum, the frequency band with the same width is divided into characteristic frequency intervals and non-characteristic frequency intervals which alternate in sequence, and the highest energy value of each frequency interval is obtained. According to the relationship of fluctuation in energy of each frequency interval and the magnitude relationship of the highest energy value of each frequency interval as a discrimination standard, good detection performance can be realized.
Description
Technical Field
The invention relates to the field of sound detection of unmanned aerial vehicles, in particular to an unmanned aerial vehicle detection method based on voiceprint multi-harmonic recognition.
Background
The general sound detection method mainly comprises the following steps: (1) a harmonic detection technique based on fourier transform; (2) harmonic detection techniques based on wavelet transforms. Compared with the latter technology, the harmonic detection technology based on Fourier transform has low calculation complexity, accurately determines the frequency, amplitude and phase of each harmonic in a stationary signal, and is widely applied to harmonic detection.
However, the conventional harmonic detection method based on fourier transform is to perform analysis based on the assumption that signals are stationary, and obtain frequency domain characteristics of the signals by using time domain information of the signals, so as to accurately determine the frequency, amplitude and phase of each subharmonic in the stationary signals. The method has the disadvantages that non-stationary signals cannot be processed, so that the method is not suitable for analyzing dynamic harmonics and abrupt signals, and in practical harmonic detection applications, the harmonics are likely to be dynamically changed, and the method needs to be improved.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle detection method based on voiceprint multi-harmonic identification, aiming at the defects of the prior art.
The purpose of the invention is realized by the following technical scheme: an unmanned aerial vehicle detection method based on voiceprint multi-harmonic identification comprises the following steps:
(1) processing sound signals of the unmanned aerial vehicle in the takeoff stage, and comparing time-frequency graphs before and after takeoff to obtain a characteristic frequency interval [ f1L′,f1R′],[f2L′,f2R′],…,[fNL′,fNR′]，[f1L′,f1R′]The frequency is fundamental frequency, and the rest is harmonic frequency of integral multiple;
(2) dividing the frequency band into successive alternate characteristic frequency intervals [ f ] according to the positions of the characteristic frequencies in the frequency spectrum1L,f1R][f3L,f3R][f5L,f5R]… and non-characteristic frequency interval [ f2L,f2R][f4L,f4R][f6L,f6R]…, total number of intervals is m;
(3) performing Fourier transform on the sound signal to be identified, taking the square sum of the highest k amplitude values in the Fourier transform result of each frequency interval divided in the step (2) as the highest energy value, and obtaining the highest energy values e1, e2... em of m frequency intervals;
(4) if the highest energy values of the m frequency intervals are in fluctuation in sequence, namely the magnitude relation of e1> e2, e2< e3, e3> e4, e4< e5, e5> e6 and e6< e7., the sound of the unmanned aerial vehicle is judged, and otherwise, the sound of the unmanned aerial vehicle is not judged.
The unmanned aerial vehicle detection method based on voiceprint multi-harmonic recognition provided by the invention has the advantages of good detection effect on the sound signal of the unmanned aerial vehicle, low omission ratio and low false alarm. Compared with the prior art, the invention has the following advantages:
1. the traditional harmonic detection technology based on Fourier transform only utilizes the stable relationship between each harmonic frequency and the integral multiple of fundamental frequency, but the harmonic of the actual signal is not absolutely stable, has frequency shift in a certain frequency range and cannot meet the standard frequency multiplication relationship. The invention divides the frequency band into the characteristic frequency interval and the non-characteristic frequency interval which are sequentially alternated and have certain width, allows the harmonic wave to fluctuate in a certain range, utilizes the fluctuation characteristic of the highest energy of the interval when the harmonic wave exists as the judgment criterion, can effectively monitor the dynamic harmonic wave of the unmanned aerial vehicle, and has good detection effect.
2. The invention uses the fluctuation characteristic of the highest energy of the characteristic frequency interval and the non-characteristic frequency interval which are sequentially alternated as the judgment criterion, has simple calculation and can well represent the voiceprint harmonic wave characteristic of the unmanned aerial vehicle.
Drawings
FIG. 1 is a flow chart of the voiceprint multi-harmonic identification based unmanned aerial vehicle detection method of the present invention;
FIG. 2 is a diagram showing the actual test results of the test method of the present invention in a campus environment.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples.
As shown in fig. 1, the method for detecting an unmanned aerial vehicle based on voiceprint multi-harmonic recognition provided by the invention comprises the following steps:
(1) processing sound signals of the unmanned aerial vehicle in the takeoff stage, and comparing time-frequency graphs before and after takeoff to obtain a characteristic frequency interval [ f1L′,f1R′],[f2L′,f2R′],…,[fNL′,fNR′]，[f1L′,f1R′]The frequency is fundamental frequency, and the rest is harmonic frequency of integral multiple;
(2) root of herbaceous plantDividing the frequency band into successive alternate characteristic frequency intervals [ f ] according to the positions of the characteristic frequencies in the frequency spectrum1L,f1R][f3L,f3R][f5L,f5R]… and non-characteristic frequency interval [ f2L,f2R][f4L,f4R][f6L,f6R]…, total number of intervals is m; take m ═ 7 as an example, [ f ═ f1L,f1R][f3L,f3R][f5L,f5R][f7L,f7R]Is a characteristic frequency interval, [ f ]2L,f2R][f4L,f4R][f6L,f6R]Is a non-characteristic frequency interval, the interval bandwidths are the same and are 100 HZ;
(3) performing Fourier transform on the sound signal to be identified, taking the square sum of the highest k amplitude values in the Fourier transform result of each frequency interval divided in the step (2) as the highest energy value, and obtaining the highest energy values e1, e2... em of m frequency intervals; k is typically 1 to one tenth of the bandwidth;
(4) and judging whether the sound is the sound of the unmanned aerial vehicle or not according to the relation of fluctuation of energy of each frequency interval as a judgment standard. When the unmanned aerial vehicle exists, the highest energy value of the characteristic frequency interval is larger than that of the non-characteristic frequency interval because the characteristic frequency interval has harmonic waves and the non-characteristic frequency interval has no harmonic waves. If harmonic waves exist, the relation of fluctuation in energy, namely e1> e2, e2< e3, e3> e4, e4< e5, e5> e6 and e6< e7, is satisfied, and whether the sound is the sound of the unmanned aerial vehicle is judged according to the magnitude relation of e1, e2, e3, e4, e5, e6 and e7 as a judgment standard.
Examples
Controlling the Xinjiang eidolon-4 four-rotor unmanned aerial vehicle to fly above the area where the cross-shaped sound array is located, operating a Labview program on a computer, collecting sound signals through a microphone of the sound array, wherein the sampling frequency is 5120HZ, the array element interval is 0.17m, the array element number is 3, and the target information source number is 1. At the current sampling frequency, a characteristic frequency interval can be obtained: [140,200],[300,380],[470,550],[650,740]. (unit: HZ).
The first 7 sequentially alternating signature frequency intervals and non-signature frequency intervals [100,200] [200,300] [300,400] [400,500] [500,600] [600,700] [700,800 ]. (unit: HZ). In this division, k generally takes 1 to 10, preferably 5.
Good detection results can be achieved within the range of 30 m. Fig. 2 is a graph of the actual detection result of the detection method of the present invention in the campus environment of the urban area, wherein the false alarm is 2.2%. In a quieter environment, the false alarm may be lower.
Claims (1)
1. An unmanned aerial vehicle detection method based on voiceprint multi-harmonic recognition is characterized by comprising the following steps:
(1) processing sound signals of the unmanned aerial vehicle in the takeoff stage, and comparing time-frequency graphs before and after takeoff to obtain a characteristic frequency interval [ f1L′,f1R′],[f2L′,f2R′],…,[fNL′,fNR′]，[f1L′,f1R′]The frequency is fundamental frequency, and the rest is harmonic frequency of integral multiple;
(2) dividing the frequency band into successive alternate characteristic frequency intervals [ f ] according to the positions of the characteristic frequencies in the frequency spectrum1L,f1R][f3L,f3R][f5L,f5R]… and non-characteristic frequency interval [ f2L,f2R][f4L,f4R][f6L,f6R]…, total number of intervals is m;
(3) performing Fourier transform on the sound signal to be identified, taking the square sum of the highest k amplitude values in the Fourier transform result of each frequency interval divided in the step (2) as the highest energy value, and obtaining the highest energy values e1, e2., em of m frequency intervals;
(4) if the highest energy values of the m frequency intervals are in fluctuation in sequence, namely e1> e2, e2< e3, e3> e4, e4< e5, e5> e6 and e6< e7..
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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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| 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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