EP3152534A1 - Procédé de classification d'un objet nautique, dispositif, sonar et véhicule nautique ou plateforme fixe - Google Patents

Procédé de classification d'un objet nautique, dispositif, sonar et véhicule nautique ou plateforme fixe

Info

Publication number
EP3152534A1
EP3152534A1 EP15737967.8A EP15737967A EP3152534A1 EP 3152534 A1 EP3152534 A1 EP 3152534A1 EP 15737967 A EP15737967 A EP 15737967A EP 3152534 A1 EP3152534 A1 EP 3152534A1
Authority
EP
European Patent Office
Prior art keywords
sound signal
determining
underwater sound
water object
parameters
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP15737967.8A
Other languages
German (de)
English (en)
Inventor
Ralf Michael MEYER
Sebastian KNABE
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Atlas Elektronik GmbH
Original Assignee
Atlas Elektronik GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Atlas Elektronik GmbH filed Critical Atlas Elektronik GmbH
Publication of EP3152534A1 publication Critical patent/EP3152534A1/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H3/00—Measuring characteristics of vibrations by using a detector in a fluid
    • G01H3/04—Frequency
    • 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
    • G01S7/539—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/001—Acoustic presence detection
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B63—SHIPS OR OTHER WATERBORNE VESSELS; RELATED EQUIPMENT
    • B63G—OFFENSIVE OR DEFENSIVE ARRANGEMENTS ON VESSELS; MINE-LAYING; MINE-SWEEPING; SUBMARINES; AIRCRAFT CARRIERS
    • B63G8/00—Underwater vessels, e.g. submarines; Equipment specially adapted therefor
    • B63G8/39—Arrangements of sonic watch equipment, e.g. low-frequency, sonar
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/10—Aspects of acoustic signal generation or detection
    • G01V2210/14—Signal detection
    • G01V2210/142—Receiver location
    • G01V2210/1423—Sea
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/40—Transforming data representation
    • G01V2210/43—Spectral

Definitions

  • the invention relates to a method for classifying a water object, in particular a ship or a submarine, using detected underwater sound signals, as well as a device for carrying out the method and a sonar and a watercraft or stationary platform.
  • Classification features are partially extracted automatically, in addition to which an operator of the sonar gives specific characteristics recognized by him to a continuously evolving model and accordingly classifies. Thus, the classification is especially on a manually created rules.
  • DEMON Detection of Envelope Modulation on Noise
  • LOFAR Low Frequency Analysis and Recording
  • the object of the invention is to improve the state of the art.
  • the object is achieved by a method for classifying a water object, in particular a ship or a submarine, the method comprising the following steps:
  • a water object can be classified automatically based on the determined Parameterat zes.
  • additional or additional information from an operator of the sonar is not needed, so that the This classification is not based on a manually created set of rules.
  • Classification means, in particular, classification in a specific (vehicle) category Since the "water objects" are, in particular, ships, submarines, manned or unmanned vessels or sound-emitting buoys, these can be described in certain categories are taken. For example, fish trawlers or small motorized vessels can form a first category, submarines (submarines) and AUV 's (autonomous underwater vehicle) or ROV 's (remotely operated vehicle). Another class, for example, form large container ships. The classes may be arbitrarily finely divided, the summary criteria being generally speed and / or size and / or applications.
  • a “hydrophone” is in particular a sound-sensitive sensor that converts underwater sound signals into electrical signals, which may be part of a passive or active sonar.
  • An "underwater sound signal” is generally a signal emitted or reflected by the water object, in the present case particularly good results being obtained with original underwater sound signals emitted by the object.
  • the signals recorded by the hydrophones are recorded especially in the time domain. These underwater sound signals present in the time domain can be converted, for example, by a transformation into a frequency domain, so that a "frequency spectrum" is present in this respect
  • a "cepstrum” comprises a renewed transformation of the frequency spectrum
  • a "set of parameters" can be extracted that adequately describes the cepstrum, and parameters directly derived from the cepstrum are called direct parameters in the present case.
  • a water object can be assigned to a water object class. This can be done, for example, by determining the water object class based on the parameter set by means of a comparison, a correlation or other comparative functions.
  • a "water object class” is in particular a category into which a water object is classified.
  • Weighting is understood to mean, in particular, that gains and / or attenuations of the signal occur in a linear or non-linear manner., The weighting can be done both in the time domain and in the frequency domain.
  • a window function in particular a Hamming window function, can be applied to the underwater sound signal and / or the weighted underwater sound signal.
  • this window function can be used to determine the weighting with which samples of a signal are sampled within a section
  • Window in subsequent calculations.
  • the window function can be applied both in the time domain and in the frequency domain.
  • the determination of the frequency spectrum is effected by means of a fast Fourier transformation.
  • a fast Fourier transformation an effective method for transforming into the frequency domain.
  • functions can be realized by means of computer or separately designed for building blocks, such as FPGAs (Field Programmable Gate Arrays).
  • determining the cepstrum can be logarithmic Absolute value and / or a logarithm of several absolute values and / or a logarithm of all absolute values of the frequency spectrum.
  • the improvement of the parameter set is that when determining the cepstrum a discrete
  • Cosine transformation is one of the real, discrete, linear, orthogonal ones
  • Transformations that generally transform a discrete-time signal from the time domain or a spatial domain into a frequency domain, similar to the discrete Fourier transform.
  • parameters derived from the direct parameters of the parameter set can be formed so that the parameter set includes derived parameters (with). It should be noted at this point that, of course, only derived parameters can be used.
  • a "derived parameter" may be, for example, a delta value or a difference between two direct parameters or a direct parameter with a derived parameter, the direct parameters being, in particular, real valued numerical values.
  • the direct parameters and / or the derived parameters can be weighted.
  • a weighting function may be, for example, a linear or a nonlinear function, which amplifies and / or attenuates the parameters.
  • the water object class can be determined by means of a comparison of the parameter data with a comparison parameter set, wherein in particular a correlation and / or a detection by means of a Gaussian mixing distribution takes place and / or the comparison takes place by means of a trained neural network.
  • a mixed distribution is understood to mean in particular a composite distribution.
  • K is the number of mixed components
  • k is the mean of a component E k
  • the covariance matrix is the weight of the mixture of a component.
  • the GMM is trained for each individual class.
  • the water object class that has reached the highest probability is assigned the corresponding water object.
  • the neural networks present here are, in particular, artificial neural networks which are simulated, for example, by means of a computer. These neural networks are also trained on the individual water object classes, wherein a trained neural network, for example, in a hardware structure such as an FPGA can be transferred.
  • the object is achieved by a device which is set up such that a previously described method can be carried out.
  • Such a device may be, for example, a computer with corresponding input and output means.
  • An FPGA is also included in this device.
  • the object is achieved by a sonar, in particular a passive sonar, which has a device described above.
  • Figure 1 is a schematic representation of a sent out for a water object
  • a container ship is powered by propellers at sea.
  • the propeller introduces an underwater sound signal into the seawater.
  • This underwater sound signal is detected in the present case by a passive sonar and processed accordingly electronically.
  • S n is the weighted signal
  • Si is the signal at time step i
  • a is the weighting parameter
  • Si -1 is the signal at time step i-1 is.
  • the range of values of a is between 0 and 1, typically close to one.
  • this weighted input signal S n is transferred into two overlapping segments by means of a Hamming window.
  • a reasonable time resolution is achieved for the downstream parameters.
  • the associated frequency spectrum is determined by means of a Fast Fourier Transform. [45] Subsequently, the absolute values of each individual frequency spectrum and the associated logarithms to the base 10 are formed.
  • this logarithmic spectrum is adapted by means of bandpass filters, so that in particular a frequency axis adapted to human frequency perception is present.
  • the advantage here is especially in a dimensional reduction.
  • delta parameters are formed so that there are further (derived) parameters for the thirteen direct parameters through difference formation.
  • both adjacent parameters and the first with the third, the second with the fourth, the third with the fifth, etc. derived parameters are formed.
  • the first class includes submarines 105
  • the second class includes trawlers and small craft 109
  • the third class includes speedboats 113
  • the fourth class includes container ships 117.
  • the known underwater sound signals were used and the associated direct parameters and derived parameters were formed. The arithmetic mean was then calculated from these parameters.
  • the measured subsonic sound signal and the associated direct and derived parameters are correlated with the arithmetic-mean value parameter sets of the associated water object classes and the probability in percent is determined.
  • the water object classes for submarines 105, and the trawler class 109 and the speed boat class 113 give a lower probability ( ⁇ 30%) of belonging to this class again (107, 111, 115), with the probability 119 belonging to the
  • Water object class of container ships 117 is over 90% very high.
  • the probability axis 103 indicates a value between 0 and 100% and four areas are indicated on the abscissa, which are each associated with one of the water object classes.
  • the respectively associated probability 107 for the submarine class 105, the probability 111 for the trawlers 109 and the probability 115 for the speed boats 113 is so small that it can be ruled out in the present case that the water object which has transmitted the subsea sound signal belongs to these classes is associated. Due to the high probability 119, the ship mentioned above is classified as a container ship.

Abstract

L'invention concerne un procédé de classification d'un objet nautique, en particulier un navire ou un sous-marin, ledit procédé comprenant les étapes suivantes : identifier un signal sonore sous-marin au moyen d'un hydrophone, ledit signal sonore sous-marin présentant un signal sonore de l'objet nautique, déterminer un spectre de fréquence du signal sonore sous-marin identifié, déterminer un cepstre du spectre de fréquence, déterminer un jeu de paramètres dudit cepstre, ledit jeu de paramètres comportant en particulier des paramètres directs et déterminer une classe d'objets nautiques sur la base du jeu de paramètres.
EP15737967.8A 2014-06-05 2015-04-20 Procédé de classification d'un objet nautique, dispositif, sonar et véhicule nautique ou plateforme fixe Withdrawn EP3152534A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102014107979.5A DE102014107979A1 (de) 2014-06-05 2014-06-05 Verfahren zum Klassifizieren eines Wasserobjekts, Vorrichtung, Sonar und Wasserfahrzeug oder stationäre Plattform
PCT/DE2015/100164 WO2015185032A1 (fr) 2014-06-05 2015-04-20 Procédé de classification d'un objet nautique, dispositif, sonar et véhicule nautique ou plateforme fixe

Publications (1)

Publication Number Publication Date
EP3152534A1 true EP3152534A1 (fr) 2017-04-12

Family

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Family Applications (1)

Application Number Title Priority Date Filing Date
EP15737967.8A Withdrawn EP3152534A1 (fr) 2014-06-05 2015-04-20 Procédé de classification d'un objet nautique, dispositif, sonar et véhicule nautique ou plateforme fixe

Country Status (3)

Country Link
EP (1) EP3152534A1 (fr)
DE (1) DE102014107979A1 (fr)
WO (1) WO2015185032A1 (fr)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110807365B (zh) * 2019-09-29 2022-02-11 浙江大学 一种基于gru与一维cnn神经网络融合的水下目标识别方法
EP3816664A1 (fr) 2019-11-04 2021-05-05 Fundación Tecnalia Research & Innovation Procédé, système et produit-programme d'ordinateur permettant d'identifier des objets sous-marins
CN111024207B (zh) * 2019-11-26 2022-09-02 中国船舶重工集团有限公司第七一0研究所 一种矢量水听器线谱自动检测与判决方法
CN112183225B (zh) * 2020-09-07 2022-07-05 中国海洋大学 一种基于概率潜在语义分析的水下目标信号特征提取方法

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Publication number Priority date Publication date Assignee Title
DE10130297C2 (de) * 2001-06-22 2003-12-24 Stn Atlas Elektronik Gmbh Verfahren zum Bestimmen der Zielposition eines schallabstrahlenden Ziels
DE102010056526B4 (de) * 2010-12-29 2016-08-11 Atlas Elektronik Gmbh Verfahren zum Bestimmen einer oder mehrerer relativer Richtungen als Zielpeilung oder Zielpeilungen sowie Vorrichtung zur Ausführung des Verfahrens
DE102012000788B4 (de) * 2012-01-17 2013-10-10 Atlas Elektronik Gmbh Verfahren und Vorrichtung zum Verarbeiten von Wasserschallsignalen
DE102012015638A1 (de) * 2012-08-07 2014-02-13 Atlas Elektronik Gmbh Verfahren und Vorrichtung zum Klassifizieren von Wasserfahrzeugen

Non-Patent Citations (1)

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Title
See references of WO2015185032A1 *

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Publication number Publication date
WO2015185032A1 (fr) 2015-12-10
DE102014107979A1 (de) 2015-12-17

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