Electric pole chuck detection technology based on passive low-frequency RFID and RLS adaptive antenna array

Abstract

The invention relates to an electric pole chuck detection technology based on a passive low-frequency RFID and an RLS adaptive antenna array. The method comprises the following steps: firstly, activating an RFID tag through an RFID reader, and sending a response by the tag after activation; then, adaptive antenna array design and signal processing based on RLS are carried out on a radio frequency channel formed by a passive antenna array, and the main steps are as follows: initialization is carried out, and an initial weight vector and an error covariance matrix are given; calculating an output signal using the current weight vector; calculating an error signal according to the difference between the output signal and the expected signal; updating the weight vector by using an RLS algorithm; calculating an array factor; and repeating the steps until a preset number of iterations is reached or a certain stop condition is met. And finally, the receiver decodes the signal to obtain the data, and then analyzes and displays the data. The weight of the antenna array is optimized and adjusted, and the precision of the passive low-frequency RFID in pole chuck detection is improved.

Description

Technical Field

[0001] The invention belongs to the field of pole chuck distance measurement, and specifically relates to a pole chuck detection technology based on passive low-frequency RFID and RLS adaptive antenna array. Background Art

[0002] At present, in order to stabilize cement poles and prevent them from falling over during the installation and construction of 10kV distribution lines, three plates are generally used for fixing. Pull plates, chuck plates or chassis are placed under the poles to prevent them from sinking. The pull plates use a pull wire to hold the poles to prevent them from falling over. There is a hole in the middle of the chuck plate to clamp the poles. It is buried underground, about halfway underground, to prevent the poles from being pulled up and sinking. Since there is currently no special pole chuck detection tool, it is difficult to check whether the distribution pole chuck is installed during the construction and acceptance of the distribution network line, and the depth is difficult to measure, which affects the reliability of the project acceptance results.

[0003] Radio Frequency Identification (RFID) is a contactless automatic identification technology that automatically identifies the target object and obtains relevant data through radio frequency signals. The identification work does not require human intervention. It can identify high-speed moving objects and multiple tags at the same time, and the operation is quick and convenient. The technology has the advantages of anti-magnetic, waterproof, high temperature resistance, large reading distance, long service life, large storage data capacity, changeable storage information, and data encryption that other automatic identification technologies do not have. Using passive low-frequency RFID detection technology, the detector can detect electronic tags buried at a depth of 2 meters. However, low-frequency RFID detection systems face many challenges in the environment of metal interference, low signal-to-noise ratio and high humidity. Summary of the invention

[0004] The technical problem to be solved by the present invention is that metal interference, low signal-to-noise ratio and high humidity environment at the pole construction site bring difficulties to the antenna electrical characteristic parameters and impedance matching of the RFID reader. To overcome the shortcomings of the prior art, a pole chuck detection technology based on passive low-frequency RFID and RLS adaptive antenna array is provided. The specific implementation steps of the present invention are as follows:

[0005] Step 1: Activate the RFID tag by sending a radio signal from the RFID reader to activate the RFID tag.

[0006] Step 2: Tag response, the RFID tag sends a response after activation.

[0007] Step 3: RF channel processing, filtering, amplification and mixing of passive antenna array signals.

[0008] Step 4: RLS-based adaptive antenna array design and signal processing, including initialization, calculation of output signal, calculation of error signal, updating weight vector, calculation of array factor and digital iteration.

[0009] Step 5: Receiver signal decoding: The RFID reader decodes the information after receiving the tag response.

[0010] Step 6: Data analysis and display. The RFID reader further processes the data, analyzes the depth information of the tag and displays it on the terminal.

[0011] The RLS-based adaptive antenna array design and signal processing described therein include the following specific steps:

[0012] s1: Initialization is completed by giving the initial weight vector and error covariance matrix;

[0013] s2: For each new input signal, use the current weight vector to calculate the output signal;

[0014] s3: Calculate the error signal based on the difference between the output signal and the expected signal;

[0015] s4: Use the RLS algorithm to update the weight vector and calculate the array factor;

[0016] Repeat the above steps until the preset number of iterations is reached or the stopping condition is met.

[0017] Preferably, the goal of the RLS algorithm is to minimize the square error. According to f(n) as the received signal, H(z) as the transfer function of the system, y(n) as the output, d(n) as the expected output, e(n) as the error, and b(n) as the updated filter coefficient, the variable ε(n) between the expected signal d(n) and the output signal y(n) in each iteration can be obtained as Where λ represents the forgetting factor 0.98<λ<1 and determines the weight given to previous data points.

[0018] Also because where b k represents the kth filter weight and f(nk) represents the kth input signal, resulting in a new equation for the sum of squared errors: Further deriving the equation

[0019] To minimize the smoothing error, take the derivative of ε(n) with respect to the filter weights and set it to zero, yielding the equation The matrix is ​​represented as R(n)b(n)=P(n).

[0020] Solving the equation for the filter weight b(n) is b(n) = R -1(n)P(n), R(n) and P(n) are described as and Where f(n) is the column vector of system outputs and d(n) is the desired output.

[0021] Preferably, the RLS algorithm iterates the R(n) matrix by incremental updating, and the specific process is R(n)=λR(n-1)+f(n)f T (n), where R(n-1) is the matrix of R(n) at the previous moment. We can further use the matrix inverse lemma to deduce To simplify the equation, we can define the vector of Kalman gains k(n) as Simplified equation is R -1 (n) = λ -1 [R -1 (n-1)-k(n)f T (n)R -1 (n-1)].

[0022] Similarly, P(n) can be defined as the incremental update equation P(n) = λP(n-1) + df(n). -1 The new definition of P(n) and P(n) can be rewritten as b(n)=b(n-1)+k(n)e(n) for the iterative calculation of b(n), where e(n) represents the error between the output signal y(n) and the expected signal d(n).

[0023] Preferably, given an initial weight vector and error covariance matrix, a value must be chosen for the first instance of R(n), the best choice being a matrix filled with correlation values ​​between antenna elements, which in the present case is equivalent to the covariance; the covariance between two elements is calculated as where cov(X,Y) represents the covariance between X and Y, X i represents the value of the i-th element of the data set X, represents the mean value of data set X, Y i represents the value of the i-th element of the data set Y, represents the mean of the data set Y, and M represents the number of values ​​in each data set.

[0024] For an antenna array, the data sets X and Y represent the signals received at two antenna elements. If the antenna array contains N elements, the N×N correlation matrix of the array is defined as Among them C ij Represents the correlation between the i-th and j-th elements of an array.

[0025] Preferably, the calculated output signal and the calculated error signal are obtained by e(n)=d(n)-y(n) and R-1 (n) = λ -1 [R -1 (n-1)-k(n)f T (n)R -1 (n-1)], where k(n) is the Kalman gain and f(n) represents the unweighted signal from each array element.

[0026] Preferably, the weight vector is updated using and

[0027] b(n)=b(n-1)+k(n)e(n) can obtain R(n) and the new filter weight b(n).

[0028] Preferably, the array factor is calculated by It is calculated that where b(n) represents the complex weight on the nth element of the array, d represents the spacing between elements (in wavelengths), n represents the number of elements, and θ represents the angle.

[0029] The calculation equation in vector form is AF = w T e -j2πdn×sin(θ) , where w is the N×1 array element weight column vector, n is the N×1 array element number column vector [0,1,...,N-1] T , θ is a 1×Q row vector, where Q represents the number of samples of the array factor AF, which itself is a 1×Q row vector.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] Originality, for the first time using passive radio frequency devices instead of manual detection, solved the problem of no special detection tools for cement pole chassis chucks. Achieved 100% chassis chuck installation rate. Can detect chassis chuck installation status anytime and anywhere, fundamentally solved the problem of chassis chucks "should be installed but not installed".

[0032] Intelligent, the first to embed RFID tags into cement products, achieving full coverage of the position coordinates and depth detection of the chassis chuck before and after completion. Intelligent means improve the quality and efficiency of acceptance. The device can be used for rapid and comprehensive detection, eliminating the problems of incomplete traditional manual spot checks and lax intermediate acceptance. The acceptance time is reduced from 30 minutes to 1 minute, and the efficiency is increased by 30 times.

[0033] Versatility: It is the first to adopt long-distance, anti-interference and miniaturized design, which completely solves the pain points of accurate detection in different environments and depths, and has strong versatility. It can accurately detect multi-source data, and the process acceptance + completion acceptance guarantees a 100% qualified rate of buried depth. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0035] Figure 1 This is a flow chart of the technical solution for pole chuck detection based on passive low-frequency RFID and RLS adaptive antenna array of the present invention.

[0036] Figure 2 It is a structural block diagram of the RLS algorithm in the pole chuck detection technology based on passive low-frequency RFID and RLS adaptive antenna array of the present invention.

[0037] Figure 3 It is an error curve between the signal received by the RLS algorithm at each iteration and the expected signal in the pole chuck detection technology based on the passive low-frequency RFID and the RLS adaptive antenna array of the present invention.

[0038] Figure 4 It is an array factor of the weight generated based on the RLS algorithm in the pole chuck detection technology based on the passive low-frequency RFID and RLS adaptive antenna array of the present invention.

[0039] Figure 5 This is a two-dimensional antenna array arrangement diagram in the pole chuck detection technology based on passive low-frequency RFID and RLS adaptive antenna array of the present invention.

[0040] Figure 6 It is the array factor generated based on the RLS algorithm in the pole chuck detection technology based on the passive low-frequency RFID and RLS adaptive antenna array of the present invention. DETAILED DESCRIPTION

[0041] For example, certain words are used in the specification and claims to refer to specific components. Those skilled in the art should understand that hardware manufacturers may use different terms to refer to the same component. This specification and claims do not use differences in names as a way to distinguish components, but use differences in the functions of components as the criteria for distinction. For example, "including" mentioned throughout the specification and claims is an open term, so it should be interpreted as "including but not limited to". "Approximately" means that within an acceptable error range, those skilled in the art can solve the technical problem within a certain error range and basically achieve the technical effect.

[0042] In the description of the present invention, it should be understood that the directions or positional relationships indicated by the terms "upper", "lower", "front", "rear", "left", "right", "horizontal", etc. are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operate in a specific direction, and therefore cannot be understood as limiting the present invention.

[0043] The following is a further detailed description of the pole chuck detection technology based on passive low-frequency RFID and RLS adaptive antenna array of the present invention in conjunction with the accompanying drawings, but it is not intended to limit the present invention.

[0044] like Figure 1 As shown, the technical solution process of the pole chuck detection technology based on passive low-frequency RFID and RLS adaptive antenna array is as follows:

[0045] First, the RFID tag is activated by sending out a radio signal through the RFID reader, which will activate nearby RFID tags.

[0046] Tag response, when the RFID tag is activated, it will send a response, which contains the tag's unique identification code and other possible data.

[0047] RF channel, which filters, amplifies and mixes passive antenna array signals;

[0048] RLS-based adaptive antenna array design and signal processing, including initialization, calculation of output signal, calculation of error signal, updating weight vector, calculation of array factor and digital iteration.

[0049] The RLS algorithm aims to minimize the square error. According to f(n) being the received signal, H(z) being the transfer function of the system, y(n) being the output, d(n) being the expected output, e(n) being the error, and b(n) being the updated filter coefficient, Figure 2 shown.

[0050] The variable ε(n) between the expected signal d(n) and the output signal y(n) in each iteration is Where λ represents the forgetting factor, which is usually chosen to be 0.98<λ<1, and determines how much weight to give to previous data points.

[0051] Also because where b k represents the kth filter weight and f(nk) represents the kth input signal, resulting in a new equation for the sum of squared errors: Further deriving the equation

[0052] To minimize the smoothing error, take the derivative of ε(n) with respect to the filter weights and set it to zero, yielding the equation The matrix is ​​represented as R(n)b(n)=P(n).

[0053] Solving the equation for the filter weight b(n) is b(n) = R -1 (n)P(n), R(n) and P(n) are described as and Where f(n) is the column vector of system outputs and d(n) is the desired output.

[0054] The RLS algorithm iterates the R(n) matrix through incremental updates. The specific process is as follows:

[0055] R(n)=λR(n-1)+f(n)f T (n), where R(n-1) is the matrix of R(n) at the previous moment. We can further use the matrix inverse lemma to deduce

[0056] To simplify the equation, we can define the vector of Kalman gains k(n) as Simplified equation is R -1 (n) = λ -1 [R -1 (n-1)-k(n)f T (n)R -1 (n-1)].

[0057] Similarly, P(n) can be defined as the incremental update equation P(n)=λP(n-1)+df(n).

[0058] Through these R -1 The new definition of P(n) and P(n) can be rewritten as b(n)=b(n-1)+k(n)e(n) for the iterative calculation of b(n), where e(n) represents the error between the output signal y(n) and the expected signal d(n).

[0059] The initialization described in the RLS-based adaptive antenna array design and signal processing steps is to give an initial weight vector and error covariance matrix. A value must be selected for the first instance of R(n). The best choice is a matrix filled with correlation values ​​between antenna elements. In the current case, this matrix is ​​equivalent to the covariance.

[0060] The formula for calculating the covariance between two elements is where cov(X,Y) represents the covariance between X and Y, X i represents the value of the i-th element of the data set X, represents the mean value of data set X, Y i represents the value of the i-th element of the data set Y, represents the mean of the data set Y, and M represents the number of values ​​in each data set.

[0061] For an antenna array, data sets X and Y represent the signals received at two antenna elements.

[0062] If the antenna array contains N array elements, the N×N correlation matrix of the array is defined as Among them C ij Represents the correlation between the i-th and j-th elements of an array.

[0063] The calculation output signal and the calculation error signal described in the RLS-based adaptive antenna array design and signal processing steps are specifically performed by e(n)=d(n)-y(n) and R -1 (n) = λ -1 [R -1 (n-1)-k(n)f T (n)R -1 (n-1)], where k(n) is the Kalman gain and f(n) represents the unweighted signal from each array element.

[0064] like Figure 3 Shown is the error between the received signal y(n) and the desired signal d(n) at each iteration, and shows how quickly the RLS algorithm converges.

[0065] In the calculation output signal and the calculation error signal described in the RLS-based adaptive antenna array design and signal processing steps, the specific method is to update the weight vector using Sum b(n)=b(n-1)+k(n)e(n) to obtain R(n) and new filter weights b(n).

[0066] The calculation method of the array factor described in the RLS-based adaptive antenna array design and signal processing steps is to use It is calculated that where b(n) represents the complex weight on the nth element of the array, d represents the spacing between elements (in wavelengths), n represents the number of elements, and θ represents the angle.

[0067] The calculation equation in vector form is AF = w T e -j2πdn×sin(θ) , where w is the N×1 array element weight column vector, n is the N×1 array element number column vector [0,1,...,N-1] T , θ is a 1×Q row vector, where Q represents the number of samples of the array factor AF, which itself is a 1×Q row vector.

[0068] Using the final calculated element weights, the array factor can be calculated. Figure 4 As shown, the array factor shows the direction in which the antenna array receives the signal and clearly shows the received desired signal and the cancelled interfering signal.

[0069] To test the RLS algorithm on a 2D antenna array, a similar process is followed. Figure 5 As shown, it is located in the xz plane.

[0070] The resulting array factor is Figure 6 As shown, the interference direction is shown in red and the desired direction is shown in green. It can be seen that the simulation experiment of the recursive least squares algorithm for determining the weights of the two-dimensional antenna array is effective.

[0071] Receiver signal decoding, after receiving the tag response, the RFID reader will decode the information and obtain the data.

[0072] Data analysis and display, the RFID reader further processes the data, performs database query and update operations, analyzes the returned tag information, infers the depth information of the tag, and finally displays the classification information of the pole chuck and chassis on the terminal.

[0073] The embodiments of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in the relevant technical field without departing from the purpose of the present invention.

Claims

1. A pole chuck detection technology based on passive low-frequency RFID and RLS adaptive antenna array, characterized in that: The specific implementation steps are: Step 1: Activate the RFID tag by sending a radio signal through the RFID reader; Step 2: Tag response, the RFID tag sends a response after activation; Step 3: RF channel processing, filtering, amplifying and mixing the passive antenna array signal; Step 4: RLS-based adaptive antenna array design and signal processing, including initialization, calculation of output signals, calculation of error signals, updating of weight vectors, calculation of array factors, and digital iteration; Step 5: Receiver signal decoding: the RFID reader decodes the information after receiving the tag response; Step 6: Data analysis and display. The RFID reader further processes the data, analyzes the depth information of the tag and displays it on the terminal.

2. The pole chuck detection method based on passive low-frequency RFID and RLS adaptive antenna array according to claim 1 is characterized in that: The RLS-based adaptive antenna array design and signal processing specifically include the following steps: s41: Initialization is completed by giving the initial weight vector and error covariance matrix; s42: For each new input signal, use the current weight vector to calculate the output signal; s43: Calculate the error signal based on the difference between the output signal and the expected signal; s44: Use the RLS algorithm to update the weight vector and calculate the array factor; Repeat the above steps until the preset number of iterations is reached or the stopping condition is met.

3. The pole chuck detection technology based on passive low-frequency RFID and RLS adaptive antenna array according to claim 2 is characterized in that: The goal of the RLS algorithm is to minimize the square error. According to f(n) as the received signal, H(z) as the transfer function of the system, y(n) as the output, d(n) as the expected output, e(n) as the error, and b(n) as the updated filter coefficient, the variable ε(n) between the expected signal d(n) and the output signal y(n) in each iteration is obtained as Where λ represents the forgetting factor 0.98<λ<1, and determines the weight given to the previous data points; Also because where b k represents the kth filter weight and f(nk) represents the kth input signal, resulting in a new equation for the sum of squared errors: Further deriving the equation To minimize the smoothing error, take the derivative of ε(n) with respect to the filter weights and set it to zero, yielding the equation The matrix is ​​represented as R(n)b(n)=P(n); Solving the equation for the filter weight b(n) is b(n) = R -1 (n)P(n), R(n) and P(n) are described as and Where f(n) is the column vector of system outputs and d(n) is the desired output.

4. The pole chuck detection technology based on passive low-frequency RFID and RLS adaptive antenna array according to claim 3 is characterized in that: The RLS algorithm iterates the R(n) matrix by incremental updating. The specific process is R(n)=λR(n-1)+f(n)f T (n), where R(n-1) is the matrix of R(n) at the previous moment. We can further use the matrix inverse lemma to deduce To simplify the equation, we can define the vector of Kalman gains k(n) as Simplified equation is R -1 (n) = λ -1 [R -1 (n-1)-k(n)f T (n)R -1 (n-1)]; Similarly, P(n) can be defined as the incremental update equation P(n) = λP(n-1) + df(n); through these R -1 The new definition of P(n) and P(n) can be rewritten as b(n)=b(n-1)+k(n)e(n) for the iterative calculation of b(n), where e(n) represents the error between the output signal y(n) and the expected signal d(n).

5. The pole chuck detection technology based on passive low-frequency RFID and RLS adaptive antenna array according to claim 4 is characterized in that: The given initial weight vector and error covariance matrix are initialized, and a value must be selected for the first instance of R(n). The best choice is a matrix filled with correlation values ​​between antenna elements, which is equivalent to the covariance in the current case. The formula for calculating the covariance between two elements is where cov(X,Y) represents the covariance between X and Y, X i represents the value of the i-th element of the data set X, represents the mean value of data set X, Y i represents the value of the i-th element of the data set Y, represents the mean value of the data set Y, and M represents the number of values ​​in each data set; For an antenna array, the data sets X and Y represent the signals received at two antenna elements. If the antenna array contains N elements, the N×N correlation matrix of the array is defined as Among them C ij Represents the correlation between the i-th and j-th elements of an array.

6. The pole chuck detection technology based on passive low-frequency RFID and RLS adaptive antenna array according to claim 5 is characterized in that: The calculated output signal and the calculated error signal are obtained by e(n)=d(n)-y(n) and R -1 (n) = λ -1 [R -1 (n-1)-k(n)f T (n)R -1 (n-1)], where k(n) is the Kalman gain and f(n) represents the unweighted signal from each array element.

7. The pole chuck detection technology based on passive low-frequency RFID and RLS adaptive antenna array according to claim 6 is characterized in that: The updated weight vector is Sum b(n)=b(n-1)+k(n)e(n) to obtain R(n) and new filter weights b(n).

8. The pole chuck detection technology based on passive low-frequency RFID and RLS adaptive antenna array according to claim 7 is characterized in that: The calculation array factor is obtained by It is calculated that, where b(n) represents the complex weight on the nth element of the array, d represents the spacing between the elements (in wavelengths), n represents the number of elements, and θ represents the angle; The calculation equation in vector form is AF = w T e -j2πdn×sin(θ) , where w is the N×1 array element weight column vector, n is the N×1 array element number column vector [0,1,...,N-1] T , θ is a 1×Q row vector, where Q represents the number of samples of the array factor AF, which itself is a 1×Q row vector.

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