A satellite navigation spoofing jamming detection method based on signal propagation fingerprint characteristics

Abstract

The present invention relates to the field of satellite navigation technology, and in particular to a method for detecting deception interference in a satellite navigation receiving device. A satellite navigation deception interference detection method based on signal propagation fingerprint features, characterized in that it includes the following steps: (1) establishing a baseline fingerprint feature database for each satellite; (2) calculating the current satellite fingerprint feature data; (3) calculating the Pearson correlation coefficient and making a deception judgment. The beneficial effects of the present invention are: deception interference detection is performed through the signal propagation fingerprint feature, making full use of the prior information that the position of the receiving device is fixed, without the need to modify the hardware of the receiving device, and only the software needs to be upgraded to achieve it, which greatly reduces the complexity of implementation. It can provide deception interference detection capabilities for various satellite navigation receiving devices in core civilian fields such as finance, electricity and communications.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation technology, and in particular to a method for detecting deception interference in a satellite navigation receiving device. Background Art

[0002] Satellite navigation systems can provide high-precision positioning, speed measurement and timing services for ground and air users. They have the advantages of wide coverage, all-weather, and high precision, and have been widely used in both civil and military fields. At present, many core civil infrastructures are increasingly relying on the positioning and timing services provided by satellite navigation. For example, the normal operation of financial systems, power systems, and communication systems all rely on the precise time and location information provided by satellite navigation. Once the satellite navigation system is interrupted by electromagnetic interference, it may cause these core infrastructures to fail or even be paralyzed, causing immeasurable losses to people's production and life. Therefore, when satellite navigation is applied to core civil fields, electromagnetic interference protection should be given priority. In terms of type, electromagnetic interference can be divided into suppression interference and deception interference. Compared with suppression interference, deception interference can cause satellite navigation receiving equipment to output incorrect time or location information, which is more concealed and more harmful.

[0003] At present, there are a lot of research results related to satellite navigation deception interference detection. Generally speaking, deception interference can be detected from aspects such as signal power, arrival time, and arrival angle. These methods have their own advantages and disadvantages, and are suitable for different application scenarios. These traditional deception interference detection methods have certain shortcomings when applied to core civilian fields such as finance, electricity, and communications. For example, although the detection method based on signal power can detect electromagnetic interference, it cannot distinguish whether the type of electromagnetic interference is suppression interference or deception interference. The detection method based on the angle of arrival requires the use of an array antenna, and the resulting substantial increase in the power consumption, volume, and implementation cost of the receiving equipment limits its application scale.

[0004] The application of satellite navigation in core civilian fields such as finance, electricity and communications has a significant feature: the location of satellite navigation receiving equipment is generally fixed. In these application scenarios, in order to reduce signal obstruction, satellite navigation receiving antennas are generally installed in fixed locations such as the roof of a building and the top of a signal transmitter. Navigation satellites orbit the earth periodically. For example, the medium and high orbit satellites of the Beidou system and GPS system orbit the earth for a period of about 12 hours. When the satellite signal reaches the receiving device, there are direct and multipath reflections, and its propagation path characteristics (such as the combined power of direct and transmitted signals) show periodic changes with the satellite elevation angle. Reflected on the receiving device, the result is that the carrier-to-noise ratio of all satellite signals shows repeated periodic fluctuations, and the carrier-to-noise ratio fluctuation law is as unique as a person's fingerprint. As for deception interference, it is almost impossible to achieve the signal propagation path characteristics that are exactly the same as those of the real satellite, so the signal propagation fingerprint characteristics can be used for deception interference detection. Summary of the invention

[0005] In order to overcome the shortcomings of the prior art, the present invention provides a satellite navigation deception interference detection method based on signal propagation fingerprint characteristics. Different from the traditional method, the method of the present invention makes full use of the prior information that the position of the receiving device is fixed, does not require changes to the hardware of the receiving device, and has low implementation complexity. It can be implemented by upgrading the software. It can provide deception interference detection capabilities for various satellite navigation receiving devices in core civilian fields such as finance, electricity and communications.

[0006] The technical solution of the present invention provides a satellite navigation deception interference detection method based on signal propagation fingerprint characteristics, which is characterized by comprising the following steps:

[0007] (1) Establishing a database of satellite fingerprint features

[0008] After the satellite navigation receiving equipment is installed, the satellite reference fingerprint feature database can be established. Assume that the satellite navigation receiving equipment can normally receive L satellite navigation signals, and the PRN numbers (pseudo-code sequence numbers) of these L satellite signals are PRN-1, PRN-2, ..., PRN-L, respectively. For satellite signal i (whose PRN number is PRN-i), its pitch angle and carrier-to-noise ratio sequence is {θ PRN-i (k),C PRN-i (k)};

[0009] Among them, θ PRN-i (k) is the elevation angle of satellite signal i relative to the satellite navigation receiving device at time k, C PRN-i(k) is the output carrier-to-noise ratio of satellite signal i at time k; the carrier-to-noise ratio of satellite signal is a standard output quantity of satellite navigation receiving equipment, and the elevation angle of satellite signal can be calculated according to the satellite ephemeris and the position coordinates of the receiving equipment. The specific calculation method is common knowledge in the field of satellite navigation and positioning, and will not be repeated here;

[0010] Since the satellite orbits the earth periodically, its pitch angle relative to the satellite navigation receiving device will also increase, decrease, increase, and change periodically over time; according to the change trend of the satellite pitch angle, the pitch angle is discretized respectively, and the discretization process is as follows:

[0011] 3) Find the sequence {θ PRN-i (k),C PRN-i (k)}, the subset of sequences in which the satellite elevation angle increases with time is set to A, and Indicates that the sequence subset of satellite elevation angles decreasing over time is set to B, and express;

[0012] 4) Discretize the satellite pitch angle at intervals of 0.1 degrees, and the discretized satellite pitch angle sequence is: There are N = 901 elements in total; and The corresponding carrier-to-noise ratio is calculated as follows: For example When, from the sequence subset Find all the satisfying The k value can be set as M≤k≤P, then The corresponding carrier-to-noise ratio is:

[0013]

[0014] Based on this, we can obtain the baseline fingerprint feature database of satellite signal i, that is, the sequence and sequence Each sequence has N = 901 data pairs;

[0015] (2) Calculate the current satellite fingerprint feature data;

[0016] Assume that the elevation angle and carrier-to-noise ratio sequence of the satellite signal i currently output by the satellite navigation receiving device is According to the changing trend of the satellite pitch angle, the pitch angle is discretized according to step (1) to obtain the current satellite fingerprint feature data; if the satellite pitch angle increases over time, the sequence If the satellite elevation angle decreases over time, the sequence

[0017] (3) Calculate the Pearson correlation coefficient and make a fraud judgment

[0018] For satellite signal i, the Pearson correlation coefficient is calculated according to the following formula:

[0019] 3) If the satellite elevation angle increases over time, then:

[0020]

[0021] 4) If the satellite elevation angle is small over time, then:

[0022]

[0023] Compare the calculated Pearson correlation coefficient with the threshold T. If or Then the satellite signal i is judged to be a real signal, otherwise it is judged to be deceptive interference; where the decision threshold T can be calculated according to the constant false alarm probability, and the empirical value is generally 0.9;

[0024] (4) Update the baseline fingerprint feature database

[0025] If the current satellite signal is determined to be a real signal in step (3), the satellite reference fingerprint feature database is updated. The specific updating process is as follows:

[0026]

[0027]

[0028] In the formula, and That is the updated satellite benchmark fingerprint feature database, r is the forgetting factor, and the empirical value is generally 0.99.

[0029] The beneficial effects of the present invention are: deception interference detection is performed through the signal propagation fingerprint feature, the prior information that the receiving device is fixed is fully utilized, and the hardware of the receiving device does not need to be modified, and only the software needs to be upgraded to achieve it, which greatly reduces the complexity of implementation. It can provide deception interference detection capabilities for various satellite navigation receiving devices in core civilian fields such as finance, electricity and communications. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flow chart of a satellite navigation deception interference detection method based on signal propagation fingerprint characteristics provided by the present invention;

[0031] Figure 2 It is a signal fingerprint feature distribution diagram obtained by the method of the present invention. DETAILED DESCRIPTION

[0032] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0033] Figure 1 The present invention provides a flow chart of a satellite navigation deception interference detection method based on signal propagation fingerprint characteristics, as shown in the figure, comprising the following steps:

[0034] (1) Establish a database of satellite fingerprint features

[0035] Assume that the satellite navigation receiving device can normally receive L satellite navigation signals. The PRN numbers (pseudo-code sequence numbers) of these L satellite signals are PRN-1, PRN-2, ..., PRN-L. For satellite signal i (whose PRN number is PRN-i), its pitch angle and carrier-to-noise ratio sequence is {θ PRN-i (k),C PRN-i (k)}. Among them, θ PRN-i (k) is the elevation angle of satellite signal i relative to the satellite navigation receiving device at time k, C PRN-i (k) is the output carrier-to-noise ratio of satellite signal i at time k.

[0036] According to the changing trend of the satellite pitch angle, the pitch angle is discretized respectively. The discretization process is as follows:

[0037] 1) Find the sequence {θ PRN-i (k),C PRN-i (k)}, the subset of sequences in which the satellite elevation angle increases with time is set to A, and Indicates that the sequence subset of satellite elevation angles decreasing over time is set to B, and express.

[0038] 2) Discretize the satellite pitch angle at intervals of 0.1 degrees, and the discretized satellite pitch angle sequence is: There are N=901 elements in total. and The corresponding carrier-to-noise ratio is calculated as follows: For example When, from the sequence subset Find all the satisfying The k value can be set as M≤k≤P, then The corresponding carrier-to-noise ratio is:

[0039]

[0040] Based on this, we can obtain the baseline fingerprint feature database of satellite signal i, that is, the sequence and sequence Each sequence has N=901 data pairs.

[0041] (2) Calculate the current satellite fingerprint feature data

[0042] Assume that the elevation angle and carrier-to-noise ratio sequence of the satellite signal i currently output by the satellite navigation receiving device is According to the change trend of the satellite pitch angle, the pitch angle is discretized according to step S11 to obtain the current satellite fingerprint feature data. If the satellite pitch angle increases over time, the sequence If the satellite elevation angle decreases over time, the sequence

[0043] (3) Calculate the Pearson correlation coefficient and make a fraud judgment

[0044] For satellite signal i, the Pearson correlation coefficient is calculated according to the following formula:

[0045] 1) If the satellite elevation angle increases over time, then:

[0046]

[0047] 2) If the satellite elevation angle is small over time, then:

[0048]

[0049] Compare the calculated Pearson correlation coefficient with the threshold T. If or Then the satellite signal i is judged to be a real signal, otherwise it is judged to be deceptive interference. In the formula, the decision threshold T can be calculated according to the constant false alarm probability, and the empirical value is generally 0.9.

[0050] (4) Update the baseline fingerprint feature database

[0051] If the current satellite signal is determined to be a real signal in step S13, the satellite reference fingerprint feature database is updated. The specific updating process is performed as follows:

[0052]

[0053]

[0054] In the formula, and That is the updated satellite benchmark fingerprint feature database, r is the forgetting factor, and the empirical value is generally 0.99.

[0055] Figure 2It is a signal fingerprint feature distribution diagram obtained by the method of the present invention. In this embodiment, the antenna of the navigation receiving device is installed in a fixed position and can receive GPS signals normally. The blue curve in the figure is the benchmark fingerprint feature database of satellite signal 1 obtained by the method of the present invention. The elevation angle of the satellite in the figure increases with time. The red curve is the current fingerprint feature database obtained by the method of the present invention when there is no deception interference. The calculated Pearson correlation coefficient is 0.97, which is greater than the decision threshold and is determined to be a real signal. The green curve in the figure is the current fingerprint feature database obtained by the method of the present invention after the deception interference is turned on. The calculated Pearson correlation coefficient is 0.29, which is less than the decision threshold and is determined to be deception interference.

[0056] The above includes the description of the preferred embodiment of the present invention, which is to explain the technical features of the present invention in detail, and is not intended to limit the invention to the specific form described in the embodiment. Other modifications and variations based on the main idea of ​​the present invention are also protected by this patent. The main idea of ​​the present invention is defined by the claims, not by the specific description of the embodiment.

Claims

1. A satellite navigation spoofing interference detection method based on signal propagation fingerprint characteristics, characterized in that: The steps include: (1) Establishment of a database of satellite baseline fingerprint features: After the satellite navigation receiving equipment is installed, a satellite fingerprint feature database is established. Assume that the satellite navigation receiving equipment can normally receive L satellite navigation signals. The PRN numbers of these L satellite signals are PRN-1, PRN-2, ..., PRN-L. Then, for the PRN number of satellite signal i, PRN-i, its pitch angle and carrier-to-noise ratio sequence is {θ PRN-i (k),C PRN-i (k)}; Among them, θ PRN-i (k) is the elevation angle of satellite signal i relative to the satellite navigation receiving device at time k, C PRN-i (k) is the output carrier-to-noise ratio of satellite signal i at time k; According to the changing trend of satellite pitch angle, the pitch angle is discretized respectively; (2) Calculate the current satellite fingerprint feature data: Assume that the elevation angle and carrier-to-noise ratio sequence of the satellite signal i currently output by the satellite navigation receiving device is According to the changing trend of the satellite pitch angle, the pitch angle is discretized according to step (1) to obtain the current satellite fingerprint feature data; if the satellite pitch angle increases over time, the sequence If the satellite elevation angle decreases over time, the sequence (3) Calculate the Pearson correlation coefficient and make a fraud judgment: For satellite signal i, the Pearson correlation coefficient is calculated according to the following formula: 1) If the satellite elevation angle increases over time, then: 2) If the satellite elevation angle is small over time, then: Compare the calculated Pearson correlation coefficient with the threshold T. If or Then the satellite signal i is judged to be a real signal, otherwise it is judged to be deceptive interference; where the decision threshold T can be calculated according to the constant false alarm probability, and the empirical value is 0.

9.

2. The satellite navigation spoofing interference detection method based on signal propagation fingerprint characteristics according to claim 1 is characterized in that: Also includes step 4: (4) Update the baseline fingerprint feature database: If the current satellite signal is determined to be a real signal in step (3), the satellite reference fingerprint feature database is updated. The specific updating process is as follows: In the formula, and That is the updated satellite benchmark fingerprint feature database, r is the forgetting factor, and the empirical value can be 0.

99.

3. The satellite navigation spoofing interference detection method based on signal propagation fingerprint characteristics according to claim 1 is characterized in that: According to the changing trend of the satellite pitch angle, the pitch angle is discretized respectively. The discretization process is as follows: 1) Find the sequence {θ PRN-i (k),C PRN-i (k)}, the subset of sequences in which the satellite elevation angle increases with time is set to A, and Indicates that the sequence subset of satellite elevation angles decreasing over time is set to B, and express; 2) Discretize the satellite pitch angle at intervals of 0.1 degrees, and the discretized satellite pitch angle sequence is: There are N = 901 elements in total; and The corresponding carrier-to-noise ratio is calculated as follows: Based on this, we can obtain the baseline fingerprint feature database of satellite signal i, that is, the sequence and sequence Each sequence has N=901 data pairs.

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