A method for leading-edge signal detection and frame synchronization in the MAC layer of a battery cell voltage sensing network

Abstract

This invention discloses a method for leading-edge signal detection and frame synchronization in the MAC layer of a battery cell pressure sensing network. By employing a frame start delimiter (SFD) leading-edge signal vector detection optimization algorithm, the efficiency and accuracy of leading-edge detection are improved. Algorithms such as sliding window analysis, noise likelihood function, and leading-edge likelihood function are used to accurately detect the leading-edge signal vector, further enhancing detection efficiency and accuracy. Simultaneously, the Bayesian likelihood ratio method is used for leading-edge signal vector detection, significantly reducing the false positive and false negative rates and increasing the reliability of the detection results. This invention enables rapid leading-edge signal vector detection and frame synchronization, improving the real-time performance of data transmission and making it suitable for applications requiring high real-time performance, such as lithium battery management systems. The method proposed in this invention can be widely applied to the MAC layer of battery cell pressure sensing networks, providing strong support for establishing more efficient and reliable sensor network communication.

Description

Technical Field

[0001] This invention relates to the field of wireless sensor network technology, and more specifically to a method for front-end signal detection and frame synchronization in the MAC layer of a battery cell pressure sensor network. Background Technology

[0002] With global economic development, energy demand continues to increase. However, the reliance on traditional fossil fuel energy is becoming increasingly prominent. In this context, the development of new energy technologies has become a focus of global attention. Among these, energy storage battery packs, as an important form of renewable energy storage, are being increasingly widely used in many fields.

[0003] Currently, energy storage battery packs are widely used in electric vehicles, photovoltaic power plants, home energy storage, small emergency power supplies, and military applications. However, the widespread use of energy storage battery packs has also brought a series of safety issues. Due to the special properties of energy storage battery packs, such as high energy density and voltage, and their susceptibility to external factors such as temperature, humidity, and mechanical damage, ensuring the safe and stable operation of energy storage battery packs has become an urgent problem to be solved.

[0004] To better ensure battery pack safety and extend its lifespan, a battery monitoring system is built. Surface-mount sensor nodes are placed inside or outside the battery cells to acquire internal cell data, including voltage, temperature, current, electrochemical reactions, and physical state, enabling online monitoring, early warning, and management. When an anomaly occurs, timely control measures are taken to achieve dynamic safety control of the battery pack.

[0005] Cell pressure sensing networks are an important component of non-destructive battery monitoring systems, and they are widely used in battery application research and development and maintenance. The leading-edge signal detection and frame synchronization methods of the Medium Access Control (MAC) layer are also crucial technologies for ensuring real-time monitoring of cell pressure sensing networks.

[0006] In signal transmission, data packets are typically used for transmission. These packets contain data information, synchronization information, and transmission control information. To ensure the normal transmission and reception of data packets, the MAC layer of the cell pressure sensing network needs to perform operations such as decoding, synchronization, and detection to guarantee the stability and quality of transmission.

[0007] However, in traditional advanced signal detection and frame synchronization technologies, methods such as frequency domain analysis and time domain analysis are used to detect and synchronize data packets. While these methods can identify the packet header, locating the packet tail is extremely difficult. This deficiency means that traditional frame synchronization methods cannot adequately meet the real-time monitoring requirements of battery cell pressure sensing networks, thus failing to accurately monitor the real-time status of the battery cells. Summary of the Invention

[0008] The technical problem to be solved by the present invention is to provide a method for detecting leading-edge signals and synchronizing frames in the MAC layer of a battery cell pressure sensing network, so as to achieve high-efficiency and high-precision detection of leading-edge signals in wireless data frames of the battery cell sensor network, and ensure the reliability and stability of battery cell pressure sensing network communication.

[0009] To address the aforementioned technical problems, this invention provides a method for leading-edge signal detection and frame synchronization in the MAC layer of a battery cell voltage sensing network, comprising:

[0010] Step S1: The sensor node acquires the leading edge signal vector, noise vector, and wireless data frame vector;

[0011] Step S2: Based on the set sliding window size, perform window analysis on the wireless data frames acquired within the acquisition time and obtain the data vector within the window.

[0012] Step S3: Calculate the noise likelihood parameter of the probability density function of noise given the wireless data frame vector within a given window;

[0013] Step S4: The time of intercepting the leading edge signal vector is used to shift the wireless data frame obtained in step S2 to the left by the same number of units as the interception time, resulting in a new wireless data frame.

[0014] Step S5: Perform the sliding window analysis from step S2 again on the wireless data frame obtained in step S4 to obtain the data vector within the window.

[0015] Step S6: Capture the position of the leading edge signal vector, segment the data within the window obtained in step S5, and calculate the leading edge likelihood function.

[0016] Step S7: Calculate the threshold for the detection of the front edge of the frame start delimiter (SFD). If the threshold is greater than 1, it is determined that a front edge signal vector has appeared; otherwise, there is no front edge signal vector.

[0017] Step S8: Repeat steps S6 and S7 to detect the leading edge signal vector in the entire wireless data frame and traverse the entire window.

[0018] Preferably, in step S1, the leading edge signal vector acquired after time t is s(t) = [s1, s2, s3…s].t The noise vector is n(t) = [n1, n2, n3…n t The wireless data frame vector is x(t) = [x1, x2, x3…x]. t ];

[0019] Among them, the leading edge signal vector s(t) refers to a short segment of signal at the beginning of the waveform, the noise vector n(t) is the rest of the waveform excluding the leading edge signal vector, and the wireless data frame vector x(t) is the basic unit of transmission on the wireless channel.

[0020] Preferably, in step S3, the probability density function noise likelihood parameter P(n|X) of noise n(t) given a wireless data frame X(t) is calculated using the following formula:

[0021]

[0022] Where N is the sliding window size, ||n||2 is the 2-norm of the noise vector n(t), and σ 2 n represents the variance of the noise. i This represents the noise parameters collected at different times within a data window of size N, where n is the sliding window. average Let be the mean of all noise parameters collected in a sliding window of size N, and let e represent the natural exponential function.

[0023] Preferably, step S3 specifically includes:

[0024] Step S31, calculate the 2-norm of the noise vector n(t):

[0025]

[0026] Step S32: Calculate the sample mean n of all noise parameters collected in the data window of size N. average :

[0027]

[0028] Step S33: Calculate the variance σ of the noise. 2 :

[0029]

[0030] Preferably, in step S4, all wireless data frames X(t) in the original window are shifted to the left by a length equal to the intercepted leading edge signal vector time, resulting in a new wireless data frame Y(t):

[0031] Y(t) = X(t+t0).

[0032] Preferably, in step S5, a sliding window analysis is performed on the new wireless data frame Y(t) to obtain the data within the window as follows:

[0033] Y(t) = [y1, y2, y3…y N ].

[0034] Preferably, in step S6, let the position of the intercepted leading edge signal vector be K, and divide the waveform Y(t) into segments to obtain two segments: before and after the sample position K, where K = 0 represents the first time. The leading edge likelihood function P(s|y) is calculated using the following formula. K ):

[0035]

[0036] Wherein, P(y K |s) represents the waveform Y(t) exhibiting the observed value y given the known leading-edge signal vector s(t). K The probability density function, P(y) K |s=0) indicates that in the absence of leading-edge signal vector interference, the waveform Y(t) exhibits the existing observed value y. K The probability density function.

[0037] Preferably, step S6 specifically includes:

[0038] Step S61: Given the leading-edge signal vector s(t), calculate the waveform Y(t) as shown by the existing observed value y. K The probability density function P(y) K |s):

[0039]

[0040] Among them, y K Here, s is the input observation vector, ε is the leading-edge signal vector, |ε| represents the covariance matrix of the difference between the sample mean and the leading-edge signal vector, and |ε| represents the determinant of ε. K -s) T It means (y) K The transpose of the -s) matrix;

[0041] Step S62: Calculate the determinant of ε, |ε|:

[0042]

[0043] Here, sgn(x) is the sign function, which takes the value 1 when x is greater than 0, 0 when x is equal to 0, and -1 or -1 when x is less than 0. iiLet L be the diagonal elements of the N-dimensional matrix L; the covariance matrix ε is a real symmetric matrix, and its determinant |ε| represents the product of the eigenvalues ​​of ε.

[0044] Step S63: Calculate the waveform Y(t) under the condition of no leading-edge signal vector interference, which represents the existing observed value y. K The probability density function P(y) K |s=0):

[0045]

[0046] Among them, ||y K ||2 represents a wireless data frame y K The 2-norm;

[0047] Step S64, calculate the covariance matrix ε of the difference between the sample mean and the leading edge signal vector:

[0048] ε=E[(y K -s)(y K -s) T ]

[0049] Among them, (y K -s)(y K -s) T It is a matrix, and each element is y. Ki -s i and y jK -s K The product of i, j, and K, where i, j, and K range from 1 to N;

[0050] Step S65, calculate the frontier likelihood function:

[0051]

[0052] Preferably, in step S7, the threshold δ for detecting the start-of-frame delimiter (SFD) front edge is calculated using the following formula:

[0053]

[0054] Wherein, P(s>0|y K ) indicates that a new wireless data frame y has been observed. K In the case of , the probability that the corresponding noise vector s is greater than 0; P(s=0|y K ) indicates that a new wireless data frame y has been observed. K In the case of δ, the probability that the corresponding noise vector s is equal to 0 is determined; when δ>1, it is determined that a leading edge signal vector has appeared, otherwise there is no leading edge signal vector; if the leading edge signal vector exists, frame synchronization is performed through the leading edge signal vector.

[0055] Preferably, step S7 specifically includes:

[0056] Step S71, calculate the prior probability P(s=0) that the leading-edge signal vector s equals 0 and the prior probability P(s>0) that the leading-edge signal vector is greater than 0:

[0057] The t data points of Y(t) are classified according to whether the leading edge signal exists or not. Then, the proportion of the number of samples in which the leading edge signal vector s is equal to 0 or greater than 0 is counted to the total number of samples. This is the prior probability P(s=0) and P(s>0) that the leading edge signal does not exist.

[0058] Step S72, calculate the collected y K The probability P(y) K ):

[0059] P(y K )=P(y K |s=0)P(s=0)+P(y K |s>0)P(s>0)

[0060] Wherein, P(y K |s=0) represents the waveform Y(t) in the absence of a leading edge vector, where the waveform Y(t) reflects the existing observation y. K The probability density function, P(s=0) is the prior probability that the leading edge signal vector does not exist, P(y K |s>0) represents the waveform Y(t) exhibiting the current observed value y when there is no leading-edge signal vector greater than 0. K The probability density function is given by P(s>0), where P(s>0) is the prior probability that the leading edge signal vector is greater than 0.

[0061] Step S73, calculate the value of the observed new wireless data frame y. K In the case where the corresponding frontier vector s equals 0, the probability P(s=0|y K ):

[0062]

[0063] Step S74, calculate the value of the observed new wireless data frame y. K In the case of , the probability P(s>0|y) that the corresponding noise vector s is greater than 0 is . K ):

[0064]

[0065] The implementation of this invention has the following beneficial effects: This invention employs a frame start delimiter (SFD) leading-edge signal vector detection optimization algorithm, improving the efficiency and accuracy of leading-edge detection; this invention uses algorithms such as sliding window analysis, noise likelihood function, and leading-edge likelihood function to accurately detect leading-edge signal vectors, improving the efficiency and accuracy of leading-edge detection; simultaneously, it uses the Bayesian likelihood ratio method to achieve leading-edge signal vector detection, greatly reducing the false positive rate and false negative rate, and increasing the reliability of the detection results; this invention can quickly perform leading-edge signal vector detection and frame synchronization, improving the real-time performance of data transmission, and is applicable to applications requiring high real-time performance, such as lithium battery management systems; this invention can stably detect different leading-edge signal vectors and can adapt to the complex channel environment within systems such as lithium batteries, making sensor network communication more stable. This invention can be widely applied to the MAC layer of cell pressure sensing networks, providing strong support for establishing more efficient and reliable sensor network communication. Attached Figure Description

[0066] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0067] Figure 1 This is a flowchart illustrating a method for front-end signal detection and frame synchronization in the MAC layer of a battery cell pressure sensing network according to an embodiment of the present invention.

[0068] Figure 2 This is a schematic diagram illustrating the general process of a source node transmitting data packets to a target node in an embodiment of the present invention. Detailed Implementation

[0069] The following description of the embodiments is taken with reference to the accompanying drawings, which illustrate specific embodiments in which the invention can be implemented.

[0070] Please refer to Figure 1 As shown, this embodiment of the invention provides a method for leading-edge signal detection and frame synchronization in the MAC layer of a battery cell voltage sensing network, comprising the following steps:

[0071] Step S1: Let the leading edge signal vector acquired after time t be s(t) = [s1, s2, s3…s t The noise vector is n(t) = [n1, n2, n3…n t The wireless data frame vector is x(t) = [x1, x2, x3…x]. t ];

[0072] Among them, the leading edge signal vector s(t) refers to a short segment of signal at the beginning of the waveform, the noise vector n(t) is the rest of the waveform excluding the leading edge signal vector, and the wireless data frame vector x(t) is the basic unit of transmission on the wireless channel.

[0073] Step S2: Set the sliding window size to N, and process the wireless data frames x(t) = [x1, x2, x3…x1] collected in step S1. t Perform window analysis and obtain the data within the window: X(t) = [x1, x2, x3, ... x N ].

[0074] Step S3: Calculate the probability density function noise likelihood parameter P(n|X) of noise n(t) given a wireless data frame X(t). The calculation process is as follows:

[0075]

[0076] Where N is the sliding window size, ||n||2 is the 2-norm of the noise vector n(t), and σ 2 Let n be the variance of the noise. i This represents the noise parameters collected at different times within a data window of size N. average Let N be the mean of all noise parameters collected in the sliding window of size N, and let e represent the natural exponential function.

[0077] In one embodiment, the specific process of the algorithm in step S3 is as follows:

[0078] Step S31: Calculate the 2-norm of the noise vector n(t) as follows:

[0079]

[0080] Step S32: Calculate the sample mean n of all noise parameters collected in a data window of size N. average Specifically:

[0081]

[0082] Step S33: Calculate the variance σ of the noise. 2 Specifically:

[0083]

[0084] Where, n i This represents the noise parameters collected at different times within a data window of size N;

[0085] Step S4: Shift all wireless data frames X(t) in the original window to the left by a length equal to the intercepted leading edge signal vector time, to obtain a new wireless data frame Y(t). The calculation process is as follows:

[0086] Y(t) = X(t + t0)

[0087] Step S5: Perform sliding window analysis on the new wireless data frame Y(t) to obtain the data within the window:

[0088] Y(t) = [y1, y2, y3…y N ]

[0089] Step S6: Calculate the leading edge likelihood function P(s|y) by dividing the waveform Y(t) into two segments: before and after the sample position K (where K = 0 represents the first time). Let K be the position of the intercepted leading edge signal vector. K The formula s(t) is used to detect the leading edge signal vector, where s(t) is the leading edge signal vector, and the calculation process is as follows:

[0090]

[0091] Wherein, P(y K |s) represents the waveform Y(t) exhibiting the observed value y given the known leading-edge signal vector s(t). K The probability density function, P(y) K |s=0) indicates that in the absence of leading-edge signal vector interference, the waveform Y(t) exhibits the characteristics of the existing observation y. K The probability density function.

[0092] In one embodiment, the specific process of the algorithm in step S6 is as follows:

[0093] Step S61: Given the leading-edge signal vector s(t), the waveform Y(t) exhibits the existing observed value y. K The probability density function P(y) K |s) Specifically:

[0094]

[0095] Among them, y K Here, s is the input observation vector, ε is the leading-edge signal vector, |ε| represents the covariance matrix of the difference between the sample mean and the leading-edge signal vector, and |ε| represents the determinant of ε. K -s) T It means (y) K The transpose of the -s matrix.

[0096] Step S62: Calculate the determinant |ε| of ε. Specifically:

[0097]

[0098] Here, sgn(x) is the sign function, which takes the value 1 when x is greater than 0, 0 when x is equal to 0, and -1 or -1 when x is less than 0. ii Let L be the diagonal elements of an N-dimensional matrix L;

[0099] The covariance matrix ε is a real symmetric matrix, and its determinant |ε| represents the product of the eigenvalues ​​of ε. The calculation process for the determinant |ε| of an N-dimensional covariance matrix ε is as follows:

[0100] 1. Perform LU decomposition on matrix ε to obtain an upper triangular matrix U and a lower triangular matrix L, such that ε = LU;

[0101] 2. Calculate the product of the diagonal elements of matrix U, i.e. u ii These are the diagonal elements of an N-dimensional matrix U;

[0102] 3. Multiply the result obtained in the previous step by the sign of the sum of the diagonal elements of matrix L to obtain |ε|.

[0103] Step S63: Calculate the waveform Y(t) as observed by the existing value y in the absence of leading-edge signal vector interference. K The probability density function P(y) K Specifically, |s=0) means:

[0104]

[0105] Among them, ||y K ||2 represents a wireless data frame y K The 2-norm.

[0106] Step S64: Calculate the covariance matrix ε of the difference between the sample mean and the leading edge signal vector. Specifically:

[0107] ε=E[(y K -s)(y K -s) T ]

[0108] Among them, (y K -s)(y K -s) T It is a matrix, and each element is y. Ki -s i and y jK -s K The product of , where i, j, and K range from 1 to N.

[0109] Step S65: Calculating the frontier likelihood function specifically involves:

[0110]

[0111] Step S7: Calculate the threshold δ for the detection of the front edge of the Start of the Frame Delimiter (SFD). The calculation process is as follows:

[0112]

[0113] Wherein, P(s>0|y K ) indicates that a new wireless data frame y has been observed. K In the case of , the probability that the corresponding noise vector s is greater than 0, P(s=0|y K ) indicates that a new wireless data frame y has been observed. K In the case of , the probability that the corresponding noise vector s is equal to 0;

[0114] A leading edge signal vector is determined to exist when δ>1; otherwise, no leading edge signal vector exists. If a leading edge signal vector exists, frame synchronization can be performed using that leading edge signal vector.

[0115] In one embodiment, the specific process of the algorithm in step S7 is as follows:

[0116] Step S71: Calculate the prior probability P(s=0) that the leading-edge signal vector s is equal to 0 and the prior probability P(s>0) that the leading-edge signal vector is greater than 0. The specific implementation process is as follows:

[0117] The t data points of Y(t) are classified according to whether the leading edge signal exists or not. Then, the proportion of the number of samples in which the leading edge signal vector s is equal to 0 or greater than 0 is counted to the total number of samples. This is the prior probability P(s=0) and P(s>0) that the leading edge signal does not exist.

[0118] Step S72: Calculate the collected y K The probability P(y) K Specifically:

[0119] P(y K )=P(y K |s=0)P(s=0)+P(y K |s>0)P(s>0)

[0120] Where P(y) K |s=0) represents the waveform Y(t) in the absence of a leading edge vector, where the waveform Y(t) reflects the existing observation y. K The probability density function, P(s=0) is the prior probability that the leading edge signal vector does not exist, P(y K|s>0) represents the waveform Y(t) exhibiting the current observed value y when there is no leading-edge signal vector greater than 0. K The probability density function is P(s>0), which is the prior probability that the leading edge signal vector is greater than 0.

[0121] Step S73: Calculate the value of the observed new wireless data frame y. K In the case where the corresponding frontier vector s is equal to 0, the probability P(s=0|y) is equal to 0. K Specifically:

[0122]

[0123] Step S74: Calculate the value of the observed new wireless data frame y. K In the case of , the probability P(s>0|y) that the corresponding noise vector s is greater than 0 is . K Specifically:

[0124]

[0125] Step S8: Repeat steps S6 and S7 to detect the leading edge signal vector in the entire wireless data frame, traversing the entire window. When K = t, the detection ends.

[0126] See Figure 2 In this invention, the patch sensor node is deployed outside the battery module and communicates with the battery management system through the cell sensor network.

[0127] As can be seen from the above description, compared with the prior art, the beneficial effects of this invention are as follows: This invention employs an optimized algorithm for leading-edge signal vector detection (SFD), improving the efficiency and accuracy of leading-edge detection; this invention uses algorithms such as sliding window analysis, noise likelihood function, and leading-edge likelihood function to accurately detect leading-edge signal vectors, improving the efficiency and accuracy of leading-edge detection; simultaneously, it uses the Bayesian likelihood ratio method to achieve leading-edge signal vector detection, greatly reducing the false positive rate and false negative rate, and increasing the reliability of the detection results; this invention can quickly perform leading-edge signal vector detection and frame synchronization, improving the real-time performance of data transmission, and is suitable for applications requiring high real-time performance, such as lithium battery management systems; this invention can stably detect different leading-edge signal vectors and can adapt to the complex channel environment within systems such as lithium batteries, making sensor network communication more stable. This invention can be widely applied to the MAC layer of cell pressure sensing networks, providing strong support for establishing more efficient and reliable sensor network communication.

[0128] The above description is merely a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. Therefore, any equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for leading-edge signal detection and frame synchronization in the MAC layer of a battery cell voltage sensing network, characterized in that, include: Step S1: The sensor node acquires the leading edge signal vector, noise vector, and wireless data frame vector; Step S2: Based on the set sliding window size, perform window analysis on the wireless data frames acquired within the acquisition time and obtain the data vector within the window. Step S3: Calculate the noise likelihood parameter of the probability density function of noise given the wireless data frame vector within a given window; Step S4: The time of intercepting the leading edge signal vector is used to shift the wireless data frame obtained in step S2 to the left by the same number of units as the interception time, resulting in a new wireless data frame. Step S5: Perform the sliding window analysis from step S2 again on the wireless data frame obtained in step S4 to obtain the data vector within the window. Step S6: Capture the position of the leading edge signal vector, segment the data within the window obtained in step S5, and calculate the leading edge likelihood function. Step S7: Calculate the threshold for the detection of the front edge of the frame start delimiter (SFD). If the threshold is greater than 1, it is determined that a front edge signal vector has appeared; otherwise, there is no front edge signal vector. Step S8: Repeat steps S6 and S7 to detect the leading edge signal vector in the entire wireless data frame and traverse the entire window.

2. The method according to claim 1, characterized in that, In step S1, let the leading-edge signal vector acquired after time t be s(t)=[s1,s2,s3…s t The noise vector is n(t) = [n1, n2, n3…n t The wireless data frame vector is x(t) = [x1, x2, x3…x]. t ]; Among them, the leading edge signal vector s(t) refers to a short segment of signal at the beginning of the waveform, the noise vector n(t) is the rest of the waveform excluding the leading edge signal vector, and the wireless data frame vector x(t) is the basic unit of transmission on the wireless channel.

3. The method according to claim 1, characterized in that, In step S3, the probability density function noise likelihood parameter P(n|X) of noise n(t) given a wireless data frame X(t) is calculated using the following formula: Where N is the sliding window size, ||n||2 is the 2-norm of the noise vector n(t), and σ 2 n represents the variance of the noise. i This represents the noise parameters collected at different times within a data window of size N, where n is the sliding window. average Let be the mean of all noise parameters collected in a sliding window of size N, and let e represent the natural exponential function.

4. The method according to claim 3, characterized in that, Step S3 specifically includes: Step S31, calculate the 2-norm of the noise vector n(t): Step S32: Calculate the sample mean n of all noise parameters collected in the data window of size N. average : Step S33: Calculate the variance σ of the noise. 2 :

5. The method according to claim 1, characterized in that, In step S4, all wireless data frames X(t) in the original window are shifted to the left by a length equal to the intercepted leading edge signal vector time, resulting in a new wireless data frame Y(t): Y(t) = X(t+t0).

6. The method according to claim 5, characterized in that, In step S5, a sliding window analysis is performed on the new wireless data frame Y(t) to obtain the data within the window: Y(t)=[y1,y2,y3…y N ]。 7. The method according to claim 6, characterized in that, In step S6, let the position of the intercepted leading-edge signal vector be K. The waveform Y(t) is segmented to obtain two segments: before and after the sample position K, where K = 0 represents the first time. The leading-edge likelihood function P(s|y) is calculated using the following formula. K ): Wherein, P(y K |s) represents the waveform Y(t) exhibiting the observed value y given the known leading-edge signal vector s(t). K The probability density function, P(y) K |s=0) indicates that in the absence of leading-edge signal vector interference, the waveform Y(t) exhibits the existing observed value y. K The probability density function.

8. The method according to claim 7, characterized in that, Step S6 specifically includes: Step S61: Given the leading-edge signal vector s(t), calculate the waveform Y(t) as shown by the existing observed value y. K The probability density function P(y) K |s): Among them, y K Here, s is the input observation vector, ε is the leading-edge signal vector, |ε| represents the covariance matrix of the difference between the sample mean and the leading-edge signal vector, and |ε| represents the determinant of ε. K -s) T It means (y) K The transpose of the -s) matrix; Step S62: Calculate the determinant of ε, |ε|: Here, sgn(x) is the sign function, which takes the value 1 when x is greater than 0, 0 when x is equal to 0, and -1 or -1 when x is less than 0. ii Let L be the diagonal elements of the N-dimensional matrix L; the covariance matrix ε is a real symmetric matrix, and its determinant |ε| represents the product of the eigenvalues ​​of ε. Step S63: Calculate the waveform Y(t) under the condition of no leading-edge signal vector interference, which represents the existing observed value y. K The probability density function P(y) K |s=0): Among them, ||y K ||2 represents a wireless data frame y K The 2-norm; Step S64, calculate the covariance matrix ε of the difference between the sample mean and the leading edge signal vector: ε=E[(y K -s)(y K -s) T ] Among them, (y K -s)(y K -s) T It is a matrix, and each element is y. Ki -s i and y jK -s K The product of i, j, and K, where i, j, and K range from 1 to N; Step S65, calculate the frontier likelihood function:

9. The method according to claim 8, characterized in that, In step S7, the threshold δ for detecting the start-of-frame delimiter (SFD) front edge is calculated using the following formula: Wherein, P(s>0|y K ) indicates that a new wireless data frame y has been observed. K In the case of , the probability that the corresponding noise vector s is greater than 0; P(s=0|y K ) indicates that a new wireless data frame y has been observed. K In the case of δ, the probability that the corresponding noise vector s is equal to 0 is determined; when δ>1, it is determined that a leading edge signal vector has appeared, otherwise there is no leading edge signal vector; if the leading edge signal vector exists, frame synchronization is performed through the leading edge signal vector.

10. The method according to claim 9, characterized in that, Step S7 specifically includes: Step S71, calculate the prior probability P(s=0) that the leading-edge signal vector s equals 0 and the prior probability P(s>0) that the leading-edge signal vector is greater than 0: The t data points of Y(t) are classified according to whether the leading edge signal exists or not. Then, the proportion of the number of samples in which the leading edge signal vector s is equal to 0 or greater than 0 is counted to the total number of samples. This is the prior probability P(s=0) and P(s>0) that the leading edge signal does not exist. Step S72, calculate the collected y K The probability P(y) K ): P(y K )=P(y K |s=0)P(s=0)+P(y K |s>0)P(s>0) Wherein, P(y K |s=0) represents the waveform Y(t) in the absence of a leading edge vector, where the waveform Y(t) reflects the existing observation y. K The probability density function, P(s=0) is the prior probability that the leading edge signal vector does not exist, P(y K |s>0) represents the waveform Y(t) exhibiting the current observed value y when there is no leading-edge signal vector greater than 0. K The probability density function is given by P(s>0), where P(s>0) is the prior probability that the leading edge signal vector is greater than 0. Step S73, calculate the value of the observed new wireless data frame y. K In the case where the corresponding frontier vector s equals 0, the probability P(s=0|y K ): Step S74, calculate the value of the observed new wireless data frame y. K In the case of , the probability P(s>0|y) that the corresponding noise vector s is greater than 0 is . K ):

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