CN105046880B - A kind of method that intelligent mobile terminal carries out hazards monitoring and early warning based on Doppler effect - Google Patents

A kind of method that intelligent mobile terminal carries out hazards monitoring and early warning based on Doppler effect Download PDF

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Publication number
CN105046880B
CN105046880B CN201510283722.7A CN201510283722A CN105046880B CN 105046880 B CN105046880 B CN 105046880B CN 201510283722 A CN201510283722 A CN 201510283722A CN 105046880 B CN105046880 B CN 105046880B
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mobile terminal
sound wave
early warning
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CN105046880A (en
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韩劲松
苟莹颖
蒋志平
王志
赵季中
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Xian Jiaotong University
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/04—Alarms for ensuring the safety of persons responsive to non-activity, e.g. of elderly persons
    • G08B21/0438—Sensor means for detecting
    • G08B21/0469—Presence detectors to detect unsafe condition, e.g. infrared sensor, microphone
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/02—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems using reflection of acoustic waves
    • G01S15/50—Systems of measurement, based on relative movement of the target
    • G01S15/52—Discriminating between fixed and moving objects or between objects moving at different speeds
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/02—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems using reflection of acoustic waves
    • G01S15/50—Systems of measurement, based on relative movement of the target
    • G01S15/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
    • G01S15/88—Sonar systems specially adapted for specific applications
    • G01S15/93—Sonar systems specially adapted for specific applications for anti-collision purposes

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  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Acoustics & Sound (AREA)
  • Human Computer Interaction (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Gerontology & Geriatric Medicine (AREA)
  • Business, Economics & Management (AREA)
  • Emergency Management (AREA)
  • Emergency Alarm Devices (AREA)
  • Alarm Systems (AREA)

Abstract

The invention provides a kind of method that intelligent mobile terminal carries out hazards monitoring and early warning based on Doppler effect, current research is seldom directed to use with the potential danger detection that smart machine is brought, and the existing method based on image and extras has its intrinsic limitation and deficiency.The present invention judges whether user is in away line state first with accelerometer data, surrounding environment is monitored in real time using sound Doppler effect and sound ranging afterwards, environmental information is judged whether by grader potentially dangerous, and danger is subjected to early warning according to danger classes, the presence of warning consumers risk in time, with convenience, real-time, accuracy, it is user friendly, the features such as low in energy consumption.Test result indicates that, the present invention has high accuracy and real-time, can carry out early warning to danger in time.It is believed that the present invention can play the characteristics of it is convenient, efficient, accurate, long-term and universal application is obtained in mobile hazard detection field and guide field.

Description

A kind of intelligent mobile terminal carries out hazards monitoring and early warning based on Doppler effect Method
Technical field
The invention belongs to signal transacting, grader and sensor field, and in particular to a kind of intelligent mobile terminal is based on many It is general to strangle the method that effect carries out hazards monitoring and early warning.
Background technology
Continuous progressive and science and technology with society is maked rapid progress, and the life style of people is constantly changed.With Smart mobile phone is simple to operate with its for the high-performance intelligent mobile terminal of new generation of representative, and the features such as being easy to carry gives people life Great convenience in work, in this case, people are also gradually stepped up for the degree of dependence of smart machine.On the other hand, just It is that dependence to mobile phone causes many people to be all absorbed in mobile phone whenever and wherever possible, is also played during trip in hand, from And it is easy to ignore potential hazards in surrounding environment due to diverting one's attention, and potential safety hazard is produced, is gently then fallen, it is heavy then occur car Misfortune.Therefore how to be prevented effectively from due to playing mobile phone to cause to divert one's attention and occurring unexpected is urgent problem to be solved.
Traditional Environment Obstacles detection method is based primarily upon ranging technology, including laser ranging, infrared distance measuring, ultrasonic wave Ranging etc..Such Environment Obstacles detection technique precision is high, identification is accurate, belongs to precise distance measurement, is widely used in military field, industry Measurement and control area etc..But above ranging technology needs that specific sensor and corresponding hardware supported, cost are high, be not easy to Body is carried, and is not suitable for the use of personal and family.
Detection of obstacles application is also occurred in that on smart machine, it is main to include two kinds of solutions.The first is direct Ambient image is obtained by the rear camera of smart machine and then judges to whether there is in environment using computer vision technique Barrier.This method can be judged environmental risk factors with more accurate, but its own is to equipment posture, ambient light Source brightness and line of sight direction have very high requirement, are typically only capable to detect the barrier in bright light environments on specific direction, and And resource consumption is big during detection, real-time is low.Be for second by smart machine additionally one laser range finder of increase or Ultrasonic wave module obtains environmental information, is handled on smart machine, obtains the distance of barrier.This method precision is high, but It is to need extra hardware device, portability is low.
The content of the invention
In order to solve the problems of the prior art, the present invention proposes that one kind judges to walk line state, and base using accelerometer Ambient condition information is obtained in sound Doppler effect and sound ranging, the danger classes that combining classification device is obtained carries out early warning, Evade people because the intelligent mobile terminal for the potential safety hazard for causing to divert one's attention to produce using mobile phone is endangered based on Doppler effect Danger monitoring and the method for early warning.
In order to realize the above object the technical solution adopted in the present invention is：Comprise the following steps：
1) state-detection：Judge whether user is in walking states, and whether use intelligent mobile terminal equipment；
2) environmental monitoring：The time difference t of detection transmitting sound wave and reflection sound wave, calculates the distance between sound source and barrier S, when sound source carries out relative motion with user, the acoustic vibration frequency generation frequency different from the frequency that sound source is sent that user receives Move, the frequency shift (FS) of contrast transmitting sound wave and reflection sound wave, you can mobile object and intelligent mobile terminal in detection surrounding environment Speed of related movement between equipment, so as to judge whether occur dangerous；
3) danger classes classification and early warning：Danger signal is determined whether by support vector machine classifier, if dangerous Signal, then judge that dangerous degree carries out early warning and obtained after threat data, it is empty to build vector using KNN algorithms by KNN algorithms Between model degree of danger is classified and to user's early warning.
Described step 1) in judge whether user is using according to whether lighting for intelligent mobile terminal equipment screen Mobile phone, lights expression and uses, non-lit up to represent to be not used.
Described step 1) in judge that people is using the regular vibration of the accelerometer data of intelligent mobile terminal equipment It is no in walking, accelerometer data microvibration near 0 under static state, data can be to shake by a relatively large margin when on foot It is dynamic.
Described step 2) in intelligent mobile terminal equipment periodic transmission particular amplitude, frequency and sample rate sound wave, The time difference t of detection transmitting sound wave and reflection sound wave, sound wave spread speed v=332m/s in atmosphere, so as to obtain intelligent sliding The distance between dynamic terminal device and barrier S is：
Described step 2) in intelligent mobile terminal equipment and barrier have relative motion, the reflection sound wave and hair received Penetrating sound wave has frequency shift (FS), and the aerial spread speed of sound wave is v=332m/s, and f is the original transmitted frequency of sound wave, is occurred The reflection sound wave frequency of frequency displacement is f ', then user and the relative velocity v of barrier0Drawn by following formula：
Wherein ,+represent that both have close relative motion ,-represent that both have remote relative motion.
Described step 2) transmitting sound wave and reflection sound wave detection in using Butterworth LPF reduce around Ambient noise obtains reflection sound wave.
Described step 3) in obtain threat data after, KNN algorithms are classified using vector space model, identical category Data similarity is high, predicts that the possibility of unknown categorical data is classified by the similarity with known class data is calculated, utilizes Hamming distances calculate Hamming distances and K in classification minimum distance, you can endangered as distance metric by majority voting Dangerous grade.
Described step 3) obtain after danger classes, shaken according to danger classes classification is different with different intelligent mobile terminal Intensity is to user's early warning.
Described step 3) in need Training Support Vector Machines grader, by Doppler frequency shift, obstacle distance, motion shape State, movement velocity input grader are trained to adjust classifier parameters, when rate of accuracy reached of the grader in certain distance Deconditioning after to threshold value.
Described SVMs builds higher-dimension hyperplane to be classified, and training grader finds optimal hyperlane, seeks Looking for optimal hyperlane is minimized under constraints | w |, w is hyperplane method vector：
Meet yi(wxi- b) >=1, wherein 1=1 ..., n；
Use non-negative Lagrange's multiplier αiSolve：
Utilize Quadratic Programming Solution：
Corresponding xiIt is exactly supporting vector, these supporting vectors are on edge and meet yi(wxi- b)=1.
Compared with prior art, the present invention judges that user walks line state using accelerometer, and based on sound Doppler effect Surrounding environment should be monitored in real time with sound distance-finding method, environmental information is classified using sorter model, according to danger etc. Level carries out early warning, on the basis of special equipment is not increased additionally, realizes and is easy to carry, accuracy in detection is high, real-time is good, It is user friendly, the features such as low in energy consumption, surrounding environment danger early warning is provided to be absorbed in the people of intelligent mobile terminal in time, and can With further simple and reliable guide service is provided for the crowd that has visual function disorder.
Further, judge whether user uses intelligent mobile terminal using whether lighting for device screen, light table Show and use, it is non-lit up to represent to be not used；People's body when on foot can rise and fall with paces, be shaken within the specific limits It is dynamic.The regular vibration of accelerometer data can be for judging whether people is walking.The purpose of state-detection is to select to use Family needs the state of risk prediction, other when etching system in a dormant state, to reduce the consumption of electricity.
Further, environmental monitoring can launch sound wave and gather reflection sound wave, and general principle is detection transmitting sound wave and anti- The time difference of sound wave is penetrated, the distance between sound source and barrier can be calculated.Doppler effect is that sound source carries out phase with observer During to motion, the acoustic vibration frequency generation frequency displacement different from the frequency that sound source is sent that observer receives, contrast transmitting sound wave With the frequency shift (FS) of reflection sound wave, you can the speed of related movement in detection surrounding environment between mobile object and smart machine, So as to judge whether occur dangerous.
Further, the noise in environment is removed using Butterworth LPF, so as to obtain reflection sound wave and carry High accuracy.
Further, in daily life, the security threat caused by the motor vehicle of fast running is far above fixed obstacle Threaten, on the other hand, barrier closely is also more dangerous than slightly remote barrier, so the present invention sets three kinds of danger Dangerous grade：It is high, medium and low.The potential danger of motor vehicle in traveling is high-grade, and the fixed obstacle within 3 meters is middle grade, Fixed obstacle within 5 meters is inferior grade.Different degrees of is dangerous with different shockproofness prompting users, and higher grade, Vibrations are stronger.The classification of danger classes is realized by two steps.
Brief description of the drawings
Fig. 1 is system overall flow figure；
Fig. 2 is walking states overhaul flow chart；
Fig. 3 is the vibrorecord of walking states；
Fig. 4 is that Doppler and sound ranging carry out environment measuring flow chart.
Embodiment
Below in conjunction with the accompanying drawings and specific embodiment the present invention is further illustrated.
Referring to Fig. 1, the present invention comprises the following steps：
1) state-detection：Judge whether user is in walking states, and whether use intelligent mobile terminal equipment；
2) environmental monitoring：The time difference t of detection transmitting sound wave and reflection sound wave, calculates the distance between sound source and barrier S, when sound source carries out relative motion with user, the acoustic vibration frequency generation frequency different from the frequency that sound source is sent that user receives Move, the frequency shift (FS) of contrast transmitting sound wave and reflection sound wave, you can mobile object and intelligent mobile terminal in detection surrounding environment Speed of related movement between equipment, so as to judge whether occur dangerous；
3) danger classes classification and early warning：Danger signal is determined whether by support vector machine classifier, if dangerous Signal, then judge that dangerous degree carries out early warning and obtained after threat data, it is empty to build vector using KNN algorithms by KNN algorithms Between model degree of danger is classified and to user's early warning.
Referring to Fig. 2 and Fig. 3, state-detection judges whether user is in walking states and whether uses equipment, when User when walking using mobile phone when need carry out danger early warning.Judge whether user makes using whether lighting for device screen With mobile phone, light expression and use, it is non-lit up to represent to be not used.People's body when on foot can rise and fall with paces, certain In the range of vibrated.The regular vibration of accelerometer data can be for judging whether people is walking.The mesh of state-detection Be select user need the state of risk prediction, other when etching system in a dormant state, to reduce the consumption of electricity.
Referring to Fig. 4, environmental monitoring can launch sound wave and gather reflection sound wave, and general principle is detection transmitting sound wave and anti- The time difference of sound wave is penetrated, the distance between sound source and barrier can be calculated.Doppler effect is that sound source carries out phase with observer During to motion, acoustic vibration frequency that observer receives is different from the frequency that sound source is sent to occur frequency displacement, contrast launch sound wave with The frequency shift (FS) of reflection sound wave, you can the speed of related movement in detection surrounding environment between mobile object and smart machine, from And judge whether occur dangerous., it is necessary to remove the noise in environment using bandpass filter after voice data is obtained, So as to obtain reflection sound wave and improve the degree of accuracy.Two kinds of present invention detection is dangerous close：The active of motor vehicle in traveling is leaned on It is near passive close with fixed barrier.Motor vehicle speed in traveling is very fast, if the frequency rise expression machine of reflection sound wave Motor-car is just close to people, if the frequency reduction of reflection sound wave represents motor vehicle from people.Due to the hair during motor-driven vehicle going Motivation and tire and the frequency that pavement friction is produced are relatively low, and the present invention launches 1500Hz sound using smart machine, receive Its reflection sound wave.If motor vehicle speed is 15m/s, the Doppler frequency shift of generation is about Δ f=67Hz, easily detection.Due to The potential danger of motor vehicle around user in any direction appearance traveling is all very high, as long as being approached so detecting by motor vehicle Early warning just is carried out to user.For fixed obstacle, the average speed of people's walking is generally 1.5m/s, the Doppler now occurred Frequency displacement Δ f=1.5Hz, frequency change is smaller, it is not easy to detect, so obtaining the distance of barrier using distance measuring method.People In outdoor walking, ranging can mainly detect the barrier of four direction all around, in order to distinguish whether barrier is in In front of user, the present invention retains the historical information of the obstacle distance detected, by the distance of newest detection and historical information ratio Compared with.If one group of range information is held essentially constant, illustrate that it represents user and the distance of the barrier similar to wall, because Advancing for user, so wall is in the left side or the right of user, the two directions are all without causing danger.If one group Range information becomes larger, and illustrates user away from this barrier, and this barrier in after one's death, does not result in danger Danger.If one group of range information is tapered into, illustrate that user is just moving closer to the barrier, now need to user's early warning.This The barrier that user or so and rear are rejected by this method is invented, barrier and the early warning in front is only focused on.
Danger classes classification determines dangerous degree.In daily life, the safe prestige caused by the motor vehicle of fast running The side of body is far above the threat of fixed obstacle, and on the other hand, barrier closely is also more dangerous than slightly remote barrier, So the present invention sets three kinds of danger classes：It is high, medium and low.The potential danger of motor vehicle in traveling is high-grade, within 3 meters Fixed obstacle be that fixed obstacle within middle grade, 5 meters is inferior grade.Different degrees of danger is strong with different vibrations Degree reminds user, and higher grade, shakes stronger.The classification of danger classes is realized by two steps.Pass through SVMs point first Class device determines whether danger signal, and because SVMs is typically used as two-value classification, filtering out dangerous signal can be with The efficiency of raising system.If dangerous signal, judge that dangerous degree carries out early warning by KNN algorithms.
Specific implementation step is as follows：
Step 1: state-detection：
1. user installation hazards monitoring system；
2. system dormancy；
If 3. screen is lighted, jumping to 4, otherwise jumping to 2；
4. background thread is waken up, accelerometer data is collected；
5. accelerometer data is removed into denoising by low pass filter；
6. judged whether by sliding window in walking；
7. if it is determined that user jumps to 8 in walking, otherwise jump to 2；
If 8. screen is closed, jumping to 2, otherwise jumping to step 2.
Step 2: environmental monitoring：
1. original sound data is launched and gathered：Launch fixed frequency, amplitude and sample rate using smart machine audio amplifier Sound wave, while collecting sound wave using sound receiver；
2. bandpass filtering：The sonic data of collection is subjected to noise reduction by Butterworth filter, transmitting sound wave is obtained Reflection sound wave；
3. extracting doppler information and range information, step 3 is jumped to.
Step 3: danger classes is classified：
1. carry out Experiment Training grader before system issue；
2. by Doppler frequency shift, obstacle distance, motion state, movement velocity input grader are trained and divided with adjusting Class device parameter；
3. deconditioning after rate of accuracy reached of the grader in certain distance is to threshold value；
4. be judged as danger for being classified device, high, medium and low three danger etc. is classified as using KNN algorithms Level；
5. vibrations early warning is carried out to user according to different danger classes.
The present invention is specifically included：
1. state-detection
Device screen lights wake-up system, collects accelerometer data.Body can rise and fall with paces when on foot, show It is the vibration of numerical value on accelerometer data.Referring to Fig. 3, accelerometer numerical value microvibration near 0 under static state, Numerical value can be to vibrate by a relatively large margin when on foot；
Using this feature, it is 20 to set sliding window size, first that numerical value is negative to remove plus the absolute value of minimum value Number, the average value M and variance S in calculation window afterwards.If M ＜ 3S, are judged as walking line state.
2. bandpass filtering noise reduction
The recorder of existing smart mobile phone all has decrease of noise functions, and because model is different, noise reduction is also not quite similar.Sound Ripple detection environment needs to reduce the ambient noise of surrounding, and the present invention selects Butterworth filter, is characterized in passband Frequency response curve it is most smooth, do not rise and fall, it is zero that suppressed frequency band, which is gradually reduced,.
Butterworth LPF can use square representing the formula of frequency for following amplitude：
Wherein n represents the exponent number of wave filter.The present invention is from 3 exponent numbers as wave filter, now suppressed frequency band fall off rate It hurry up, meet filtering requirements.Ambient noise is generally high-frequency noise, and people around speaks noise for 500-700Hz, and detection frequency of sound wave exists 1500Hz or so, therefore present invention design Butterworth digital band-pass filter, band connection frequency 1200-1600Hz, the stopband upper limit Frequency 1700Hz, stopband lower frequency limit 1100Hz, pass band damping are less than 1dB, and stopband maximum attenuation is more than 20dB, sample rate 2000Hz.By bandpass filter, most of noise can be filtered out, to obtain target sound waves.The intensity (amplitude) of sound wave exists Being propagated in air to decay, and unifrequency sound is compared with long-distance communications, and sound pressure level L decay (acoustic pressure takes log) is substantially line Property, i.e.,
L=as
Wherein s is distance, and a is attenuation coefficient, can be tabled look-up and obtained according to temperature humidity frequency.By detecting acoustic pressure and frequency Rate, can obtain reflection sound wave.
3. sound ranging
The sound wave of smart machine periodic transmission particular amplitude, frequency and sample rate, detection transmitting sound wave and reflection sound wave Time difference t, sound wave spread speed v=332m/s in atmosphere, can be with the distance between computational intelligence equipment and barrier S For：
4. Doppler effect
When Doppler effect refers to that smart machine and barrier have relative motion, the reflection sound wave received and transmitting sound wave There is certain frequency shift (FS) phenomenon.General principle is if user has relative velocity v with barrier0, sound wave is aerial Spread speed is v=332m/s, and f is the original transmitted frequency of the sound wave, then the reflection sound wave frequency f' for occurring frequency displacement is：
Wherein ,+represent that both have close relative motion ,-represent that both have remote relative motion.
Relative velocity v between smart machine and barrier0For：
Wherein ,-represent that both have close relative motion ,+represent that both have remote relative motion.
5. Training Support Vector Machines grader
SVMs builds one or more higher-dimension hyperplane to be classified, and hyperplane is classification boundaries.Classification Device training is exactly to find optimal hyperlane.Find optimal hyperlane be under constraints minimize | w |, w be hyperplane method to Amount：
Meet yi(wxi- b) >=1, wherein i=1 ..., n.
Use non-negative Lagrange's multiplier αiSolve：
Utilize Quadratic Programming Solution：
Corresponding xiIt is exactly supporting vector, these supporting vectors are on edge and meet
yi(wxi- b)=1
6. carry out danger classes classification using KNN
Obtain after threat data, its degree of danger is classified using KNN.KNN is classified using vector space model, The data similarity of identical category is high.Unknown categorical data can be predicted by the similarity with known class data is calculated May classification.Distance metric is used as by the use of Hamming distances.Hamming distances are calculated with K in classification (by evolving by majority voting Algorithm obtains K for 3) individual minimum distance, you can obtain danger classes belong to it is high, medium and low which kind of, and according to classification it is different with Different shockproofnesses are to user's early warning.

Claims (9)

1. a kind of method that intelligent mobile terminal carries out hazards monitoring and early warning based on Doppler effect, it is characterised in that including Following steps：
1) state-detection：Judge whether user is in walking states, and whether use intelligent mobile terminal equipment；
2) environmental monitoring：The time difference t of detection transmitting sound wave and reflection sound wave, calculates the distance between sound source and barrier S, sound When source carries out relative motion with user, the acoustic vibration frequency generation frequency displacement different from the frequency that sound source is sent that user receives is right Than the frequency shift (FS) of transmitting sound wave and reflection sound wave, you can in detection surrounding environment mobile object and intelligent mobile terminal equipment it Between speed of related movement so that judge whether i.e. by it is dangerous occur；
3) danger classes classification and early warning：Danger signal is determined whether by support vector machine classifier, if dangerous signal, Then judge that dangerous degree carries out early warning and obtained after threat data by KNN algorithms, KNN algorithms are divided using vector space model Class, the data similarity of identical category is high, and unknown categorical data is predicted by the similarity with known class data is calculated May classification, by the use of Hamming distances as distance metric, by majority voting calculate Hamming distances with classification K minimum away from From, you can danger classes is obtained, and to user's early warning.
2. a kind of intelligent mobile terminal according to claim 1 carries out the side of hazards monitoring and early warning based on Doppler effect Method, it is characterised in that：Described step 1) in judge user whether just according to whether lighting for intelligent mobile terminal equipment screen Mobile phone is being used, expression is being lighted and uses, it is non-lit up to represent to be not used.
3. a kind of intelligent mobile terminal according to claim 2 carries out the side of hazards monitoring and early warning based on Doppler effect Method, it is characterised in that：Described step 1) in sentenced using the regular vibration of the accelerometer data of intelligent mobile terminal equipment Whether disconnected people is in walking.
4. a kind of intelligent mobile terminal according to claim 1 carries out the side of hazards monitoring and early warning based on Doppler effect Method, it is characterised in that：Described step 2) in intelligent mobile terminal equipment periodic transmission particular amplitude, frequency and sample rate The time difference t, sound wave spread speed v=332m/s in atmosphere, so as to obtain intelligence of sound wave, detection transmitting sound wave and reflection sound wave Can the distance between mobile terminal device and barrier S be：
5. a kind of intelligent mobile terminal according to claim 4 carries out the side of hazards monitoring and early warning based on Doppler effect Method, it is characterised in that：Described step 2) in intelligent mobile terminal equipment and barrier have relative motion, the reflected sound received Ripple has frequency shift (FS) with transmitting sound wave, and the aerial spread speed of sound wave is v=332m/s, and f is the original transmitted frequency of sound wave Rate, the reflection sound wave frequency for occurring frequency displacement is f ', then user and the relative velocity v of barrier0Drawn by following formula：
Wherein ,+represent that both have close relative motion ,-represent that both have remote relative motion.
6. a kind of intelligent mobile terminal according to claim 5 carries out the side of hazards monitoring and early warning based on Doppler effect Method, it is characterised in that：Described step 2) transmitting sound wave and reflection sound wave detection in using Butterworth LPF drop Low ambient noise obtains reflection sound wave.
7. a kind of intelligent mobile terminal according to claim 1 carries out the side of hazards monitoring and early warning based on Doppler effect Method, it is characterised in that：Described step 3) obtain after danger classes, moved eventually with different intelligent according to danger classes classification is different Shockproofness is held to user's early warning.
8. a kind of intelligent mobile terminal according to claim 7 carries out the side of hazards monitoring and early warning based on Doppler effect Method, it is characterised in that：Described step 3) in need Training Support Vector Machines grader, by Doppler frequency shift, obstacle distance, Motion state, movement velocity input grader are trained to adjust classifier parameters, when standard of the grader in certain distance True rate reaches deconditioning after threshold value.
9. a kind of intelligent mobile terminal according to claim 8 carries out the side of hazards monitoring and early warning based on Doppler effect Method, it is characterised in that：Described SVMs builds higher-dimension hyperplane to be classified, and training grader finds optimal super flat Face, finding optimal hyperlane is minimized under constraints | w |, w is hyperplane method vector：
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Meet yi(wxi- b) >=1, wherein i=1 ..., n；
Use non-negative Lagrange's multiplier αiSolve：
<mrow> <mi>arg</mi> <mi> </mi> <msub> <mi>min</mi> <mrow> <mi>w</mi> <mo>,</mo> <mi>b</mi> </mrow> </msub> <msub> <mi>max</mi> <mrow> <mi>&amp;alpha;</mi> <mo>&amp;GreaterEqual;</mo> <mn>0</mn> </mrow> </msub> <mo>{</mo> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <mo>|</mo> <mo>|</mo> <mi>w</mi> <mo>|</mo> <msup> <mo>|</mo> <mn>2</mn> </msup> <mo>-</mo> <munderover> <mi>&amp;Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <mo>&amp;lsqb;</mo> <msub> <mi>y</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>wx</mi> <mi>i</mi> </msub> <mo>-</mo> <mi>b</mi> <mo>)</mo> </mrow> <mo>-</mo> <mn>1</mn> <mo>&amp;rsqb;</mo> <mo>}</mo> </mrow>
Utilize Quadratic Programming Solution：
<mrow> <mi>w</mi> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>&amp;alpha;</mi> <mi>i</mi> </msub> <msub> <mi>y</mi> <mi>i</mi> </msub> <msub> <mi>x</mi> <mi>i</mi> </msub> </mrow>
Corresponding xiIt is exactly supporting vector, these supporting vectors are on edge and meet yi(wxi- b)=1.
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