CN107741579B - TOA mine target positioning method based on compressed sensing subspace reconstruction - Google Patents
TOA mine target positioning method based on compressed sensing subspace reconstruction Download PDFInfo
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Abstract
The invention discloses a TOA mine target positioning method based on compressed sensing subspace reconstruction in the field of underground coal mine safety monitoring and communication, which enables sensing nodes to restore original signals by using sample information less than twice of transmitting frequency, and mainly solves the problems of positioning energy consumption acquired in real time due to frequent sensing of the sensing nodes and influence of environmental factors on positioning errors. According to the method, compressed sensing subspace reconstruction is introduced into the TOA-based mine target positioning method, so that under the condition that the sampling data quantity of sensing nodes is reduced, accurate positioning of the target can be performed, and the accuracy of the positioning method is further improved. Meanwhile, the method can shorten the positioning time, reduce the energy consumption of positioning and is more beneficial to the practical application of the target positioning method in mines.
Description
Technical Field
The invention belongs to the field of underground coal mine safety monitoring and communication, relates to a TOA mine target positioning method based on compressed sensing subspace reconstruction, and particularly relates to a situation that positioning performance is improved, sensing node sampling data quantity is reduced and positioning time is shortened in an underground coal mine positioning algorithm.
Background
The target positioning is used as one of important research contents in underground coal mines, is also an important guarantee of efficient and safe production of the mines, and can help mine management staff to know what kind of event happens to a roadway only if the information acquired by the system contains position information, so as to assist in reasonably allocating resources; when accidents such as collapse and the like occur in the mine, rescue personnel are helped to master the position of a miner so as to quickly formulate and implement a rescue scheme; meanwhile, the coal cutting depth, the supporting strength, the personnel distance and the like can be determined in an auxiliary mode, and the production process is monitored. Therefore, it is particularly important to realize accurate positioning of the underground coal mine targets.
Meanwhile, due to the characteristics of long and narrow mine laneway, more dust, personnel, coal walls and other mobile equipment, other positioning technologies aiming at ground environment are not used.
The existing method for realizing underground positioning of the coal mine is mainly based on ranging and non-ranging, wherein the ranging aspect is mainly based on time of arrival TOA, time difference of arrival TDOA, angle of arrival AOA, power arrival intensity RSSI and the like, and the method is complex in structure, high in cost and low in positioning precision. The basic idea of TOA-based positioning is that the distance from a transmitting node to a receiving node is proportional to the propagation time of an electric wave, and the measurement principle is that the condition that the transmission speed c of the electromagnetic wave is known is utilized, and the arrival time t of the transmitting signal of the transmitting node received by the receiving node is utilized TOA The velocity multiplied by the time gives the distance c t between the receiving node and the transmitting node TOA ＝T 1 -T 0 。
However, in recent years, with rapid development of sensor technology, embedded technology and wireless communication technology, a wireless sensor network has a wide application prospect as an emerging network in the fields of national defense and military, environmental monitoring, urban and rural medical treatment, agricultural production and the like. Meanwhile, the positioning is used as a supporting technology of the wireless sensor network, and the acquired information is required to contain position information in order to meet the requirements that the information acquired by the sensor is connected with the real world and has a certain practical meaning. Meanwhile, to realize accurate positioning of a target, a sensor node needs to frequently sense the target node, acquire the target node in real time, the obtained data is huge in quantity, and the energy consumption for processing a large quantity of data is large, meanwhile, high requirements are put on the signal sampling rate and the data processing speed, and the sensor node is a great challenge for the traditional shannon-nyquist sampling theorem. Therefore, the compressive sensing theory proposed by Candes et al in 2006 provides a new idea for solving the above-mentioned problems. He breaks through the bottleneck of shannon's theorem, and the signal sampling rate can acquire high-resolution signals even under the condition that the nyquist sampling theorem requirement is not met, namely, the reconstructed signals containing enough information of the original signals can be reconstructed with high probability under the condition of less signal acquisition quantity.
In summary, in the practical application of accurate positioning of the underground coal mine target, multipath interference and error accumulation of the roadway wireless signals are difficult to completely eliminate, so that the positioning accuracy in the complex transmission environment is reduced, and as the sampled data is increased, the energy consumption of the system is increased sharply, and the implementation of the accurate positioning method of the underground coal mine target is limited. For this reason, we have to find a way to implement the above-described scheme-to make a target positioning method that uses a small amount of sensing node sampling data to achieve accurate positioning in complex environments downhole in coal mines.
Disclosure of Invention
The invention aims to solve the problems that the traditional shannon nyquist sampling theorem cannot meet the requirement on the limitation of the signal sampling rate and the environment of a coal mine underground roadway is complex in the process of frequent sensing of a sensing node on a target node and real-time acquisition, and provides a TOA coal mine underground target positioning method based on compressed sensing subspace reconstruction. The method is characterized in that a compressed sensing subspace reconstruction method is introduced into a coal mine underground positioning method based on TOA, so that the accuracy of the positioning method is improved under the condition that the sampling data quantity of sensing nodes is reduced. Meanwhile, the invention can shorten the positioning time, reduce the energy consumption of positioning and is more beneficial to the practical application of the target positioning method in underground coal mines.
In order to achieve the above object, the present invention adopts the following technical scheme: the TOA coal mine underground target positioning method based on compressed sensing subspace reconstruction comprises the following steps of target area division, measurement matrix establishment in a compressed sensing method, rough positioning of a compressed sensing SP positioning reconstruction method and accurate positioning of a TOA ranging method, wherein the method comprises the following steps of:
(1) Because the mine roadway is narrow, sensing nodes cannot be arranged in the center or the bottom of the roadway, sensing nodes with known positions can be deployed only at two sides of the roadway, the sensing nodes send signals to target nodes, the target nodes in the communication radius of the sensing nodes can receive the signals sent by the sensing nodes, and the area where the target is located is determined according to the signals received by the target nodes;
(2) Determining the actual moment when the target node receives the signal, and obtaining a distance measurement value between the target node and the sensing node according to the propagation speed of the wireless signal in the air;
(3) Collecting signal transmission time t between each sensing node i and target node j by using TOA ranging model i,j Establishing an M multiplied by N measuring matrix phi;
(4) Obtaining a grid sequence number of a target node by using a M-dimensional measured value vector and a compressed sensing subspace reconstruction positioning method, and realizing rough positioning;
(5) And (3) obtaining the distance between each target node and the sensing node obtained in the step (2), and obtaining the position coordinates of the target node to be positioned by utilizing a TOA ranging model, so as to finally realize accurate positioning.
Compressed sensing theory indicates that the compressible (sparse) signal can still accurately recover the original signal after data sampling at a rate far below the nyquist sampling rate, i.e. as long as the signal is compressible or sparse in a certain transform domain, the transformed high-dimensional signal can be projected onto a low-dimensional space by an observation matrix that is not related to the transform basis, and then the original signal can be reconstructed with high probability from these small projections (or observations) by solving an optimization problem. The research on compressed sensing is mostly from the research on image processing of European and American students. The original compressed sensing positioning method is characterized in that firstly, sparse signal representation is adopted, a measurement matrix meeting constraint equidistance (RIP) conditions is constructed, and a reconstruction method is adopted to position a target. Compared with the traditional target positioning method based on TOA, the method greatly reduces the energy consumption of positioning.
The invention has the core that the traditional TOA positioning algorithm adopts a compressed sensing subspace reconstruction technology, and the accurate positioning is carried out by a compressed sensing principle and a TOA ranging method:
(1) Firstly, dividing a positioning area according to the radius of a sensing node to select grids, and carrying out area screening on a network with low sparsity can obviously improve positioning accuracy.
(2) And then sampling TOA information of the sensing node and generating an observation matrix, and effectively restoring the sparse signal by adopting a signal restoration method based on compressed sensing.
(3) And finally, TOA ranging processing is carried out on the restored sparse signals (the obtained target node grid sequence numbers), so that the positioning accuracy is further improved.
In order to achieve the above purpose, another technical scheme adopted by the invention is as follows: a TOA coal mine underground target positioning method based on compressed sensing subspace reconstruction comprises the steps that a sensing node generates a wireless electromagnetic wave emission signal, and the area where a target node is located is obtained according to the sensing radius of the sensing node; the sensing matrix is constructed by the signal arrival time of the wireless electromagnetic wave signals sent by the sensing nodes received by all grid points; the subspace reconstruction method takes a perception matrix as input, and the correlation degree between K columns of the perception matrix selected through iterative calculation and residual errors is maximum until the residual error value is enlarged relative to the previous iterative process, so that the effect of determining a target grid is achieved in the positioning process; the TOA ranging method calculates the position coordinates of the target node by knowing the Euclidean distance value between the target node and the sensing node and by using a circumference model in the TOA ranging method. Thus, the method performs positioning with high accuracy.
The novel beneficial effects of the invention are as follows:
1. according to the TOA coal mine underground positioning method based on compressed sensing subspace reconstruction, which is provided by the invention, the sparse measurement matrix can be obtained by sampling TOA information, and the target grid sequence number is recovered and met by the compressed sensing subspace reconstruction method, so that the positioning time is shortened.
2. Compared with the prior art, the TOA coal mine underground target positioning method based on compressed sensing can enable the sensing node to restore the original signal by using sample information which is less than twice of the transmitting frequency, reduce the positioning energy consumption of frequent sensing real-time acquisition, reduce the influence of environmental factors on positioning errors and improve the positioning accuracy.
Drawings
FIG. 1 is a flow chart of the target positioning method of the present invention
FIG. 2 is a schematic diagram of TOA ranging according to the object locating method of the present invention
FIG. 3 is a system model diagram of the object locating method of the present invention
FIG. 4 is a flowchart of the recovery of sparse signals for the target positioning method of the present invention
Detailed Description
In order to make the contents and advantages of the technical scheme of the invention more clear, the TOA coal mine underground target positioning method based on compressed sensing subspace reconstruction is further described in detail below with reference to the accompanying drawings and the specific embodiments. It should be emphasized that the following description is merely exemplary in nature and is in no way intended to limit the scope of the invention or its applications.
As shown in fig. 1, the target positioning method of the present invention includes target area division, measurement matrix establishment in a compressed sensing method, rough positioning in a compressed sensing subspace reconstruction positioning method, and accurate positioning in a TOA ranging method, and the method includes the following steps:
(1) A plurality of sensing nodes with known positions which are randomly deployed at two sides of a roadway send signals to a target node, the target node in the communication radius of the sensing node can receive the signals sent by the sensing node, and the area where a target is located is determined according to the signals received by the target node;
(2) Determining the actual moment when the target node receives the signal, and obtaining a distance measurement value between the target node and the sensing node according to the propagation speed of the wireless signal in the air;
as shown in fig. 2, assuming time synchronization of the target node and the sensing node, the arrival time t of the sensing node transmitting signal received by the target node is calculated TOA Obtaining a distance measurement value between the target node and the sensing node by using the known electromagnetic wave emission speed c, namely D i,j ＝c*t TOA ＝c*(T 1 -T 0 )。
(3) Collecting sensations using TOA ranging modelKnowing the signal transmission time t between node i and target node j i,j Establishing an M multiplied by N measuring matrix phi;
as shown in fig. 3, the underground coal mine roadway is divided into grids for calibration, the higher the required positioning precision is, the smaller the grid division is, and the larger the formed observation matrix is. The total number of grid points in a roadway area is N, the number of sensing nodes is M, on each grid, sampling is carried out for multiple times, TOA (time of arrival) values of grid points sensed by all sensing nodes are collected, and an M multiplied by N measuring matrix is obtainedWherein t is i,j The TOA value representing the sampling of the jth grid point at the ith sensing node.
(4) Obtaining a grid sequence number of a target node by using a M-dimensional measured value vector and a compressed sensing subspace reconstruction positioning method, and realizing rough positioning;
as shown in fig. 4, the sparse sampling matrix obtained in step (3) may not have an incoherence with the signal itself. According to the compressed sensing theory, the signal processed by compressed sensing must have sparse characteristics or compressibility, but in practice, most of the time domain signal f is not sparse, so that sparse transformation is required to be performed on the sampled signal. With a set of base ψ= { ψ 1 ,ψ 2 ……ψ N Sparse transforming the sampled signal f, i.eWhere ψ is N collection matrix and f and x are N-dimensional vectors. After sparse transformation, a sparse measurement value vector y=Φψx is obtained, wherein ψ is the TOA characteristic of a sensing node.
Let a=Φψ, matrixIs the pseudo-inverse of matrix a, q=orth (a T ) T Orth (A) is the orthorhombic base of A. Then there is orthogonal transformation->So that y=tsif. Substituting phi, f, T to obtain
Because ofIs the pseudo-inverse of A, then->So y=qx. Where Q is an orthogonal matrix and Q satisfies constraint equidistance (RIP) due to grid point number N>>M (number of sensing nodes), when the length of the measured value vector y is greater than m=o (KlogN), the original signal can be accurately reconstructed>By calculation ofThe optimal solution of (2) can obtain the original information number +.>And finally, obtaining the grid sequence number of the target node.
(5) And (3) obtaining the distance between each target node and the sensing node obtained in the step (2), and obtaining the position coordinates of the target node to be positioned by utilizing a TOA ranging model, so as to finally realize accurate positioning.
The invention designs a compressed sensing subspace reconstruction method introduced into a TOA-based target positioning method for limiting the frequent sensing of a sensing node to a target node by a traditional Shannon Nyquist sampling theorem and collecting the signal sampling rate in real time, so that the accurate positioning of the target can be performed under the condition that the sampling data quantity of the sensing node is reduced, and the accuracy of the positioning method is further improved. Meanwhile, the invention can also shorten the positioning time, reduce the energy consumption of positioning and is more beneficial to the practical application of the target positioning method in underground coal mines.
The foregoing is only illustrative of the preferred embodiments of the present invention, but the scope of the present invention is not limited thereto, and all equivalent modifications and variations within the scope of the present invention will be within the scope of the present invention as those skilled in the art will readily appreciate.
Claims (6)
1. The TOA mine target positioning method based on compressed sensing subspace reconstruction is characterized by comprising the steps of target area division, measurement matrix establishment in the compressed sensing method, rough positioning of the compressed sensing subspace reconstruction positioning method and accurate positioning of the TOA ranging method;
the TOA mine target positioning method based on compressed sensing subspace reconstruction comprises the following steps:
(1) A plurality of sensing nodes with known positions which are randomly deployed at two sides of a roadway send signals to a target node, the target node in the communication radius of the sensing node can receive the signals sent by the sensing node, and the area where a target is located is determined according to the signals received by the target node;
(2) Collecting signal transmission time t between each sensing node i and target node j by using TOA ranging model i,j Establishing an M multiplied by N measuring matrix phi;
(3) Obtaining a grid sequence number of a target node by using a compressed sensing SP reconstruction positioning method by using an M-dimensional measured value vector, and realizing rough positioning;
(4) Determining the actual moment of the target node receiving the signal based on the measurement matrix phi on the obtained grid sequence number of the target node, and obtaining a distance measurement value between the target node and the sensing node according to the propagation speed of the wireless electromagnetic wave signal in the air;
(5) And (3) obtaining the position coordinates of the target node to be positioned by utilizing the Euclidean distance formula according to the distance between each target node obtained in the step (4) and the sensing node, and finally realizing accurate positioning.
2. The method for locating TOA mine targets based on compressed sensing subspace reconstruction according to claim 1, wherein the measurement matrix Φ in the step (2) is composed of signal arrival time between each grid point and sensing node, namely
3. The TOA mine target positioning method based on compressed sensing subspace reconstruction according to claim 1, wherein the grid sequence number of the target node in the step (3) is represented by a measured value vector Y M×1 Obtained as input by a compressed perceptual subspace reconstruction method, wherein Y M×1 From formula Y M× 1＝Φ M×N X N×1 Obtained.
4. The method for locating TOA mine targets based on compressed sensing subspace reconstruction according to claim 1, wherein in the step (4), the distance measurement value between the target node and the sensing node is measured, and the signal transmission time t of the signal received by the target node is measured TOA The speed of the wireless signal in the air is the light speed c, and a distance measurement value D between the target node and the sensing node is calculated i,j D is i,j ＝c*t TOA ＝c*(T 1 -T 0 )。
5. The method for positioning TOA mine targets based on compressed sensing subspace reconstruction according to claim 1, wherein the position coordinates (x j ，y j ) Is defined by a sensing node (x i ,y i ) Calculated by European distance formula, i.e
6. The device adopting the TOA mine target positioning method based on compressed sensing subspace reconstruction according to claim 1, wherein the sensing node generates a wireless electromagnetic wave emission signal, and the area where the target node is located is obtained according to the sensing radius of the sensing node; the measurement matrix is constructed by the signal arrival time of the wireless electromagnetic wave signals transmitted by the sensing nodes received by all grid points; the subspace reconstruction takes a measuring matrix as input, and the correlation degree between K columns of the measuring matrix and the residual error is maximum through iterative calculation, so that the residual error value is enlarged relative to the previous iterative process, and the function of determining a target grid is realized in the positioning process; the TOA ranging calculates the position coordinates of the target node through the known Euclidean distance value between the target node and the sensing node and through a circumference model in the TOA ranging; thus, the target positioning method performs positioning with high accuracy.
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| CN108519577B (en) * | 2018-03-12 | 2023-09-15 | 中国矿业大学(北京) | Distributed positioning method based on compressed sensing TOA characteristic signal fingerprint library |
| CN109140241B (en) * | 2018-08-21 | 2019-10-29 | 吉林大学 | A kind of compressed sensing based pipeline leakage positioning method |
| CN112285695B (en) * | 2020-10-21 | 2024-01-12 | 浙江大学 | Interactive positioning system and method based on compressed sensing |
| CN112765207B (en) * | 2021-04-07 | 2021-06-18 | 中国人民解放军国防科技大学 | Resource big data processing, storing and inquiring method |
| CN114740433B (en) * | 2022-04-27 | 2023-04-25 | 电子科技大学 | Time synchronization method based on compressed sensing under influence of multipath effect |
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