Method for adaptively identifying multi-channel deception signals by GNSS (Global Navigation Satellite System) receiver

Abstract

The invention provides a method for a GNSS receiver to adaptively identify multiple deception signals. The method comprises the following steps: acquiring an input signal of the GNSS receiver; determining whether the current input signal is a deception signal based on a plurality of preset algorithms; based on the judgment of a preset algorithm on the input signal, performing joint decision on whether the current input signal is a deception signal or not by using a preset neural network, and outputting a decision result; and based on the decision result, performing PVT solution on the input signal to output a credible positioning result. Based on various joint detection methods for detecting the carrier-to-noise ratio, the clock error jump, the Doppler threshold and the like of the GNSS signal, the probability of detecting multiple deception signals is effectively improved through the designed calculation decision algorithm based on the Bayesian neural network, and the credibility, the safety and the reliability of the GNSS service are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite navigation systems (GNSS), and in particular to a method for a GNSS receiver to adaptively identify multi-path spoofing signals. Background Art

[0002] At present, satellite navigation systems (GNSS) have been widely used in communications, finance, electricity, telecommunications, aviation and other industries, providing high-precision positioning, navigation and timing services for industry users, enabling industry users to develop efficiently. However, satellite navigation signals have inherent radio characteristics such as long transmission distance and low signal power, and are easily affected by various intentional and unintentional interference. Among them, deception interference is more harmful than suppression interference, as it can cause users to obtain wrong information and mislead users. In addition, it has attracted extensive attention from experts and scholars at home and abroad because of its low power requirements and strong concealment. Especially when the deception signal interference-to-signal ratio is 20-40dB and the delay of the deception signal is as low as 1 code chip compared to the real signal, the correlation peak of the real signal is easily submerged by the deception signal, and traditional deception interference suppression technology is difficult to apply. Targeted research is needed on medium-intensity and low-delay deception interference suppression technology. Summary of the invention

[0003] The technical problem to be solved by the present invention is how to improve the probability of detecting multi-path spoofing signals and enhance the credibility, security and reliability of GNSS services; in view of this, the present invention provides a method for a GNSS receiver to adaptively identify multi-path spoofing signals.

[0004] The technical solution adopted by the present invention is a method for a GNSS receiver to adaptively identify multi-path spoofing signals, comprising:

[0005] Step S1, obtaining an input signal of a GNSS receiver;

[0006] Step S2, determining whether the current input signal is a fraudulent signal based on a plurality of preset algorithms;

[0007] Step S3, based on the determination of the input signal by the preset algorithm, using a preset neural network, making a joint decision on whether the current input signal is a fraudulent signal, and outputting a decision result;

[0008] Step S4: Based on the decision result, perform PVT solution on the input signal to output a reliable positioning result.

[0009] In one embodiment, the input signal includes: real carrier-to-noise ratio observation data, real satellite pseudorange, and user known position.

[0010] In one embodiment, the preset algorithm includes:

[0011] Obtain real carrier-to-noise ratio observation data;

[0012] Configuration parameters, including detection threshold and sliding window size;

[0013] Determine whether the difference between the currently detected carrier-to-noise ratio and the actual carrier-to-noise ratio observation data exceeds the detection threshold, and the continuous duration exceeds the preset detection value of the sliding window;

[0014] If so, it is determined that the current input signal is a fraudulent signal.

[0015] In one embodiment, the preset algorithm includes:

[0016] Obtain the real satellite pseudorange and the user's known position to determine the satellite clock error;

[0017] Configuration parameters, including detection threshold and sliding window size;

[0018] Acquire the real satellite pseudorange, and determine whether the clock error jump calculated by the receiver currently monitored is higher than the actual satellite clock error fluctuation and reaches a threshold;

[0019] If so, it is determined that the signal received by the receiver is a spoofed signal.

[0020] In one embodiment, the preset algorithm includes:

[0021] Get the velocity value of the inertial sensor;

[0022] Determining a theoretical Doppler value based on the velocity value and the ephemeris;

[0023] Determining a Doppler threshold using the theoretical Doppler value;

[0024] When the receiver measures that the Doppler frequency shift value of the signal exceeds the Doppler threshold or a jump occurs, it is determined that a spoofing signal is detected.

[0025] Another aspect of the present invention further provides a GNSS receiver adaptively identifying multi-path spoofing signals, comprising:

[0026] An acquisition unit configured to acquire an input signal of a GNSS receiver;

[0027] A determination unit, configured to determine whether the current input signal is a fraudulent signal based on a plurality of preset algorithms;

[0028] A decision unit, configured to make a joint decision on whether the current input signal is a spoofing signal based on the preset algorithm and using a preset neural network, and output a decision result;

[0029] The output unit is configured to perform PVT solution on the input signal based on the decision result to output a reliable positioning result.

[0030] Another aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method for adaptively identifying multi-path spoofing signals for a GNSS receiver as described in any one of the above items.

[0031] Another aspect of the present invention further provides a computer storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method for adaptively identifying multi-path spoofing signals for a GNSS receiver as described in any one of the above items are implemented.

[0032] Compared with the prior art, the present invention has at least the following advantages:

[0033] The present invention is based on multiple joint detection methods such as detecting the carrier-to-noise ratio, clock difference jump and Doppler threshold of GNSS signals. By designing a Bayesian neural network-based inference decision algorithm, the probability of detecting multi-path deception signals is effectively improved, and the credibility, security and reliability of GNSS services are enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a flowchart of a method for adaptively identifying multi-path spoofing signals by a GNSS receiver according to an embodiment of the present invention;

[0035] Figure 2 It is a schematic diagram showing the principle of a method for adaptively identifying multi-path spoofing signals by a GNSS receiver according to an embodiment of the present invention;

[0036] Figure 3 A logical diagram of a method for adaptively identifying multi-path spoofing signals by a GNSS receiver according to an embodiment of the present invention;

[0037] Figure 4 A logical schematic diagram of a spoofing signal detection method based on receiver carrier-to-noise ratio jump according to an embodiment of the present invention;

[0038] Figure 5 A logical diagram of a spoofing signal detection method based on receiver clock error according to an embodiment of the present invention;

[0039] Figure 6 A logical schematic diagram of a deceptive signal detection method based on a receiver Doppler threshold according to an embodiment of the present invention;

[0040] Figure 7A schematic diagram of a device for adaptively identifying multi-path spoofing signals by a GNSS receiver according to an embodiment of the present invention;

[0041] Figure 8 The figure is a schematic diagram of the composition of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose, the present invention is described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0043] Unless otherwise defined, all terms (including technical terms and scientific terms) used in this article have the same meaning as commonly understood by ordinary technicians in the field to which this application belongs. It should also be understood that terms (such as terms defined in commonly used dictionaries) should be interpreted as having the same meaning as their meaning in the context of the relevant technology, and will not be interpreted in an idealized or overly formal sense unless explicitly defined in this article.

[0044] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0045] In an embodiment of the present invention, a method for adaptively identifying multi-path spoofing signals by a GNSS receiver is provided. Figure 1 As shown, including:

[0046] Step S1, obtaining an input signal of a GNSS receiver;

[0047] Step S2, determining whether the current input signal is a fraudulent signal based on a plurality of preset algorithms;

[0048] Step S3, based on the determination of the input signal by the preset algorithm, using a preset neural network, making a joint decision on whether the current input signal is a fraudulent signal, and outputting a decision result;

[0049] Step S4: Based on the decision result, perform PVT solution on the input signal to output a reliable positioning result.

[0050] refer to Figures 1 to 3 , the method provided in this embodiment will be described in detail step by step below.

[0051] Step S1, obtaining an input signal of a GNSS receiver.

[0052] In this embodiment, the input signal may include, for example: real carrier-to-noise ratio observation data, real satellite pseudorange, and a user's known position.

[0053] Step S2: determining whether the current input signal is a fraudulent signal based on a plurality of preset algorithms.

[0054] In this embodiment, three algorithms are provided as "preset algorithms", specifically: a deceptive signal detection method based on receiver carrier-to-noise ratio jump, a deceptive signal detection method based on receiver clock difference, and a deceptive signal detection method based on receiver Doppler threshold.

[0055] refer to Figure 4 , the deception signal detection method based on the receiver carrier-to-noise ratio jump may specifically include:

[0056] Obtain the real satellite pseudorange and the user's known position to determine the satellite clock error;

[0057] Configuration parameters, including detection threshold and sliding window size;

[0058] Acquire the real satellite pseudorange, and determine whether the clock error jump calculated by the receiver currently monitored is higher than the actual satellite clock error fluctuation and reaches a threshold;

[0059] If so, it is determined that the signal received by the receiver is a fraudulent signal; if not, it is determined that the signal received by the receiver is a reliable signal.

[0060] Specifically, the algorithm constructs a sliding window with dual carrier-to-noise ratio threshold variance statistics by detecting the carrier-to-noise ratio of the real GNSS signal. When the detected carrier-to-noise ratio is 20dB higher than the real GNSS satellite signal and the continuous duration exceeds 100s, it is determined to be a deceptive interference signal. The key parameters such as the movement of the sliding window, the window duration, and the detection threshold can be revised by monitoring the on-site environment, adaptively or by remote injection to complete the parameter matrix table.

[0061] refer to Figure 5 , a spoofing signal detection method based on receiver clock error may specifically include:

[0062] Obtain the real satellite pseudorange and the user's known position to determine the satellite clock error;

[0063] Configuration parameters, including detection threshold and sliding window size;

[0064] Acquire the real satellite pseudorange, and determine whether the clock error jump calculated by the receiver currently monitored is higher than the actual satellite clock error fluctuation and reaches a threshold;

[0065] If so, it is determined that the signal received by the receiver is a fraudulent signal; if not, it is determined that the signal received by the receiver is a reliable signal.

[0066] Specifically, the algorithm measures the real GNSS satellite one-way pseudo-range. When the clock difference jump calculated by the receiver is detected to be 20ns higher than the actual satellite clock difference fluctuation, the signal received by the receiver is determined to be a spoofing signal. The detection software module mainly includes: one-way ranging solution module and satellite clock fluctuation monitoring module.

[0067] refer to Figure 6 , a deceptive signal detection method based on a receiver Doppler threshold may specifically include:

[0068] Get the velocity value of the inertial sensor;

[0069] Determining a theoretical Doppler value based on the velocity value and the ephemeris;

[0070] Determining a Doppler threshold using the theoretical Doppler value;

[0071] When the receiver measures that the Doppler frequency shift value of the signal exceeds the Doppler threshold or a jump occurs, it is determined that a spoofing signal is detected.

[0072] Specifically, for a user receiver whose motion state has been determined, the algorithm can dynamically and adaptively determine the frequency shift threshold based on the almanac or prior frequency shift information. When the receiver measures that the Doppler frequency shift value of the signal exceeds the threshold or jumps, it can be determined that a spoofing signal has been detected.

[0073] Step S3, based on the determination of the input signal by the preset algorithm, using a preset Bayesian neural network, making a joint decision on whether the current input signal is a fraudulent signal, and outputting a decision result;

[0074] Step S4: Based on the decision result, perform PVT solution on the input signal to output a reliable positioning result.

[0075] Compared with the prior art, this embodiment has at least the following effects:

[0076] 1) This embodiment is based on multiple joint detection methods such as the carrier-to-noise ratio, clock error jump, and Doppler threshold of the GNSS signal. By designing a Bayesian neural network-based inference decision algorithm, the probability of detecting multi-path spoofing signals is effectively improved, and the credibility, security, and reliability of GNSS services are enhanced;

[0077] 2) This embodiment does not require modification of the receiver hardware, and only requires a small part of the software to be updated to achieve the functions of monitoring spoofing signals and protecting the receiver security.

[0078] The second embodiment of the present invention corresponds to the first embodiment. This embodiment introduces a device for adaptively identifying multi-path spoofing signals by a GNSS receiver, such as Figure 7 As shown, it includes the following components:

[0079] An acquisition unit configured to acquire an input signal of a GNSS receiver;

[0080] A determination unit, configured to determine whether the current input signal is a fraudulent signal based on a plurality of preset algorithms;

[0081] A decision unit, configured to make a joint decision on whether the current input signal is a spoofing signal based on the preset algorithm and using a preset neural network, and output a decision result;

[0082] The output unit is configured to perform PVT solution on the input signal based on the decision result to output a reliable positioning result.

[0083] In this embodiment, the input signal includes: real carrier-to-noise ratio observation data, real satellite pseudorange, and user known position.

[0084] In this embodiment, the preset algorithm may specifically include:

[0085] Obtain real carrier-to-noise ratio observation data;

[0086] Configuration parameters, including detection threshold and sliding window size;

[0087] Determine whether the difference between the currently detected carrier-to-noise ratio and the actual carrier-to-noise ratio observation data exceeds the detection threshold, and the continuous duration exceeds the preset detection value of the sliding window;

[0088] If so, it is determined that the current input signal is a fraudulent signal.

[0089] In this embodiment, the preset algorithm may specifically include:

[0090] Obtain the real satellite pseudorange and the user's known position to determine the satellite clock error;

[0091] Configuration parameters, including detection threshold and sliding window size;

[0092] Acquire the real satellite pseudorange, and determine whether the clock error jump calculated by the receiver currently monitored is higher than the actual satellite clock error fluctuation and reaches a threshold;

[0093] If so, it is determined that the signal received by the receiver is a spoofed signal.

[0094] In this embodiment, the preset algorithm may specifically include:

[0095] Get the velocity value of the inertial sensor;

[0096] Determining a theoretical Doppler value based on the velocity value and the ephemeris;

[0097] Determining a Doppler threshold using the theoretical Doppler value;

[0098] When the receiver measures that the Doppler frequency shift value of the signal exceeds the Doppler threshold or a jump occurs, it is determined that a spoofing signal is detected.

[0099] A third embodiment of the present invention is an electronic device, such as Figure 8 As shown, it can be understood as a physical device, including a processor and a memory storing instructions executable by the processor. When the instructions are executed by the processor, the following operations are performed:

[0100] Step S1, obtaining an input signal of a GNSS receiver;

[0101] Step S2, determining whether the current input signal is a fraudulent signal based on a plurality of preset algorithms;

[0102] Step S3, based on the determination of the input signal by the preset algorithm, using a preset neural network, making a joint decision on whether the current input signal is a fraudulent signal, and outputting a decision result;

[0103] Step S4: Based on the decision result, perform PVT solution on the input signal to output a reliable positioning result.

[0104] The fourth embodiment of the present invention, the process of the method for adaptively identifying multi-path spoofing signals by a GNSS receiver in this embodiment is the same as that of the first, second or third embodiment, except that, in terms of engineering implementation, this embodiment can be implemented by means of software plus a necessary general hardware platform, or of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the method of the present invention can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a device to execute the method described in the embodiment of the present invention.

[0105] Through the description of the specific implementation methods, a deeper and more specific understanding of the technical means and effects adopted by the present invention to achieve the predetermined purpose should be obtained. However, the accompanying drawings are only for reference and illustration purposes and are not intended to limit the present invention.

Claims

1. A method for adaptively identifying multi-path spoofing signals by a GNSS receiver, characterized in that: include: Step S1, obtaining an input signal of a GNSS receiver; Step S2, determining whether the current input signal is a fraudulent signal based on a plurality of preset algorithms; Step S3, based on the determination of the input signal by the preset algorithm, using a preset neural network, making a joint decision on whether the current input signal is a fraudulent signal, and outputting a decision result; Step S4: Based on the decision result, perform PVT solution on the input signal to output a reliable positioning result.

2. The method for adaptively identifying multi-path spoofing signals by a GNSS receiver according to claim 1, characterized in that: The input signal includes: real carrier-to-noise ratio observation data, real satellite pseudo-range, and user known position.

3. The method for adaptively identifying multi-path spoofing signals by a GNSS receiver according to claim 2, characterized in that: The preset algorithm includes: Obtain real carrier-to-noise ratio observation data; Configuration parameters, including detection threshold and sliding window size; Determine whether the difference between the currently detected carrier-to-noise ratio and the actual carrier-to-noise ratio observation data exceeds the detection threshold, and the continuous duration exceeds the preset detection value of the sliding window; If so, it is determined that the current input signal is a fraudulent signal.

4. The method for adaptively identifying multi-path spoofing signals by a GNSS receiver according to claim 2, characterized in that: The preset algorithm includes: Obtain the real satellite pseudorange and the user's known position to determine the satellite clock error; Configuration parameters, including detection threshold and sliding window size; Acquire the real satellite pseudorange, and determine whether the clock error jump calculated by the receiver currently monitored is higher than the actual satellite clock error fluctuation and reaches a threshold; If so, it is determined that the signal received by the receiver is a spoofed signal.

5. The method for adaptively identifying multi-path spoofing signals by a GNSS receiver according to claim 2, characterized in that: The preset algorithm includes: Get the velocity value of the inertial sensor; Determining a theoretical Doppler value based on the velocity value and the ephemeris; Determining a Doppler threshold using the theoretical Doppler value; When the receiver measures that the Doppler frequency shift value of the signal exceeds the Doppler threshold or a jump occurs, it is determined that a spoofing signal is detected.

6. A device for adaptively identifying multi-path spoofing signals by a GNSS receiver, characterized in that: include: An acquisition unit configured to acquire an input signal of a GNSS receiver; A determination unit, configured to determine whether the current input signal is a fraudulent signal based on a plurality of preset algorithms; A decision unit, configured to make a joint decision on whether the current input signal is a spoofing signal based on the preset algorithm and using a preset neural network, and output a decision result; The output unit is configured to perform PVT solution on the input signal based on the decision result to output a reliable positioning result.

7. An electronic device, characterized in that: The electronic device comprises: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method for adaptively identifying multi-path spoofing signals by a GNSS receiver as claimed in any one of claims 1 to 5.

8. A computer storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the method for adaptively identifying multi-path spoofing signals by a GNSS receiver as claimed in any one of claims 1 to 5 are implemented.

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