A GNSS anti-narrowband interference method and device

Abstract

The present invention belongs to the field of satellite communication technology, and discloses a GNSS anti-narrowband interference method and device, the device comprising: a frequency downconverter, used to convert the received original signal r(t) into a signal of an intermediate frequency; an analog-to-digital converter, used to sample the signal of the intermediate frequency at a predetermined sampling frequency, and display the observed value at the sampling k to form a digital signal r(k); an adaptive notch filter, used to filter out part of the narrowband interference of the digital signal r(k) to obtain a signal sum product neural network interference identification predictor, used to parse out the narrowband interference signal in the signal subtractor, used to obtain an effective signal z(k) by subtracting the narrowband interference signal from the signal. The present invention adopts a method of cascading a notch filter and a neural network predictor, which can suppress narrowband interference at multiple frequency points, improve the anti-interference performance of the satellite navigation receiver, and effectively improve the positioning and timing accuracy in complex environments.

Description

Technical Field

[0001] The present invention belongs to the field of satellite communication technology, and in particular relates to a GNSS narrowband interference resistance method and device. Background Art

[0002] The fragility of satellite navigation signals makes them sensitive to interference and are easily affected by various intentional and unintentional interference. In practice, the working environment of the receiver is relatively complex, and there may be a lot of time-varying environmental interference around, such as additive interference, multiplicative interference, multipath interference, multi-user interference, etc. In order to improve the robustness of the system, the performance and flexibility of interference suppression must be improved.

[0003] Suppressive interference includes narrowband and broadband interference. The frequency band occupied by narrowband interference is much smaller than the spectrum width of the spread spectrum signal. It includes single-tone, multi-tone interference, swept-frequency interference, narrow-band Gaussian interference and other single-frequency or narrow-bandwidth interference. The frequency band occupied by broadband interference is equivalent to the spectrum width of the spread spectrum signal. It includes pulse-type interference, broadband Gaussian white noise, etc. According to the stability of the interference, it can be divided into stable interference with stable or slowly changing spectrum characteristics and non-stationary interference with drastic changes. The former includes single-tone multi-tone interference with constant frequency and narrow-band Gaussian noise, and the latter includes swept-frequency interference with time-varying frequency and sine wave pulses.

[0004] Since narrowband interference is easily generated in the actual environment and has a significant impact on the performance of satellite navigation receivers, the prior art suppresses narrowband interference by setting filters.

[0005] Traditional filters only filter out one or some specific frequencies, but adaptive filters avoid this defect. Adaptive filters are special filters that can use some adaptive algorithms to change filter parameters or structures according to changes in the environment. The structures commonly used in existing time domain adaptive filters are FIR and IIR.

[0006] Adaptive FIR filter is a linear prediction filter that can automatically adjust according to interference. Due to its own feedback structure, adaptive IIR filter can achieve the same notch bandwidth and approximate suppression effect by repeatedly using fewer orders. It can be divided into three types according to the structure: direct type, lattice type and cascade type.

[0007] However, the existing FIR and IIR notch filters still have the following defects:

[0008] In order to better suppress narrowband interference in spread spectrum communication systems, traditional FIR filters need to design a sufficiently sharp band-stop filter, hoping to filter out narrowband interference separately, and its center frequency needs to be consistent with the frequency of the suppressed interference signal. In order to achieve the required sharp cutoff characteristics, a higher order is usually required to update the tap weight coefficients, which will increase the amount of calculation and is not cost-effective.

[0009] For direct IIR filters, it is hoped that the bandwidth of the designed notch filter is as small as possible, and only zero notches are generated at the interference frequency point. The depth should be deep enough to suppress the interference. However, the depth and width of the notch filter are mutually restricted. The depth of the notch filter increases with the increase of the width, and the depth of the narrow bandwidth is not deep. Therefore, when designing the bandwidth parameters of the notch filter, a trade-off needs to be made to ensure that the notch filter reaches a certain depth under a narrow bandwidth. Summary of the invention

[0010] The purpose of the present invention is to provide a GNSS narrowband interference resistance method and device to solve the above technical problems.

[0011] In order to achieve the above object, the present invention adopts the following technical solution:

[0012] In a first aspect, the present invention provides a GNSS narrowband interference prevention device, comprising:

[0013] A down-converter, used for converting the received original signal r(t) into a signal of an intermediate frequency;

[0014] an analog-to-digital converter, used for sampling the signal of the intermediate frequency at a predetermined sampling frequency and displaying an observation value at a sampling point k to form a digital signal r(k);

[0015] The adaptive notch filter is used to filter out some narrowband interference from the digital signal r(k) to obtain a signal

[0016] Sum-product neural network interference identification predictor for parsing out the signal Narrowband interference signal in

[0017] Subtractor, used to pass the signal Subtract narrowband interference signal Get the effective signal z(k).

[0018] A further improvement of the present invention is that the original signal r(t) is expressed as:

[0019] r(t)=s(t)+j(t)+n(t)

[0020] Where s(t) is the effective signal of the GNSS signal after bandpass filtering, amplification, and down-conversion to the intermediate frequency; j(t) is the narrowband interference signal, which is less than 10% of the GNSS signal bandwidth; n(t) is the variance of σ 2 Gaussian white noise.

[0021] A further improvement of the present invention is that the adaptive notch filter is a second-order IIR lattice adaptive notch filter; the transfer function of the second-order IIR lattice adaptive notch filter is:

[0022]

[0023] Where X(z) and Y(z) represent the z-transform of the input r(k) and output y(k) of the notch filter, respectively; α is the pole control factor of the notch filter; and β is the zero control factor of the notch filter.

[0024] A further improvement of the present invention is that: output y(k) (i.e. output for:

[0025] y(k)=W T θ(k)

[0026] in,

[0027] θ(k)=[r(k)r(k-1)r(k-2)y(k-1)y(k-2)] T

[0028]

[0029] Notch center frequency ω0 = arccos (-β);

[0030] The guidelines require:

[0031] J(β)=E(|y(k)| p )

[0032] Where E is the expectation, when p = 2, it is the LMS minimum mean square error criterion, when p = 1, it is the sign-LMS minimum mean square error criterion;

[0033] Update the coefficient β according to the steepest descent algorithm

[0034]

[0035] Among them, the coefficient μ represents the size of the step size, The instantaneous value of is used to replace the statistical average value, and we get:

[0036]

[0037] Among them, the sign function sgn(x) = x / |x|, p = 1, and the expression of the sign-LMS adaptive algorithm coefficient β is:

[0038] β(k+1)=β(k)-μsgn{[sgny(k)]·g(k)}

[0039] g(k) represents the gradient signal, which is expressed as:

[0040] g(k)≈η(k) T ω

[0041] η(k)=[x(k-1)y(k-1)] T ,ω=[-11] T .

[0042] A further improvement of the present invention is that: the sum-product neural network interference identification predictor comprises an input layer, two hidden layers and an output layer;

[0043] The number of input layer nodes is N, x1 to x N is the discrete value of the received signal at N moments, the number of hidden layer nodes is M, the connection weight between the first hidden layer and the input layer is fixed to 1, and the hidden layer threshold is θ j , the output is y j ; The connection weight between the second hidden layer and the first hidden layer is w j , the output is u.

[0044] A further improvement of the present invention is that the calculation steps of the sum-product neural network interference identification predictor are as follows:

[0045] Step 1: Initialization of weight vector:

[0046] Set all weights and thresholds of the sum-product neural network to small uniformly distributed random numbers;

[0047] Step 2: Based on the input vector, calculate the output y of the first hidden layer at the nth iteration j and the second hidden layer output u;

[0048]

[0049]

[0050] Calculate the output layer output o:

[0051]

[0052] Step 3: Learning Process

[0053] The BP algorithm is used to minimize the square error cost function to minimize the sum of square errors between the neural network output o(n) and the expected output d(n), which is defined as:

[0054]

[0055] Using the gradient descent method, calculate the change Δw of the connection weights of the hidden layer j :

[0056]

[0057] Using the gradient descent method, calculate the change in threshold Δθ j :

[0058]

[0059] Step 4: Iteration

[0060] Repeat the second and third steps to iteratively update the connection weights and thresholds of the summed neural network until the maximum number of learning times is reached or the error E(n) of the neural network is less than the expected threshold value η, and the final output is obtained.

[0061] A further improvement of the present invention is that the expected threshold value η is 2.5.

[0062] In a second aspect, the present invention provides a GNSS narrowband interference resistance method, comprising:

[0063] Obtaining the original signal r(t), and transforming the received original signal r(t) into a signal of an intermediate frequency;

[0064] The signal of the intermediate frequency is sampled at a predetermined sampling frequency, and the observed value is displayed at sampling k to form a digital signal r(k);

[0065] Filter out some narrowband interference from the digital signal r(k) to obtain a signal

[0066] Analyze the signal Narrowband interference signal in

[0067] By signal Subtract narrowband interference signal Get the effective signal z(k).

[0068] A further improvement of the present invention is that: the digital signal r(k) is filtered to remove part of the narrowband interference to obtain a signal The steps are performed by a second-order IIR lattice adaptive notch filter; the transfer function of the second-order IIR lattice adaptive notch filter is:

[0069]

[0070] Where X(z) and Y(z) represent the input r(k) and output of the notch filter, respectively. z-transform, α is the pole control factor of the notch filter; β is the zero control factor of the notch filter.

[0071] A further improvement of the present invention is that: the signal is parsed Narrowband interference signal in The steps are performed by a sum product neural network interference recognition predictor; the sum product neural network interference recognition predictor includes an input layer, two hidden layers and an output layer;

[0072] The number of input layer nodes is N, x1 to x N is the discrete value of the received signal at N moments, the number of hidden layer nodes is M, the connection weight between the first hidden layer and the input layer is fixed to 1, and the hidden layer threshold is θ j , the output is y j ; The connection weight between the second hidden layer and the first hidden layer is w j , the output is u.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] The present invention provides a GNSS anti-narrowband interference method and device, the device comprising: a down-converter, used to convert a received original signal r(t) into a signal of an intermediate frequency; an analog-to-digital converter, used to sample the signal of the intermediate frequency at a predetermined sampling frequency, and display the observed value at the sampling point k to form a digital signal r(k); an adaptive notch filter, used to filter out part of the narrowband interference from the digital signal r(k) to obtain a signal Sum-product neural network interference identification predictor for parsing out the signal Narrowband interference signal in Subtractor, used to pass the signal Subtract narrowband interference signal The effective signal z(k) is obtained. The present invention adopts a method of cascading a notch filter and a neural network predictor, which can suppress narrowband interference of multiple frequency points and improve the anti-interference performance of the satellite navigation receiver; the present invention can increase the number of satellite observations in the signal receiving stage, increase the signal-to-noise ratio, and provide more effective data for subsequent navigation positioning and solution; the present invention can effectively improve the positioning and timing accuracy in complex environments.

[0075] The present invention improves the transfer function of the notch filter and reduces the bandwidth of the notch filter by multiples through data downsampling, and the reduction multiple and the downsampling multiple are kept consistent;

[0076] The present invention utilizes an IIR grid-type adaptive notch filter and adopts a sign-LMS adaptive algorithm to filter out part of the narrowband interference;

[0077] The present invention proposes an interference adaptive algorithm based on a sum-product neural network, which iteratively adjusts the adaptive weights and thresholds by means of hidden layer product and summation to obtain an effective interference signal;

[0078] The invention cascades an IIR lattice notch filter and an interference identification predictor based on a sum-product neural network to filter out narrowband interference signals twice, and finally obtains accurate positioning information.

[0079] The present invention aims to increase the cascade structure of an IIR grid adaptive notch filter and a neural network interference identification predictor in the original GNSS receiver signal receiving stage, and utilize the sign-LMS adaptive algorithm and the sum-product neural network algorithm to suppress the narrowband interference of multiple frequency points and improve the timing and positioning accuracy of the satellite navigation receiver in complex environments.

[0080] The cascaded adaptive notch filter of the present invention has a reasonably designed notch filter bandwidth, reduces the loss of useful signals, and ensures that narrowband interference is effectively filtered out; the sum-product neural network interference identification proposes a new neural network structure that can effectively identify interference signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0082] Figure 1 It is a structural schematic diagram of a GNSS narrowband interference prevention device of the present invention;

[0083] Figure 2 It is a structural diagram of a second-order IIR lattice adaptive notch filter;

[0084] Figure 3 It is the flow chart of Sign-LMS adaptive algorithm;

[0085] Figure 4 Neural network diagram for the sum-product neural network interference identification predictor. DETAILED DESCRIPTION

[0086] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present invention can be combined with each other without conflict.

[0087] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0088] Explanation of relevant terms:

[0089] GNSS—Global Navigation Satellite System, global satellite navigation system.

[0090] IIR—Infinite impulse response, infinite impulse response.

[0091] LMS—Least Mean Square, least mean square algorithm.

[0092] Narrowband interference signals are interference signals with less than 10% of the GNSS signal bandwidth.

[0093] After the satellite navigation receiving terminal receives the GNSS signal, it can obtain the navigation message and pseudo-range observation information of the received satellite, thereby constructing the pseudo-range observation equation group and calculating the specific position information. However, due to the natural environment or human behavior, the received signal may be interfered, resulting in a complex amount of received signal information, which ultimately leads to inaccurate positioning and failure to achieve the expected effect.

[0094] The present invention proposes a GNSS anti-narrowband interference method and device, improves the notch filter in the GNSS receiver, and embeds an interference identification algorithm to effectively identify the narrowband interference in the signal received by the GNSS receiver, and improves the accuracy of the positioning result by filtering out the narrowband interference.

[0095] Example 1

[0096] See also Figure 1 As shown, the present invention provides a GNSS anti-narrowband interference device, including a downconverter, an analog-to-digital converter (ADC), an adaptive notch filter (ANF) and a sum-product neural network interference identification predictor (∑-∏NN).

[0097] The present invention provides a GNSS narrowband interference resistance device, the main innovations of which include two parts: 1. an improved adaptive notch filter ANF, and 2. a sum-product neural network interference identification predictor ∑-∏NN.

[0098] In the present invention, the received original signal r(t) can be expressed as:

[0099] r(t)=s(t)+j(t)+n(t)

[0100] Where s(t) is the effective signal of the GNSS signal after bandpass filtering, amplification, and down-conversion to the intermediate frequency. j(t) is the narrowband interference signal, which is less than 10% of the GNSS signal bandwidth, and n(t) is the variance σ 2 Gaussian white noise.

[0101] The down converter is a circuit that converts the received original signal r(t) into a signal of an intermediate frequency. In a specific implementation, the intermediate frequency may be 4 MHz.

[0102] The analog-to-digital converter ADC samples the signal converted to the intermediate frequency at a predetermined sampling frequency and displays the observed value at sampling k to form a digital signal r(k). The cap number (^) is used to display the value of the predicted signal. First, the IIR cascade adaptive notch filter ANF is used to filter out part of the narrowband interference and obtain the signal Then the signal is analyzed using the sum-product neural network interference recognition predictor ∑-∏NN The narrowband interference signal still exists in By signal Subtract narrowband interference signal The effective signal z(k) after filtering out narrowband interference is obtained as follows:

[0103]

[0104] See also Figure 2 As shown in FIG. 1 , the structure of the second-order IIR grid adaptive notch filter (ANF) used in the present invention is shown. The transfer function of the second-order IIR grid adaptive notch filter is:

[0105]

[0106] Where X(z) and Y(z) represent the input r(k) and output of the notch filter, respectively. z-transform, α is the pole control factor of the notch filter, indicating the stability of the filter. The value of α is less than 1. The larger the α, the narrower the notch width. β is the zero control factor of the notch filter, and the notch center frequency ω0 is controlled by β. For ease of description, replace the signal with y(k) The output y(k) is defined as:

[0107] y(k)=W T θ(k)

[0108] in,

[0109] θ(k)=[r(k)r(k-1)r(k-2)y(k-1)y(k-2)] T

[0110]

[0111] The center frequency of the notch ω0 = arccos (-β), and the narrowband interference is filtered out by solving the appropriate β.

[0112] The guidelines require:

[0113] J(β)=E(|y(k)| p )

[0114] Where E is the expectation. When p=2, it is the LMS minimum mean square error criterion. When p=1, it is called the sign-LMS minimum mean square error criterion.

[0115] Update the coefficient β according to the steepest descent algorithm

[0116]

[0117] Among them, the coefficient μ represents the size of the step size, The instantaneous value of

[0118]

[0119] Where the sign function sgn(x) = x / |x|, p = 1, then the expression of the sign-LMS adaptive algorithm coefficient β is:

[0120] β(k+1)=β(k)-μsgn{[sgny(k)]·g(k)}

[0121] g(k) represents the gradient signal, which is expressed as:

[0122] g(k)≈η(k) T ω

[0123] η(k)=[x(k-1)y(k-1)] T ,ω=[-11] T

[0124] See also Figure 3 As shown, the process of the Sign-LMS adaptive algorithm includes:

[0125] S1, initialize β(0) and coefficient μ;

[0126] S2, read θ(k) and W(k);

[0127] S3. Calculate the output signal: y(k) = WT θ(k);

[0128] S4. Calculate the gradient signal: g(k)≈η(k) T ω;

[0129] S5. Calculate the filter coefficient β(k+1)=β(k)-μsgn{[sgny(k)]·g(k)} at time k+1;

[0130] S6. k increases to K+1, and returns to step S2 to calculate the adaptive algorithm coefficient at the next moment.

[0131] See also Figure 4 As shown, the sum product neural network interference identification predictor proposed in the present invention takes as input the received signal of the receiver after bandpass filtering, amplification, down-conversion to intermediate frequency, and discretization. The output function is a narrowband interference signal

[0132] The network of the sum-product neural network interference recognition predictor is divided into an input layer, two hidden layers and an output layer. The number of nodes in the input layer is N, and x1 to x N is the discrete value of the received signal at N moments, the number of hidden layer nodes is M, the connection weight between the first hidden layer and the input layer is fixed to 1, and the hidden layer threshold is θ j , the output is y j The connection weight between the second hidden layer and the first hidden layer is w j , the output is u. The calculation steps of the sum-product neural network are as follows:

[0133] Step 1: Initialization of weight vector:

[0134] Set all weights and thresholds of the sum-product neural network to small uniformly distributed random numbers.

[0135] Step 2: Based on the input vector, calculate the output y of the first hidden layer at the nth iteration j And the second hidden layer outputs u.

[0136]

[0137] Calculate the output layer output o:

[0138]

[0139] Step 3: Learning Process

[0140] The BP algorithm is used to minimize the square error cost function to minimize the sum of square errors between the neural network output o(n) and the expected output d(n), which is defined as:

[0141]

[0142] Using the gradient descent method, calculate the change Δw of the connection weights of the hidden layer j :

[0143]

[0144] Using the gradient descent method, calculate the change in threshold Δθ j :

[0145]

[0146] Step 4: Iteration

[0147] Repeat the second and third steps to iteratively update the connection weights and thresholds of the summed neural network until the maximum number of learning times is reached, or the error E(n) of the neural network is less than the expected threshold value η. The threshold value η depends on the receiver capture threshold and can be 2.5. The final output is obtained.

[0148] Example 2

[0149] The present invention provides a GNSS narrowband interference resistance method, comprising the following steps:

[0150] Obtaining the original signal r(t), and transforming the received original signal r(t) into a signal of an intermediate frequency;

[0151] The signal of the intermediate frequency is sampled at a predetermined sampling frequency, and the observed value is displayed at sampling k to form a digital signal r(k);

[0152] Filter out some narrowband interference from the digital signal r(k) to obtain a signal

[0153] Analyze the signal Narrowband interference signal in

[0154] By signal Subtract narrowband interference signal Get the effective signal z(k).

[0155] In a specific implementation manner: the digital signal r(k) is filtered to remove part of the narrowband interference, and the signal is obtained. The steps are performed by a second-order IIR lattice adaptive notch filter; the transfer function of the second-order IIR lattice adaptive notch filter is:

[0156]

[0157] Where X(z) and Y(z) represent the input r(k) and output of the notch filter, respectively. z-transform, α is the pole control factor of the notch filter; β is the zero control factor of the notch filter.

[0158] In a specific implementation, for ease of description, the signal y(k) is replaced by The output y(k) is:

[0159] y(k)=W T θ(k)

[0160] in,

[0161] θ(k)=[r(k)r(k-1)r(k-2)y(k-1)y(k-2)] T

[0162]

[0163] Notch center frequency ω0 = arccos (-β);

[0164] The guidelines require:

[0165] J(β)=E(|y(k)| p )

[0166] Where E is the expectation, when p = 2, it is the LMS minimum mean square error criterion, when p = 1, it is the sign-LMS minimum mean square error criterion;

[0167] Update the coefficient β according to the steepest descent algorithm

[0168]

[0169] Among them, the coefficient μ represents the size of the step size, The instantaneous value of is used to replace the statistical average value, and we get:

[0170]

[0171] Among them, the sign function sgn(x) = x / |x|, p = 1, and the expression of the sign-LMS adaptive algorithm coefficient β is:

[0172] β(k+1)=β(k)-μsgn{[sgny(k)]·g(k)}

[0173] g(k) represents the gradient signal, which is expressed as:

[0174] g(k)≈η(k) T ω

[0175] η(k)=[x(k-1)y(k-1)]T ,ω=[-11] T .

[0176] In a specific implementation manner: the signal is parsed Narrowband interference signal in The steps are performed by a sum product neural network interference recognition predictor; the sum product neural network interference recognition predictor includes an input layer, two hidden layers and an output layer;

[0177] The number of input layer nodes is N, x1 to x N is the discrete value of the received signal at N moments, the number of hidden layer nodes is M, the connection weight between the first hidden layer and the input layer is fixed to 1, and the hidden layer threshold is θ j , the output is y j ; The connection weight between the second hidden layer and the first hidden layer is w j , the output is u.

[0178] In one specific implementation, the calculation steps of the sum-product neural network interference identification predictor are as follows:

[0179] Step 1: Initialization of weight vector:

[0180] Set all weights and thresholds of the sum-product neural network to small uniformly distributed random numbers;

[0181] Step 2: Based on the input vector, calculate the output y of the first hidden layer at the nth iteration j and the second hidden layer output u;

[0182]

[0183] Calculate the output layer output o:

[0184]

[0185] Step 3: Learning Process

[0186] The BP algorithm is used to minimize the square error cost function to minimize the sum of square errors between the neural network output o(n) and the expected output d(n), which is defined as:

[0187]

[0188] Using the gradient descent method, calculate the change Δw of the connection weights of the hidden layer j :

[0189]

[0190] Using the gradient descent method, calculate the change in threshold Δθ j :

[0191]

[0192] Step 4: Iteration

[0193] Repeat the second and third steps to iteratively update the connection weights and thresholds of the summed neural network until the maximum number of learning times is reached or the error E(n) of the neural network is less than the expected threshold value η, and the final output is obtained.

[0194] In a specific implementation manner: the expected threshold value η is 2.5.

[0195] It is known from common technical knowledge that the present invention can be implemented by other embodiments that do not deviate from its spirit or essential features. Therefore, the above disclosed embodiments are only illustrative in all respects and are not exclusive. All changes within the scope of the present invention or within the scope equivalent to the present invention are included in the present invention.

[0196] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0197] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, 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 instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0198] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0199] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0200] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A GNSS anti-narrowband interference device, characterized in that: include: A down-converter, used for converting the received original signal r(t) into a signal of an intermediate frequency; an analog-to-digital converter, used for sampling the intermediate frequency signal at a predetermined sampling frequency and displaying an observation value at sampling k to form a digital signal r(k); An adaptive notch filter, used to filter out part of narrowband interference from the digital signal r(k) to obtain a signal y(k); A sum-product neural network interference identification predictor is used to parse out narrowband interference signals in the signal y(k) Subtractor, used to subtract the narrowband interference signal from the signal y(k) Get the effective signal z(k); The adaptive notch filter is a second-order IIR lattice adaptive notch filter; the transfer function of the second-order IIR lattice adaptive notch filter is: Where X(z) and Y(z) represent the z-transform of the input r(k) and output y(k) of the notch filter, respectively; α is the pole control factor of the notch filter; β is the zero control factor of the notch filter; The output y(k) is: y(k)=W T θ(k) in, θ(k)=[r(k)r(k-1)r(k-2)y(k-1)y(k-2)] T Notch center frequency ω0 = arccos (-β); The guidelines require: J(β)=E(|y(k)| p ) Where E is the expectation, when p = 2, it is the LMS minimum mean square error criterion, when p = 1, it is the sign-LMS minimum mean square error criterion; Update the coefficient β according to the steepest descent algorithm Among them, the coefficient μ represents the size of the step size, The instantaneous value of is used to replace the statistical average value, and we get: Among them, the sign function sgn(x) = x / |x|, p = 1, and the expression of the sign-LMS adaptive algorithm coefficient β is: β(k+1)=β(k)-μsgn{[sgny(k)]·g(k)} g(k) represents the gradient signal, which is expressed as: g(k)≈η(k) T oh η(k)=[x(k-1)y(k-1)] T ,ω=[-11] T 。 2. A GNSS anti-narrowband interference device according to claim 1, characterized in that: The original signal r(t) is expressed as: r(t)=s(t)+j(t)+n(t) Where s(t) is the effective signal of the GNSS signal after bandpass filtering, amplification, and down-conversion to the intermediate frequency; j(t) is the narrowband interference signal, which is less than 10% of the GNSS signal bandwidth; n(t) is the variance of σ 2 Gaussian white noise.

3. A GNSS anti-narrowband interference device according to claim 1, characterized in that: The sum-product neural network interference identification predictor includes an input layer, two hidden layers and an output layer; The number of input layer nodes is N, x1 to x N is the discrete value of the received signal at N moments, the number of hidden layer nodes is M, the connection weight between the first hidden layer and the input layer is fixed to 1, and the hidden layer threshold is θ j , the output is y j ; The connection weight between the second hidden layer and the first hidden layer is w j , the output is u.

4. The GNSS anti-narrowband interference device according to claim 1, characterized in that: The calculation steps of the sum-product neural network interference identification predictor are as follows: Step 1: Initialization of weight vector: Set all weights and thresholds of the sum-product neural network to small uniformly distributed random numbers; Step 2: Based on the input vector, calculate the output y of the first hidden layer at the nth iteration j and the second hidden layer output u; Calculate the output layer output o: Step 3: Learning Process The BP algorithm is used to minimize the square error cost function to minimize the sum of square errors between the neural network output o(n) and the expected output d(n), which is defined as: Using the gradient descent method, calculate the change Δw of the connection weights of the hidden layer j : Using the gradient descent method, calculate the change in threshold Δθ j : Step 4: Iteration Repeat the second and third steps to iteratively update the connection weights and thresholds of the summed neural network until the maximum number of learning times is reached or the error E(n) of the neural network is less than the expected threshold value η, and the final output is obtained.

5. A GNSS anti-narrowband interference device according to claim 4, characterized in that: The expected threshold value η is 2.

5.

6. A GNSS narrowband interference resistance method, characterized in that: include: Obtaining the original signal r(t), and transforming the received original signal r(t) into a signal of an intermediate frequency; The signal of the intermediate frequency is sampled at a predetermined sampling frequency, and the observed value is displayed at sampling k to form a digital signal r(k); Filtering out part of narrowband interference from the digital signal r(k) to obtain a signal y(k); Analyze the narrowband interference signal in the signal y(k) Subtract the narrowband interference signal from the signal y(k) Get the effective signal z(k); The step of filtering out part of the narrowband interference of the digital signal r(k) to obtain the signal y(k) is performed by a second-order IIR lattice adaptive notch filter; the transfer function of the second-order IIR lattice adaptive notch filter is: Where X(z) and Y(z) represent the z-transform of the input r(k) and output y(k) of the notch filter, respectively; α is the pole control factor of the notch filter; β is the zero control factor of the notch filter; The output y(k) is: y(k)=W T θ(k) in, θ(k)=[r(k)r(k-1)r(k-2)y(k-1)y(k-2)] T Notch center frequency ω0 = arccos (-β); The guidelines require: J(β)=E(|y(k)| p ) Where E is the expectation, when p = 2, it is the LMS minimum mean square error criterion, when p = 1, it is the sign-LMS minimum mean square error criterion; Update the coefficient β according to the steepest descent algorithm Among them, the coefficient μ represents the size of the step size, The instantaneous value of is used to replace the statistical average value, and we get: Among them, the sign function sgn(x) = x / |x|, p = 1, and the expression of the sign-LMS adaptive algorithm coefficient β is: β(k+1)=β(k)-μsgn{[sgny(k)]·g(k)} g(k) represents the gradient signal, which is expressed as: g(k)≈η(k) T oh η(k)=[x(k-1)y(k-1)] T ,ω=[-11] T 。 7. A GNSS narrowband interference resistance method according to claim 6, characterized in that: The narrowband interference signal in the signal y(k) is analyzed The steps are performed by a sum product neural network interference recognition predictor; the sum product neural network interference recognition predictor includes an input layer, two hidden layers and an output layer; The number of input layer nodes is N, x1 to x N is the discrete value of the received signal at N moments, the number of hidden layer nodes is M, the connection weight between the first hidden layer and the input layer is fixed to 1, and the hidden layer threshold is θ j , the output is y j ; The connection weight between the second hidden layer and the first hidden layer is w j , the output is u.

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