A method and system for detecting weak signals through wireless ground electrode current field

Abstract

The present invention relates to a method and system for detecting weak signals wirelessly through the ground using a ground electrode current field. The method comprises: using a maximum likelihood algorithm to calculate a coarse estimate of the sampling frequency offset of the time domain information of a received signal; based on the coarse estimate of the sampling frequency offset, using an ergodic method to correct a local long synchronization signal to generate a local long synchronization signal that has undergone frequency offset correction; performing a sliding correlation calculation between the local long synchronization signal that has undergone frequency offset correction and the received signal to accurately estimate the sampling frequency offset, thereby determining the starting point of the long synchronization signal in the received signal and completing the detection of the weak signal. The present invention can compensate for the impact of the sampling frequency offset on the long synchronization signal in the received signal under low-frequency and low signal-to-noise ratio detection conditions, improve the received signal-to-noise ratio through long-term accumulation, detect the position of the long synchronization signal in the received signal, and thus achieve TTE communication. The present invention can be applied in the field of wireless communication in the ground electrode current field.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication of ground electrode current field, and in particular to a method and system for detecting weak signals of ground electrode current field wirelessly through the ground. Background Art

[0002] Wireless through-the-earth (TTE) communication, using the ground current field, is an information transmission technology that uses the earth as a channel. The extremely low-frequency signals used in TTE communication have strong penetration, but the low transmission frequency often requires a long time for signal transmission and reception. In this case, sampling frequency offsets caused by the different crystal oscillator frequencies of the transceiver amplify the impact on synchronization at the receiving end. Furthermore, TTE communication signals are severely attenuated during transmission, making them difficult to detect in the presence of noise. Existing correlation detection methods for low-frequency, long synchronization signals are limited and unsuitable for TTE communication systems. Summary of the Invention

[0003] To address the above-mentioned issues, the present invention aims to provide a method and system for detecting weak signals wirelessly through the ground using the ground electrode current field. This method and system can compensate for the impact of sampling frequency offset on the long synchronization signal in the received signal under low-frequency and low signal-to-noise ratio detection conditions, improve the received signal-to-noise ratio through long-term accumulation, detect the position of the long synchronization signal in the received signal, and thus achieve TTE communication.

[0004] To achieve the above objectives, in a first aspect, the present invention adopts a technical solution: a method for detecting weak signals through ground electrode current fields wirelessly through the ground, comprising: using a maximum likelihood algorithm to calculate a coarse estimate of the sampling frequency offset of time domain information of a received signal; based on the coarse estimated sampling frequency offset, using a traversal method to correct a local long synchronization signal to generate a local long synchronization signal that has undergone frequency offset correction; performing a sliding correlation calculation on the local long synchronization signal that has undergone frequency offset correction and the received signal to accurately estimate the sampling frequency offset, thereby determining a starting point of the long synchronization signal in the received signal and completing weak signal detection.

[0005] Furthermore, generating a local long synchronization signal after frequency offset correction includes:

[0006] Assume that the number of samples that actually accumulate a sampling clock deviation during the reception process is N′, and the value of N′ is in the range [Nx, N+x], where the value of x is N / 10, and N is the coarse estimate of the sampling frequency deviation;

[0007] Traverse N′ in [Nx,N+x] and, in each traversal, insert or remove a sampling point every N′ of the local long synchronization signal according to the positive or negative value of the phase rotation angle θ between the training signals to correct the residual deviation and generate a frequency-offset-corrected local long synchronization signal.

[0008] Furthermore, according to the positive or negative value of the phase rotation angle θ between the training signals, a sampling point is inserted or removed every N′ sampling points, including:

[0009] When the phase rotation angle θ between training signals is positive, a sampling point is inserted every N′ sampling points, and the average of the two sampling points before and after the inserted sampling value is taken to correct the residual deviation;

[0010] When the phase rotation angle θ between training signals is negative, one sampling point is removed every N′ sampling points to correct the residual deviation.

[0011] Furthermore, accurate estimation of the sampling frequency offset is performed, including:

[0012] Perform sliding correlation calculation on each local long synchronization signal after frequency offset correction and the received signal, and record the N′ value and the first correlation peak K in each traversal;

[0013] According to the maximum first correlation peak K max The sampling frequency deviation precise estimation value is determined, and the local long synchronization signal is corrected again by interpolation method based on the sampling frequency deviation precise estimation value to generate the local long synchronization signal after the long correlation algorithm.

[0014] Furthermore, the local long synchronization signal is corrected again by interpolation based on the precise estimated value of the sampling frequency offset, including:

[0015] According to the positive and negative values ​​of the inter-symbol phase rotation angle θ, the local long synchronization signal is rotated every N pre Insert or remove a sampling point; N pre is the precise estimate of the sampling frequency bias;

[0016] The phase rotation angle θ between symbols is positive, and the local long synchronization signal is rotated every N pre When the phase rotation angle θ between training signals is negative, the local long synchronization signal is inserted into one sampling point every N pre Remove one sampling point from each sampling point.

[0017] Further, determining a starting point of a long synchronization signal in the received signal includes:

[0018] Perform sliding correlation calculation on the local long synchronization signal after the long correlation algorithm and the received signal. Assume that the second correlation peak is K re , determine the correlation threshold based on the second correlation peak value, and if the second correlation peak value exceeds the correlation threshold value in a sliding correlation detection, it is determined that the synchronization signal is detected.

[0019] Furthermore, the relevant threshold is: 0.9K re .

[0020] In the second aspect, the technical solution adopted by the present invention is: a weak signal detection system with wireless ground penetration of the ground electrode current field, which includes: a coarse estimation module, which uses the maximum likelihood algorithm to calculate a coarse estimate of the sampling frequency deviation of the time domain information of the received signal; a correction module, which uses the ergodic method to correct the local long synchronization signal based on the coarse estimation value of the sampling frequency deviation to generate a local long synchronization signal corrected for the frequency deviation; a fine estimation module, which performs a sliding correlation calculation on the local long synchronization signal corrected for the frequency deviation and the received signal to accurately estimate the sampling frequency deviation, thereby determining the starting point of the long synchronization signal in the received signal to complete weak signal detection.

[0021] In a third aspect, the technical solution adopted by the present invention is: a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, and when the instructions are executed by a computing device, the computing device executes any one of the above methods.

[0022] In a fourth aspect, the technical solution adopted by the present invention is: a computing device, comprising: one or more processors, a memory and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any of the above methods.

[0023] The present invention has the following advantages due to the adoption of the above technical solution:

[0024] The through-the-ground communication system of the present invention can compensate for the impact of sampling frequency offset on the long synchronization signal in the received signal under low-frequency and low signal-to-noise ratio detection conditions. It improves the received signal-to-noise ratio through long-term accumulation, detects the position of the long synchronization signal in the received signal, and improves synchronization accuracy. Compared with the existing method that only uses conventional sliding correlation synchronization, the long correlation algorithm of the present invention, which uses a coarse sampling frequency offset estimation combined with a fine sampling frequency offset estimation, achieves a better signal-to-noise ratio, a lower false alarm probability, and a higher synchronization point accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a flow chart of a weak signal detection method according to an embodiment of the present invention;

[0026] Figure 2 1 is a schematic diagram of a sampling frequency deviation precise estimation process according to an embodiment of the present invention;

[0027] Figure 3 It is a schematic diagram of the correlation value using the conventional sliding correlation algorithm;

[0028] Figure 4 Schematic diagram of correlation values ​​using the long correlation algorithm in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be described clearly and completely below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the described embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art belong to the scope of protection of the present application.

[0030] It should be noted that the terms used herein are only intended to describe specific embodiments and are not intended to limit exemplary embodiments according to the present application. As used herein, the singular form is intended to include the plural form, unless the context clearly indicates otherwise, and it should also be understood that, when the terms "comprise" and / or "include" are used in the specification, there is a presence of a feature, step, operation, device, component and / or a combination thereof.

[0031] The weak signal detection method of the long correlation algorithm is applied to the ground electrode current field wireless through-the-earth communication system. Because of the particularity of electrode current field information transmission, the signal received by the receiving end is usually very weak, and it is difficult to determine the accurate position of the signal, so that information missed detection and partial information loss are caused. In order to solve this problem, the length of the local synchronization signal is lengthened (hereinafter referred to as the local long synchronization signal), and the received signal-to-noise ratio is improved by long-time accumulation, so that the problem of difficult determination of the starting position of the weak signal is solved. Because the frequencies of the transceiver crystal oscillators are different, the signal of the receiving end is affected by the sampling frequency offset, and the local long synchronization signal is also affected. Therefore, the long correlation algorithm for correcting the local long synchronization signal according to the sampling frequency offset of the transceiver is adopted.

[0032] The present application provides a kind of weak signal detection method and system of ground electrode current field wireless through-the-earth based on long correlation algorithm, its steps are: first, the maximum likelihood algorithm is used to carry out sampling frequency offset coarse estimation to received signal. After coarse estimation of sampling deviation, correlation calculation is carried out using traversal method to complete fine estimation of sampling frequency offset. Then, according to the fine estimation value, the local long synchronization signal is modified using interpolation method, to generate the local long synchronization signal after long correlation algorithm, and sliding correlation calculation is carried out with received signal, when correlation peak value exceeds correlation detection threshold, it is determined that long synchronization signal in received signal is detected.

[0033] In one embodiment of the present application, a weak signal detection method of ground electrode current field wireless through-the-earth is provided. Figure 1 、 Figure 2 As shown in the figure, the method comprises the following steps:

[0034] 1) the maximum likelihood algorithm is used to calculate the sampling frequency offset coarse estimation of the time domain information of received signal;

[0035] 2) Based on the sampling frequency offset coarse estimation value, the local long synchronization signal is corrected by using the traversal method to generate the local long synchronization signal after frequency offset correction;

[0036] 3) The sliding correlation calculation is performed between the local long synchronization signal after frequency offset correction and the received signal to perform the accurate estimation of the sampling frequency offset, and then the starting point of the long synchronization signal in the received signal is determined to complete the weak signal detection.

[0037] In the above step 1), in the embodiment, the maximum likelihood algorithm is used to perform the sampling frequency offset coarse estimation on the received signal to reduce the difference between N' and N.

[0038] Specifically, the maximum likelihood algorithm used in the embodiment is run on the time domain information of the received signal, wherein the training signal is two continuous repeated symbols in the received signal, and the length of each symbol is L.

[0039] Suppose that the transmitted signal is x n , the sampling frequency of the transmitter is f s , the sampling frequency of the receiver is f s ', the transmitter sampling period T s =1 / f s , the receiver sampling period T s '=1 / f s ', Δf is the frequency offset, Δf=f s '-f s . The delay between the two symbols in the training signal is D sampling points.

[0040] 1.1) Calculate the delay correlation sum r of the training signal:

[0041]

[0042] In the formula, r n is the received complex baseband signal at the receiving end, x n is the transmitted signal, * is the conjugate, L is the length of each symbol, and the delay between the two continuous repeated symbols is D sampling points.

[0043] 1.2) The influence of the frequency offset Δf is reflected on According to the Euler formula and the complex angle operation formula, the phase rotation radian ∠r of the training signal is calculated:

[0044] e jθ =cosθ+jsinθ=a+bi

[0045] ∠r=arctan(r)=arCtan(b / a)

[0046] The two same symbol time intervals in the training signal are short, and the phase rotation angle θ is between -π / 2 and π / 2, so the phase rotation angle θ can be calculated according to the phase rotation radian, and is expressed as:

[0047]

[0048] When θ is positive, the phase rotates clockwise; when θ is negative, the phase rotates counterclockwise.

[0049] 1.3) The sampling frequency offset will cause the accumulation of the phase rotation angle, and when the phase rotation angle accumulates to -2π, the sampling point loss will occur; when the phase rotation angle accumulates to 2π, the sampling point increase will occur. According to the value of θ in step 1.2), the sampling number of the receiving end accumulated when a sampling clock offset is calculated as N, and then:

[0050]

[0051] N is the coarse estimation value of the sampling frequency offset.

[0052] In the above step 2), since the through-the-earth communication system uses the earth as a channel, the signal-to-noise ratio is low, and the received signal at the receiving end is greatly changed by the noise compared with the transmitted signal. The originally same two symbols in step 1.1) are also changed by the noise, so the calculation result of the coarse estimation value of the sampling frequency offset in step 1.3) will have a residual deviation. In the low-frequency transmission process, the transmission time is long, and the TTE communication needs a long synchronization signal to improve the received signal-to-noise ratio, so the influence of the residual deviation on the synchronization performance will be amplified.

[0053] In this embodiment, as shown in Figure 2 the local long synchronization signal after frequency offset correction is generated, including the following steps:

[0054] 2.1) Assuming that the actual number of samples accumulated in the receiving process is N', the value of N' is within [N-x, N+x], and the value of x is N / 10, and N is the coarse estimation value of the sampling frequency offset;

[0055] 2.2) N' is traversed within [N-x, N+x], and in each traversal, the local long synchronization signal is inserted or removed by one sampling point every N' sampling points according to the positive or negative value of the phase rotation angle θ between the training signals, to correct the residual deviation, and generate the local long synchronization signal after frequency offset correction. The local long synchronization signal after frequency offset correction can be used to overcome the influence of the residual deviation on the synchronization performance, thereby increasing the synchronization accuracy.

[0056] In step 2.2 above, when θ is a positive number, the phase rotates clockwise, resulting in lost samples; when θ is a negative number, the phase rotates counterclockwise, resulting in added samples. Therefore, in this embodiment, a sampling point is inserted or removed every N′ sampling points, depending on the positive or negative value of the phase rotation angle θ between the training signals. The following steps are included:

[0057] 2.2.1) When the phase rotation angle θ between training signals is positive, insert a sampling point every N′ sampling points, and take the average of the two sampling points before and after the inserted sampling value to correct the residual bias;

[0058] 2.2.2) When the phase rotation angle θ between training signals is negative, one sampling point is removed every N′ sampling points to correct the residual bias.

[0059] Take inserting a sampling point every N′ sampling points as an example:

[0060] Assume that the local long synchronization signal is [x1, x2……x n ], x1 is the first point in the local long synchronization signal. The signal after inserting the sampling point is [x1, x2……x N′-1 , x N′ ,y N′ , x N′+1 ...x 2N′-1 , x 2N′ ,y 2N′ , x 2N′+1 ...x n+m ], where y lN′ (l=1,2,…) is the inserted sampling point, and the sampling value is the average of the two sampling points before and after, such as:

[0061]

[0062] This generates 2x+1 local long synchronization signals that have been corrected for frequency offset.

[0063] In the above step 3), accurate estimation of the sampling frequency offset is performed, including the following steps:

[0064] 3.1) Perform sliding correlation calculation on each local long synchronization signal after frequency offset correction and the received signal, and record the N′ value and the first correlation peak K in each traversal;

[0065] The first correlation peak K is the correlation peak between the local long synchronization signal corrected for frequency offset and the received signal;

[0066] 3.2) According to the maximum first correlation peak K maxThe sampling frequency offset fine estimation value is determined, and the local long synchronization signal is corrected again by interpolation method according to the sampling frequency offset fine estimation value to generate the local long synchronization signal after long correlation algorithm.

[0067] Specifically, the maximum correlation peak value K max The corresponding N' value is the sampling frequency offset fine estimation value, denoted as N pre .

[0068] In step 3.2), the local long synchronization signal is corrected again by interpolation method according to the sampling frequency offset fine estimation value, specifically:

[0069] According to the positive or negative value of the inter-symbol phase rotation angle θ, one sampling point is inserted or removed every N pre sampling points of the local long synchronization signal; N pre is the sampling frequency offset fine estimation value.

[0070] When the inter-symbol phase rotation angle θ is positive, one sampling point is inserted every N pre sampling points of the local long synchronization signal; when the inter-symbol phase rotation angle θ is negative, one sampling point is removed every N pre sampling points of the local long synchronization signal.

[0071] In step 3) above, the starting point of the long synchronization signal in the received signal is determined, specifically:

[0072] The local long synchronization signal after long correlation algorithm is calculated by sliding correlation with the received signal, and it is assumed that the second correlation peak value is K re , the correlation threshold is determined according to the second correlation peak value, and if the second correlation peak value exceeds the correlation threshold in one sliding correlation detection, it is determined that the synchronization signal is detected. Wherein, the second correlation peak value K re is the correlation peak value between the local long synchronization signal after long correlation algorithm and the received signal.

[0073] In this embodiment, preferably, the correlation threshold is: 0.9K re .

[0074] Embodiment: In this embodiment, the long correlation algorithm of the present application is simulated and compared with the conventional sliding correlation algorithm to further illustrate the present application.

[0075] As shown in Figure 3 , the correlation value is plotted after the sliding correlation calculation of the local long synchronization signal without frequency offset correction and the received signal with frequency offset under the transmission condition of low frequency and low signal-to-noise ratio. Figure 3 The correlation peak value in the left long box is about 5 times the absolute value of the average value of the correlation value in the right box. As Figure 4The correlation value is drawn after the long correlation algorithm under the low frequency and low SNR transmission condition. Figure 4 The correlation peak in the left frame is about 6 times of the absolute value of the correlation value mean in the right frame. In the low frequency and low SNR earth channel, the long correlation algorithm using coarse estimation and fine estimation proposed by the present application has better SNR and lower false alarm probability than the direct synchronization using the conventional sliding correlation algorithm. In the simulation, the false alarm probability of the long correlation algorithm is about 6%, while the false alarm probability of the conventional sliding correlation algorithm is 7%, as shown in Table 1.

[0076] Table 1 False alarm probability of two synchronization algorithms

[0077] Algorithm type Conventional sliding correlation algorithm Long correlation algorithm False alarm times 35 30 False alarm probability 7％ 6％

[0078] The probability that the point of the correlation peak is completely correct is about 10.2% after the sliding correlation calculation of the local long synchronization signal without frequency offset correction and the received signal with frequency offset under the low frequency and low SNR transmission condition. The long correlation algorithm using coarse estimation and fine estimation proposed by the present application has better synchronization accuracy than the direct synchronization using the conventional sliding correlation algorithm. After the long correlation algorithm, the probability that the point of the correlation peak is completely correct is about 19.6% under the low frequency and low SNR transmission condition.

[0079] In the low frequency and low SNR earth channel, the long correlation algorithm using coarse estimation and fine estimation proposed by the present application has better SNR, lower false alarm probability and higher synchronization point accuracy than the synchronization using only the conventional sliding correlation algorithm, as shown in Table 2.

[0080] Table 2 Synchronization point accuracy of two synchronization algorithms

[0081] Algorithm type Conventional sliding correlation algorithm Long correlation algorithm Synchronization point position correct probability 10.2％ 19.6％

[0082] In summary, the existing sampling frequency offset coarse estimation algorithm can approximately estimate the sampling frequency offset of the transceiver. However, since the earth is used as the channel in the through-the-earth communication system, the SNR is low, the received signal at the receiving end is greatly changed by the noise, and the originally same two symbols are also changed by the noise. Therefore, there is a residual deviation between the calculation result N of the sampling frequency offset coarse estimation value and the actual accumulated sampling number N' of the sampling clock deviation at the receiving end, which will affect the synchronization performance. The long correlation algorithm using coarse estimation and fine estimation twice can reduce the influence of the residual deviation on the synchronization performance and improve the synchronization accuracy of the through-the-earth communication system under the low frequency and low SNR detection condition.

[0083] In an embodiment of the present application, a weak signal detection system of a ground electrode current field wireless through-the-earth is provided, which comprises:

[0084] a coarse estimation module, which calculates a sampling frequency offset coarse estimation of time domain information of the received signal by using a maximum likelihood algorithm;

[0085] a correction module, which corrects the local long synchronization signal by using a traversal method based on the sampling frequency offset coarse estimation value, to generate the local long synchronization signal after frequency offset correction;

[0086] a fine estimation module, which performs fine estimation of the sampling frequency offset by performing sliding correlation calculation on the local long synchronization signal after frequency offset correction and the received signal, and further determines a starting point of the long synchronization signal in the received signal to complete the weak signal detection.

[0087] In the above embodiment, the local long synchronization signal after frequency offset correction is generated by:

[0088] assuming that the actual accumulated sampling number of a sampling clock deviation during the receiving process is N', and the value of N' is within [N-x, N+x], and the value of x is N / 10, and N is the sampling frequency offset coarse estimation value;

[0089] traversing N' within [N-x, N+x], and in each traversal, inserting or removing a sampling point every N' sampling points according to the positive or negative value of the phase rotation angle θ between the training signals, to correct the residual deviation, and generate the local long synchronization signal after frequency offset correction.

[0090] In the above embodiment, the inserting or removing a sampling point every N' sampling points according to the positive or negative value of the phase rotation angle θ between the training signals includes:

[0091] when the phase rotation angle θ between the training signals is positive, inserting a sampling point every N' sampling points, and taking the average of the values of the two sampling points before and after the inserted sampling point as the value of the inserted sampling point, to correct the residual deviation;

[0092] when the phase rotation angle θ between the training signals is negative, removing a sampling point every N' sampling points, to correct the residual deviation.

[0093] In the above embodiment, the fine estimation of the sampling frequency offset includes:

[0094] performing sliding correlation calculation on each local long synchronization signal after frequency offset correction and the received signal, and recording the value of N' in each traversal and the first correlation peak value K;

[0095] determining the sampling frequency offset fine estimation value according to the maximum first correlation peak value K max determining the sampling frequency offset fine estimation value according to the maximum first correlation peak value K

[0096] The local long synchronization signal is corrected again by interpolation using the precise estimated value of the sampling frequency offset, including:

[0097] According to the positive and negative values ​​of the inter-symbol phase rotation angle θ, the local long synchronization signal is rotated every N pre Insert or remove a sampling point; N pre is the precise estimate of the sampling frequency bias;

[0098] The phase rotation angle θ between symbols is positive, and the local long synchronization signal is rotated every N pre When the phase rotation angle θ between training signals is negative, the local long synchronization signal is inserted into one sampling point every N pre Remove one sampling point from the sampling points.

[0099] In the above embodiment, determining the starting point of the long synchronization signal in the received signal includes:

[0100] Perform sliding correlation calculation on the local long synchronization signal after the long correlation algorithm and the received signal. Assume that the second correlation peak is K re , determine the correlation threshold based on the second correlation peak value, and if the second correlation peak value exceeds the correlation threshold value in a sliding correlation detection, it is determined that the synchronization signal is detected.

[0101] In this embodiment, preferably, the correlation threshold is: 0.9K re .

[0102] The system provided in this embodiment is used to execute the above-mentioned method embodiments. Please refer to the above-mentioned embodiments for specific processes and detailed contents, which will not be repeated here.

[0103] The computing device provided in the embodiment of the present application can be a terminal, which can include a processor, a communications interface, a memory, a display screen and an input device. The processor, the communications interface and the memory can communicate with each other through a communication bus. The processor is configured to provide computing and control capabilities. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program, which is executed by the processor to implement a method for detecting a weak signal in a ground electrode current field wirelessly through the ground. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium. The communications interface is configured to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, a management network, NFC (near field communication) or other technologies. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device can be a touch layer overlaid on the display screen, or a button, a trackball or a touchpad arranged on the shell of the computing device, or an external keyboard, a touchpad or a mouse, etc. The processor can call logical instructions in the memory.

[0104] In addition, the logical instructions in the memory described above can be implemented in the form of a software functional unit and sold or used as an independent product, which can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk and various media that can store program codes.

[0105] In an embodiment of the present application, a computer program product is provided, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the method provided by the above-mentioned method embodiments.

[0106] In an embodiment of the present application, a non-transitory computer-readable storage medium is provided, which stores server instructions. The computer instructions enable a computer to execute the method provided by the above-mentioned embodiments.

[0107] The computer readable storage medium provided by the above embodiment has similar implementation principles and technical effects to the method embodiment, and thus will not be described here again.

[0108] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate a device implemented in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that implements the functions specified in the flow or flows and / or block or blocks.

[0109] These computer program instructions can also be stored in a computer readable memory capable of directing the computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including instruction apparatus, which implements the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that implements the functions specified in the flow or flows and / or block or blocks.

[0110] These computer program instructions can also be loaded into a computer or other programmable data processing device, so that a series of operation steps are performed on the computer or other programmable device to generate a computer implemented process, so that the instructions executed on the computer or other programmable device provide a process for implementing the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that implements the functions specified in the flow or flows and / or block or blocks.

[0111] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; for example, the traversal range of N' can be set according to the sampling frequency, the sampling values of the inserted sampling points, and the parameters and threshold values of each step can be changed, and on the basis of the technical solutions of the present application, these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for detecting weak signals through wireless ground electrode current field, characterized in that: include: The maximum likelihood algorithm is used to calculate the rough estimation of the sampling frequency offset of the time domain information of the received signal; Based on the rough estimated value of the sampling frequency offset, the local long synchronization signal is corrected by using the ergodic method to generate a local long synchronization signal with frequency offset correction; The local long synchronization signal corrected for frequency offset is subjected to sliding correlation calculation with the received signal to accurately estimate the sampling frequency offset, thereby determining the starting point of the long synchronization signal in the received signal and completing weak signal detection. Generates a frequency-offset-corrected local long synchronization signal, including: Assume that the number of samples that actually accumulate a sampling clock deviation during the receiving process is , The value of Inside, x The value of N / 10, N is a rough estimate of the sampling frequency bias; Will exist Internal traversal, judgment Is it equal to N+x +1, if it is equal to, the precise estimate is determined and the local long synchronization signal is generated. If it is not equal to, the local long synchronization signal is rotated according to the phase angle between the training signals in each traversal. The positive and negative values ​​of each interval Insert or remove a sampling point from each sampling point to correct the residual deviation and generate a local long synchronization signal that has been corrected for frequency offset; if the generated local long synchronization signal is slidingly correlated with the received signal, the traversal ends; The training signal is two consecutive repeated symbols in the received signal, each symbol length is L; the delay between two consecutive repeated symbols is D sampling points, and the phase rotation angle between symbols is exist arrive The phase rotation angle for: ； Where, is the phase rotation radian between training signals; According to the phase rotation angle between training signals Positive or negative value, every interval Insert or remove a sampling point, including: Phase rotation angle between training signals When it is positive, each interval A sampling point is inserted into each sampling point, and the average of the two sampling points before and after the inserted sampling value is taken to correct the residual deviation; Phase rotation angle between training signals When it is a negative value, each interval Remove one sampling point from the sampling points to correct the residual bias.

2. The method for detecting weak signals by wirelessly penetrating the ground using the ground electrode current field as claimed in claim 1, characterized in that: Accurately estimate the sampling frequency offset, including: Each local long synchronization signal after frequency offset correction is subjected to sliding correlation calculation with the received signal, and the Value and first correlation peak K ; According to the maximum first correlation peak K max The sampling frequency deviation precise estimation value is determined, and the local long synchronization signal is corrected again by interpolation based on the sampling frequency deviation precise estimation value to generate the final local long synchronization signal.

3. The method for detecting weak signals by wirelessly penetrating the ground using the ground electrode current field as claimed in claim 2, characterized in that: The local long synchronization signal is corrected again by interpolation based on the precise estimation of the sampling frequency offset, including: According to the phase rotation angle between symbols The positive and negative values ​​of the local long synchronization signal N pre Insert or remove a sampling point; N pre is the precise estimate of the sampling frequency bias; Intersymbol phase rotation angle is a positive value, the local long synchronization signal is N pre Sampling points are inserted into one sampling point; Phase rotation angle between training signals When it is a negative value, the local long synchronization signal is N pre Remove one sampling point from each sampling point.

4. The method for detecting weak signals by wirelessly penetrating the ground using the ground electrode current field as claimed in claim 1, wherein: Determine the starting point of the long sync signal in the received signal, including: Perform sliding correlation calculation on the local long synchronization signal and the received signal. Assume that the second correlation peak is K re , determine the correlation threshold based on the second correlation peak value, and if the second correlation peak value exceeds the correlation threshold value in a sliding correlation detection, it is determined that the synchronization signal is detected.

5. The method for detecting weak signals by wirelessly penetrating the ground using the ground electrode current field as claimed in claim 4, characterized in that: The correlation threshold is: 0.9 K re .

6. A weak signal detection system for wirelessly penetrating the ground using a ground electrode current field, for implementing the weak signal detection method for wirelessly penetrating the ground using a ground electrode current field as claimed in any one of claims 1 to 5, characterized in that: include: A coarse estimation module uses a maximum likelihood algorithm to calculate a coarse estimate of the sampling frequency offset of the time domain information of the received signal; The correction module corrects the local long synchronization signal using the ergodic method based on the rough estimated value of the sampling frequency offset to generate a local long synchronization signal that has been corrected for the frequency offset; The precise estimation module performs sliding correlation calculation between the local long synchronization signal corrected for frequency offset and the received signal to accurately estimate the sampling frequency offset, thereby determining the starting point of the long synchronization signal in the received signal and completing weak signal detection.

7. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any one of the methods of claims 1 to 5 .

8. A computing device, characterized in that include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for executing any one of the methods according to claims 1 to 5.

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