Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R29/00—Arrangements for measuring or indicating electric quantities not covered by groups G01R19/00 - G01R27/00
  • G01R29/02—Measuring characteristics of individual pulses, e.g. deviation from pulse flatness, rise time or duration
  • G01R29/027—Indicating that a pulse characteristic is either above or below a predetermined value or within or beyond a predetermined range of values
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
  • G01R23/16—Spectrum analysis; Fourier analysis
  • G01R23/165—Spectrum analysis; Fourier analysis using filters
  • G01R23/167—Spectrum analysis; Fourier analysis using filters with digital filters
• • 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
  • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
  • G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
  • G01S7/523—Details of pulse systems
  • G01S7/526—Receivers
  • G01S7/527—Extracting wanted echo signals
  • G01S7/5273—Extracting wanted echo signals using digital techniques

Definitions

• the present invention relates to methods which make it possible to detect, and possibly identify, the transient acoustic signals characteristic of various phenomena which it is desired to study. It can be applied as well to aerial acoustic signals as to underwater acoustic signals, for applications as diverse as the identification of breakdowns in an automobile and the identification of ships.
• the invention proposes a method for assisting in the detection of transient signals, in particular in acoustic signals, mainly characterized in that in a first step the parameters of a filter are determined making it possible to modify the received signal to obtain a signal formed by a first substantially continuous and low-level part and a second impulse part having a level significantly higher than this low level and representing the transient signals to be detected, and that in a second step the signal is filtered reception by a filter having the parameters thus determined.
• the electrical signal representative of the audio signal received is applied to an anti-aliasing filter 101 adapted to the maximum frequency received, to the maximum frequency audible by the operator (approximately 20 KHz) and at the sampling frequency of a converter analog-digital (102) located after this filter 101.
• This analog-digital converter delivers a digital signal Y (T) which is applied to several processing chains whose outputs lead to a switch 103.
• One of the inputs of this switch is directly connected to converter 102 in order to allow the operator to listen directly to the received signal.
• the output of the switch 103 is connected to the input of a digital-analog converter 104, followed by a low-pitch filter 105 and an amplifier 106 which supplies headphones 107.
• a digital-analog converter 104 which supplies headphones 107.
• the operator provided with the headset 107 hears the same signal as that received by the hydrophone, the passage by the digital channel not causing significant distortion of this signal.
• the operator directs the output signal of the converter 102, using a switch 108, to a learning module 109
• This training module makes it possible to model the signal listened to according to a stationary broadband component synthesized by a predictive model, and a prediction residue corresponding to the difference between the signal predicted by the predictive model and the true signal.
• This modeling will preferably be carried out by the known method of autoregressive modeling of the signal with a P order.
• the order P of such an autoregressive model corresponds to the P temporal previous samples of the output of the autoregressive filter
• these P predictor coefficients can be obtained in a known manner by solving the Yule-Walker equations using the Levinson algorithm. This algorithm converges quickly and we can get the P coefficients in the space of a few seconds during which the operator closes the switch 108.
• this selection of the learning sequences can be carried out automatically, either repeatedly (for example 1 second every 10 seconds), or by detection of an abnormal phenomenon identified in any way.
• This gives a vector formed of P coefficients a. which can be used in different ways, for example by reverse filtering or by so-called "caricatural” filtering. In the case of the use of reverse filtering, the coefficients a.
• the signal e (kT) obtained at the output of this adapted average filter MA is applied via the switch 103 to the converter 104, and the processed signal finally arrives on the headphones 107.
• AR recursive filters of the self-regressive type which receive the coefficients a. and apply a processing defined by the formula to the input signal:
• the signal at the output of converter 102 is applied to a first AR filter
• the inventors have proposed to call signal c. (kT) thus obtained, order 1 caricature of the signal s (t) because it has a power spectral density substantially equal to the square of the power spectral density of this signal s (t), which amounts to saying that the permanent noise contained in the signal s (t) has been "flattened” by bringing out the impulse noise contained in this same signal.
• This impulse noise is thus exaggerated by reducing the permanent noise, which corresponds pictorially to a caricature of the signal s (t). In this way the operator who listens to the signal resulting from this processing in the helmet 107 more easily perceives this impulse signal, which emerges better from the permanent signal.
• the invention further proposes to further increase the influence of this processing by applying the signal at the output of the first AR filter in a second AR filter 111.2 then possibly in a succession of AR filters put in series up to an n th AR filter 111. n.
• the signal cj thus obtained at the output of the AR filter 111. j will quite naturally be called caricature of order j of the input signal, and its power spectral density is substantially equal to the power n of the power spectral density of the signal s .
• the treatment thus carried out is defined by the formula:
• the outputs of the AR filters are respectively connected to inputs of the switch 103 and the operator can thus select, in addition to the direct signal and the signal filtered by reverse filtering using the MA filter, a caricature of order between 1 and n of the input signal.
• the operator after having learned will successively select the different outputs and then come back to the one which seems to him to have particularly interesting characteristics. Indeed the passage in such a system, whether by reverse filtering or by j-order caricature, deeply distorts the signal, since in the best of cases there is a continuous signal of very low level barely audible from which come out more or less regular pulses which are clearly perceptible to the operator but which are distorted with respect to the initial signal received.
• this signal is very difficult to analyze, even by a trained operator, to obtain precise information on the source of the disturbing signal, because the deformations which are made to this disturbing signal, if they make it more detectable, distort it too much compared to the original signal to allow effective identification.
• these modifications are essentially variable according to the modalities brought, within the framework of the invention, to the processing chains, for example the order P of the auto-regression or the order j of the caricature.
• the method according to the invention therefore essentially serves as a detection aid for an operator, who, after having identified the presence of a disturbing signal, may return for example to direct listening without filtering to concentrate his attention on the identification of the source of this disturbing signal. Possibly he can pass the baton to a more trained operator who will more easily carry out this identification.
• the invention has been described in the case where the selection of the learning range is made by the operator, and the possibility of using automatic criteria based on scales has been mentioned. of time.
• LEVINSON for example those of BURG, ITAKURA, GUEGUEN-LEROUX, MORF and FALCONER, or MORF and LEE. . .
• the invention extends beyond self-regressive analysis to any technique for modeling the initial signal which allows a decomposition into a set of parameters characteristic of the chosen model and an "innovation" ( difference between the prediction provided by the model and the true signal).
• ARMA model the KALMAN filter
• neural networks . . .
• a device applying the method according to the invention in order to obtain an automatic function making it possible to improve the performance of a device for detecting and automatically classifying acoustic signals.
• the adapted medium filter to produce a pre-processing module making it possible to whiten and minimize the energy of a stationary signal, in order to favor the automatic detection of transient signals contained in an acoustic signal, by controlling for example the learning phases by a clock validated by a transient non-detection signal.

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• Physics & Mathematics (AREA)
• General Physics & Mathematics (AREA)
• Engineering & Computer Science (AREA)
• Computer Networks & Wireless Communication (AREA)
• Radar, Positioning & Navigation (AREA)
• Remote Sensing (AREA)
• Mathematical Physics (AREA)
• Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
• Filters That Use Time-Delay Elements (AREA)
• Measurement Of Velocity Or Position Using Acoustic Or Ultrasonic Waves (AREA)

Applications Claiming Priority (3)

Publications (1)

Family

ID=9415882

Family Applications (1)

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Family Cites Families (4)

• 1991
  • 1991-08-02 FR FR9109874A patent/FR2680006B1/fr not_active Expired - Fee Related
• 1992
  • 1992-07-31 CA CA 2114632 patent/CA2114632A1/fr not_active Abandoned
  • 1992-07-31 WO PCT/FR1992/000760 patent/WO1993003395A1/fr not_active Application Discontinuation
  • 1992-07-31 AU AU24708/92A patent/AU657792B2/en not_active Expired - Fee Related
  • 1992-07-31 EP EP92918452A patent/EP0597040A1/de not_active Withdrawn

Non-Patent Citations (1)

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