CN109270295B - Underwater acoustic Doppler flow velocity measurement method based on autocorrelation estimation and effective data screening - Google Patents

Underwater acoustic Doppler flow velocity measurement method based on autocorrelation estimation and effective data screening Download PDF

Info

Publication number
CN109270295B
CN109270295B CN201810950528.3A CN201810950528A CN109270295B CN 109270295 B CN109270295 B CN 109270295B CN 201810950528 A CN201810950528 A CN 201810950528A CN 109270295 B CN109270295 B CN 109270295B
Authority
CN
China
Prior art keywords
flow velocity
autocorrelation
value
estimation
echo
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.)
Active
Application number
CN201810950528.3A
Other languages
Chinese (zh)
Other versions
CN109270295A (en
Inventor
方世良
杨永寿
孙兆文
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.)
Nanjing Shihai Acoustics Technology Co ltd
Original Assignee
Nanjing Shihai Acoustics Technology Co ltd
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 Nanjing Shihai Acoustics Technology Co ltd filed Critical Nanjing Shihai Acoustics Technology Co ltd
Priority to CN201810950528.3A priority Critical patent/CN109270295B/en
Publication of CN109270295A publication Critical patent/CN109270295A/en
Application granted granted Critical
Publication of CN109270295B publication Critical patent/CN109270295B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P5/00—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft
    • G01P5/24—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft by measuring the direct influence of the streaming fluid on the properties of a detecting acoustical wave
    • G01P5/241—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft by measuring the direct influence of the streaming fluid on the properties of a detecting acoustical wave by using reflection of acoustical waves, i.e. Doppler-effect

Landscapes

  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • General Physics & Mathematics (AREA)
  • Measurement Of Velocity Or Position Using Acoustic Or Ultrasonic Waves (AREA)

Abstract

The invention discloses an underwater acoustic Doppler flow velocity measurement method based on autocorrelation estimation and effective data screening, which relates to the technical field of acoustic Doppler measurement, and comprises the steps of estimating a complex autocorrelation function value R (tau), estimating the total signal power R (0), calculating the precision criterion eta (v) of a sonar echo sample, and finally determining whether to retain the current sample through threshold judgment. According to the method, the measured data samples are screened according to the relation between the echo autocorrelation estimation precision criterion value and the set threshold value, the precision of underwater acoustic flow velocity estimation is improved, and the analysis of measured data shows that the method can obviously reduce the variance of flow velocity estimation and improve the key performance of underwater acoustic velocity measurement equipment.

Description

Underwater acoustic Doppler flow velocity measurement method based on autocorrelation estimation and effective data screening
Technical Field
The invention relates to the technical field of acoustic Doppler measurement, in particular to an underwater acoustic Doppler flow velocity measurement method based on autocorrelation estimation and effective data screening.
Background
The flow velocity of a water body, the movement velocity of an underwater target and the like are very important measurement objects in underwater acoustic measurement. The common underwater acoustic flow velocity measurement method is mostly based on the doppler principle, and the flow measurement equipment comprises an acoustic doppler flow velocity profile meter, an acoustic doppler flow velocity meter and the like. The working process of a common speed measuring sonar is as follows: firstly, a modulation short pulse with the carrier frequency of tens kHz to hundreds kHz is transmitted to a detection water area, sound waves encounter scatterers in water to generate sound scattering, the scattered sound waves with extremely small power are received by a receiving transducer and converted into electric signals, and then the echo intensity, the water flow speed and the like of each water layer of the detection water area are obtained after signal conditioning, orthogonal demodulation, analog-to-digital conversion and signal processing.
When the measured data of the speed measuring sonar is analyzed, some sampling points with flow velocity values seriously deviating from the mean value are found to be frequently generated, so that the judgment of data quality and the screening of echo data are necessary in the signal processing stage. The most direct reaction on the spectrum of fluctuations in flow velocity values is the broadening of the spectral width of the echo. The factors causing the broadening of the spectrum width of the echo are analyzed to provide theoretical support for generating a criterion of data quality. (ii) consistency of diffuser velocity. The volume of scatterers in natural water is usually very small compared to the range of emitted beam illumination of sonar. For example, the particle size of scatterers in seawater is mostly distributed in the range of 20 μm to 200 μm, and the irradiation diameter of the scatterers at the distance R is approximately equal to 0.052R and is far larger than the particle size of the scatterers, taking the beam with the beam width of 3 degrees as an example. Therefore, the doppler information of the echo at the distance R is obtained by the superposition of the motion of all scatterers within the beam irradiation range. If the speed consistency of the scatterers is good, the broadening of the echo spectrum is not obvious; if their velocity difference is large, it will bring about a significant broadening of the echo spectrum. The wider the spectral width, the larger the estimated standard deviation of the doppler shift, and the larger the estimated flow velocity deviation from the mean. This is the theoretical support for judging the data quality based on the estimated value of the spectral width. (II) width and emission bandwidth of the sound wave beam. The wider the width of the transmit beam means a larger beam footprint at the same depth, i.e. a superposition of more scatterer motion characteristics, which increases the width of the echo spectrum and the standard deviation of the flow velocity estimate. The larger the operating bandwidth of the transmitter and transducer, the wider the bandwidth of the waveform actually transmitted into the water, which in turn leads to an increase in the echo spectral width. Similar to the pulse width factor, for an actual sonar equipment, the influence degree of the factor on the spectrum width is determined. And (iii) factors that reduce the signal-to-noise ratio. Factors influencing the echo signal-to-noise ratio mainly include transmission power, propagation distance, scatterer concentration and the like. After the signal-to-noise ratio is reduced, the spectrum width of the echo is widened, and the standard deviation of the flow velocity estimation is increased. It is obvious that the smaller the transmission power, the longer the propagation distance, the smaller the echo signal-to-noise ratio. The number of scatterers in the water rarely causes a significant drop in the echo signal-to-noise ratio. On the contrary, the concentration of the scatterer is too high, so that the echo of the deep water body is shielded, and the signal to noise ratio of the deep water body is further reduced. (IV) non-uniformity of flow velocity distribution. The influence of the non-uniform flow velocity on the width of the echo spectrum has two aspects. One is that the faster the flow velocity changes along the beam direction, the larger the difference in velocity of scatterers in each water layer, the wider the echo spectrum width naturally widens, and the standard deviation increases when estimating the layered flow velocity. Secondly, the flow of the upper water body is the movement of the sound wave propagation medium for the echo of the lower water body, which brings extra Doppler frequency shift, and further broadens the spectrum of the echo. It should be noted that the larger the difference between the flow rates of the upper and lower layers, the more significant this effect is. In a word, it is feasible to establish a method for judging echo quality from the aspect of estimating spectral width, and the increase of flow rate estimation standard deviation caused by the inconsistent motion characteristics of scatterers and the reduction of echo signal-to-noise ratio can be compensated to a certain extent.
Disclosure of Invention
The invention aims to provide an underwater acoustic Doppler flow velocity measurement method based on autocorrelation estimation and effective data screening.
In order to achieve the purpose, the technical scheme of the invention is as follows:
an underwater acoustic Doppler flow velocity measurement method based on autocorrelation estimation and effective data screening comprises the following steps:
s1, according to
Figure BDA0001771372530000021
Estimating the value of the complex autocorrelation function R (tau) by means of the complex echoes of the effective water layer, based on
Figure BDA0001771372530000022
Estimating the total power of the signal R (0), where x1,x2,...,xmIs the complex data of m current valid water layers, fsIn order to be able to measure the echo sampling rate,
Figure BDA0001771372530000023
n is expressed at a sampling rate fsNumber of data points, operator corresponding to time-dependent interval tau
Figure BDA0001771372530000024
Represents rounding down;
s2, estimating the accuracy criterion according to the flow rate autocorrelation
Figure BDA0001771372530000025
A velocimeter sonar estimates the eta (v) value of a sampling echo in real time, wherein v is the radial flow velocity, lambda is the wavelength of a transmitted sound wave, tau is the interval of complex autocorrelation, R (tau) is the autocorrelation function value of an effective water layer, and R (0) is the total power of signals;
and S3, judging a threshold, discarding the current sample when the eta (v) value is smaller than the set threshold, and keeping the current sample when the eta (v) value is larger than or equal to the set threshold.
In the scheme, the threshold value is 0.2-0.4.
The underwater acoustic Doppler flow velocity measurement method based on the autocorrelation estimation and the effective data screening provides a flow velocity autocorrelation-based estimation precision criterion according to the characteristics of echo signals in underwater acoustic flow velocity measurement and the knowledge of signal power spectrum estimation, and then screens measurement data samples in real time according to the relation between the criterion value and a set threshold value so as to improve the accuracy of underwater acoustic flow velocity measurement. The analysis of measured data shows that the method can obviously reduce the variance of the flow velocity estimation and improve the key performance of the underwater sound flow velocity measuring equipment.
Drawings
FIG. 1 is a flow chart of a method for measuring underwater acoustic Doppler flow velocity based on autocorrelation estimation and efficient data screening in an embodiment of the present invention;
FIG. 2 is a radial flow velocity profile in accordance with an embodiment of the present invention;
FIG. 3 is a graph illustrating flow rate autocorrelation estimation accuracy criteria in an embodiment of the present invention;
FIG. 4 is a graph of the relationship between the forward standard deviation of the flow rate and the accuracy criterion η (v) of the flow rate autocorrelation estimation in an embodiment of the present invention;
FIG. 5 is a graph illustrating filtered flow autocorrelation estimation accuracy criteria in an embodiment of the present invention.
Detailed Description
The technical solution of the present invention is further described in detail with reference to the accompanying drawings and examples.
The underwater acoustic Doppler flow velocity measurement method based on autocorrelation estimation and effective data screening, as shown in figure 1, comprises the following steps:
estimating a complex autocorrelation function R (tau) according to a formula 1 and estimating a value of total signal power R (0) according to a formula 2 through complex echoes of an effective water layer;
Figure BDA0001771372530000031
wherein, { x1,x2,…,xmIs the complex data of m current valid water layers, fsIn order to be able to measure the echo sampling rate,
Figure BDA0001771372530000032
n is expressed at a sampling rate fsNumber of data points, operator corresponding to time-dependent interval tau
Figure BDA0001771372530000033
Indicating a rounding down.
Secondly, estimating eta (v) values of sampled echoes in real time by a speed measuring sonar according to flow velocity autocorrelation estimation precision criteria;
Figure BDA0001771372530000034
wherein v is the radial flow velocity, λ is the wavelength of the emitted sound wave, τ is the interval of the complex autocorrelation, R (τ) is the function value of the complex autocorrelation of the effective water layer, R (0) is the total power of the signal, and R (τ) and R (0) estimated by formula 1 and formula 2 are substituted into formula 3 to obtain the value of the sampled echo η (v).
And step three, comparing the accuracy criterion eta (v) of the sonar echo sample with a set threshold, abandoning the current sample when the eta (v) value is smaller than the threshold, and keeping the current sample when the eta (v) value is larger than or equal to the threshold, wherein the value range of the threshold is 0.2-0.4 under the normal condition.
In actual measurement, the flow velocity autocorrelation estimation accuracy criterion η (v) of a shallow water body is generally greater than that of a deep water body, so that the acoustic doppler flow profiler is used as a sample after obtaining echo data of one transmission, the flow velocity echo autocorrelation estimation accuracy criterion η (v) of a second layer of an effective water layer is firstly calculated, when a threshold value is 0.3, if the value of η (v) is less than the threshold value 0.3, the current sample is discarded, and if the value of η (v) is greater than or equal to the threshold value 0.3, the current sample is retained.
Taking the first-time test data of the Nanjing Qinhuai river as an example, the water depth of the sampled river reach is more than 5m, the 1 st layer is a blind area, so the 2 nd to 10 th layers are effective water layers, the 2 nd layer of the beam 3 is selected as an analysis object, and the total sample number is 100. Parameters of an Acoustic Doppler Current Profiler (ADCP) used in the test are that the emission frequency is 600kHz, the wavelength lambda is 2.5mm, the complex autocorrelation calculation interval tau is 140 mu s, the water body layering thickness is 0.42m, and the emission waveform is a coding phase modulation signal.
Fig. 2 is a graph plotting the radial flow velocity estimated by the complex autocorrelation method in 100 samples, and the error between the radial flow velocity estimated value and the mean value of a plurality of samples in 100 samples is significantly larger than that of other samples, and belongs to the outlier. Fig. 3 is a graph of the original 100 samples using the flow rate autocorrelation estimation accuracy criterion η (v), and the relationship between the flow rate autocorrelation estimation accuracy criterion η (v) and the value of the flow rate estimation cannot be clearly seen from fig. 2 and 3, for this purpose, the values of the flow rate autocorrelation estimation accuracy criterion η (v) of the 100 samples in fig. 3 are sorted from small to large, rearranging the data in the figure 1 according to the sample sequence of the ordering result, calculating the standard deviation of all forward samples at each sample point in the figure 1 after rearrangement, fig. 4 is a graph showing the relationship between the forward standard deviation of the flow velocity and the autocorrelation estimation accuracy criterion η (v) of the flow velocity in 100 samples, and it can be seen from fig. 4 that the standard deviation of the ADCP flow velocity estimation decreases approximately monotonically with the monotonic increase of the value of η (v), and this phenomenon is consistent with the theoretical analysis, thereby verifying the effectiveness of the method of the present invention.
As shown in FIG. 2, the standard deviation of the radial flow velocity of 100 sample points without data screening was 0.779 m/s; as shown in fig. 5, after the flow velocity estimation accuracy criterion screening, the data of 70 sample points are retained, and the standard deviation of the remaining 70 sample points is 0.422m/s, the underwater acoustic doppler flow velocity measurement method based on autocorrelation estimation and effective data screening of the present invention reduces the flow velocity estimation standard deviation by about 46%, significantly reduces the variance of flow velocity estimation, and improves the performance of the underwater acoustic flow velocity measurement apparatus.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (2)

1. An underwater acoustic Doppler flow velocity measurement method based on autocorrelation estimation and effective data screening is characterized by comprising the following steps of:
s1, according to
Figure FDA0001771372520000011
Estimating the value of the complex autocorrelation function R (tau) by means of the complex echoes of the effective water layer, based on
Figure FDA0001771372520000012
Estimating the total power of the signal R (0), where x1,x2,...,xmIs the complex number data of m current effective water layers,
Figure FDA0001771372520000014
fsfor the echo sampling rate, n is at a sampling rate of fsThe number of data points corresponding to the time correlation interval tau;
s2, estimating the accuracy criterion according to the flow rate autocorrelation
Figure FDA0001771372520000013
The speed measuring sonar estimates eta (v) value of sampling echo in real time, wherein v is radial flow velocity, lambda is wavelength of transmitting sound wave, tau is interval of complex autocorrelation, R (tau) is function value of complex autocorrelation, and R (0) is total work of signalRate;
and S3, judging a threshold, discarding the current sample when the eta (v) value is smaller than the set threshold, and keeping the current sample when the eta (v) value is larger than or equal to the set threshold.
2. The method of claim 1, wherein the method comprises: the threshold value is 0.2-0.4.
CN201810950528.3A 2018-08-20 2018-08-20 Underwater acoustic Doppler flow velocity measurement method based on autocorrelation estimation and effective data screening Active CN109270295B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810950528.3A CN109270295B (en) 2018-08-20 2018-08-20 Underwater acoustic Doppler flow velocity measurement method based on autocorrelation estimation and effective data screening

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810950528.3A CN109270295B (en) 2018-08-20 2018-08-20 Underwater acoustic Doppler flow velocity measurement method based on autocorrelation estimation and effective data screening

Publications (2)

Publication Number Publication Date
CN109270295A CN109270295A (en) 2019-01-25
CN109270295B true CN109270295B (en) 2021-03-30

Family

ID=65153677

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810950528.3A Active CN109270295B (en) 2018-08-20 2018-08-20 Underwater acoustic Doppler flow velocity measurement method based on autocorrelation estimation and effective data screening

Country Status (1)

Country Link
CN (1) CN109270295B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110824193A (en) * 2019-11-11 2020-02-21 南京世海声学科技有限公司 Non-uniform water velocity estimation method based on multi-beam radial flow velocity measurement
CN116027307B (en) * 2022-11-02 2023-09-01 哈尔滨工程大学 Acoustic Doppler instantaneous speed measurement quality evaluation method
CN116125459B (en) * 2023-02-03 2023-07-11 水利部南京水利水文自动化研究所 Determination method for effective speed measurement unit of side scanning radar

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA1093676A (en) * 1977-04-21 1981-01-13 Noel Clavelloux Liquid flow rate metering system
CN1438496A (en) * 2003-03-19 2003-08-27 中国科学院声学研究所 Method for measuring flow-layer speed by related velocity sonar, and system thereof
CN102564508A (en) * 2011-12-14 2012-07-11 河海大学 Method for implementing online tests of stream flow based on video images
CN105572418A (en) * 2016-01-19 2016-05-11 浙江工业大学 FPGA-based acoustic doppler current profiler signal processing method and system
CN105572650A (en) * 2015-12-15 2016-05-11 宁波大学 Broadband multiple correlation flow velocity measurement method

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA1093676A (en) * 1977-04-21 1981-01-13 Noel Clavelloux Liquid flow rate metering system
CN1438496A (en) * 2003-03-19 2003-08-27 中国科学院声学研究所 Method for measuring flow-layer speed by related velocity sonar, and system thereof
CN102564508A (en) * 2011-12-14 2012-07-11 河海大学 Method for implementing online tests of stream flow based on video images
CN105572650A (en) * 2015-12-15 2016-05-11 宁波大学 Broadband multiple correlation flow velocity measurement method
CN105572418A (en) * 2016-01-19 2016-05-11 浙江工业大学 FPGA-based acoustic doppler current profiler signal processing method and system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
直流偏置对ADCP系统测量的影响及去除;项宁 等;《声学与电子工程》;20161231(第1期);24-28 *
脉冲对实现回波频偏测量的算法研究;朱昊 等;《传感技术学报》;20050331;第18卷(第1期);195-200 *

Also Published As

Publication number Publication date
CN109270295A (en) 2019-01-25

Similar Documents

Publication Publication Date Title
CN109270295B (en) Underwater acoustic Doppler flow velocity measurement method based on autocorrelation estimation and effective data screening
CN110673108B (en) Airborne marine laser radar signal processing method based on iteration Klett
KR100195576B1 (en) Apparatus for measuring the velocity of moving body
CN110836981A (en) Layered water flow high-resolution radial acoustic Doppler frequency measurement method
AU2013282701B2 (en) An improved suspended sediment meter
US20130235699A1 (en) System and method of range estimation
CN112780259A (en) Method and device for determining well cementation quality and storage medium
US7363177B2 (en) Apparatus and method for performing the time delay estimation of signals propagating through an environment
CN103845080A (en) Ultrasonic umbilical cord blood measuring system and method based on linear frequency modulation coding
CN115166817A (en) Ice sound positioning method based on ice layer modal group slowness difference characteristics
Colin et al. False-alarm reduction for low-frequency active sonar with BPSK pulses: experimental results
GB2509817B (en) Speed sensor
Cook et al. Synthetic aperture sonar image contrast prediction
CN109471113B (en) Multi-beam sonar submarine topography measurement quality real-time evaluation method based on phase method
US20140142887A1 (en) Speed Sensor
Manik et al. Quantifying suspended sediment using acoustic doppler current profiler in Tidung Island seawaters
Hansen Oceanic incoherent Doppler sonar spectral analysis by conventional and finite-parameter modeling methods
Reynisson Evaluation of threshold-induced bias in the integration of single-fish echoes
Hansen Asymptotic performance of a pulse-to-pulse incoherent Doppler sonar in an oceanic environment
Dias et al. Co-Prime Sampling and Cross-Correlation Estimation
Williams Forward scattering from a rippled sand/water interface: Modeling, measurements, and determination of the plane wave, flat surface reflection coefficient
Wang et al. Transmitting Waveforms Ambiguity Function based Complementary Coding in Broadband Acoustic Doppler Current Profiler
Shi et al. A broadband ADCP scattering echo model
Ross et al. Testing of Ice Profiler Sonar (IPS) targets using a logarithmic detector
RU2789812C1 (en) Echo sounder

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant