CN117319939A - Positioning system and positioning method for underwater acoustic sensor network node - Google Patents
Positioning system and positioning method for underwater acoustic sensor network node Download PDFInfo
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- 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
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- H04B13/00—Transmission systems characterised by the medium used for transmission, not provided for in groups H04B3/00 - H04B11/00
- H04B13/02—Transmission systems in which the medium consists of the earth or a large mass of water thereon, e.g. earth telegraphy
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- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/029—Location-based management or tracking services
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- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
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- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
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Abstract
The invention relates to a positioning system and a positioning method for a network node of a underwater acoustic sensor, wherein the system comprises the following components: transmitting node: deployed in an underwater environment for broadcasting a sound source signal to an underwater receiving node; and a time measurement module: for measuring the time difference of arrival between transmission and reception; transmitting transducer: the system is used for realizing the electric-acoustic conversion of the sound source signal and propagating through the underwater sound channel; the receiving node: the system is deployed in an underwater environment and is used for collecting converted acoustic signals subjected to multipath propagation; receiving a hydrophone: for effecting an acousto-electric conversion of an acoustic wave signal; and the acoustic wave signal denoising subsystem: the method is used for processing the sound wave signals and inhibiting noise components; and a positioning calculation subsystem: and the method is used for carrying out positioning calculation according to the acoustic wave signals to obtain the positions of the fixed node and the mobile node in the transmitting node. Compared with the prior art, the invention has the advantages of improving the positioning precision and accuracy and the like.
Description
Technical Field
The invention relates to a network node positioning technology, in particular to a underwater acoustic sensor network node positioning system and a positioning method.
Background
The underwater acoustic sensor network node positioning is an emerging hot spot technology combining underwater acoustic communication and positioning technologies, and can be widely applied to the fields of underwater navigation, target tracking, resource detection and the like. With the increase of underwater activities and the development and utilization of ocean resources, most application fields of underwater acoustic sensor networks depend on reliable position information, so that the requirement for accurately positioning underwater environments is increasingly urgent. In recent years, many research institutions and scholars have focused on the research of underwater acoustic sensor network node positioning technology and have made a series of important progress. By utilizing the mutual communication among multiple nodes and the ranging data and combining an advanced positioning algorithm, the underwater acoustic sensor network node positioning technology can realize the high-precision positioning of the underwater target, and provides important support for underwater resource development, scientific research, environmental protection and the like. With the continuous development of underwater acoustic sensor technology and communication algorithms, underwater acoustic sensor network node positioning technology can provide a more accurate and efficient solution for underwater environment monitoring and management.
In the positioning process of the underwater acoustic sensor network nodes, the nodes need to communicate and range by utilizing acoustic waves propagated in water, and each node mutually sends and receives underwater acoustic signals to communicate and form network connection among the nodes. By analyzing the received signals, the node can measure information such as signal arrival time difference, signal strength, multipath effect and the like, and utilize the information to perform positioning calculation.
Network node location technology on land generally relies on satellite navigation systems to determine the location of a node by receiving satellite signals. The underwater acoustic sensor network node positioning presents greater challenges in terms of technology implementation and algorithms than the network node positioning on land. In an underwater environment, the use of satellite signals is strongly limited due to the damping effect of water and the constraints of the acoustic wave propagation characteristics. In addition, due to the complex characteristics of the underwater acoustic sensor network environment, sea waves, wind speeds, ship movement and other factors, the underwater acoustic sensor network nodes move along with the water flow, the propagation of underwater acoustic signals in the underwater acoustic sensor network can be strongly attenuated under the influence of environmental factors such as depth, water flow and the like, the accuracy and the accuracy of node positioning are seriously affected, and higher uncertainty exists.
Disclosure of Invention
The invention aims to provide a underwater acoustic sensor network node positioning system and a positioning method for improving node positioning precision.
The aim of the invention can be achieved by the following technical scheme:
a hydroacoustic sensor network node positioning system, comprising:
transmitting node: deployed in an underwater environment for broadcasting a sound source signal to a receiving node;
and a time measurement module: for measuring the time difference of arrival between transmission and reception;
transmitting transducer: for effecting an electro-acoustic conversion of said acoustic source signal and propagating through an underwater acoustic channel;
the receiving node: deployed in an underwater environment for collecting acoustic signals that have propagated through multipath;
receiving a hydrophone: for effecting an acousto-electric conversion of said acoustic wave signal;
and the acoustic wave signal denoising subsystem: the device is used for processing the acoustic wave signals converted by the receiving hydrophone and inhibiting noise components;
and a positioning calculation subsystem: and the method is used for carrying out positioning calculation according to the acoustic wave signals to obtain the positions of the fixed node and the mobile node in the transmitting node.
Further, the positioning system further comprises a power supply for powering the transmitting node, the time measurement module and the receiving node.
Further, the acoustic wave signal denoising subsystem comprises a digital signal input interface, a signal processing unit, a band-pass filter and an adaptive filter, and is used for receiving the acoustic wave signal subjected to acoustic-electric conversion by the input interface, performing digital processing and analysis on the acoustic wave signal, and filtering noise by the band-pass filter and the adaptive filter.
Further, the positioning computing subsystem comprises a data storage module, a processing and control module, a positioning computing module, a position predicting module and a power management module, wherein the data storage module is used for storing acoustic signals and water flow velocity vector data, the processing and control module is used for processing and analyzing the acoustic signals, configuring and controlling nodes and communicating with the nodes, the positioning computing module is used for receiving the data transmitted by the processing and control module and computing the positions of the fixed nodes, the position predicting module is used for computing the positions of the mobile nodes, and the power management module is used for providing power supply for the positioning computing subsystem.
The invention also provides a positioning method based on the underwater acoustic sensor network node positioning system, which comprises the following steps:
broadcasting the sound source signal to a receiving node through a transmitting node, and starting the work of a time measurement module;
adopting a transmitting transducer to realize the electric-acoustic conversion of the sound source signal and propagating through an underwater acoustic channel;
the receiving node collects the sound wave signals subjected to multipath propagation and electric-acoustic conversion, and the time measuring module finishes working;
the acoustic-electric conversion of the acoustic wave signal is realized by adopting a receiving hydrophone;
filtering the acoustic wave signal subjected to the acoustic-electric conversion to suppress noise components;
dividing the acoustic wave signal with the noise component removed into a line-of-sight component and a non-line-of-sight component, and calculating a signal oblique projection based on the line-of-sight component and the non-line-of-sight component;
calculating the position of a fixed node in the transmitting node based on the signal oblique projection;
and collecting historical water flow velocity vector data, constructing an ARIMA model to predict a water flow velocity vector, and calculating the position of a mobile node in the transmitting node with the data comprising the sound wave signals.
Further, the specific step of suppressing the noise component includes:
collecting the acoustic wave signals converted by the receiving hydrophone;
performing signal preprocessing on the converted sound wave signals based on Fourier transformation;
and filtering and denoising the preprocessed signals by adopting a band-pass filter and an adaptive filter.
Further, the specific step of calculating the oblique projection of the signal includes:
dividing the acoustic wave signal with the noise component removed into a line-of-sight component and a non-line-of-sight component, and constructing a data matrix received by a receiving node, wherein the expression of the data matrix is as follows:
wherein Y is a data matrix, g i a i s i As a component of the line of sight,as a non-line-of-sight component,the measurement noise matrix is the measurement noise matrix in the underwater sound environment;
calculating an oblique projection operator based on the data matrix, wherein the expression of the oblique projection operator is as follows:
wherein E is i←[P]/i In order to be a diagonal projection operator,representation matrix->Orthogonal projection matrix of (2) satisfying->Matrix->Projection matrix of (2) is
Multiplying the oblique projection operator by the acoustic wave signal converted by the receiving hydrophone to obtain signal oblique projection, and realizing that the non-line-of-sight component is obliquely projected to the line-of-sight component, wherein the expression of the signal oblique projection is as follows:
Z(f)＝E i←[P]/i ·Y＝(g i a i )s i +E i←[P]/i ·W
wherein Z (f) is signal oblique projection, E i←[P]/i Y is a data matrix, g, for the oblique projection operator i a i s i As a component of the line of sight,is a measurement noise matrix in the underwater sound environment.
Further, the specific calculation step of the fixed node position includes:
and recovering the sound source signal through an optimization problem based on the signal oblique projection, wherein the expression of the optimization problem is as follows:
wherein S is a sound source signal, and I.I.I A Representing atomic norms, Y being the data matrix, Z (f) being the letterAnd the number is projected obliquely, delta is a noise substrate, and the requirements of W are met 2 ≤δ；
Converting the optimization problem into a dual problem, wherein the expression of the dual problem is as follows:
wherein I is an identity matrix, E i←[P]/i For the oblique projection operator, F (θ) is a linear operator mapping the continuous signal to the observed value Y, c is a dual variable;
converting the dual problem into a semi-positive problem, and solving by adopting an interior point method to obtain the angle of a fixed node, wherein the expression of the semi-positive problem is as follows:
wherein M is the number of receiving nodes;
and calculating the distance based on the acoustic wave signals of the denoising components and the propagation loss, and realizing the positioning of the fixed node.
Further, the step of calculating the position of the mobile node specifically includes:
collecting historical data of a water flow velocity vector, and preprocessing;
constructing an ARIMA model, predicting a water flow velocity vector based on the ARIMA model,
in the process of constructing an ARIMA model, determining a model order based on the preprocessed data, constructing a Yule-Walker equation based on the model order, and adopting a Levinson-Durbin recursion algorithm to obtain model parameters;
based on the predicted water flow velocity vector, calculating the positions of a passive mobile node and an active mobile node, wherein the calculation expression of the positions of the passive mobile node is as follows:
in the method, in the process of the invention,is a passive movable node n pas Distance from the reference point->Is a passive movable node n pas Angle relative to the reference point, T is time, d n V is the distance of the position of the nth fixed node relative to the reference point wat For the flow velocity vector of the water flow in time T +.>For the offset angle, θ, when the node begins to move with the water flow wat And theta n V respectively wat Corresponding angle and angle of the position of the fixed node relative to the reference point, n fix Representing a fixed node;
the calculation expression of the position of the active mobile node is:
in the method, in the process of the invention,is a passive movable node n act Angle relative to the reference point->Is a passive movable node n act Distance from reference point v auv V for the speed of the mounted mobile device wat For the flow velocity vector of the water flow within the time T, theta wat For the corresponding angle of the two-way valve,θ auv for the angle of the mobile device carried, +.>For time slot index, t b 、t a The flow velocity vector v corresponding to the last time slot b And the current time slot water flow velocity vector v a Corresponding time.
Further, the preprocessing operation includes a data linearization process and a data stabilization process.
Compared with the prior art, the invention has the following beneficial effects:
(1) According to the invention, noise existing in a complex environment is considered, the noise component is suppressed by adopting the acoustic wave denoising subsystem, and the multipath propagation is considered, and the positioning denoising subsystem obtains high-precision node position information by reconstructing the signal, so that the wireless positioning of the acoustic wave signal is realized.
(2) The invention considers that under the environment with underwater sound transmission loss and multipath effect, the data matrix of multipath signals is obliquely projected, the signals from expected dominant transmission paths are enhanced, and then the optimization problem is further converted and then solved, so that the high-precision positioning of the fixed nodes is realized.
(3) Aiming at the nodes with movement characteristics due to water flow movement, the method acquires the historical data of the water flow velocity vector, fits the constructed ARIMA model to the water flow velocity vector of the time sequence prediction future moment, and realizes accurate prediction and positioning of the mobile nodes through a designed positioning algorithm.
(4) The invention utilizes the oblique projection matrix to realize the enhancement of the signals of the expected dominant propagation path and the suppression of the signals of other unexpected paths, and has better anti-interference performance, thus being more suitable for complex environments in the underwater acoustic sensor network.
(5) The positioning system provided by the invention is suitable for various deployment modes by utilizing the characteristics of the sparse deployment modes of the underwater acoustic sensor network nodes, meets the application requirements of any node position distribution and network deployment topological graph, and can effectively avoid the problem of mismatching of the node position distribution and the network deployment topological graph.
Drawings
FIG. 1 is a schematic diagram of a positioning system according to the present invention;
FIG. 2 is a flow chart of a positioning method of the present invention;
FIG. 3 is a flow chart of mobile node positioning in the positioning method of the present invention;
fig. 4 is a schematic diagram of a real position and an estimated position of a transmitting node in scenario 1 of the present invention;
fig. 5 is a schematic diagram of a real position and an estimated position of a transmitting node in scenario 2 of the present invention.
Detailed Description
The invention will now be described in detail with reference to the drawings and specific examples. The present embodiment is implemented on the premise of the technical scheme of the present invention, and a detailed implementation manner and a specific operation process are given, but the protection scope of the present invention is not limited to the following examples.
In a hydroacoustic sensor network, nodes transmit acoustic signals for a specific time interval. Nodes can be divided into three classes: fixed nodes, passive mobile nodes, and active mobile nodes. The fixed node may be attached to the seabed or mounted on a bottoming platform. The passive mobile node changes its position due to the movement of water flow, and when the node is mounted on a mobile device such as an AUV (Autonomous Underwater Vehicle ), the node has both active and passive movement characteristics. Therefore, the fixed node can obtain the accurate position by only positioning once, and the passive mobile node and the active mobile node also need to predict and position on the basis.
To this end, the present embodiment provides a positioning system for a network node of a underwater acoustic sensor to achieve positioning of a fixed node and a mobile node, as shown in fig. 1, the positioning system includes a transmitting node 1, a transmitting transducer 3, a receiving node 4, a receiving hydrophone 5, a time measurement module 2, a noise removal subsystem 6 for acoustic signals, a positioning calculation subsystem 7, and a power supply 8.
Wherein, the rectangular body shown in the figure is an underwater environment, the transmitting node 1 and the receiving node 4 are properly selected and deployed in the underwater environment according to specific application requirements, the transmitting node 1 transmits signals in a broadcast form through the transmitting transducer 3 and performs electro-acoustic conversion, namely, the transmitting transducer 3 is used as an output interface of the transmitting node 1 to convert electric signals generated inside the transmitting node 1 into sound source signals, the sound source signals are transmitted in a sound wave form through a plurality of paths due to multiple reflection, scattering and refraction caused by encountering different transmission paths and barriers when being transmitted underwater, and the signals are weakened and can be specific sound wave pulses or continuous sound waveforms; the receiving node 4 collects multipath propagation sound wave signals, and the receiving hydrophone 5 completes sound-electricity conversion on the collected sound wave signals; the time measuring module 2 starts a timer when the sound source signal is transmitted in a broadcasting mode, the time measuring module 2 closes the timer after the receiving node 4 collects N bits of information, so that an arrival time difference (Time Difference of Arrival, TDoA) is obtained, and the distance between the nodes is calculated by combining the underwater sound propagation speed; the power supply 8 supplies power to the transmitting node 1, the receiving node 4 and the time measuring module 2; the underwater acoustic denoising subsystem designs a band-pass filter and an adaptive filter to filter the received underwater acoustic signal according to the frequency spectrum characteristics of noise in the underwater acoustic sensor network environment, and suppresses low-frequency noise components such as underwater thermal noise by setting appropriate low cut-off frequencies (Lower Cut off Frequency, LCF). Attenuating high frequency noise components, such as turbulent noise, by setting a suitable high cut-off frequency (Upper Cut off Frequency, UCF), and further denoising the signal by adjusting adaptive filter parameters; the positioning calculation subsystem 7 designs a node positioning algorithm according to the propagation characteristics of the underwater sound signals and the motion characteristics of the nodes. And (3) utilizing the oblique projection matrix to realize beam forming in the target direction, thereby enhancing the signals of the expected dominant propagation path and inhibiting the signals of other unexpected paths, and selecting the direction with the maximum propagation power as the positioning direction of the transmitting node 1. In addition, based on the underwater sound propagation model and by utilizing the characteristic of the sparsity of the underwater sound signals, the atomic norm and the super-resolution are applied to signal recovery, and a high-resolution source signal is obtained. And (3) performing node positioning calculation by using the distance information among the nodes and the position information of the known reference point, and converting the high-resolution signal into the node position of the underwater acoustic sensor network to realize high-precision node positioning under any underwater acoustic sensor network topology. Finally, after the node positioning is completed, the communication equipment is used for outputting or transmitting the node position information to other related applications. The information can be used in various application fields such as underwater resource monitoring, ocean research and the like.
In the above, the underwater sound denoising subsystem comprises an input interface, a digital signal processing unit, a band-pass filter and an adaptive filter. The input interface is used for receiving the original underwater sound signal and providing input data for a subsequent processing module. The digital signal processing unit is used for carrying out digital processing and analysis on the acquired signals. The band-pass filter is a core module, and is used for selecting signals with specific frequency ranges to pass through according to the frequency spectrum characteristics of noise and filtering noise with other frequencies. The adaptive filter uses an adaptive filtering algorithm to adjust filter parameters based on real-time environmental noise conditions to further denoise the signal. Specifically, the subsystem designs a band-pass filter and an adaptive filter to filter the received underwater acoustic signal according to the spectral characteristics of noise in the underwater acoustic sensor network environment, and suppresses low-frequency noise components such as underwater thermal noise by setting a proper LCF. The signal is further denoised by setting an appropriate UCF to attenuate high frequency noise components, such as turbulent noise, and by adjusting adaptive filter parameters.
In the foregoing, the positioning computing subsystem 7 includes a data storage module, a processing and control module, a positioning computing module, a position predicting module, and a power management module. The data storage module is used for storing the signals acquired by the receiving node 4 and the water flow velocity vector data. The processing and control module is responsible for data processing and analysis, configuration and control of the nodes and communication with other nodes of the signals collected by the receiving node 4. The positioning calculation module receives the auxiliary data transmitted by the processing and control module, executes a positioning algorithm and calculates the position of the fixed node. The position prediction module uses the water flow velocity vector data in the data storage module to construct an autoregressive integral moving average model (Autoregressive Integrated Moving Average, ARIMA) to achieve mobile node positioning. The power management module is used for providing stable power supply for the subsystem. Specifically, the subsystem designs a node location algorithm based on the propagation characteristics of the underwater acoustic signal and the motion characteristics of the nodes. And (3) utilizing the oblique projection matrix to realize beam forming in the target direction, thereby enhancing the signals of the expected dominant propagation path and inhibiting the signals of other unexpected paths, and selecting the direction with the maximum propagation power as the positioning direction of the transmitting node 1. In addition, based on the underwater sound propagation model and by utilizing the characteristic of the sparsity of the underwater sound signals, the atomic norm and the super-resolution are applied to signal recovery, and a high-resolution source signal is obtained. And (3) performing node positioning calculation by using the distance information among the nodes and the position information of the known reference point, and converting the high-resolution signal into the node position of the underwater acoustic sensor network to realize high-precision node positioning under any underwater acoustic sensor network topology. Finally, after the node positioning is completed, the communication equipment is used for outputting or transmitting the node position information to other related applications. The information can be used in various application fields such as underwater resource monitoring, ocean research and the like.
The whole structure of the positioning system of the embodiment is described above, and the positioning method using the positioning system is shown in fig. 2, and the specific process is as follows:
step 1: broadcast transmission of sound source signals: the time measurement module 2 starts to work and the timer starts. In the underwater acoustic sensor network, the transmitting node 1 broadcasts a source signal S to the various underwater sensor nodes via acoustic links.
Step 2: the transmitting transducer 3 is used to effect electro-acoustic conversion of the source signal and propagation through the underwater acoustic channel. During the propagation, the source signal corresponding to each transmitting node 1n e 1, n is reflected, scattered and refracted multiple times in the form of sound waves, and reaches the receiving node 4m e 1, m, P e 1, P along P paths, with propagation loss G (f, d). The propagation loss G (f, d) of the underwater acoustic signal at the frequency f after the propagation distance d is:
wherein, xi p Represents the scattering loss of the p-th path, alpha (f) represents the absorption coefficient, d p The propagation distance of the p-th path is shown.
Step 3: receiving the underwater acoustic signal: and in the K time slots, after the receiving node 4 collects the receiving data matrix corresponding to the sending signal, the time measuring module 2 finishes working, and the timer is closed to calculate the TDoA. Each receiving node 4m epsilon 1, M]Measurement data consisting of K single frequency signals, i.e. K bits of information, are collected. Let the manifold matrix of the transmitting node 1 on the hydrophone be a (f) = { a (f) n )} n∈[1,N] Response a (f n ) Can be expressed as:
wherein τ m,p (θ n ) Is the propagation delay of the mth receiving node 4 p-th path with respect to the reference point. Considering the propagation loss of the underwater acoustic signal in the underwater acoustic channel, the data matrix received by the sensor node can be expressed as:
Y＝G H (f,d)A(f)S+W (3)
wherein S is a source signal matrix,for the measurement noise matrix in the underwater acoustic environment, A (f) is the manifold matrix on the hydrophone, G H (f, d) is the conjugate transpose of the propagation loss matrix.
Step 4: receiving hydrophone 5: and converting the received sound wave energy into electric energy through an energy conversion device to complete sound-electric conversion.
Step 5: denoising underwater acoustic signals: a filter is used to filter the underwater sound signal to suppress unnecessary noise components.
In the process of denoising the underwater acoustic signal, the method generally comprises the following steps: signal acquisition, signal preprocessing, filter design and filter application. The specific operation flow is as follows:
step 5.1: and (3) data acquisition: collecting underwater acoustic signals by using equipment such as a hydrophone and the like;
step 5.2: signal pretreatment: signal pretreatment: performing time-frequency transformation on the collected underwater sound signals through Fourier transformation;
step 5.3: and (3) designing a filter: according to the frequency spectrum characteristic of the signal and the statistical characteristic of noise, a band-pass Filter and an adaptive Filter are designed to carry out filtering treatment on the received underwater sound signal, a proper LCFLow-Cut Filter is arranged to inhibit low-frequency noise components, a proper UCFUpper-Cut Filter is arranged to weaken high-frequency noise components, and parameters of the adaptive Filter are adjusted according to the real-time environmental noise condition to further denoise the signal;
step 5.4: filter application: the designed filter is applied to the frequency domain signal, and the filtering and denoising of the signal are realized by multiplying or convolving the frequency domain signal.
Step 6: signal oblique projection: and analyzing multipath transmission of the acoustic wave signal, so that a path i represents a propagation path corresponding to a line of sight (LOS), and a path P epsilon [ P ]/i represents propagation paths corresponding to other non-line-of-sight (NLOS) components. And performs a oblique projection calculation to project the NLOS component to the LOS component by oblique projection. The method comprises the following steps:
dividing the denoised underwater sound signal into LOS components Y L And NLOS component Y N Wherein:
step 6.1: to LOS component G i (f,d)a i (f)s i (f) Recorded as g i a i s i To NLOS componentMarked as->The data matrix received by the sensor node may be expressed as:
step 6.2: to project the NLOS component to the LOS component by oblique projection, first the oblique projection operator is calculated:
wherein,representation matrix->Orthogonal projection matrix of (2) satisfying->Wherein matrix->Projection matrix of (2) is
Step 6.3: the oblique projection operator is multiplied with the signal processed by the receiving hydrophone 5 to realize the oblique projection of the signal, namely:
Z(f)＝E i←[P]/i ·Y＝(g i a i )s i +E i←[P]/i ·W (7)
by obliquely projecting the NLOS component to the LOS component, signals of a desired dominant propagation path, i.e., signals propagated by paths corresponding to the LOS component, are enhanced, and signals of other undesired paths, i.e., signals propagated by paths corresponding to other NLOS components, are suppressed, thereby reducing the influence of multipath effects on the signals and improving the reliability of signal recovery and positioning.
Step 7: positioning a fixed node: and according to the signals obtained after oblique projection, performing target detection and positioning by utilizing atomic norm minimization and super-resolution technology.
Assume that the low pass band range of noisy observed data is [ -f l ,f l ]The spectral range of the target signal to be estimated is [ -f h ,f h ]The super resolution factor SRF may be defined as:
wherein lambda is h ,λ l Respectively f h ，f l Corresponding wavelengths. If SRF is equal to 2, this means that the resolution is increased by a factor of 1, and if SRF is equal to 4, this means that the resolution is increased by a factor of 3.
According to the embodiment, the characteristics of the sparse deployment mode of the underwater acoustic sensor network nodes are utilized, and the node positioning under the condition of no grid is provided, so that the method is suitable for various deployment modes, and the problem that the node position distribution is not matched with a network deployment topological graph can be effectively avoided. Enhancement of signals in the target direction, i.e. from the desired dominant propagation path, and suppression of signals in other undesired paths is achieved with the oblique projection matrix, with the direction of maximum propagation power being the node location direction. In the embodiment, the signals corresponding to the LOS component are used as expected signals and enhanced, and the signals corresponding to other NLOS components are used as unexpected signals and suppressed, so that the influence of multipath effect on the signals is reduced, and meanwhile, the reliability of signal recovery and positioning is improved. And according to the signals obtained after oblique projection, performing target detection and positioning by utilizing atomic norm minimization and super-resolution technology.
The specific steps of positioning are as follows:
step 7.1: because the source nodes are sparse in the underwater acoustic sensor network deployment topology, the source signal S can be recovered by using the data matrix received by the sensor nodes in equation (3) through the following optimization problem:
wherein I A Representing atomic norms, delta is a noise substrate, and satisfies the condition of W 2 ≤δ。
Step 7.2: since the original optimization problem 9 is infinitely dimensional in continuous space, it is difficult to solve. Thus, it is translated into a corresponding dual problem:
where I is the identity matrix, F (θ) is a linear operator mapping the continuous signal to the observed value Y, and c is the dual variable. And then the dual problem 10 is converted into a semi-positive programming:
solving the formula (11) by adopting an interior point method, converting an optimization problem with linear equality and inequality constraint into a series of simpler linear equality constraint problems, and iteratively solving by using a Newton method to improve the approximation precision of each step. Satisfy |F (θ) H Angle θ of c|=1 n I.e. the angular position where the transmitting node 1 is located. Due to angle theta n And a (theta) n ) There is a one-to-one mapping relationship θ n →a(θ n ) By estimating the frequency f n Can obtain theta n 。
The above steps complete the positioning of the stationary transmitting node 1. The angle of the position of the fixed node relative to the reference point is theta n . Based on denoised underwater sound signalDistance d from propagation loss n 。
As shown in fig. 3, a prediction and positioning method of the mobile node will be next given.
Step 8: prediction and positioning of mobile nodes:
first, a passive mobile node n pas : the underwater nodes have passive movement characteristics due to water flow movement. When the nodes under the conditions are positioned, the passive movement track of the underwater nodes is predicted, and the positions of the nodes are updated. The present invention uses ARIMA as a predictive tool for performing a water flow velocity vector. The method comprises the following specific steps:
step 8.1: and (3) data collection: historical data of the water flow velocity vector is collected and used for predicting the water flow velocity vector of the next time slot, and passive mobility data of the sending node 1 are obtained through calculation. The data acquisition module records data samples of the water flow velocity vector once per second, including taking 3 features of flow velocity magnitude, direction and time stamp.
Step 8.2: data preprocessing: because the ARIMA model is good at processing linear and stable data, the nonlinear or non-stationarity of the flow velocity vector data, which is caused by the complex environment influence of the underwater acoustic sensor network, is preprocessed, and the approximation of the ARIMA model of each flow velocity vector event is realized, so that an effective time sequence model is obtained.
Step 8.2 is specifically:
8.2.1 data linearization processing: in order to enable the ARIMA model to better capture the nonlinear relation in the water flow velocity vector data and improve the capability of solving the non-stationary water flow velocity vector data sequence, 3 characteristics of flow velocity, direction and time stamp are extracted from the water flow velocity vector, and the following semi-positive definite matrixes are constructed as kernel functions by combining the characteristics of a network environment of the underwater acoustic sensor to design weight vectors so as to process the data by adopting kernel smoothing:
wherein v is a And v b Respectively represent the flow velocity vectors of water flow corresponding to the current time slot and the adjacent previous time slot, and theta a 、Θ b 、t a And t b V respectively a And v b Corresponding angles and moments. The time a E [1, omega ] is smoothed according to the following kernel]Processing the received data of (a):
8.2.2 due to two major features of the stationary time series: constant mean and variance, fitting the ARIMA model to a time series also requires the following two steps:
data equal variance form transformation: a time sequence based on a flow velocity vector is constructed, and non-stationary data is normalized by using Box-Cox transformation so as to improve prediction accuracy. By estimating historical data X of the original water flow velocity vector t To determine the stationarity of the variance of the data using a single parameter Box-Cox transformation R,
when λ=1, it indicates that the variance of the original data has stationarity, otherwise, the original data needs to be converted by the above equation.
Data equi-mean formal transformation: the stationarity of the data mean is checked by the decay pattern of the sample autocorrelation function values, which if it shows a slow decay trend, the data is not yet stationary on the mean and therefore differential pair time series processing is required until the series mean is stable. Conversely, if it shows a rapid decay trend, the data is already smooth on average. The corresponding differential operation times when the data mean value is stable are used as the differential order eta of the ARIMA model.
Step 8.3: model grading: from the preprocessed data, an auto-correlation function of the stationary data and a partial auto-correlation function of the stationary data are used to determine an auto-regression order ρ and a moving average order q, respectively. When there are a plurality of combinations of values of ρ, η, q satisfying the above conditions, further, an optimum order combination is determined using Akaike akak information criterion, the values of ρ, η, q having the smallest values determining the optimum ARIMA model of the time series.
Step 8.4: parameter estimation: after ρ, η and q are known, a Yule-Walker equation is constructed, and a Levinson-Durbin recursion algorithm is adopted to obtain an ARIMA modelParameter phi in (a) i 、e j And epsilon n-j Wherein B is a hysteresis operator.
Step 8.5: and (3) water flow velocity vector prediction: based on the ARIMA model obtained, a recursive method is used for a period of time in the futureIs the water flow velocity vector v of (a) wat Predicting the angle theta wat 。
Step S8.6: positioning of mobile nodes: passive mobile node n pas Angle of position relative to reference pointDistance->The method comprises the following steps:
wherein n is pas ←n fix Representing a fixed node n fix After transmitting the signal, the fixed cable is untwisted to form a passive mobile node n pas In the case of (2), T is time, d n V is the distance of the position of the nth fixed node relative to the reference point wat For the water flow velocity vector over time T,for the offset angle, θ, when the node begins to move with the water flow wat And theta n V respectively wat Corresponding angle and angle of the position of the fixed node relative to the reference point, n fix Representing a fixed node. .
When the transmitting node 1 is mounted on a mobile device such as an AUV, both active mobility and passive mobility are provided. Let AUV have velocity v in horizontal coordinate system auv Angle of theta auv Active mobile node n act Angle of position relative to reference pointAnd distance->Can be expressed as:
/>
in the formula, v auv V for the speed of the mounted mobile device wat For θ wat 、θ auv The two kinds of the materials are respectively that,for t b 、t a Respectively, are as follows. The active mobile node n can be uniquely determined according to formula (16) act Is a position of (c).
In order to verify the effectiveness of the positioning system and the positioning method, the embodiment performs performance verification on a Matlab R2023a simulation platform. The computer used was equipped with an i5-1135G7 processor. The simulation environment established is as follows: the transmitting nodes 1 are randomly deployed in a 100m multiplied by 100m underwater acoustic sensor network area, the number of the receiving nodes 4 is 40, the moving speed of the active moving type nodes is 2m/s, the experimental result adopts the average value when the Monte Carlo simulation times are 5000, and the convex optimization tool CVX is used for solving the formula (11). The simulation scene 1 is set to be that 18 sending nodes 1 are randomly deployed in a network area of a 100m×100m underwater acoustic sensor, 9 sending nodes are fixed nodes, 5 sending nodes are passive mobile nodes, 4 sending nodes are active mobile nodes, and fig. 4 is a schematic diagram of the actual positions and estimated positions of the sending nodes 1 in the scene 1 in the embodiment.
Setting the simulation scene 2 as 10 sending nodes 1, wherein 4 sending nodes are fixed nodes, 3 sending nodes are passive mobile nodes, and 3 sending nodes are active mobile nodes, and fig. 5 is a schematic diagram of the actual positions and estimated positions of the sending nodes 1 in the scene 2 of the embodiment, and comparing fig. 4 and fig. 5, when the number of the sending nodes 1 is smaller, the node positioning accuracy is higher. At this time, fewer source signals reduce mutual interference between the signals to some extent, so that the receiving node 4 can receive the target signal more accurately.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
Claims (10)
1. A hydroacoustic sensor network node positioning system, comprising:
transmitting node (1): deployed in an underwater environment for broadcasting sound source signals to receiving nodes (4);
time measurement module (2): for measuring the time difference of arrival between transmission and reception;
transmitting transducer (3): for effecting an electro-acoustic conversion of said acoustic source signal and propagating through an underwater acoustic channel;
receiving node (4): deployed in an underwater environment for collecting acoustic signals that have propagated through multipath;
receiving hydrophone (5): for effecting an acousto-electric conversion of said acoustic wave signal;
acoustic wave signal denoising subsystem (6): the device is used for processing the acoustic wave signals converted by the receiving hydrophone (5) and inhibiting noise components;
positioning calculation subsystem (7): and the method is used for carrying out positioning calculation according to the acoustic wave signals to obtain the positions of the fixed node and the mobile node in the transmitting node (1).
2. A hydroacoustic sensor network node positioning system according to claim 1, characterized in that the positioning system further comprises a power supply (8) for powering the transmitting node (1), the time measurement module (2) and the receiving node (4).
3. A positioning system of a network node of a hydroacoustic sensor according to claim 1, wherein the acoustic signal denoising subsystem (6) comprises a digital signal input interface, a signal processing unit, a band-pass filter and an adaptive filter, and is configured to receive the acoustic signal after being subjected to the acoustic-to-electric conversion through the input interface, to the signal processing unit for digital processing and analysis, and to filter noise through the band-pass filter and the adaptive filter.
4. A hydroacoustic sensor network node location system according to claim 1, wherein the location calculation subsystem (7) comprises a data storage module for storing acoustic signals and water flow velocity vector data, a processing and control module for processing and analyzing acoustic signals, configuration and control of nodes and communication with nodes, a location calculation module for receiving data transferred by the processing and control module, calculating the location of a fixed node, a location prediction module for calculating the location of a mobile node, and a power management module for providing power supply to the location calculation subsystem (7).
5. A positioning method based on a hydroacoustic sensor network node positioning system according to any of claims 1-4, comprising the steps of:
broadcasting the sound source signal to a receiving node (4) through a transmitting node (1), and starting the work of a time measuring module (2);
adopting a transmitting transducer (3) to realize the electric-acoustic conversion of the sound source signal and propagating through an underwater acoustic channel;
the receiving node (4) collects the sound wave signals subjected to multipath propagation and electric-acoustic conversion, and the time measuring module (2) finishes working;
-effecting an acoustic-to-electrical conversion of said acoustic signals using a receiving hydrophone (5);
filtering the acoustic wave signal subjected to the acoustic-electric conversion to suppress noise components;
dividing the acoustic wave signal with the noise component removed into a line-of-sight component and a non-line-of-sight component, and calculating a signal oblique projection based on the line-of-sight component and the non-line-of-sight component;
calculating the position of a fixed node in the transmitting node (1) based on the signal oblique projection;
historical water flow velocity vector data are collected, an ARIMA model is built to predict the water flow velocity vector, and the position of a mobile node in the sending node (1) is calculated according to the data comprising the sound wave signals.
6. The positioning method according to claim 5, wherein the specific step of suppressing the noise component includes:
collecting the acoustic wave signals converted by the receiving hydrophone (5);
performing signal preprocessing on the converted sound wave signals based on Fourier transformation;
and filtering and denoising the preprocessed signals by adopting a band-pass filter and an adaptive filter.
7. The positioning method according to claim 5, wherein the step of calculating the oblique projection of the signal comprises:
dividing the acoustic wave signal with noise components removed into a line-of-sight component and a non-line-of-sight component, and constructing a data matrix received by a receiving node (4), wherein the expression of the data matrix is as follows:
wherein Y is a data matrix, g i a i s i As a component of the line of sight,for non-line-of-sight components>The measurement noise matrix is the measurement noise matrix in the underwater sound environment;
calculating an oblique projection operator based on the data matrix, wherein the expression of the oblique projection operator is as follows:
wherein E is i←[P]/i In order to be a diagonal projection operator,representation matrix->Is satisfied by the orthogonal projection matrix of (2)Matrix->Is +.>
Multiplying the oblique projection operator by the acoustic wave signal converted by the receiving hydrophone (5) to obtain signal oblique projection, and realizing that the non-line-of-sight component is obliquely projected to the line-of-sight component, wherein the expression of the signal oblique projection is as follows:
Z(f)＝E i←[P]/i ·Y＝(g i a i )s i +E i←[P]/i ·W
wherein Z (f) is signal oblique projection, E i←[P]/i Y is a data matrix, g, for the oblique projection operator i a i s i As a component of the line of sight,is a measurement noise matrix in the underwater sound environment.
8. The positioning method according to claim 5, wherein the specific calculation step of the fixed node position includes:
and recovering the sound source signal through an optimization problem based on the signal oblique projection, wherein the expression of the optimization problem is as follows:
wherein S is a sound source signal, and I.I.I A Representing atomic norms, Y is a data matrix, Z (f) is signal oblique projection, delta is a noise substrate, and the requirements of W are met 2 ≤δ；
Converting the optimization problem into a dual problem, wherein the expression of the dual problem is as follows:
wherein I is an identity matrix, E i←[P]/i For the oblique projection operator, F (θ) is a linear operator mapping the continuous signal to the observed value Y, c is a dual variable;
converting the dual problem into a semi-positive problem, and solving by adopting an interior point method to obtain the angle of a fixed node, wherein the expression of the semi-positive problem is as follows:
wherein M is the number of receiving nodes (4);
and calculating the distance based on the acoustic wave signals of the denoising components and the propagation loss, and realizing the positioning of the fixed node.
9. The positioning method according to claim 5, wherein the step of calculating the position of the mobile node specifically includes:
collecting historical data of a water flow velocity vector, and preprocessing;
constructing an ARIMA model, predicting a water flow velocity vector based on the ARIMA model,
in the process of constructing an ARIMA model, determining a model order based on the preprocessed data, constructing a Yule-Walker equation based on the model order, and adopting a Levinson-Durbin recursion algorithm to obtain model parameters;
based on the predicted water flow velocity vector, calculating the positions of a passive mobile node and an active mobile node, wherein the calculation expression of the positions of the passive mobile node is as follows:
in the method, in the process of the invention,is a passive movable node n pas Distance from the reference point->Is a passive movable node n pas Angle relative to the reference point, T is time, d n V is the distance of the position of the nth fixed node relative to the reference point wat For the flow velocity vector of the water flow in time T +.>For the offset angle, θ, when the node begins to move with the water flow wat And theta n V respectively wat Corresponding angle and angle of the position of the fixed node relative to the reference point, n fix Representing a fixed node;
the calculation expression of the position of the active mobile node is:
in the method, in the process of the invention,is a passive movable node n act Angle relative to the reference point->Is a passive movable node n act Distance from reference point v auv V for the speed of the mounted mobile device wat For the flow velocity vector of the water flow within the time T, theta wat For its corresponding angle, θ auv For the angle of the mobile device carried, +.>For time slot index, t b 、t a The flow velocity vector v corresponding to the last time slot b And the current time slot water flow velocity vector v a Corresponding time.
10. The positioning method of claim 9, wherein the preprocessing operation comprises a data linearization process and a data stationarity process.
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