CN119177881A - Long-distance wireless sensing method for coal mine unstructured environment - Google Patents
Long-distance wireless sensing method for coal mine unstructured environment Download PDFInfo
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Abstract
The invention discloses a long-distance wireless sensing method for a coal mine unstructured environment, which is characterized in that an intelligent detection robot is provided with a directional transmitting antenna and a pair of receiving antennas which are arranged side by side left and right, directional transmitting antennas which face the same direction are arranged in a coal mine underground roadway along the length trend direction of the intelligent detection robot according to set interval distances, and reflecting plates are arranged at the optimal reflection installation positions on the roadway walls of the coal mine underground roadway, the periodic working transmitting antennas and receiving antennas are utilized for detection, the specific positions of trapped people can be judged according to the amplitude change direction of gesture signals of the trapped people and the signal source positions after disasters occur, and the smooth MUSIC algorithm and the ISAR algorithm are combined for accurately separating and analyzing reflected signals, so that the number and the relative positions of people in the environment are determined, the effective transmission and detection of signals can be guaranteed in the complex coal mine underground roadway environment, and the method is particularly suitable for the rescue detection of the trapped people in the coal mine underground environment after disasters.
Description
Technical Field
The invention relates to a long-distance wireless sensing method, in particular to a long-distance wireless sensing method for a coal mine unstructured environment, which can realize long-distance personnel positioning and personnel number detection in a complex coal mine underground environment, and belongs to the technical field of coal mine safety production.
Background
Coal is used as strategic core energy source in China, and provides a solid rear shield for the stable and high-speed growth of economy for a long time. However, the coal mining process has complex underground mining environment and complex mining flow, on one hand, the underground coal mine environment is complex and changeable, and is in severe conditions such as high temperature, high humidity, dust, gas and the like for a long time, the roadway structure is complex, and a plurality of potential safety hazards exist, on the other hand, under the background that the coal mining intensity is continuously improved and the mining depth is continuously extended, various uncertain factors in the coal mining process are accompanied, and the risk of coal mine disaster accidents is gradually increased. In the emergency rescue of the underground environment of the coal mine after the disaster, the traditional personnel detection methods mostly depend on contact sensors or video monitoring systems, and the methods have the following main problems that the contact sensors are required to be widely arranged underground, the installation and maintenance workload is large, the sensors are easily affected by environmental factors such as humidity and dust, the detection precision is reduced, the detection range is limited, the effective detection can only be carried out near the sensor arrangement points, the remote detection is difficult to realize, the video monitoring systems have high initial investment and maintenance cost due to the fact that a large number of cameras and video transmission equipment are required, and video signals are easily interfered by the underground light, dust and other factors, so that the image quality is reduced, and the privacy protection of personnel is also provided.
In order to achieve effective rescue detection in post-disaster coal mine downhole environments, researchers in the industry have begun to explore non-contact detection techniques. The wireless sensing detection method based on the radio signal is paid attention to gradually due to the advantages of simplicity and convenience in installation, low cost, strong adaptability and the like. Among the radio technologies, the LoRa technology is an ideal choice for wireless sensing detection of the underground environment due to the characteristics of low power consumption, long-distance transmission and strong penetrating power. However, the wireless sensing detection technology based on the LoRa signal still faces many challenges in a complex underground environment, such as the stability of the signal is greatly affected by the narrow and tortuous of an underground roadway, reflection, attenuation and the like, the wireless sensing precision is poor in identifying and positioning the specific position and state of trapped personnel, and the problem of how to ensure effective transmission and detection of the signal in an unstructured environment with complex corners and in an obstacle environment under a coal mine is still needed to be solved in the industry.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a long-distance wireless sensing method for a coal mine unstructured environment, which can effectively sense, accurately separate and analyze reflected signals in the coal mine unstructured environment and in the obstacle environment, further obtain accurate personnel number information and is particularly suitable for rescue detection for searching trapped personnel in the coal mine underground environment after disaster.
In order to achieve the above purpose, the non-contact advanced detection system used in the long-distance wireless sensing method facing the non-structural coal mine environment comprises an intelligent detection robot, a receiving antenna, a transmitting antenna and a reflecting plate, wherein a pair of receiving antennas are arranged on the intelligent detection robot side by side left and right, the transmitting antennas are directional transmitting antennas, the transmitting antennas are arranged in a plurality, each transmitting antenna is provided with a LoRa node, one transmitting antenna is arranged on the intelligent detection robot, the other transmitting antennas are arranged in a coal mine underground roadway, the reflecting plate of a metal sheet structure is arranged on the roadway wall of the coal mine underground roadway, and the intelligent detection robot capable of moving independently comprises a signal processing module which is electrically connected with a pair of receiving antennas and one transmitting antenna on the intelligent detection robot;
The long-distance wireless sensing method for the coal mine unstructured environment specifically comprises the following steps:
Step1, equipment is arranged, namely transmitting antennas are arranged in a coal mine underground roadway along the length trend direction of the coal mine underground roadway according to a set interval distance, the transmitting antennas face the same direction, the transmitting period and the transmitting time of each transmitting antenna are set, and the mounting position and the specific working time of each transmitting antenna are recorded after the transmitting antennas are arranged;
Step2, detecting after disaster, namely inputting coal mine tunnel layout diagram data marked with good detection effect positions into an intelligent detection robot after disaster occurs, controlling the intelligent detection robot to enter a coal mine underground tunnel and go to the marked good detection effect positions to detect signals, and waiting for at least one complete signal transmission period at each good detection effect position by the intelligent detection robot so as to receive signals sent by all transmitting antennas;
Step2-1, determining the position of trapped personnel, namely recording and analyzing the received signals by a signal processing module, determining whether gesture signals exist in the signals, when the gesture signals are detected, judging a transmitting source of the gesture signals by the signal processing module firstly, if the transmitting source is a transmitting antenna in a coal mine underground roadway, judging that the trapped personnel is positioned near the transmitting antenna by the signal processing module, and if the transmitting source is a transmitting antenna on an intelligent detection robot, judging the trend of the amplitude of the gesture signals by the signal processing module firstly, and then judging which side of the trapped personnel is positioned at an intersection relative to the intelligent detection robot according to the fluctuation direction of the amplitude of the gesture signals;
Step2-2, determining the number of trapped persons, namely after confirming the existence of the trapped persons, processing the reflected signals by eliminating static signals and direct path signals in the received signals, separating out the reflected signals in different directions, determining the arrival angles of the reflected signals, and calculating the positions of the trapped persons relative to the receiving antenna and the number of the trapped persons.
Further, in Step2-1, when the signal processing module determines which side of the intersection the trapped person is located relative to the intelligent detection robot, the amplitude fluctuation direction of the gesture signal indicates that the trapped person is located at the right corner of the intersection, the amplitude fluctuation direction of the gesture signal indicates that the trapped person is located at the left corner of the intersection, and if the upward and downward amplitude fluctuation directions exist at the same time, the amplitude fluctuation direction indicates that the trapped person exists at the left and right corners of the intersection.
Further, in Step2-1, when the signal processing module judges the trend of the amplitude of the gesture signal, a singular spectrum analysis method, a multi-scale sliding window fluctuation detection method and an average gradient judgment method are used for judging;
the singular spectrum analysis method comprises the following specific steps:
① One-dimensional time series are embedded in a high-dimensional space. Given a time series of length N (X 1,X2,…,XN), a window length L (typically L < N/2) is selected to construct an lxkl trajectory matrix X, where k=n-l+1. Each column of the track matrix is a subsequence of the time series, represented as follows:
X=[X1,X2,…,XL],XI＝(xi,xi+1,…,xi+L-1)T
SVD decomposition is carried out on the track matrix X, and the following steps are obtained:
Wherein d is a nonlinear function, lambda i is a characteristic value of XX T, U I and Vi are left singular vectors and right singular vectors respectively, s is the number of non-zero singular values, alpha X 0 is an extra matrix for increasing the degree of freedom;
② Grouping the components obtained by decomposition according to the size and physical meaning of the characteristic values, wherein the larger characteristic value corresponds to trend and period components, and the smaller characteristic value corresponds to noise components;
③ Inversely transforming the selected components to reconstruct a time sequence, and respectively extracting trend, period and noise parts of the time sequence by selecting different components;
The multi-scale sliding window fluctuation detection method specifically comprises the following steps:
① Selecting sliding windows with different sizes for data processing;
② Calculating the fluctuation degree of the data in each window, and if the fluctuation in the window is smaller than a preset threshold value, processing the data in the window to be 0;
③ The size of each sliding window is respectively subjected to fluctuation detection, and the method is as follows:
a. processing the whole data by using the maximum window size to obtain a first-stage processing result;
b. processing the first-stage processing result by using the medium window size to obtain a second-stage processing result;
c. Processing the second-stage processing result by using the minimum window size to obtain a final result;
the average gradient judging method comprises the following specific steps:
① Calculating a gradient for the initial portion of each gesture signal amplitude data, the gradient formula being:
Wherein delta A is the amplitude difference of adjacent sampling points, delta t is the sampling interval time;
② The gradient value of each piece of data is averaged to obtain the average gradient of the piece of data:
wherein Ns is the number of sampling points of the segment of data;
③ The signal trend is determined according to the sign and magnitude of the average gradient, and if the average gradient is positive, the signal amplitude is increased, and if the average gradient is negative, the signal amplitude is decreased.
Further, in Step2-2, when the reflected signal is processed by eliminating the static signal and the direct path signal in the received signal, the inverse synthetic aperture radar algorithm and the smooth MUSIC algorithm are applied to carry out depth analysis on the signal;
when the inverse synthetic aperture radar algorithm is applied to carry out depth analysis on signals, the track formed by human gesture motion is regarded as an antenna array, and then the space direction angle calculation formula is as follows:
Wherein, angle [ theta, n ] is a signal function of measuring time n along the space direction theta, lambda is wavelength, deltad is the space distance between continuous antennas in the array, S [ n+i ] is used as the antenna array;
Estimating a spatial distance delta d between continuous antennas in the simulation array, wherein delta d is expressed as delta d=vt, wherein T is a sampling period, v is a speed of motion, and a default value of v is selected to be v=1 m/s;
when the smooth MUSIC algorithm is applied to carry out depth analysis on the signals, the specific steps are as follows:
① Constructing a covariance matrix R of a received signal, wherein R=E [ xx H ];
② Calculating eigenvalues and eigenvectors of the covariance matrix, and dividing the eigenvalues and eigenvectors into a signal subspace and a noise subspace;
③ The method comprises the steps of processing eigenvectors of a noise subspace to form a MUSIC spatial spectrum function:
Wherein W i is a transformation matrix for filtering the signal, e i is a eigenvector of the noise subspace, a (θ) is an array manifold vector;
④ And finally, obtaining a clear track formed by human body movement in a time direction spectrogram by searching the arrival direction of the peak value estimation signal of the MUSIC spectrum, and obtaining the number of people in the environment according to the number of the tracks.
Further, in Step2, when the intelligent detection robot goes to the marked position with good detection effect to perform signal detection, at least two positions with good detection effect are selected at each intersection to perform signal detection.
Further, in Step2-1, if the emission source is an emission antenna on the intelligent detection robot, the detection is repeated three times at the position with good detection effect, and the detection result is selected to be equal to or greater than two times.
Further, in Step1, when the transmitting antenna is installed in the underground tunnel of the coal mine, the transmitting antenna is not installed in the set range by taking the geometric center of the intersection as the range center according to the detection range of the receiving antenna on the intelligent detection robot for the crossroad and the T-shaped crossroad.
Further, in Step1, when determining the optimal reflection installation position of the reflecting plate, the detection equipment is used for performing on-site exploration on the underground tunnel of the coal mine, and the optimal reflection installation position of the reflecting plate is determined through experiments and data analysis.
In Step1, when the position with good signal detection effect is determined, a plurality of positions with good detection effect are determined for each intersection aiming at the intersection and the T-shaped intersection.
Compared with the prior art, the long-distance wireless sensing method for the coal mine unstructured environment is characterized in that an intelligent detection robot is used for carrying a directional transmitting antenna and a pair of receiving antennas which are arranged side by side left and right, the directional transmitting antennas facing the same direction are arranged in a coal mine underground roadway along the length trend direction of the intelligent detection robot according to the set interval distance, each transmitting antenna is provided with a LoRa node, a reflecting plate is arranged at the optimal reflection installation position on the roadway wall of the coal mine underground roadway, the periodically working transmitting antennas and receiving antennas are used for detecting signal interference, signal receiving accuracy and reliability can be ensured, the specific position of a person can be judged according to the amplitude change direction of gesture signals of the trapped person and the signal source position after disasters occur, and the reflected signals are accurately separated and analyzed by combining a smooth MUSIC algorithm and an ISAR algorithm, so that the number and the relative positions of the trapped person in the environment are determined, thus the long-distance sensing and accurate positioning of the trapped person are realized, the effective transmission and detection of the signal can be ensured under the complex coal mine underground roadway environment, and the detection of the trapped person is particularly suitable for searching the rescue workers in the underground coal mine environment.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of a gesture signal return receiving antenna according to the present invention;
FIG. 3 is a schematic diagram of the return of the present invention to different receiving antenna reflection areas;
FIG. 4 is a schematic diagram of a transmitting antenna and reflector arrangement according to the present invention;
FIG. 5 is a schematic illustration of the sensing range of the T-junction of the present invention;
fig. 6 is a schematic diagram of an array of human motion simulation antennas using ISAR in accordance with the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
The non-contact advanced detection system for the long-distance wireless sensing method of the coal mine unstructured environment comprises an intelligent detection robot, receiving antennas, transmitting antennas and reflecting plates, wherein a pair of receiving antennas are arranged on the intelligent detection robot side by side left and right, the transmitting antennas are directional transmitting antennas, the transmitting antennas are arranged in a plurality, each transmitting antenna is provided with a LoRa node, one transmitting antenna is arranged on the intelligent detection robot, the other transmitting antennas are arranged in a coal mine underground roadway, the reflecting plates of a metal sheet structure are arranged on the roadway walls of the coal mine underground roadway, and the intelligent detection robot capable of moving independently comprises a signal processing module which is electrically connected with a pair of receiving antennas and one transmitting antenna on the intelligent detection robot.
As shown in fig. 1, the long-distance wireless sensing method for the coal mine unstructured environment specifically comprises the following steps:
Step1, equipment arrangement:
Step1-1, installing transmitting antennas in a coal mine underground roadway along the length trend direction of the coal mine underground roadway at set interval distances, wherein the transmitting antennas face to the same direction, aiming at a crossroad and a T-shaped crossroad, according to the detection range of a receiving antenna on an intelligent detection robot, taking the geometric center of the crossroad as a range center, not installing the transmitting antennas in the set range, and recording the position of each transmitting antenna after the transmitting antennas are installed.
The method is characterized in that a transmitting antenna is a 9dbi directional transmitting antenna, the model of a receiving antenna on the intelligent detection robot is USRP X310, the carrier frequency of a LoRa node transmitting signal is 915MHz, the linear frequency modulation bandwidth BW is 125KHz, the spreading factor SF is 12, the coding rate CR is 4/8, and the sampling rate is 500Hz, as shown in fig. 4 and 5, one transmitting antenna is arranged in each roadway along the length direction every 500 meters, and aiming at an intersection and a T-shaped intersection, the receiving antenna on the intelligent detection robot can detect signals in a corner 150m, so that the transmitting antenna is not arranged in the range of 100 meters at each intersection, the using quantity of the transmitting antennas is reduced, and the cost is reduced.
Step1-2, in order to ensure that the transmitting antennas cannot interfere with each other, a transmitting period and a transmitting time of each transmitting antenna are set to avoid signal interference, the transmitting antennas operate periodically, the transmitting antennas which possibly generate interference cannot operate simultaneously, and the position and specific operating time of each transmitting antenna are recorded in detail so as to accurately correspond to a signal source in subsequent signal processing.
Step1-3, the detection equipment is used for carrying out field exploration on the underground roadway of the coal mine, the reflection plate is arranged on the roadway wall of the underground roadway of the coal mine after the optimal reflection installation position of the reflection plate is determined, the optimal reflection installation position of the reflection plate can be determined through experiments and data analysis, so that the signal intensity and the detection effect are ensured to be enhanced to the greatest extent, the reflection plate is ensured to be smooth and firm when being installed, shielding of other objects is avoided as much as possible, after the installation of the reflection plate is finished, the detection equipment can be used for carrying out signal detection on the installation position of the reflection plate again, the effect of the iron sheet on the signal enhancement is verified, detection data are recorded and analyzed, and the detection distance and the signal intensity are ensured to be improved remarkably.
Step1-4, performing on-site exploration in a coal mine underground roadway, determining positions with good signal detection effect, marking and recording specific coordinates and environment description of each position so as to quickly find and utilize the positions in the actual rescue process, and determining a plurality of positions with good detection effect at each intersection so as to avoid undetected when disaster is serious. These locations need to have good signal reception conditions and also provide reliable detection results in the event of possible rubble blockage after a disaster.
Step2, detecting after disaster, namely inputting the coal mine tunnel layout diagram data marked with the good detection effect positions into the intelligent detection robot after disaster, controlling the intelligent detection robot to enter a coal mine underground tunnel and go to the marked good detection effect positions to detect signals, and selecting at least two good detection effect positions at each intersection to detect signals.
Step2-1, determining the position of trapped personnel:
The signal processing module records and analyzes the received signals to determine whether gesture signals exist in the signals, when the gesture signals are detected, the signal processing module firstly judges the transmitting source of the gesture signals, if the transmitting source is a transmitting antenna in a coal mine underground roadway (namely, trapped people are positioned in the roadway where the transmitting antenna is positioned, the intelligent detection robot can directly receive signals transmitted by the transmitting antenna and reflected by the human body of the trapped people), the trapped people are positioned near the transmitting antenna, and if the transmitting source is a transmitting antenna on the intelligent detection robot (namely, the trapped people are positioned at the intersection position where a signal reflection area exists, the intelligent detection robot receives the reflecting signals transmitted by the transmitting antenna on the intelligent detection robot and reflected by the human body of the trapped people), firstly judges the amplitude trend of the gesture signals by using a Singular Spectrum Analysis (SSA), a multi-scale sliding window fluctuation detection and average gradient judgment method, and then judges which side of the intelligent detection robot the trapped people are positioned according to the amplitude fluctuation direction of the gesture signals.
SSA is a non-parametric dimension reduction method based on time series, which is mainly used for signal processing, time series decomposition and trend analysis, and the core idea is to decompose the time series into a group of independent components (such as trend, period and noise) so as to reveal hidden structures in data. The basic steps of SSA are as follows:
① One-dimensional time series are embedded in a high-dimensional space. Given a time series of length N (X 1,X2,…,XN), a window length L (typically L < N/2) is selected to construct an lxkl trajectory matrix X, where k=n-l+1. Each column of the track matrix is a subsequence of the time series, represented as follows:
X=[X1,X2,…,XK],XI＝(xi,xi+1,…,xi+L-1)T
SVD decomposition is carried out on the track matrix X, and the following steps are obtained:
Wherein f is a nonlinear function, lambda i is a characteristic value of XX T, U I and Vi are left singular vectors and right singular vectors respectively, s is the number of non-zero singular values, and alpha X 0 is an additional matrix for increasing the degree of freedom.
② And grouping the components obtained by decomposition according to the size and physical meaning of the characteristic values. In general, larger eigenvalues correspond to trend and period components, and smaller eigenvalues correspond to noise components.
③ The selected components are inverse transformed to reconstruct the time series. By selecting different components, the trend, period and noise portions of the time series can be extracted separately.
The multi-scale sliding window fluctuation detection is a method for processing data by using sliding windows with different sizes, and can process the part with insignificant fluctuation in the signal as 0, so as to highlight significant fluctuation in the signal. This method can be divided into the following steps:
① And selecting sliding windows with different sizes for data processing. The size of the sliding window should cover a range from larger to smaller, typically three or more windows of different sizes are selected.
② The degree of fluctuation is calculated for the data within each window. Common fluctuation detection methods include mean difference, variance, standard deviation, and the like. If the fluctuation in the window is less than the predetermined threshold, the data in the window is processed to 0.
③ And respectively carrying out fluctuation detection on the size of each sliding window. The specific method comprises the following steps:
a. And processing the whole data by using the maximum window size to obtain a first-stage processing result.
B. and processing the first-stage processing result by using the medium window size to obtain a second-stage processing result.
C. And processing the second-stage processing result by using the minimum window size to obtain a final result.
The average gradient determination method is a technique for determining the signal trend by calculating the gradient of the signal amplitude. The method comprises the following specific steps:
① A gradient, i.e., the difference in amplitudes of adjacent sampling points, is calculated for the beginning portion of each gesture signal amplitude data. The gradient formula is:
Where ΔA is the amplitude difference between adjacent samples and Δt is the sampling interval time.
② And averaging the gradient value of each piece of data to obtain the average gradient of the piece of data.
Where Ns is the number of sampling points for the segment of data.
③ The signal trend is determined according to the sign and magnitude of the average gradient, and if the average gradient is positive, the signal amplitude is increased, and if the average gradient is negative, the signal amplitude is decreased.
When the direction of the trapped person is determined according to the direction of the amplitude fluctuation of the gesture signal, as shown in fig. 2, the gray part is a reflected signal when the hand of the trapped person is not moving, the green part is a reflected signal caused when the hand of the trapped person moves, one part of the reflected signal is received by the left receiving antenna RX1 (blue part in the figure), the other part is received by the right receiving antenna RX2 (orange part in the figure), and since the two receiving antennas arranged side by side are relatively close to each other, the signals of the blue part and the orange part are also similar, so that the amplitude change caused by path attenuation should be smaller than the change caused by different reflecting areas. Since the transmitting antenna is a directional transmitting antenna, the signal emitted by the directional transmitting antenna is conical, and the signal reflected from the wall forms an ellipse (the signal reflected from the air can be regarded as being reflected from an intangible wall at a certain point, and a plurality of ellipses exist in the whole environment), the size of the reflecting area is related to the diameter of the ellipse. As shown in fig. 3, the elliptical diameters of the signals reflected by the wall are expressed by x and y, respectively, and according to the geometric relationship, the elliptical diameters can be calculated by the following formula:
Where d is the distance from the emission point to the wall, and θ 1、θ2、θ3 and θ 4 are the angle of incidence and angle of reflection of the signal.
According to the law of reflection, θ∈ (0 ° -90 °) and θ 1<θ2<θ3<θ4 when the signal is reflected from the right hand corner. Since the left side is symmetrical to the right side, we take the right side as an example for analysis.
When theta is within the range of 0 DEG to 90 DEG,Is the first derivative of (2)Second derivativeThus (2)Is a concave function, i.e. during a gradual increase in theta,Is smaller and smaller.
Due toAs θ increases, it can be considered that there is a higher probability of x > y at θ e (0 ° -90 °).
In order to discuss the probability of x > y, a Monte Carlo method is used for numerical simulation, difference comparison is carried out through a large number of randomly generated theta 1、θ2、θ3、θ4, and after 100 thousands of tests, when theta epsilon (0-90 DEG), the probability of x being greater than y is about 85%.
In non-line-of-sight environments, the motion of the trapped person's hand may reflect more signals to the receiving antenna. When a signal is transmitted from the right side of the receiving antenna, since the signal incident on the receiving antenna RX1 from the wall has a large reflection area, the signal intensity increment of the receiving antenna RX1 is larger than that of the receiving antenna RX 2.
The receive antennas RX1 and RX2 are typically used to cancel CFO (Carrier Frequency Offset ) and CSO (Sampling Frequency Offset, sampling frequency offset) when processing the LoRa signal. The resulting amplitude can be expressed as:
wherein amp is the acquired amplitude, abs represents the absolute value function; And The signals received by the two receiving antennas mounted on the intelligent detection robot are respectively.
Thus, when the signal is reflected from the right hand corner, there is a probability of about 85% such thatThe increase in (2) is greater thanResulting in an increased amp, and when the signal is reflected from the left corner, about 85% probability results inThe increase in (2) is less thanThereby resulting in a decrease in amp.
In order to improve the detection accuracy, once trapped people possibly exist on two sides of an intersection are detected during intersection detection, the detection is repeated twice. In the three detection, the case where the detection result is equal to or greater than two times is selected. A large number of experiments show that the detection accuracy can be improved to more than 93%.
That is, when the transmitting source is a transmitting antenna on the intelligent detection robot, the amplitude fluctuation direction of the gesture signal is upward to indicate that the trapped person is located at the right corner of the intersection, the amplitude fluctuation direction of the gesture signal is downward to indicate that the trapped person is located at the left corner of the intersection, and if the upward and downward amplitude fluctuation directions exist at the same time, the gesture signal indicates that the trapped person exists at the left and right corners of the intersection.
Step2-2, determining the number of trapped people:
After confirming that trapped personnel exist, the static signals and the direct path signals in the received signals are eliminated, the reflected signals are further processed, inverse Synthetic Aperture Radar (ISAR) algorithm and smooth MUSIC algorithm are applied to conduct deep analysis on the signals, reflected signals in different directions are separated, the arrival angles of the reflected signals are determined, the positions of the trapped personnel relative to the receiving antenna and the number of the trapped personnel are calculated, and therefore accurate rescue is conducted on rescue personnel.
ISAR is a technique that simulates an antenna array by using the motion of a target. In conventional antenna arrays, multiple antennas simultaneously receive the signals of the target and process this information to determine the direction (θ) of the target relative to the array. In ISAR, there is only one receiving antenna, but since the target is moving, the continuous time measurement simulates an inverse antenna array, i.e. the moving body can become an antenna array. By processing such continuous measurements, the spatial direction of the human body can be identified by standard antenna array beam steering.
As shown in fig. 6, regarding the trajectory formed by the gesture motion of the human body as an antenna array, the spatial direction angle can be calculated as:
Wherein Angle [ theta, n ] is a signal function of the measurement time n along the space direction theta, lambda is wavelength, deltad is the space distance between the continuous antennas in the array, and Sn+i is used as the antenna array.
At any point in time n, the value θ that produces the maximum in Angle [ θ, n ] corresponds to the direction of object motion. To calculate Angle [ θ, n ] from the above equation, it is necessary to estimate the spatial distance Δd between consecutive antennas in the simulated array, Δd may be expressed as Δd=vt, where T is the sampling period and v is the speed of motion, since human motion simulates the antennas in the array, the range of human arm motion speed is quite narrow although the exact speed of arm movement is not known, and the error in the v value translates into an underor overestimation of the exact direction of the human body, not impeding subsequent people detection, so the default value of v may be chosen to be v=1 m/s.
The smoothing MUSIC (Multiple Signal Classification) algorithm is a technique to estimate the direction of arrival (DOA) of the signal by decomposing the covariance matrix. The MUSIC algorithm achieves high resolution DOA estimation by analyzing orthogonality between the signal and noise subspaces. The basic idea is that eigenvectors of the covariance matrix can be divided into signal subspaces and noise subspaces, the eigenvectors of the signal subspaces being related to the direction of arrival of the signal, and the eigenvectors of the noise subspaces being orthogonal to these directions. The algorithm comprises the following steps:
① A covariance matrix R of the received signal is constructed, r=e [ xx H ].
② Eigenvalues and eigenvectors of the covariance matrix are calculated and divided into a signal subspace and a noise subspace.
③ The method comprises the steps of processing eigenvectors of a noise subspace to form a MUSIC spatial spectrum function:
where W i is a transform matrix used to filter the signal, e i is a eigenvector of the noise subspace, and a (θ) is an array manifold vector.
④ And finally, obtaining a clear track formed by human body movement in a time direction spectrogram by searching the arrival direction of the peak value estimation signal of the MUSIC spectrum, and obtaining the number of people in the environment according to the number of the tracks.
The long-distance wireless sensing method for the coal mine unstructured environment utilizes the periodically working transmitting antenna and receiving antenna to detect the signal interference, ensures the accuracy and reliability of signal receiving, can judge the specific position of the personnel according to the amplitude change direction of the gesture signal of the trapped personnel and the position of the signal source after the disaster occurs, combines a smooth MUSIC algorithm and an ISAR algorithm to accurately separate and analyze the reflected signals, further determines the number and the relative position of the personnel in the environment, further realizes the long-distance sensing and accurate positioning of the trapped personnel, can ensure the effective transmission and detection of the signals in the complex coal mine underground roadway environment, and is particularly suitable for searching the rescue detection of the trapped personnel in the coal mine underground environment after the disaster.
Claims (9)
1. The non-contact type advanced detection system comprises an intelligent detection robot, receiving antennas, transmitting antennas and reflecting plates, wherein a pair of receiving antennas are arranged on the intelligent detection robot side by side left and right, the transmitting antennas are directional transmitting antennas and are arranged in a plurality, each transmitting antenna is provided with a LoRa node, one transmitting antenna is arranged on the intelligent detection robot, the other transmitting antennas are arranged in a coal mine underground roadway, the reflecting plates of a metal sheet structure are arranged on the roadway walls of the coal mine underground roadway, and the intelligent detection robot capable of moving independently comprises a signal processing module which is electrically connected with a pair of receiving antennas and one transmitting antenna on the intelligent detection robot;
The long-distance wireless sensing method for the coal mine unstructured environment specifically comprises the following steps:
Step1, equipment is arranged, namely transmitting antennas are arranged in a coal mine underground roadway along the length trend direction of the coal mine underground roadway according to a set interval distance, the transmitting antennas face the same direction, the transmitting period and the transmitting time of each transmitting antenna are set, and the mounting position and the specific working time of each transmitting antenna are recorded after the transmitting antennas are arranged;
Step2, detecting after disaster, namely inputting coal mine tunnel layout diagram data marked with good detection effect positions into an intelligent detection robot after disaster occurs, controlling the intelligent detection robot to enter a coal mine underground tunnel and go to the marked good detection effect positions to detect signals, and waiting for at least one complete signal transmission period at each good detection effect position by the intelligent detection robot so as to receive signals sent by all transmitting antennas;
Step2-1, determining the position of trapped personnel, namely recording and analyzing the received signals by a signal processing module, determining whether gesture signals exist in the signals, when the gesture signals are detected, judging a transmitting source of the gesture signals by the signal processing module firstly, if the transmitting source is a transmitting antenna in a coal mine underground roadway, judging that the trapped personnel is positioned near the transmitting antenna by the signal processing module, and if the transmitting source is a transmitting antenna on an intelligent detection robot, judging the trend of the amplitude of the gesture signals by the signal processing module firstly, and then judging which side of the trapped personnel is positioned at an intersection relative to the intelligent detection robot according to the fluctuation direction of the amplitude of the gesture signals;
Step2-2, determining the number of trapped persons, namely after confirming the existence of the trapped persons, processing the reflected signals by eliminating static signals and direct path signals in the received signals, separating out the reflected signals in different directions, determining the arrival angles of the reflected signals, and calculating the positions of the trapped persons relative to the receiving antenna and the number of the trapped persons.
2. The method for long-distance wireless sensing of a coal mine unstructured environment according to claim 1, wherein in Step2-1, when the signal processing module judges which side of the intersection the trapped person is located relative to the intelligent detection robot, the amplitude fluctuation direction of the gesture signal is upward to indicate that the trapped person is located at the right corner of the intersection, the amplitude fluctuation direction of the gesture signal is downward to indicate that the trapped person is located at the left corner of the intersection, and if the upward and downward amplitude fluctuation directions are both present, the signal processing module indicates that the trapped person is located at the left and right corners of the intersection.
3. The long-distance wireless sensing method for the non-structural environment of the coal mine according to claim 1, wherein in Step2-1, when the signal processing module judges the trend of the amplitude of the hand signal, the signal processing module judges by using a singular spectrum analysis method, a multi-scale sliding window fluctuation detection method and an average gradient judgment method;
the singular spectrum analysis method comprises the following specific steps:
① One-dimensional time series are embedded in a high-dimensional space. Given a time series of length N (X 1,X2,…,XN), a window length L (typically L < N/2) is selected to construct an lxkl trajectory matrix X, where k=n-l+1. Each column of the track matrix is a subsequence of the time series, represented as follows:
X=[X1,X2,…,XK],XI＝(xi,xi+1,…,xi+L-1)T
SVD decomposition is carried out on the track matrix X, and the following steps are obtained:
Wherein f is a nonlinear function, lambda i is a characteristic value of XX T, U I and Vi are left singular vectors and right singular vectors respectively, s is the number of non-zero singular values, alpha X 0 is an extra matrix for increasing the degree of freedom;
② Grouping the components obtained by decomposition according to the size and physical meaning of the characteristic values, wherein the larger characteristic value corresponds to trend and period components, and the smaller characteristic value corresponds to noise components;
③ Inversely transforming the selected components to reconstruct a time sequence, and respectively extracting trend, period and noise parts of the time sequence by selecting different components;
The multi-scale sliding window fluctuation detection method specifically comprises the following steps:
① Selecting sliding windows with different sizes for data processing;
② Calculating the fluctuation degree of the data in each window, and if the fluctuation in the window is smaller than a preset threshold value, processing the data in the window to be 0;
③ The size of each sliding window is respectively subjected to fluctuation detection, and the method is as follows:
a. processing the whole data by using the maximum window size to obtain a first-stage processing result;
b. processing the first-stage processing result by using the medium window size to obtain a second-stage processing result;
c. Processing the second-stage processing result by using the minimum window size to obtain a final result;
the average gradient judging method comprises the following specific steps:
① Calculating a gradient for the initial portion of each gesture signal amplitude data, the gradient formula being:
Wherein delta A is the amplitude difference of adjacent sampling points, delta t is the sampling interval time;
② The gradient value of each piece of data is averaged to obtain the average gradient of the piece of data:
wherein Ns is the number of sampling points of the segment of data;
③ The signal trend is determined according to the sign and magnitude of the average gradient, and if the average gradient is positive, the signal amplitude is increased, and if the average gradient is negative, the signal amplitude is decreased.
4. The method for long-distance wireless sensing for non-structural coal mine environments according to claim 1, wherein in Step2-2, when the reflected signals are processed by eliminating static signals and direct path signals in the received signals, an inverse synthetic aperture radar algorithm and a smooth MUSIC algorithm are applied to carry out depth analysis on the signals;
when the inverse synthetic aperture radar algorithm is applied to carry out depth analysis on signals, the track formed by human gesture motion is regarded as an antenna array, and then the space direction angle calculation formula is as follows:
Wherein, angle [ theta, n ] is a signal function of measuring time n along the space direction theta, lambda is wavelength, deltad is the space distance between continuous antennas in the array, S [ n+i ] is used as the antenna array;
Estimating a spatial distance delta d between continuous antennas in the simulation array, wherein delta d is expressed as delta d=vt, wherein T is a sampling period, v is a speed of motion, and a default value of v is selected to be v=1 m/s;
when the smooth MUSIC algorithm is applied to carry out depth analysis on the signals, the specific steps are as follows:
① Constructing a covariance matrix R of a received signal, wherein R=E [ xx H ];
② Calculating eigenvalues and eigenvectors of the covariance matrix, and dividing the eigenvalues and eigenvectors into a signal subspace and a noise subspace;
③ The method comprises the steps of processing eigenvectors of a noise subspace to form a MUSIC spatial spectrum function:
Wherein W i is a transformation matrix for filtering the signal, e i is a eigenvector of the noise subspace, a (θ) is an array manifold vector;
④ And finally, obtaining a clear track formed by human body movement in a time direction spectrogram by searching the arrival direction of the peak value estimation signal of the MUSIC spectrum, and obtaining the number of people in the environment according to the number of the tracks.
5. The method for long-distance wireless sensing for non-structural coal mine environments according to claim 1, wherein in Step2, when the intelligent detection robot goes to the marked good detection effect positions for signal detection, at least two good detection effect positions are selected for signal detection at each intersection.
6. The method for long-distance wireless sensing in a non-structural coal mine environment according to claim 1, wherein in Step2-1, if the transmitting source is a transmitting antenna on the intelligent detecting robot, repeating the detection three times at the position with good detecting effect, and selecting the detection result to be greater than or equal to two times.
7. The method for long-distance wireless sensing for coal mine unstructured environment according to claim 1, wherein in Step1, when the transmitting antenna is installed in a coal mine underground roadway, the transmitting antenna is not installed in a set range by taking the geometric center of the intersection as the range center according to the detection range of the receiving antenna on the intelligent detection robot for the crossroad and the T-shaped crossroad.
8. The long-distance wireless sensing method for the non-structural coal mine environment according to claim 1, wherein in Step1, when the optimal reflection installation position of the reflecting plate is determined, the detection equipment is used for carrying out field exploration on the underground coal mine roadway, and the optimal reflection installation position of the reflecting plate is determined through experiments and data analysis.
9. The method for long-distance wireless sensing for non-structural environments of coal mines according to claim 1, wherein in Step1, when the position of the signal detection effect is determined, a plurality of positions with good detection effect are determined for the crossroad and the T-shaped intersection, and each intersection is determined.
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