CN110275157A - Vector acoustic bearing method based on genetic algorithm self-adapting random resonant - Google Patents
Vector acoustic bearing method based on genetic algorithm self-adapting random resonant Download PDFInfo
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Abstract
The vector acoustic bearing method based on genetic algorithm self-adapting random resonant that the present invention provides a kind of is that characterization is estimated with maximum output signal-to-noise ratio, using the non-linear stochastic resonance system parameter that Genetic algorithm searching is optimal, reduces system parameter optimizing duration.The present invention can obtain target Bearing Estimation values more higher than classical way precision under Low SNR, it was demonstrated that the validity of this single vector hydrophone method for estimating target azimuth based on genetic algorithm self-adapting random resonant.
Description
Technical field
The present invention relates to Acoustic Object orientation to estimate field, especially a kind of single vector hydrophone target direction estimation side
Method.
Background technique
Vector hydrophone is just becoming one of the research direction that underwater sound field attracts people's attention in recent years.Compared to traditional acoustic pressure
Hydrophone, vector hydrophone provides the vibration velocity information of sound field more, so that vector hydrophone can complete previous acoustic pressure
The target Bearing Estimation task that hydrophone array could be completed.But single vector hydrophone orientation accuracy is with the drop of signal-to-noise ratio
It is low and be remarkably decreased, limit application of the vector hydrophone under Low SNR.
Accidental resonance is an important branch of non-linear subject, it can be in nonlinear system, input signal and noise
When reaching certain matching status, signal energy is converted by noise energy, improves output signal-to-noise ratio, there is good small-signal
Reinforcing effect.It therefore, is to improve vector sound direction estimation under the conditions of very noisy using the pretreated vector acoustic bearing of accidental resonance
The effective way of precision.
There are two types of methods for the realization of accidental resonance at present: existing one is accidental resonance is generated by increasing noise intensity
As；Another kind is by regulating system inherent parameters, and the matching relationship of improvement signal, noise and nonlinear system is random to generate
Resonance.It, can not be by increasing noise intensity when interference noise intensity, which has exceeded, generates the range cooperateed with system and signal
Generate effective accidental resonance.Presently, there are single vector hydrophone target Bearing Estimation precision to be limited to signal-to-noise ratio, and tradition is adaptive
The problem for answering stochastic resonance system calculation amount excessive.
Summary of the invention
For overcome the deficiencies in the prior art, the present invention provides a kind of single arrow based on genetic algorithm self-adapting random resonant
Measure hydrophone method for estimating target azimuth.The calculation amount for reducing self-adapting random resonant system, improves Low SNR
The target Bearing Estimation precision of lower single vector hydrophone.
The present invention is directed to the problem that single vector hydrophone target Bearing Estimation precision is limited under Low SNR, propose with
The pretreated vector acoustic bearing method of machine resonance, promotes single vector hydrophone orientation estimated accuracy under strong background noise.It passes simultaneously
Self-adapting random resonant system-computed amount of uniting is excessive, is unfavorable for practical application, and the present invention is that characterization is surveyed with maximum output signal-to-noise ratio
Degree reduces system parameter optimizing duration using the non-linear stochastic resonance system parameter that Genetic algorithm searching is optimal.But in mesh
It marks in the calculating of Signal-to-Noise and needs given frequency information, therefore, the present invention proposes a kind of modulated stochastic resonance detection signal
The method of frequency detects the frequency of unknown signaling.
The step of the technical solution adopted by the present invention to solve the technical problems, is as follows:
Step 1: obtaining the received three roads signal of single vector hydrophone: p (n), vx (n), vy (n)；Wherein, p (n) is defeated
Enter sound pressure signal, vx (n) is the vibration velocity signal in the direction x, and vy (n) is the direction y vibration velocity signal；
Step 2: building stochastic resonance system；
Establish non-linear stochastic resonance system:
Wherein,For system output, s (t) is input signal, and n (t) is the ambient noise that noise intensity is D；V (x) is non-
Linear bistable state potential function；
Wherein a, b are the barrier parameters of bistable-state random resonance system, and system potential function exists there are two steady state pointPlace, by the potential barrier Δ V=a at zero point2/ 4b points are opened, and V ' (x) is to differentiate；
Step 3: being modulated accidental resonance determines signal frequency
By the mudulation effect of sinusoidal signal, the multiplication of two simple signals can obtain one and frequency signal and a difference frequency signal,
That is: cos (2 π f0t)cos(2πfcT)=0.5cos [2 π (f0-fc)t]+0.5cos[2π(f0+fc)t]
Wherein, f0For echo signal frequency, fcFor known frequency modulating signal, change fc, work as fc=f0When, modulation output
One direct current signal and one and frequency signal, by changing frequency modulating signal fc, make at the output zero-frequency of stochastic resonance system
Energy is maximum, f at this timecAs echo signal frequency f0, modulated stochastic resonance determines signal frequency, and specific step is as follows:
1. generating modulated signal: sc(n)=cos (2 π fcTsN), TsIt is the sampling interval；
2. modulated signal and input signal are modulated: u (n)=sc(n)si(n) wherein, siIt (n) is input signal, u
It (n) is modulated signal；
3. being calculated using modulated signal u (n), the differential side is solved using Runge-Kutta algorithm shown in formula (3)
Journey calculates output x (n):
Wherein, h is algorithm step-size, when sample frequency is fs,And setting a=b=1, k1、k2、k3And k4For in
Between variable；
4. calculating energy λ at the zero-frequency of output x (n) according to formula (4), the measurement index as modulated stochastic resonance:
5. changing f from preset frequency search interval limit to the upper limit by stepping of 1Hzc, repeat step 1. -4., record
Each fcCorresponding λ；
6. by f corresponding to λ value maximumcAs frequency estimation
Step 4: carrying out parameter regulation self-adapting random resonant using genetic algorithm
The Population Size popsize of genetic algorithm, code length chromlength, crossover probability pc, mutation probability are set
Pm, the number of iterations gen；
By the parameter combination [a, b] of stochastic resonance system as individual, a value range is [0.001,0.1], b value model
It encloses for [0.000001,0.001], generates popsize initial individuals at random to be uniformly distributed, it is each individual by two sections
Chromlength binary number compositions, respectively indicate the coding form of a and b, the conversion for being encoded to actual value can be by following formula
It obtains:
Wherein caIndicate that individual indicates the corresponding decimal number of binary number of a, cbIndicate that individual indicates the binary number of b
Corresponding decimal number；
It is 10000 that zoom factor α, which is arranged,；Input signal siIt (n) is x (n), the meter of x (n) by stochastic resonance system output
Calculating is formula (3), and step-length is defined as h1=α Ts；
Using the signal-to-noise ratio of x (n) as the individual adaptation degree function lambda of genetic algorithmf, λfPass through frequency estimationIt finds out,
That is:
Genetic algorithm calculating is carried out, and by the corresponding output signal x of the maximum filial generation of individual adaptation degree functionopt(n) conduct
Output of the self-adapting random resonant system to input signal；
It is exported after being handled respectively using self-adapting random resonant system three tunnel input signal of vector hydrophone: acoustic pressure output
pout(n), the direction x vibration velocity exports vxout(n), the direction y vibration velocity exports vyout(n)；
Step 5: obtaining target Bearing Estimation value using cross-spectrum sound intensity technique:
Wherein, Ang (k) is target bearing spectrum, and P (k), VX (k), VY (k) are p respectivelyout(n), vxout(n), vyout(n)
Discrete Fourier transform, angle be algorithm output target Bearing Estimation value.
The beneficial effects of the present invention are using modulated stochastic resonance to detect echo signal frequency, require no knowledge about
The priori knowledge of echo signal frequency.The present invention is also searched using optimized parameter group of the genetic algorithm to stochastic resonance system
Rope, while effectively reducing the calculation amount of self-adapting random resonant system, by parameter search dimension by it is classical adaptively with
The One-Dimensional Extended of machine resonance method keeps stochastic resonance system output signal-to-noise ratio higher to two dimension.It can also adjust at any time as needed
Whole individual amount and the number of iterations are to further increase orientation estimated accuracy.Emulation experiment shows that the present invention can obtain low letters
It makes an uproar the target Bearing Estimation value more higher than classical way precision than under the conditions of, it was demonstrated that this to be based on genetic algorithm self-adapting random
The validity of the single vector hydrophone method for estimating target azimuth of resonance.
Detailed description of the invention
Fig. 1 is genetic algorithm self-adapting random resonant detection method block diagram of the present invention.
Fig. 2 is the pure input signal of the present invention, and band is made an uproar input signal, accidental resonance output signal diagram.
Fig. 3 is three road input signal spectrum figures of the invention.
Fig. 4 is three-way output signal spectrogram of the present invention.
Fig. 5 is azimuth spectrum obtained by cross-spectrum sound intensity technique of the present invention and sound intensity spectrogram.
Specific embodiment
Present invention will be further explained below with reference to the attached drawings and examples.
The present invention uses the self-adapting random resonant based on parameter regulation.Conventional parameter regulation self-adapting random resonant with
Output signal-to-noise ratio is accidental resonance effect of the parameter of index traversal search system to be optimal, this method calculation amount mistake
Greatly.For this problem, the present invention by genetic algorithm in conjunction with self-adapting random resonant system, it is optimal using Genetic algorithm searching
System parameter, greatly reduce calculation amount, while also ensuring that stochastic resonance system has the output signal compared with high s/n ratio,
Subsequent vector acoustic bearing algorithm is allowed to export high-precision target Bearing Estimation value.
Specific implementation step of the present invention is as follows:
Step 1: obtaining the received three roads signal of single vector hydrophone: p (n), vx (n), vy (n)；Wherein, p (n) is defeated
Enter sound pressure signal, vx (n) is the vibration velocity signal in the direction x, and vy (n) is the direction y vibration velocity signal；
Step 2: building stochastic resonance system；
Establish non-linear stochastic resonance system:
Wherein,For system output, s (t) is input signal, and n (t) is the ambient noise that noise intensity is D；V (x) is non-
Linear bistable state potential function；
Wherein a, b are the barrier parameters of bistable-state random resonance system, and system potential function exists there are two steady state pointPlace, by the potential barrier Δ V=a at zero point2/ 4b points are opened, and V ' (x) is to differentiate；
Step 3: being modulated accidental resonance determines signal frequency
By the mudulation effect of sinusoidal signal, the multiplication of two simple signals can obtain one and frequency signal and a difference frequency signal,
That is: cos (2 π f0t)cos(2πfcT)=0.5cos [2 π (f0-fc)t]+0.5cos[2π(f0+fc)t]
Wherein, f0For echo signal frequency, fcFor known frequency modulating signal, change fc, work as fc=f0When, modulation output
One direct current signal and one and frequency signal, by the Lorentz characteristic of stochastic resonance system, i.e., by higher frequency signal energy to low
The output when characteristic and stochastic resonance system of frequency transfer are for direct-flow input signal concentrates on the characteristic at zero-frequency, passes through
Change frequency modulating signal fc, keep energy at the output zero-frequency of stochastic resonance system maximum, f at this timecAs echo signal frequency
f0, modulated stochastic resonance determines signal frequency, and specific step is as follows:
1. generating modulated signal: sc(n)=cos (2 π fcTsN), TsIt is the sampling interval；
2. modulated signal and input signal are modulated: u (n)=sc(n)si(n) wherein, siIt (n) is input signal, u
It (n) is modulated signal；
3. being calculated using modulated signal u (n), the differential side is solved using Runge-Kutta algorithm shown in formula (3)
Journey calculates output x (n):
Wherein, h is algorithm step-size, when sample frequency is fs,And setting a=b=1, k1、k2、k3And k4For in
Between variable；
4. calculating energy λ at the zero-frequency of output x (n) according to formula (4), the measurement index as modulated stochastic resonance:
5. changing f from preset frequency search interval limit to the upper limit by stepping of 1Hzc, repeat step 1. -4., record
Each fcCorresponding λ；
6. by f corresponding to λ value maximumcAs frequency estimation
Step 4: carrying out parameter regulation self-adapting random resonant using genetic algorithm
Genetic algorithm is a kind of searching algorithm, it is by mimic biology evolutionary mechanism, using object search as bion,
It is evolved by exchange information random between individual and structuring and vying each other, mostly instead of evolves high a of fitness afterwards
Body is as optimal solution.
The Population Size popsize=25 of genetic algorithm, code length chromlength=32, crossover probability pc are set
=0.4, mutation probability pm=0.001, the number of iterations gen=25；
By the parameter combination [a, b] of stochastic resonance system as individual, a value range is [0.001,0.1], b value model
It encloses for [0.000001,0.001].Popsize=25 initial individuals are generated at random to be uniformly distributed, and each individual is by two sections
Chromlength=32 binary number compositions, respectively indicate the coding form of a and b, the conversion for being encoded to actual value can be by
Following formula obtains:
Wherein caIndicate that individual indicates the corresponding decimal number of binary number of a, cbIndicate that individual indicates the binary number of b
Corresponding decimal number；
It is 10000 that zoom factor α, which is arranged,；Input signal siIt (n) is x (n), the meter of x (n) by stochastic resonance system output
Calculating is formula (3), and step-length is defined as h1=α Ts；
Using the signal-to-noise ratio of x (n) as the individual adaptation degree function lambda of genetic algorithmf, λfPass through frequency estimationIt finds out,
That is:
Genetic algorithm calculating is carried out, and by the corresponding output signal x of the maximum filial generation of individual adaptation degree functionopt(n) conduct
Output of the self-adapting random resonant system to input signal；
It is exported after being handled respectively using self-adapting random resonant system three tunnel input signal of vector hydrophone: acoustic pressure output
pout(n), the direction x vibration velocity exports vxout(n), the direction y vibration velocity exports vyout(n)；
Step 5: obtaining target Bearing Estimation value using cross-spectrum sound intensity technique:
Wherein, Ang (k) is target bearing spectrum, and P (k), VX (k), VY (k) are p respectivelyout(n), vxout(n), vyout(n)
Discrete Fourier transform, angle be algorithm output target Bearing Estimation value.
Claims (1)
1. a kind of vector acoustic bearing method based on genetic algorithm self-adapting random resonant, it is characterised in that include the following steps:
Step 1: obtaining the received three roads signal of single vector hydrophone: p (n), vx (n), vy (n)；Wherein, p (n) is input sound
Signal is pressed, vx (n) is the vibration velocity signal in the direction x, and vy (n) is the direction y vibration velocity signal；
Step 2: building stochastic resonance system；
Establish non-linear stochastic resonance system:
Wherein,For system output, s (t) is input signal, and n (t) is the ambient noise that noise intensity is D；V (x) is non-linear
Bistable state potential function；
Wherein a, b are the barrier parameters of bistable-state random resonance system, and system potential function exists there are two steady state pointPlace,
By the potential barrier Δ V=a at zero point2/ 4b points are opened, and V ' (x) is to differentiate；
Step 3: being modulated accidental resonance determines signal frequency
By the mudulation effect of sinusoidal signal, the multiplication of two simple signals can obtain one and frequency signal and a difference frequency signal, it may be assumed that
cos(2πf0t)cos(2πfcT)=0.5cos [2 π (f0-fc)t]+0.5cos[2π(f0+fc)t]
Wherein, f0For echo signal frequency, fcFor known frequency modulating signal, change fc, work as fc=f0When, modulation output one
Direct current signal and one and frequency signal, by changing frequency modulating signal fc, make energy at the output zero-frequency of stochastic resonance system
Maximum, f at this timecAs echo signal frequency f0, modulated stochastic resonance determines signal frequency, and specific step is as follows:
1. generating modulated signal: sc(n)=cos (2 π fcTsN), TsIt is the sampling interval；
2. modulated signal and input signal are modulated: u (n)=sc(n)si(n) wherein, siIt (n) is input signal, u (n) is
Modulated signal；
3. being calculated using modulated signal u (n), the differential equation is solved using Runge-Kutta algorithm shown in formula (3), is counted
Calculate output x (n):
Wherein, h is algorithm step-size, when sample frequency is fs,And setting a=b=1, k1、k2、k3And k4Become for centre
Amount；
4. calculating energy λ at the zero-frequency of output x (n) according to formula (4), the measurement index as modulated stochastic resonance:
5. changing f from preset frequency search interval limit to the upper limit by stepping of 1Hzc, repeat step 1. -4., record each fc
Corresponding λ；
6. by f corresponding to λ value maximumcAs frequency estimation
Step 4: carrying out parameter regulation self-adapting random resonant using genetic algorithm
The Population Size popsize of genetic algorithm, code length chromlength, crossover probability pc, mutation probability pm are set,
The number of iterations gen；
By the parameter combination [a, b] of stochastic resonance system as individual, a value range is [0.001,0.1], and b value range is
[0.000001,0.001] generates popsize initial individuals to be uniformly distributed at random, and each individual is by two sections
Chromlength binary number compositions, respectively indicate the coding form of a and b, the conversion for being encoded to actual value can be by following formula
It obtains:
Wherein caIndicate that individual indicates the corresponding decimal number of binary number of a, cbIndicate that individual indicates that the binary number of b is corresponding
Decimal number；
It is 10000 that zoom factor α, which is arranged,；Input signal siIt (n) is x (n) by stochastic resonance system output, x's (n) is calculated as
Formula (3), step-length is defined as h1=α Ts；
Using the signal-to-noise ratio of x (n) as the individual adaptation degree function lambda of genetic algorithmf, λfPass through frequency estimationIt finds out, it may be assumed that
Genetic algorithm calculating is carried out, and by the corresponding output signal x of the maximum filial generation of individual adaptation degree functionopt(n) as adaptive
Answer output of the stochastic resonance system to input signal；
Export after being handled respectively using self-adapting random resonant system three tunnel input signal of vector hydrophone: acoustic pressure exports pout
(n), the direction x vibration velocity exports vxout(n), the direction y vibration velocity exports vyout(n)；
Step 5: obtaining target Bearing Estimation value using cross-spectrum sound intensity technique:
Wherein, Ang (k) is target bearing spectrum, and P (k), VX (k), VY (k) are p respectivelyout(n), vxout(n), vyout(n) from
Fourier transformation is dissipated, angle is the target Bearing Estimation value of algorithm output.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112716462A (en) * | 2020-12-09 | 2021-04-30 | 北京航空航天大学 | Narrow-beam millimeter wave human body heartbeat/respiration sign monitoring device capable of controlling irradiation direction |
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| US6636643B1 (en) * | 1999-02-04 | 2003-10-21 | Quvis, Inc. | System and method for improving compressed image appearance using stochastic resonance and energy replacement |
| CN105701544A (en) * | 2016-01-11 | 2016-06-22 | 南京信息工程大学 | Stochastic resonance weak signal detection method based on genetic algorithm and frequency modulation |
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| US6636643B1 (en) * | 1999-02-04 | 2003-10-21 | Quvis, Inc. | System and method for improving compressed image appearance using stochastic resonance and energy replacement |
| CN105701544A (en) * | 2016-01-11 | 2016-06-22 | 南京信息工程大学 | Stochastic resonance weak signal detection method based on genetic algorithm and frequency modulation |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112716462A (en) * | 2020-12-09 | 2021-04-30 | 北京航空航天大学 | Narrow-beam millimeter wave human body heartbeat/respiration sign monitoring device capable of controlling irradiation direction |
| CN112716462B (en) * | 2020-12-09 | 2022-04-05 | 北京航空航天大学 | Narrow-beam millimeter wave human body heartbeat/respiration sign monitoring device capable of controlling irradiation direction |
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