WO2020244359A1 - 一种声源位置估计方法、可读存储介质及计算机设备 - Google Patents

一种声源位置估计方法、可读存储介质及计算机设备 Download PDF

Info

Publication number
WO2020244359A1
WO2020244359A1 PCT/CN2020/089894 CN2020089894W WO2020244359A1 WO 2020244359 A1 WO2020244359 A1 WO 2020244359A1 CN 2020089894 W CN2020089894 W CN 2020089894W WO 2020244359 A1 WO2020244359 A1 WO 2020244359A1
Authority
WO
WIPO (PCT)
Prior art keywords
signal
window
sound source
length
channel
Prior art date
Application number
PCT/CN2020/089894
Other languages
English (en)
French (fr)
Inventor
孙显彬
贾鑫明
郑轶
王振
Original Assignee
青岛理工大学
山东省科学院海洋仪器仪表研究所
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 青岛理工大学, 山东省科学院海洋仪器仪表研究所 filed Critical 青岛理工大学
Priority to AU2020287677A priority Critical patent/AU2020287677B2/en
Publication of WO2020244359A1 publication Critical patent/WO2020244359A1/zh

Links

Images

Classifications

    • 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
    • G01S3/00—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received
    • G01S3/80—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received using ultrasonic, sonic or infrasonic waves
    • 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
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/18—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using ultrasonic, sonic, or infrasonic waves
    • G01S5/20—Position of source determined by a plurality of spaced direction-finders
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks

Definitions

  • the present disclosure relates to the technical field of sound source position estimation, in particular to a sound source position estimation method, readable storage medium and computer equipment.
  • the inventors of the present disclosure have discovered in research that current single-vector hydrophones are mostly used for the estimation of target azimuth and pitch angles, while the judgment of target position distance requires the use of vector hydrophone arrays for cross-estimation. This is due to single-vector hydrophones. Hearing device is caused by the inherent defect of insufficient distance resolution; and the single vector hydrophone in practical engineering application is because on the one hand, the limitation of process conditions makes it difficult for actual parameters to meet the requirements of ideal electroacoustic parameters, which restricts the accuracy of its azimuth estimation. On the one hand, it is susceptible to uncertain changes in the attitude of the environment, which further affects the acquisition of the true orientation of the target. These reasons lead to insufficient application of single vector hydrophones in target position estimation.
  • the present disclosure provides a sound source position estimation method, a readable storage medium and a computer device.
  • a complicated vector hydrophone array is required to receive signals.
  • the sound source estimation method only needs to use a single vector hydrophone to collect signals, which not only simplifies the difficulty of layout and use cost, but also expands the scope of application.
  • the present disclosure provides a sound source position estimation method
  • a sound source position estimation method the steps are as follows:
  • Single vector hydrophone receives multi-channel signals from sound sources in the ocean
  • the received multi-channel signal is fused into an instantaneous single-channel sound intensity signal and divided into signal segments containing sufficient information.
  • the amount of data is reduced and the amount of calculating speed;
  • the maximum expectation algorithm is used to self-complement the signal, and at the same time, by expanding the distance between the signal segments, the resolution between the signal segments is improved, and to a certain extent the information lost in the previous step is supplemented;
  • the sound source position is estimated by using the equal length signal after self-supplement through the recurrent neural network.
  • the multi-channel signal is a four-channel signal, comprising three orthogonal directions of vibration velocity signals: x-axis vibrates velocity v x, y-axis vibrates velocity v y, z-axis velocity v vibrates z and a scalar sound pressure signal p.
  • the multi-channel signal is fused into an instantaneous single-channel sound intensity signal through a fixed window, dynamic windows of all lengths are traversed, the fastest rising section of information entropy is found, the best dynamic window is determined, and the best dynamic window is based on the information entropy.
  • the instantaneous single-channel sound intensity signal in a fixed window is dynamically intercepted as signals of unequal length. For the intercepted signals of unequal length, the maximum expectation algorithm is used to self-complement the signal.
  • the received multi-channel signal is divided into signal segments containing sufficient information, specifically:
  • a fixed-size time window is synchronously slipped in each channel signal, and the signal is extracted and the information is fused into an instantaneous single-channel sound intensity signal through the cross-spectrum method, specifically:
  • Is the cross-spectrum function of the three components of x, y, and z Is the spectral function of p 2 (f), f is the frequency
  • Re[] is the Laplace transform
  • ⁇ Is the pitch and azimuth angle of the sound source relative to the vector hydrophone, with the xoy plane and x axis being 0°, respectively
  • p(t), v x (t), v y (t), v z (t) It is the sound pressure signal and the vibration velocity signal of each direction received by the vector hydrophone at time t.
  • the instantaneous single channel in the fixed window Traverse dynamic windows of all lengths and find the fastest growing segment of information entropy, which is the best dynamic window Specifically:
  • x i is the possible value of random event X
  • Shannon(X) is the information entropy contained in random event X
  • m is the total number of random events
  • p(x i ) is the probability of occurrence of x i ;
  • l 0 is the preset minimum intercept length
  • l 1 is the preset maximum interception length
  • the start interception length is Signal segment is dynamic window
  • the end time of the window is
  • the maximum expectation algorithm uses the maximum expectation algorithm to self-complement the signal.
  • the segmented unequal-length signal is equivalent to the observed data X
  • the equal-length signal after the complement is equivalent to the complete data Y
  • the supplemented signal is equivalent to unobserved data Z
  • the maximum value ⁇ * of the parameter ⁇ obtained by the iterative result of the maximum expectation algorithm that is, when the maximum likelihood function L( ⁇ ) based on Y reaches the maximum value, the mean and variance u i of the complete data set are summed
  • the unknown data set Z is obtained based on the observed data set X, and then the complete data set Y is supplemented, specifically as follows:
  • step 703 fixed Regarding ⁇ (t) as a variable, the derivative of L( ⁇ (t) ) in step 702 is obtained by the formula Get ⁇ (t+1) ;
  • Q i represents a certain distribution of unknown data Z
  • p(x (i) ,z (i) ; ⁇ (t) ) is the probability of occurrence of x (i) , z (i) under the condition of ⁇ (t)
  • the superscript i is the i-th value of the corresponding parameter
  • ⁇ is the threshold, which is a small value initially given as the criterion for terminating the iteration
  • E[] is the mathematical expectation.
  • the self-complemented equal length signal is used to estimate the position of the sound source through the recurrent neural network, specifically: using the maximum expectation algorithm to take the supplemented signal segment as input, and output the location of the sound source under different signal segments Angle and distance; through cross-validation of the estimation results of different signal segments, accurate positioning of the sound source position is achieved.
  • the present disclosure provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps in the sound source position estimation method described in the present disclosure are implemented.
  • the present disclosure provides a computer device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor.
  • the processor implements the sound described in the present disclosure when the program is executed. Steps in the source position estimation method.
  • the sound source position estimation method described in this disclosure avoids the problem of arranging complicated vector hydrophone arrays to receive signals.
  • the sound source estimation method described in this application only needs to use a single The vector hydrophone collects signals, which not only simplifies the difficulty of layout and use cost, but also expands the scope of application.
  • the sound source position estimation method described in the present disclosure uses the joint sliding of the dynamic window and the fixed window to divide the short-term signal samples into a large number of signal segments. Through mutual verification of each signal segment, the accuracy and stability of the position estimation are improved. On the premise of ensuring the amount of information, reduce the amount of data and increase the computing speed.
  • the sound source position estimation method described in the present disclosure only needs to use large samples to train the network in the early stage, and does not require complex calculations during use, so it can realize real-time tracking of high-speed, high-maneuvering target trajectories.
  • Figure 1 shows the positional relationship between the single vector hydrophone and the sound source described in Embodiment 1 of the present disclosure.
  • FIG. 2 is a flowchart of the sound source position estimation method according to Embodiment 1 of the disclosure.
  • FIG. 3 is a flowchart of the fixed window-dynamic window joint sliding described in Embodiment 1 of the disclosure.
  • FIG. 4 is a graph of the position estimation result of the ultra-low frequency sound source according to Embodiment 1 of the disclosure.
  • Embodiment 1 of the present disclosure provides a method for estimating the position of a sound source. The steps are as follows:
  • a single vector hydrophone receives a multi-channel signal from a sound source in the ocean;
  • the multi-channel signal is a four-channel signal, including three orthogonal directions of vibration velocity signals: x-axis direction vibration velocity v x , y-axis direction vibration Speed v y , the vibration speed v z in the z-axis direction and a scalar sound pressure signal p;
  • the received multi-channel signal is fused into an instantaneous single-channel sound intensity signal and divided into signal segments containing sufficient information.
  • the amount of data is reduced and the amount of calculating speed;
  • the maximum expectation algorithm is used to self-complement the signal, and at the same time, by expanding the distance between the signal segments, the resolution between the signal segments is improved, and to a certain extent the information lost in the previous step is supplemented;
  • the sound source position is estimated by using the equal length signal after self-supplement through the recurrent neural network.
  • the single-channel sound intensity signal is dynamically intercepted into unequal length signals. For the intercepted unequal length signals, the maximum expectation algorithm is used to self-complement the signal.
  • the length of the internal interception window and the starting point are respectively with Dynamic window It can be considered that the dynamic window
  • the internal signal should be as short as possible under the requirement of sufficient information
  • step 302 a fixed-size time window is synchronously slipped in each channel signal, and the signal is extracted and the information is merged into an instantaneous single-channel sound intensity signal through the cross-spectrum method, specifically:
  • the specific instantaneous single-channel sound intensity signal derivation process is:
  • the vector hydrophone Q receives this signal, and its output has the following relationship:
  • Vibration velocity x component v x (t) v xs (t)+v xn (t) (3)
  • Vibration velocity y component v y (t) v ys (t)+v yn (t) (4)
  • Vibration velocity z component v z (t) v zs (t) + v zn (t) (5)
  • the sound pressure signal and the vibration velocity signal in each direction received by the vector hydrophone are p(t), v x (t), v y (t), v z (t),
  • the instantaneous single-channel sound intensity signal in the fixed window Traverse dynamic windows of all lengths and find the fastest growing segment of information entropy, which is the best dynamic window Specifically:
  • x i is the possible value of random event X
  • Shannon(X) is the information entropy contained in random event X
  • m is the total number of random events
  • p(x i ) is the probability of occurrence of x i ;
  • l 0 is the preset minimum intercept length
  • l 1 is the preset maximum interception length
  • the start interception length is Signal segment is dynamic window
  • the end time of the window is
  • the maximum expectation algorithm (EM algorithm) is used to self-complement the signal.
  • the segmented unequal-length signal is equivalent to the observation data X
  • the equal-length signal after supplementation is equivalent
  • the complete data Y the supplementary signal is equivalent to the unobserved data Z
  • the isometric signal is convenient for later calculation and comparison;
  • the maximum value ⁇ * of the parameter ⁇ is obtained through the iterative result of the maximum expectation algorithm, that is, when the maximum likelihood function L( ⁇ ) based on Y reaches the maximum value, the mean and variance u i of the complete data set are sum
  • the unknown data set Z is obtained based on the observed data set X, and then the complete data set Y is supplemented, specifically as follows:
  • step 703 fixed Regarding ⁇ (t) as a variable, the derivative of L( ⁇ (t) ) in step 702 is obtained by the formula Get ⁇ (t+1) ;
  • ⁇ ) is the probability density function of the observed data set
  • ⁇ ) is the probability density function of the complete data set
  • u i and i are the mean and variance of the probability density function.
  • Finding the maximum value of the likelihood function L( ⁇ ) is to find ⁇ in the parameter space ⁇ to maximize the likelihood function when the sample points ⁇ x (1) ,L,x (n) ⁇ are fixed, namely:
  • Q i denotes the unknown data Z certain distribution, and satisfy the condition:
  • the formula (25) can be regarded as the process of finding the lower bound of L( ⁇ ). Through continuous iteration, the lower bound is increased until the parameter ⁇ reaches the maximum value ⁇ * , the lower bound L( ⁇ (t) ) converges to the likelihood function Near L( ⁇ ), the iteration ends at this time.
  • Estimating the position of the sound source by using the self-supplemented equal-length signal through the recurrent neural network is specifically: using the maximum expectation algorithm to take the supplemented signal segment as input, and output the azimuth and distance of the sound source under different signal segments; The estimation result of the signal segment is cross-validated to realize the precise positioning of the sound source position.
  • the signal collected by a single vector hydrophone arranged in a certain position is used to test this method when a ship is sailing. After inspection, it is found that the method can locate the sound source in a short time. The position accuracy is 1.5m. Compared with the traditional method, when only a single vector hydrophone is needed, it not only improves the positioning accuracy, but also improves the stability. The estimation result is shown in Figure 4.
  • Embodiment 2 of the present disclosure provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps in the sound source position estimation method described in Embodiment 1 of the present disclosure are implemented.
  • Embodiment 3 of the present disclosure provides a computer device that includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor.
  • the processor executes the program to implement the description of Embodiment 1 of the present disclosure. The steps in the sound source position estimation method.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Theoretical Computer Science (AREA)
  • Computational Linguistics (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Measurement Of Velocity Or Position Using Acoustic Or Ultrasonic Waves (AREA)

Abstract

一种声源位置估计方法，包括：单矢量水听器接收海洋中的声源发出的多通道信号；通过固定窗-动态窗的联合滑动，将接收到的多通道信号融合为瞬时单通道声强信号，并划分为包含足够信息量的信号段，在保证信息量的前提下，减少数据量，提高运算速度；利用最大期望算法进行信号的自补足，同时通过扩大各信号段之间的距离，提高了信号段之间的分辨率，而且一定程度上补充了上一步截取损失的信息；通过循环神经网络利用自补足后的等长信号进行声源位置的估计。该方法仅需采用单矢量水听器采集信号，不仅简化了布置难度和使用成本，还扩大了适用范围。还公开了一种可读存储介质及计算机设备。

Description

一种声源位置估计方法、可读存储介质及计算机设备 技术领域
本公开涉及声源位置估计技术领域，特别涉及一种声源位置估计方法、可读存储介质及计算机设备。
背景技术
本部分的陈述仅仅是提供了与本公开相关的背景技术，并不必然构成现有技术。
随着我国经济快速持续地增长，人口也不断地增加，人们对资源的需求量和消费水平的要求也随之大幅度地增长。对于陆地上有限的资源，人们也在最大可能的开发和利用，同样与此同时也会面临资源短缺的情况，所以我们要在现有的资源得到最大化应用基础上，还要研究开发新的领域和新的资源。而海洋面积占地球表面积高达70％以上，所以对于海洋资源的开发利用至关重要。近年来世界上各个国家对于海洋资源竞争也是相当激烈。怎样开拓和使用海洋资源，使其资源能够得到最大价值的利用，已经成为近年来备受关注的重大问题。中国处于一个独特的位置，总的国土面积约为960万平方公里，其中海洋面积约占总面积的3％，并且这些海洋水域大部分处于浅海水域。所以对浅海水域的技术研究，将会对人类的生存和发展产生更大的意义。
相比于深海环境，浅海环境的时空多变性、不确定性对信号的传播影响更加严重，同时浅海海底的反射信号及浅海人类活动均会造成目标信号的混叠，进而影响声源位置估计，因此对于浅海环境下声源目标估计一直是目前该领域的研究难点。相比于传统的声压水听器，矢量水听器能共点采集声压信号和正交方向的三轴振速信号，具有很高的实用价值。
本公开发明人在研究中发现，目前单矢量水听器多用于目标方位角和俯仰角的估计，而目标位置距离的判断则需采用矢量水听器阵列进行交叉估计，这是由于单矢量水听器对于距离分辨不足的天生缺陷造成；而且单矢量水听器在实际工程应用中一方面是由于工艺条件的限制使得实际参数难以到达理想电声参数特性要求，制约其方位估计精度，另一方面，其易受环境影响发生姿态的不确定变化，更加影响目标真实方位的获取，这些原因导致单矢量水听器在目标位置估计上应用不足。
发明内容
为了解决现有技术的不足，本公开提供了一种声源位置估计方法、可读存储介质及计算机设备，相比于传统的声源估计模型需要布置复杂的矢量水听器阵列接收信号，该声源估计 方法仅需采用单矢量水听器采集信号，不仅简化了布置难度和使用成本，还扩大了适用范围。
为了实现上述目的，本公开采用如下技术方案：
第一方面，本公开提供了一种声源位置估计方法；
一种声源位置估计方法，步骤如下：
单矢量水听器接收海洋中的声源发出的多通道信号；
通过固定窗-动态窗的联合滑动，将接收到的多通道信号融合为瞬时单通道声强信号，并划分为包含足够信息量的信号段，在保证信息量的前提下，减少数据量，提高运算速度；
利用最大期望算法进行信号的自补足，同时通过扩大各信号段之间的距离，提高了信号段之间的分辨率，而且一定程度上补充了上一步截取损失的信息；
通过循环神经网络利用自补足后的等长信号进行声源位置的估计。
作为可能的一些实现方式，所述多通道信号为四通道信号，包括三个正交方向的振速信号：x轴方向振速v x，y轴方向振速v y，z轴方向振速v z和一个标量声压信号p。
作为进一步的限定，通过固定窗将多通道信号融合为瞬时单通道声强信号，遍历所有长度的动态窗，寻找信息熵最速上升段，确定最佳动态窗，通过最佳动态窗基于信息熵将固定窗口内的瞬时单通道声强信号动态截取为不等长信号，对于截取的不等长信号，利用最大期望算法进行信号的自补足。
作为更进一步的限定，通过固定窗-动态窗的联合滑动，将接收到的多通道信号划分为包含足够信息量的信号段，具体为：
401对于采集到的四通道信号p,v x,v y,v z，给定固定窗口长度l f和窗口初始起点
Figure PCTCN2020089894-appb-000001
402利用窗口长度和起始点分别为l f和
Figure PCTCN2020089894-appb-000002
的固定窗
Figure PCTCN2020089894-appb-000003
进行窗口内的四通道信息融合，得出长度为l f瞬时单通道声强信号；
403在瞬时单通道声强信号
Figure PCTCN2020089894-appb-000004
内截取窗口长度和起始点分别为
Figure PCTCN2020089894-appb-000005
和
Figure PCTCN2020089894-appb-000006
的动态窗
Figure PCTCN2020089894-appb-000007
可认为所述动态窗
Figure PCTCN2020089894-appb-000008
内信号在满足足够信息量的需求下信号长度尽可能短；
404返回402，以信号重叠率η更新固定窗
Figure PCTCN2020089894-appb-000009
的起始点
Figure PCTCN2020089894-appb-000010
循环运算。
作为更进一步的限定，所部步骤402中，通过固定大小的时间窗在各通道信号内同步滑移，提取信号通过互谱法将信息融合为瞬时单通道声强信号，具体为：
501根据信号融合程度，给定固定窗长度l f和窗口起始点
Figure PCTCN2020089894-appb-000011
502分别在声压p和各轴向振速v x,v y,v z的信号通道内，以相同起始点
Figure PCTCN2020089894-appb-000012
截取窗口大小为l f的信号段，对应的窗口信号为
Figure PCTCN2020089894-appb-000013
503基于互谱法计算各窗口信号融合后的瞬时单通道声强信号
Figure PCTCN2020089894-appb-000014
实现多传感器的信息融合，融合后的瞬时单通道声强信号的计算公式为：
Figure PCTCN2020089894-appb-000015
其中
Figure PCTCN2020089894-appb-000016
和
Figure PCTCN2020089894-appb-000017
为x、y、z三个分量的互谱函数，
Figure PCTCN2020089894-appb-000018
是p 2(f)的谱函数，f是频率，Re[]为进行拉普拉斯变换，θ、
Figure PCTCN2020089894-appb-000019
是声源相对于矢量水听器的俯仰角和方位角，分别以xoy平面和x轴为0°，p(t)、v x(t)、v y(t)、v z(t)分别为t时刻下矢量水听器的接收到的声压信号和各方向振速信号。
作为更进一步的限定，所述步骤403中，对固定窗内的瞬时单通道
Figure PCTCN2020089894-appb-000020
遍历所有长度的动态窗，找取信息熵的最速增长段，即为最佳动态窗口
Figure PCTCN2020089894-appb-000021
具体为：
601在截取的瞬时单通道声强信号I的固定窗
Figure PCTCN2020089894-appb-000022
内，以固定窗起始点
Figure PCTCN2020089894-appb-000023
出发，遍历整个窗口，通过如下公式计算所有长度信号的信息熵，构造成信息熵信号
Figure PCTCN2020089894-appb-000024
Figure PCTCN2020089894-appb-000025
其中，x i为随机事件X可能的取值；Shannon(X)为随机事件X包含的信息熵，m为随机事件的总数，p(x i)为x i发生的概率；
602根据
Figure PCTCN2020089894-appb-000026
的求导结果S′ I寻找
Figure PCTCN2020089894-appb-000027
的最速增长段，标记长度为
Figure PCTCN2020089894-appb-000028
跳至步骤604；
603若在
Figure PCTCN2020089894-appb-000029
内未找到最速增长段，则可认为该固定窗
Figure PCTCN2020089894-appb-000030
内的信号为无效信号或噪声信号，则标记长度为
Figure PCTCN2020089894-appb-000031
满足以下两条件：
当
Figure PCTCN2020089894-appb-000032
较小，则认为该信号为空信号或固定窗内信号信息熵含量不足，取
Figure PCTCN2020089894-appb-000033
l 0为预设最小截取长度；
当
Figure PCTCN2020089894-appb-000034
较大，则认为该信号为噪声信号或含信息熵较高的有用信号，取
Figure PCTCN2020089894-appb-000035
l 1为预设最 大截取长度；
604在固定窗
Figure PCTCN2020089894-appb-000036
内从起始点
Figure PCTCN2020089894-appb-000037
开始截取长度为
Figure PCTCN2020089894-appb-000038
的信号段为动态窗
Figure PCTCN2020089894-appb-000039
并标记窗口终止时间为
Figure PCTCN2020089894-appb-000040
作为更进一步的限定，利用最大期望算法进行信号的自补足，分割出的不等长信号相当于观测数据X，补足后的等长信号相当于完整数据Y，补充的信号相当于未观测到数据Z，通过最大期望算法迭代结果得参数θ的最大值θ *，即当基于Y的最大似然函数L(θ)取到最大值时，完整数据集的均值和方差u i和
Figure PCTCN2020089894-appb-000041
取到最优解，基于观察到的数据集X得到未知数据集Z，进而补足完整数据集Y，具体为：
701令迭代次数t＝0，初始化参数向量θ (0)，θ为数据集Y的均值和方差组成的参数向量，计算初始最大似然函数L (0)(θ)：
Figure PCTCN2020089894-appb-000042
702由θ (t)得到
Figure PCTCN2020089894-appb-000043
保证在给定θ (t)时，ln(E(X))≥E[ln(X)]的等号成立，以建立L(θ (t))的下界；
703固定
Figure PCTCN2020089894-appb-000044
并将θ (t)视作变量，对702步中的L(θ (t))求导，由公式
Figure PCTCN2020089894-appb-000045
得到θ (t+1)；
704如果|L(θ (t+1))-L(θ (t))|≤ε时，迭代计算结束，否则令t＝t+1，返回至702步，其中阈值ε为给定的很小值。；
其中，Q i表示未知数据Z的某种分布；p(x (i),z (i)；θ (t))为θ (t)条件下发生x (i),z (i)的概率；上标i为对应参数的第i个值；ε为阈值，为初始给定的一个很小的值，作为终止迭代的标准，E[]为数学期望。
作为更进一步的限定，通过循环神经网络利用自补足后的等长信号进行声源位置的估计，具体为：利用最大期望算法以补充后的信号段作为输入，输出不同信号段下声源的方位角和 距离；通过不同信号段的估计结果交叉验证，实现声源位置的精准定位。
第二方面，本公开提供了一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现本公开所述的声源位置估计方法中的步骤。
第三方面，本公开提供了一种计算机设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现本公开所述的声源位置估计方法中的步骤。
与现有技术相比，本公开的有益效果是：
1、本公开所述的声源位置估计方法相比于传统的声源估计模型，避免了布置复杂的矢量水听器阵列接收信号的问题，本申请所述的声源估计方法仅需采用单矢量水听器采集信号，不仅简化了布置难度和使用成本，同时扩大了适用范围。
2、本公开所述的声源位置估计方法采用动态窗和固定窗的联合滑动将短时信号样本划分为大量信号段，通过各信号段的相互验证，提高了位置估计的精度和稳定性，在保证信息量的前提下，减少数据量，提高运算速度。
3、本公开所述的声源位置估计方法只需前期使用大样本对网络进行训练，使用时不需要通过复杂的运算，故可实现高速、高机动目标轨迹的实时跟踪。
附图说明
图1为本公开实施例1所述的单矢量水听器与声源位置关系。
图2为本公开实施例1所述的声源位置估计方法流程图。
图3为本公开实施例1所述的固定窗-动态窗联合滑动的流程图。
图4为本公开实施例1所述的超低频声源位置估计结果曲线图。
具体实施方式
应该指出，以下详细说明都是例示性的，旨在对本公开提供进一步的说明。除非另有指明，本文使用的所有技术和科学术语具有与本公开所属技术领域的普通技术人员通常理解的相同含义。
需要注意的是，这里所使用的术语仅是为了描述具体实施方式，而非意图限制根据本公开的示例性实施方式。如在这里所使用的，除非上下文另外明确指出，否则单数形式也意图包括复数形式，此外，还应当理解的是，当在本说明书中使用术语“包含”和/或“包括”时，其指明存在特征、步骤、操作、器件、组件和/或它们的组合。
实施例1：
如图1-2所示，本公开实施例1提供了一种声源位置估计方法，步骤如下：
单矢量水听器接收海洋中的声源发出的多通道信号；所述多通道信号为四通道信号，包括三个正交方向的振速信号：x轴方向振速v x，y轴方向振速v y，z轴方向振速v z和一个标量声压信号p；
通过固定窗-动态窗的联合滑动，将接收到的多通道信号融合为瞬时单通道声强信号，并划分为包含足够信息量的信号段，在保证信息量的前提下，减少数据量，提高运算速度；
利用最大期望算法进行信号的自补足，同时通过扩大各信号段之间的距离，提高了信号段之间的分辨率，而且一定程度上补充了上一步截取损失的信息；
通过循环神经网络利用自补足后的等长信号进行声源位置的估计。
通过固定窗将多通道信号融合为瞬时单通道声强信号，遍历所有长度的动态窗，寻找信息熵最速上升段，确定最佳动态窗，通过最佳动态窗基于信息熵将固定窗口内的瞬时单通道声强信号动态截取为不等长信号，对于截取的不等长信号，利用最大期望算法进行信号的自补足。
通过固定窗-动态窗的联合滑动，划分为包含足够信息量的信号段，如图3所示，具体为：
301对于采集到的四通道信号p,v x,v y,v z，给定固定窗口长度l f和窗口初始起点
Figure PCTCN2020089894-appb-000046
302利用窗口长度和起始点分别为l f和
Figure PCTCN2020089894-appb-000047
的固定窗
Figure PCTCN2020089894-appb-000048
进行窗口内的四通道信息融合，得出长度为l f瞬时单通道声强信号；
303在瞬时单通道声强信号
Figure PCTCN2020089894-appb-000049
内截取窗口长度和起始点分别为
Figure PCTCN2020089894-appb-000050
和
Figure PCTCN2020089894-appb-000051
的动态窗
Figure PCTCN2020089894-appb-000052
可认为所述动态窗
Figure PCTCN2020089894-appb-000053
内信号在满足足够信息量的需求下信号长度尽可能短；
304返回302，以信号重叠率η更新固定窗
Figure PCTCN2020089894-appb-000054
的起始点
Figure PCTCN2020089894-appb-000055
循环运算。
所部步骤302中，通过固定大小的时间窗在各通道信号内同步滑移，提取信号通过互谱法将信息融合为瞬时单通道声强信号，具体为：
401根据信号融合程度，给定固定窗长度l f和窗口起始点
Figure PCTCN2020089894-appb-000056
402分别在声压p和各轴向振速v x,v y,v z的信号通道内，以相同起始点
Figure PCTCN2020089894-appb-000057
截取窗口大小为l f的信号段，对应的窗口信号为
Figure PCTCN2020089894-appb-000058
403基于互谱法计算各窗口信号融合后的瞬时单通道声强信号
Figure PCTCN2020089894-appb-000059
实现多传感器的信息融合，融合后的单通道的瞬时单通道声强信号的计算公式为：
Figure PCTCN2020089894-appb-000060
具体瞬时单通道声强信号的推导过程为：
假设声信号P在各向同性噪声场中传播，矢量水听器Q接收到此信号，其输出有如下关系：
声压                    p(t)＝p s(t)+p n(t)                         (2)
振速x分量               v x(t)＝v xs(t)+v xn(t)                      (3)
振速y分量               v y(t)＝v ys(t)+v yn(t)                      (4)
振速z分量               v z(t)＝v zs(t)+v zn(t)                      (5)
上式中，足标“s”和“n”分别表示信号和噪声。如果噪声源相互独立，均值为零，则x方向的声强为：
Figure PCTCN2020089894-appb-000061
同理可得：
Figure PCTCN2020089894-appb-000062
Figure PCTCN2020089894-appb-000063
由同时上式可看出，由矢量水听器的输出p,v x,v y,v z得到的声强不含噪声能量，即它具有抗各项同性噪声的能力；
t时刻下，矢量水听器的接收到的声压信号和各方向振速信号分别为p(t),v x(t),v y(t),v z(t)，
使用互谱法估算出目标大致空间方位。首先对声压p分别和各振速分量做互相关运算，得到互相关函数如下：
Figure PCTCN2020089894-appb-000064
Figure PCTCN2020089894-appb-000065
Figure PCTCN2020089894-appb-000066
再对上述互相关函数做傅里叶变化，得到它们的互谱函数
Figure PCTCN2020089894-appb-000067
Figure PCTCN2020089894-appb-000068
Figure PCTCN2020089894-appb-000069
这里
Figure PCTCN2020089894-appb-000070
是p 2(f)的谱函数，f是频率。Re[]为进行拉普拉斯变换。θ、
Figure PCTCN2020089894-appb-000071
是声源相对于矢量水听器的俯仰角和方位角，分别以xoy平面和x轴为0°
于是，得到目标方位角和俯仰角为
Figure PCTCN2020089894-appb-000072
Figure PCTCN2020089894-appb-000073
结合公式(6)、(7)、(10)可得矢量水听器声强为：
Figure PCTCN2020089894-appb-000074
所述步骤303中，对固定窗内的瞬时单通道声强信号
Figure PCTCN2020089894-appb-000075
遍历所有长度的动态窗，找取信息熵的最速增长段，即为最佳动态窗口
Figure PCTCN2020089894-appb-000076
具体为：
601在截取的瞬时单通道声强信号I的固定窗
Figure PCTCN2020089894-appb-000077
内，以固定窗起始点
Figure PCTCN2020089894-appb-000078
出发，遍历整个窗口，通过如下公式计算所有长度信号的信息熵，构造成信息熵信号
Figure PCTCN2020089894-appb-000079
Figure PCTCN2020089894-appb-000080
其中，x i为随机事件X可能的取值，Shannon(X)为随机事件X包含的信息熵，m为随机事件的总数，p(x i)为x i发生的概率；
602根据
Figure PCTCN2020089894-appb-000081
的求导结果
Figure PCTCN2020089894-appb-000082
寻找
Figure PCTCN2020089894-appb-000083
的最速增长段，标记长度为
Figure PCTCN2020089894-appb-000084
跳至步骤604；
603若在
Figure PCTCN2020089894-appb-000085
内未找到最速增长段，则可认为该固定窗
Figure PCTCN2020089894-appb-000086
内的信号为无效信号或噪声信号，则标记长度为
Figure PCTCN2020089894-appb-000087
满足以下两条件：
当
Figure PCTCN2020089894-appb-000088
较小，则认为该信号为空信号或固定窗内信号信息熵含量不足，取
Figure PCTCN2020089894-appb-000089
l 0为预设最小截取长度；
当
Figure PCTCN2020089894-appb-000090
较大，则认为该信号为噪声信号或含信息熵较高的有用信号，取
Figure PCTCN2020089894-appb-000091
l 1为预设最大截取长度；
604在固定窗
Figure PCTCN2020089894-appb-000092
内从起始点
Figure PCTCN2020089894-appb-000093
开始截取长度为
Figure PCTCN2020089894-appb-000094
的信号段为动态窗
Figure PCTCN2020089894-appb-000095
并标记窗口终止时间为
Figure PCTCN2020089894-appb-000096
针对固定窗-动态窗分割后的信号长短不一的缺点，利用最大期望算法(EM算法)进行信号的自补足，分割出的不等长信号相当于观测数据X，补足后的等长信号相当于完整数据Y，补充的信号相当于未观测到数据Z，信号的等长化便于后期计算、对比；
通过最大期望算法迭代结果得参数θ的最大值θ *，即当基于Y的最大似然函数L(θ)取到最大值时，完整数据集的均值和方差u i和
Figure PCTCN2020089894-appb-000097
取到最优解，基于观察到的数据集X得到未知数据集Z，进而补足完整数据集Y，具体为：
701令迭代次数t＝0，初始化参数向量θ (0)，θ为数据集Y的均值和方差组成的参数向量，计算初始最大似然函数L (0)(θ)：
Figure PCTCN2020089894-appb-000098
702由θ (t)得到
Figure PCTCN2020089894-appb-000099
保证在给定θ (t)时，ln(E(X))≥E[ln(X)]的等号成立，以建立L(θ (t))的下界；
703固定
Figure PCTCN2020089894-appb-000100
并将θ (t)视作变量，对702步中的L(θ (t))求导，由公式
Figure PCTCN2020089894-appb-000101
得到θ (t+1)；
704如果|L(θ (t+1))-L(θ (t))|≤ε时，迭代计算结束，否则令t＝t+1，返回至702步，其中阈值ε为给定的很小值；
具体迭代过程如下：
令Z表示缺失数据，即没有观测到的数据，X为观测到的数据，称之为不完整数据，将缺失数据Z和不完整数据X之和定义为完整数据Y，X是Y的函数，则有如下关系式：
Figure PCTCN2020089894-appb-000102
Figure PCTCN2020089894-appb-000103
Figure PCTCN2020089894-appb-000104
其中，p(X|θ)是观测到的数据集的概率密度函数，p(Y|θ)是完整数据集的概率密度函数，u i和
Figure PCTCN2020089894-appb-000105
分别为概率密度函数的均值和方差。
求似然函数极大值L(θ)就是在样本点{x (1),L,x (n)}固定的情况下，在参数空间Θ内寻找θ来极大化似然函数，即：
θ *＝arg max θ∈ΘL(θ)     (16)
因L(θ)与ln L(θ)在同一θ处取到极值，所以对数化似然函数：
Figure PCTCN2020089894-appb-000106
θ的极大似然估计θ *可从下述方程解得：
Figure PCTCN2020089894-appb-000107
所以公式(13)可化为
Figure PCTCN2020089894-appb-000108
Q i(z (i)):＝p(z (i)|x (i)；θ)   (20)
其中，Q i表示未知数据Z的某种分布，且满足条件：
Figure PCTCN2020089894-appb-000109
由数学期望和Jensen不等式的相关定义：
Figure PCTCN2020089894-appb-000110
ln(E(X))≥E[ln(X)]    (23)
结合公式(19)得：
Figure PCTCN2020089894-appb-000111
再结合公式(19)和公式(20)可得，在第t次迭代时：
Figure PCTCN2020089894-appb-000112
公式(25)可看成是对L(θ)求下界的过程，其通过不断迭代，提高下界，直至参数θ取到最大值θ *时，下界L(θ (t))收敛到似然函数L(θ)附近，此时迭代结束。
通过循环神经网络利用自补足后的等长信号进行声源位置的估计，具体为：利用最大期 望算法以补充后的信号段作为输入，输出不同信号段下声源的方位角和距离；通过不同信号段的估计结果交叉验证，实现声源位置的精准定位。
为了进一步说明该方法的实施过程，使用某船舶航行时，布置于某一位置的单矢量水听器采集到的信号检验该方法，经检验，发现该方法能在很短时间内定位到声源位置，精度为1.5m，相比于传统方法，在仅需使用单矢量水听器的情况下，不仅提高了定位精度，还提高了稳定性，估计结果如图4所示。
实施例2：
本公开实施例2提供了一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现本公开实施例1所述的声源位置估计方法中的步骤。
实施例3：
本公开实施例3提供了一种计算机设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现本公开实施例1所述的声源位置估计方法中的步骤。
以上所述仅为本公开的优选实施例而已，并不用于限制本公开，对于本领域的技术人员来说，本公开可以有各种更改和变化。凡在本公开的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本公开的保护范围之内。

Claims (10)

  1. 一种声源位置估计方法，其特征在于，步骤如下：
    单矢量水听器接收海洋中的声源发出的多通道信号；
    通过固定窗-动态窗的联合滑动，将接收到的多通道信号融合为瞬时单通道声强信号，并划分为包含足够信息量的信号段；
    利用最大期望算法进行信号的自补足，同时扩大各信号段之间的距离；
    通过循环神经网络利用自补足后的等长信号进行声源位置的估计。
  2. 如权利要求1所述的声源位置估计方法，其特征在于，所述多通道信号为四通道信号，包括三个正交方向的振速信号：x轴方向振速v x，y轴方向振速v y，z轴方向振速v z和一个标量声压信号p。
  3. 如权利要求2所述的声源位置估计方法，其特征在于，通过固定窗将多通道信号融合为瞬时单通道声强信号，遍历所有长度的动态窗，寻找信息熵最速上升段，确定最佳动态窗，通过最佳动态窗基于信息熵将固定窗口内的瞬时单通道声强信号动态截取为不等长信号，对于截取的不等长信号，利用最大期望算法进行信号的自补足。
  4. 如权利要求3所述的声源位置估计方法，其特征在于，通过固定窗-动态窗的联合滑动，划分为包含足够信息量的信号段，具体为：
    401对于采集到的四通道信号p,v x,v y,v z，给定固定窗口长度l f和窗口初始起点
    Figure PCTCN2020089894-appb-100001
    402利用窗口长度和起始点分别为l f和
    Figure PCTCN2020089894-appb-100002
    的固定窗
    Figure PCTCN2020089894-appb-100003
    进行窗口内的四通道信息融合，得出长度为l f瞬时单通道声强信号；
    403在瞬时单通道声强信号
    Figure PCTCN2020089894-appb-100004
    内截取窗口长度和起始点分别为
    Figure PCTCN2020089894-appb-100005
    和
    Figure PCTCN2020089894-appb-100006
    的动态窗
    Figure PCTCN2020089894-appb-100007
    可认为所述动态窗
    Figure PCTCN2020089894-appb-100008
    内信号在满足足够信息量的需求下信号长度尽可能短；
    404返回402，以信号重叠率η更新固定窗
    Figure PCTCN2020089894-appb-100009
    的起始点
    Figure PCTCN2020089894-appb-100010
    循环运算。
  5. 如权利要求4所述的声源位置估计方法，其特征在于，所部步骤402中，通过固定大小的时间窗在各通道信号内同步滑移，提取信号通过互谱法将信息融合为瞬时单通道声强信号，具体为：
    501根据信号融合程度，给定固定窗长度l f和窗口起始点
    Figure PCTCN2020089894-appb-100011
    502分别在声压p和各轴向振速v x,v y,v z的信号通道内，以相同起始点
    Figure PCTCN2020089894-appb-100012
    截取窗口大小为l f的信号段，对应的窗口信号为
    Figure PCTCN2020089894-appb-100013
    503基于互谱法计算各窗口信号融合后的瞬时单通道声强信号
    Figure PCTCN2020089894-appb-100014
    实现多传感器的信息融合，融合后的瞬时单通道声强信号的计算公式为：
    Figure PCTCN2020089894-appb-100015
    其中
    Figure PCTCN2020089894-appb-100016
    和
    Figure PCTCN2020089894-appb-100017
    为x、y、z三个分量的互谱函数，
    Figure PCTCN2020089894-appb-100018
    是p 2(f)的谱函数，f是频率，Re[]为进行拉普拉斯变换，θ、
    Figure PCTCN2020089894-appb-100019
    是声源相对于矢量水听器的俯仰角和方位角，分别以xoy平面和x轴为0°，p(t)、v x(t)、v y(t)、v z(t)分别为t时刻下矢量水听器的接收到的声压信号和各方向振速信号。
  6. 如权利要求4所述的声源位置估计方法，其特征在于，所述步骤403中，对固定窗内的瞬时单通道声强信号
    Figure PCTCN2020089894-appb-100020
    遍历所有长度的动态窗，找取信息熵的最速增长段，即为最佳动态窗口
    Figure PCTCN2020089894-appb-100021
    具体为：
    601在截取的瞬时声强信号I的固定窗
    Figure PCTCN2020089894-appb-100022
    内，以固定窗起始点
    Figure PCTCN2020089894-appb-100023
    出发，遍历整个窗口，通过如下公式计算所有长度信号的信息熵，构造成信息熵信号
    Figure PCTCN2020089894-appb-100024
    Figure PCTCN2020089894-appb-100025
    其中，x i为随机事件X可能的取值，；Shannon(X)为随机事件X包含的信息熵，m为随机事件的总数，p(x i)为x i发生的概率；
    602根据
    Figure PCTCN2020089894-appb-100026
    的求导结果
    Figure PCTCN2020089894-appb-100027
    寻找
    Figure PCTCN2020089894-appb-100028
    的最速增长段，标记长度为
    Figure PCTCN2020089894-appb-100029
    跳至步骤604；
    603若在
    Figure PCTCN2020089894-appb-100030
    内未找到最速增长段，则可认为该固定窗
    Figure PCTCN2020089894-appb-100031
    内的信号为无效信号或噪声信号，则标记长度为
    Figure PCTCN2020089894-appb-100032
    满足以下两条件：
    当
    Figure PCTCN2020089894-appb-100033
    较小，则认为该信号为空信号或固定窗内信号信息熵含量不足，取
    Figure PCTCN2020089894-appb-100034
    l 0为预设最小截取长度；
    当
    Figure PCTCN2020089894-appb-100035
    较大，则认为该信号为噪声信号或含信息熵较高的有用信号，取
    Figure PCTCN2020089894-appb-100036
    l 1为预设最大截取长度；
    604在固定窗
    Figure PCTCN2020089894-appb-100037
    内从起始点
    Figure PCTCN2020089894-appb-100038
    开始截取长度为
    Figure PCTCN2020089894-appb-100039
    的信号段为动态窗
    Figure PCTCN2020089894-appb-100040
    并标记窗口终止时间为
    Figure PCTCN2020089894-appb-100041
  7. 如权利要求1所述的声源位置估计方法，其特征在于，
    利用最大期望算法进行信号的自补足，分割出的不等长信号相当于观测数据X，补足后的等长信号相当于完整数据Y，补充的信号相当于未观测到数据Z，通过最大期望算法迭代结果得参数θ的最大值θ *，即当基于Y的最大似然函数L(θ)取到最大值时，完整数据集的均值和方差u i和
    Figure PCTCN2020089894-appb-100042
    取到最优解，基于观察到的数据集X得到未知数据集Z，进而补足完整数据集Y，具体为：
    701令迭代次数t＝0，初始化参数向量θ (0)，θ为数据集Y的均值和方差组成的参数向量，计算初始最大似然函数L (0)(θ)：
    Figure PCTCN2020089894-appb-100043
    702由θ (t)得到
    Figure PCTCN2020089894-appb-100044
    保证在给定θ (t)时，ln(E(X))≥E[ln(X)]的等号成立，以建立L(θ (t))的下界；
    703固定
    Figure PCTCN2020089894-appb-100045
    并将θ (t)视作变量，对702步中的L(θ (t))求导，由公式
    Figure PCTCN2020089894-appb-100046
    得到θ (t+1)；
    704如果|L(θ (t+1))-L(θ (t))|≤ε时，迭代计算结束，否则令t＝t+1，返回至702步，其中阈值ε为给定的很小值；
    其中，Q i表示未知数据Z的某种分布；p(x (i),z (i)；θ (t))为θ (t)条件下发生x (i),z (i)的概率；上标i为对应参数的第i个值；ε为阈值，为初始给定的一个很小的值，作为终止迭代的标准， E[]为数学期望。
  8. 如权利要求1所述的声源位置估计方法，其特征在于，通过循环神经网络利用自补足后的等长信号进行声源位置的估计，具体为：利用最大期望算法以补充后的信号段作为输入，输出不同信号段下声源的方位角和距离，通过不同信号段的估计结果交叉验证，实现声源位置的精准定位。
  9. 一种计算机可读存储介质，其上存储有计算机程序，其特征在于，该程序被处理器执行时实现如权利要求1-8任一项所述的声源位置估计方法中的步骤。
  10. 一种计算机设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其特征在于，所述处理器执行所述程序时实现如权利要求1-8任一项所述的声源位置估计方法中的步骤。
PCT/CN2020/089894 2019-06-06 2020-05-13 一种声源位置估计方法、可读存储介质及计算机设备 WO2020244359A1 (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU2020287677A AU2020287677B2 (en) 2019-06-06 2020-05-13 Sound source position estimation method, readable storage medium and computer device

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201910493374.4A CN110146846B (zh) 2019-06-06 2019-06-06 一种声源位置估计方法、可读存储介质及计算机设备
CN201910493374.4 2019-06-06

Publications (1)

Publication Number Publication Date
WO2020244359A1 true WO2020244359A1 (zh) 2020-12-10

Family

ID=67590874

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/089894 WO2020244359A1 (zh) 2019-06-06 2020-05-13 一种声源位置估计方法、可读存储介质及计算机设备

Country Status (3)

Country Link
CN (1) CN110146846B (zh)
AU (1) AU2020287677B2 (zh)
WO (1) WO2020244359A1 (zh)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113009417A (zh) * 2021-02-05 2021-06-22 中国人民解放军国防科技大学 利用声场干涉特性的海底声学阵列阵形估计方法
CN114910901A (zh) * 2022-05-30 2022-08-16 深圳市大族机器人有限公司 一种协作机器人高精度多传感器融合测距系统
CN116068494A (zh) * 2023-03-31 2023-05-05 中国人民解放军96901部队 一种基于次声传播模型的广域次声源定位方法

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110146846B (zh) * 2019-06-06 2021-04-13 青岛理工大学 一种声源位置估计方法、可读存储介质及计算机设备
CN111427008B (zh) * 2020-04-19 2022-09-16 中国电子科技集团公司电子科学研究院 一种用于水下自主航行器集群的位置测定方法及相关装置
CN111580048B (zh) * 2020-05-09 2020-12-29 中国科学院声学研究所 一种利用单矢量水听器的宽带声源深度估计方法
CN112415467B (zh) * 2020-11-06 2022-10-25 中国海洋大学 一种基于神经网络的单矢量潜标目标定位实现方法
CN113009419B (zh) * 2021-02-25 2021-11-09 中国科学院声学研究所 一种基于频域互相关匹配的目标深度估计方法

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040165479A1 (en) * 2003-02-25 2004-08-26 Lockheed Martin Corporation Bi-directional temporal correlation sonar
CN101539597A (zh) * 2009-04-29 2009-09-23 哈尔滨工程大学 一种分辨具有相同频带辐射噪声的多目标方法
CN102981146A (zh) * 2012-11-19 2013-03-20 哈尔滨工程大学 一种单矢量水听器被动定位方法
CN103267966A (zh) * 2013-05-20 2013-08-28 西北工业大学 一种单个二维压差式矢量水听器方位估计的方法
CN103954346A (zh) * 2014-05-16 2014-07-30 中国电子科技集团公司第四十九研究所 具有目标定位及判别功能的磁复合三维矢量水听器及该水听器的目标定位及判别方法
CN106249244A (zh) * 2016-07-07 2016-12-21 哈尔滨工程大学 一种声矢量圆阵宽带相干信号源方位估计方法
CN110146846A (zh) * 2019-06-06 2019-08-20 青岛理工大学 一种声源位置估计方法、可读存储介质及计算机设备

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN202383285U (zh) * 2010-12-30 2012-08-15 中国科学院声学研究所 一种网络化水声定位节点系统
CN102708374B (zh) * 2012-01-06 2014-06-25 香港理工大学 融合边缘信息和支持向量机对遥感图像进行分类的方法及装置
CN103927401B (zh) * 2014-04-16 2018-02-16 公安部第一研究所 一种多通道采样的高速信号采集与处理系统
CN104848900B (zh) * 2015-05-26 2017-03-22 国家海洋技术中心 阵列式海洋声信号测量系统
US10063965B2 (en) * 2016-06-01 2018-08-28 Google Llc Sound source estimation using neural networks
CN108337002A (zh) * 2018-02-09 2018-07-27 黄淮学院 一种多通道声信号重构方法
CN109492580B (zh) * 2018-11-08 2020-08-07 北方工业大学 一种基于全卷积网络的邻域显著性参照的多尺寸航拍图像定位方法

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040165479A1 (en) * 2003-02-25 2004-08-26 Lockheed Martin Corporation Bi-directional temporal correlation sonar
CN101539597A (zh) * 2009-04-29 2009-09-23 哈尔滨工程大学 一种分辨具有相同频带辐射噪声的多目标方法
CN102981146A (zh) * 2012-11-19 2013-03-20 哈尔滨工程大学 一种单矢量水听器被动定位方法
CN103267966A (zh) * 2013-05-20 2013-08-28 西北工业大学 一种单个二维压差式矢量水听器方位估计的方法
CN103954346A (zh) * 2014-05-16 2014-07-30 中国电子科技集团公司第四十九研究所 具有目标定位及判别功能的磁复合三维矢量水听器及该水听器的目标定位及判别方法
CN106249244A (zh) * 2016-07-07 2016-12-21 哈尔滨工程大学 一种声矢量圆阵宽带相干信号源方位估计方法
CN110146846A (zh) * 2019-06-06 2019-08-20 青岛理工大学 一种声源位置估计方法、可读存储介质及计算机设备

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
DA, LIANGLONG ET AL.: "An experiment on azimuth estimation of target by single vector hydrophone", JOURNAL OF APPLIED ACOUSTICS, vol. 34, no. 6, 17 November 2015 (2015-11-17), pages 516 - 525, XP055765519 *
SHI, SHENGGUO ET AL.: "Acoustic source location by acoustic vector-sensor and its bearing error", JOURNAL OF HARBIN ENGINEERING UNIVERSITY, vol. 24, no. 2, 30 April 2003 (2003-04-30), pages 1 - 4, XP055765575 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113009417A (zh) * 2021-02-05 2021-06-22 中国人民解放军国防科技大学 利用声场干涉特性的海底声学阵列阵形估计方法
CN114910901A (zh) * 2022-05-30 2022-08-16 深圳市大族机器人有限公司 一种协作机器人高精度多传感器融合测距系统
CN116068494A (zh) * 2023-03-31 2023-05-05 中国人民解放军96901部队 一种基于次声传播模型的广域次声源定位方法
CN116068494B (zh) * 2023-03-31 2023-07-18 中国人民解放军96901部队 一种基于次声传播模型的广域次声源定位方法

Also Published As

Publication number Publication date
AU2020287677A1 (en) 2021-03-04
CN110146846B (zh) 2021-04-13
AU2020287677B2 (en) 2022-07-07
CN110146846A (zh) 2019-08-20

Similar Documents

Publication Publication Date Title
WO2020244359A1 (zh) 一种声源位置估计方法、可读存储介质及计算机设备
Dai et al. Root sparse Bayesian learning for off-grid DOA estimation
Sheng et al. Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks
CN108802674B (zh) 一种针对直接定位的联合搜索方法及装置
WO2021164282A1 (zh) 基于低秩矩阵重建的水声宽频散射源的定位方法
CN108802683A (zh) 一种基于稀疏贝叶斯学习的源定位方法
CN108957403B (zh) 一种基于广义互相关的高斯拟合包络时延估计方法及系统
Cheng et al. Node selection algorithm for underwater acoustic sensor network based on particle swarm optimization
CN109661030A (zh) 无线传感器网络中基于动态网格的未知目标定位算法
CN109724592A (zh) 一种基于进化梯度搜索的auv地磁仿生导航方法
CN108549052A (zh) 一种时频-空域联合加权的圆谐域伪声强声源定位方法
WO2023213052A1 (zh) 一种深海声源定位方法及计算机设备和存储介质
CN110082741A (zh) 一种基于伪数据重构的超分辨波达角估计算法
Luo et al. Target location and height estimation via multipath signal and 2D array for sky-wave over-the-horizon radar
WO2020134797A1 (zh) 通过构建虚拟sar图像实现wsn节点定位的方法及装置
Ben et al. A novel cooperative navigation algorithm based on factor graph with cycles for AUVs
Chen et al. Underwater terrain positioning method based on least squares estimation for AUV
Tong et al. A misalignment angle error calibration method of underwater acoustic array in strapdown inertial navigation system/ultrashort baseline integrated navigation system based on single transponder mode
Yan et al. Broad learning-based localization for underwater sensor networks with stratification compensation
Liu et al. Error analysis of a distributed node positioning algorithm in underwater acoustic sensor networks
CN109100798B (zh) 实现折射多次波层析反演的方法、装置及处理终端
Huang et al. Underwater sound speed inversion by joint artificial neural network and ray theory
CN110568496B (zh) 一种复杂介质条件下射线追踪方法
CN112197914A (zh) 一种基于鲸鱼music算法的气体泄漏源估计方法
Liu et al. A sound source localization method based on microphone array for mobile robot

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20818576

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2020287677

Country of ref document: AU

Date of ref document: 20200513

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20818576

Country of ref document: EP

Kind code of ref document: A1