1. A GNSS forwarding type deception jamming detection method based on C/N0-MV comprises the following specific steps:

(1) preprocessing the GNSS intermediate frequency signal, searching the code phase of Doppler frequency when a software receiver captures the signal, estimating a noise base of the receiver, and analyzing the influence of the noise base on the total power TSP of deceptive signals;

the step (1) specifically comprises the following steps:

(1.1) processing and storing GNSS intermediate frequency signals, and capturing intermediate frequency data by a software receiver;

(1.2) performing code phase search of Doppler frequency, wherein the output of a Kth time interval correlator obtained by searching the ith local pseudo code sequence is represented as:

wherein the content of the first and second substances,a correlation integral value representing the l-th target satellite signal in the acquisition process;representing interference generated by other pseudo-code signals;representing interference resulting from spoofed signals; η (k) represents the variance σ in the environment²White gaussian noise,/N.

In the formula, R_il[ω_l,τ_l,K]And R_kl[ω_l,τ_l,K]The expression of (a) is:

wherein the content of the first and second substances,is the signal power;the carrier phase difference of the current capture signal and a local number l of the replica signal is obtained; r_il[ω_l,τ_l,K]And R_kl[ω_l,τ_l,K]Respectively outputting the rest real signals except the first numbered and deceptive signals and the local copy signals; n is a radical of_AuthThe number of satellites that are true signals; n is a radical of_SpoofThe number of satellites that are spoofed signals; n is the number of sampling points sent to the correlator in the coherent integration; c. C_l(n) is a locally replicated pseudo-code, c_i(n-τ_ilK) Pseudo-code of the remaining true signals, c_k(n-τ_klK) A pseudo code that is a spoof signal; Δ ω_ilKFor the frequency difference of the local replica carrier and the remaining real signal carriers,phase differences between the locally replicated carrier and the remaining real signal carriers; Δ ω_klKTo locally replicate the carrier frequency difference of the carrier and the spoofed signal,copying the carrier phase difference of the carrier and the deceptive signal for the local area; tau is_ilKAnd τ_klKCode delay differences of the other real signals, the deception signals and the local copy signals are respectively; eta (k) is a complex Gaussian random process with a mean of zero and a variance of sigma²N, where σ²White noise in the input signal;

(1.3) estimating receiver noise floor:

assuming a satellite that is neither a true signal nor a spoofed signal, PRN number f, then the noise floor:

where the first two terms are the cross-correlation function R between the PRN pseudo-code numbered i or k and the PRN pseudo-code numbered f_f(ω_f,τ_fThe variance of K);

wherein R is_f(ω_f,τ_fK) obeys the following distribution:

in the formula, N (a, b) represents a circularly symmetric Gaussian distribution with a mean value a and a covariance b, and the variance of the cross-correlation function

(1.4) defining total power TSP of the deception signal, analyzing the influence process of the TSP on the noise base through a simulation experiment:

(2) calculating carrier-to-noise ratio C/N according to signal-to-noise ratio SNR₀；

(3) Setting a fixed-length sliding window, calculating a data set variance MV, and creating a variance sequence;

(4) setting a detection threshold, and detecting a variance sequence;

(5) after the semi-physical simulation experiment, the method can be used for detecting whether deception jamming occurs.

2. The C/N based according to claim 1₀-a GNSS forwarded deception jamming detection method of MV, characterized by:

the step (3) specifically comprises the following steps:

(3.1) setting a sliding window of length w, calculating the variance of the data subsets within the window by dividing the subset squares and the mean square by the difference between the subsets;

(3.2) moving the window forward by a fixed sliding interval, and calculating the variance of the new data subset;

(3.3) repeating the above process over the entire data set, creating a sequence of variances:

the MV expression for the nth sliding window is:

wherein x (i) is the ith C/N in the data₀The value of the sample, w is the length of the MV window, k is the sliding interval, and N is the total number of sliding windows.

Background

With the continuous progress of science and technology, Global Navigation Satellite System (GNSS) is widely applied to various fields of modern society, but the signal structure and modulation mode of the civil credit are public, so that the GNSS is easily subjected to deception interference, and the security of GNSS service and the use of users are greatly threatened. The major current jamming artifacts include jamming and spoofing, among others, which are more threatening. Deceptive jamming produces false location information after the receiver receives an erroneous signal by broadcasting a signal similar to the true signal. Therefore, the research on cheating and anti-cheating mechanisms is very important in modern GNSS application, and the development of the research on the GNSS anti-cheating interference technology has great significance on national and social security.

The traditional carrier-to-noise ratio detection method detects deception jamming by setting a certain threshold, and the method fails when the carrier-to-noise ratio is reduced due to satellite elevation.

Disclosure of Invention

In order to solve the above problems, the present invention provides a GNSS forwarding spoofing interference detection method based on C/N0-MV, which reflects the effect of spoofing signals on satellites by measuring the fluctuation degree of the carrier-to-noise ratio, and has the advantage of shielding satellite elevation.

The invention provides a GNSS forwarding type deception jamming detection method based on C/N0-MV, which comprises the following steps:

(1) preprocessing the GNSS intermediate frequency signal, searching the code phase of Doppler frequency when a software receiver captures the signal, estimating a noise base of the receiver, and analyzing the influence of the noise base on the total power TSP of deceptive signals;

the step (1) specifically comprises the following steps:

(1.1) processing and storing GNSS intermediate frequency signals, and capturing intermediate frequency data by a software receiver;

(1.2) performing code phase search of Doppler frequency, wherein the output of a Kth time interval correlator obtained by searching the ith local pseudo code sequence is represented as:

wherein the content of the first and second substances,a correlation integral value representing the l-th target satellite signal in the acquisition process;representing interference generated by other pseudo-code signals;representing interference resulting from spoofed signals; η (k) represents the variance σ in the environment²White gaussian noise,/N.

In the formula, R_il[ω_l,τ_l,K]And R_kl[ω_l,τ_l,K]The expression of (a) is:

wherein the content of the first and second substances,is the signal power;the carrier phase difference of the current capture signal and a local number l of the replica signal is obtained; r_il[ω_l,τ_l,K]And R_kl[ω_l,τ_l,K]Respectively outputting the rest real signals except the first numbered and deceptive signals and the local copy signals; n is a radical of_AuthThe number of satellites that are true signals; n is a radical of_SpoofThe number of satellites that are spoofed signals; n is the number of sampling points sent to the correlator in the coherent integration; c. C_l(n) is a locally replicated pseudo-code, c_i(n-τ_ilK) Pseudo-code of the remaining true signals, c_k(n-τ_klK) A pseudo code that is a spoof signal; Δ ω_ilKFor the frequency difference of the local replica carrier and the remaining real signal carriers,phase differences between the locally replicated carrier and the remaining real signal carriers; Δ ω_klKTo locally replicate the carrier frequency difference of the carrier and the spoofed signal,copying the carrier phase difference of the carrier and the deceptive signal for the local area; tau is_ilKAnd τ_klKCode delay differences of the other real signals, the deception signals and the local copy signals are respectively; eta (k) is a complex Gaussian random process with a mean of zero and a variance of sigma²N, where σ²White noise in the input signal;

(1.3) estimating receiver noise floor:

assuming a satellite that is neither a true signal nor a spoofed signal, PRN number f, then the noise floor:

where the first two terms are the cross-correlation function R between the PRN pseudo-code numbered i or k and the PRN pseudo-code numbered f_f(ω_f,τ_fThe variance of K);

wherein R is_f(ω_f,τ_fK) obeys the following distribution:

in the formula, N (a, b) represents a circularly symmetric Gaussian distribution with a mean value a and a covariance b, and the variance of the cross-correlation function

(1.4) defining total power TSP of the deception signal, analyzing the influence process of the TSP on the noise base through a simulation experiment:

(2) calculating carrier-to-noise ratio C/N according to signal-to-noise ratio SNR₀；

(3) Setting a fixed-length sliding window, calculating a data set variance MV, and creating a variance sequence;

(4) setting a detection threshold, and detecting a variance sequence;

(5) after the semi-physical simulation experiment, the method can be used for detecting whether deception jamming occurs.

As a further improvement of the invention, the step (3) specifically comprises the following steps:

(3.1) setting a sliding window of length w, calculating the variance of the data subsets within the window by dividing the subset squares and the mean square by the difference between the subsets;

(3.2) moving the window forward by a fixed sliding interval, and calculating the variance of the new data subset;

(3.3) repeating the above process over the entire data set, creating a sequence of variances:

the MV expression for the nth sliding window is:

wherein x (i) is the ith C/N in the data₀The value of the sample, w is the length of the MV window, k is the sliding interval, and N is the total number of sliding windows.

Compared with the prior art, the invention has the following remarkable advantages:

and detecting the deception signal by utilizing the characteristic that the variance represents the data dispersity and capturing the fluctuation degree of the satellite carrier-to-noise ratio during the deception attack. The traditional carrier-to-noise ratio detection method is easily influenced by the satellite elevation angle, and the method has the advantages of shielding the influence of the satellite elevation angle and having high detection efficiency.

Drawings

FIG. 1 is a schematic flow diagram of one embodiment of the present invention;

FIG. 2 is a schematic diagram of the MV model established by the invention.

Detailed Description

The invention is described in further detail below with reference to the following detailed description and accompanying drawings:

the invention provides a GNSS forwarding type deception jamming detection method based on C/N0-MV, when deception signals occur, through comprehensive investigation of a noise base and a carrier-to-noise ratio of a receiver, the carrier-to-noise ratio of a satellite is found to fluctuate more than an original value at the time of deception signal broadcasting, and the deception jamming signals are detected through calculating a data set variance. The method utilizes the characteristic that the variance can represent the data dispersibility, has the advantage of shielding the satellite elevation influence while realizing deception jamming detection, and improves the detection efficiency and the accuracy.

As a specific embodiment of the present invention, the present invention provides a C/N-based solution₀A GNSS forward spoofing interference detection method for MV, with a flowchart as shown in fig. 1, includes the following specific steps;

preprocessing a GNSS intermediate frequency signal, searching a code phase of Doppler frequency when a software receiver captures the signal, estimating a noise base of the receiver, and analyzing the influence of the noise base on the total power TSP of a deceptive signal;

the method specifically comprises the following steps:

(1.1) processing and storing GNSS intermediate frequency signals, and capturing intermediate frequency data by a software receiver;

(1.2) performing code phase search of Doppler frequency, wherein the output of the Kth time interval correlator obtained by the search of the l local pseudo code sequence can be represented as:

wherein the content of the first and second substances,represents capturedThe correlation integral value of the ith target satellite signal in the process;representing interference generated by other pseudo-code signals;representing interference resulting from spoofed signals; η (k) represents the variance σ in the environment²White gaussian noise,/N.

In the formula, R_il[ω_l,τ_l,K]And R_kl[ω_l,τ_l,K]The expression of (a) is:

wherein the content of the first and second substances,is the signal power;the carrier phase difference of the current capture signal and a local number l of the replica signal is obtained; r_il[ω_l,τ_l,K]And R_kl[ω_l,τ_l,K]Respectively outputting the correlation between the other real signals (except the No. l) and the deception signal and the local copy signal; n is a radical of_AuthThe number of satellites that are true signals; n is a radical of_SpoofThe number of satellites that are spoofed signals; n is the number of sampling points sent to the correlator in the coherent integration; c. C_l(n) is a locally replicated pseudo-code, c_i(n-τ_ilK) Pseudo-code of the remaining true signals, c_k(n-τ_klK) A pseudo code that is a spoof signal; Δ ω_ilKFor frequency differences between the locally replicated carrier and the remaining real signal carriers，Phase differences between the locally replicated carrier and the remaining real signal carriers; Δ ω_klKTo locally replicate the carrier frequency difference of the carrier and the spoofed signal,copying the carrier phase difference of the carrier and the deceptive signal for the local area; tau is_ilKAnd τ_klKCode delay differences of the other real signals, the deception signals and the local copy signals are respectively; eta (k) is a complex Gaussian random process with a mean of zero and a variance of sigma²/N(σ²White noise in the input signal).

(1.3) estimating receiver noise floor:

assuming a satellite that is neither a true signal nor a spoofed signal, PRN number f, then the noise floor:

where the first two terms are the cross-correlation function R between the PRN pseudo-code numbered i or k and the PRN pseudo-code numbered f_f(ω_f,τ_fAnd K) variance.

Wherein R is_f(ω_f,τ_fK) obeys the following distribution:

in the formula, N (a, b) represents a circularly symmetric Gaussian distribution with a mean value a and a covariance b, and the variance of the cross-correlation function

(1.4) defining total power TSP of the deception signal, analyzing the influence process of the TSP on the noise base through a simulation experiment:

step two, calculating the carrier-to-noise ratio C/N according to the SNR₀。

The method specifically comprises the following steps:

(2.1) calculating to obtain a signal-to-noise ratio (SNR);

N＝kTB_n

in the formula, P_RIs signal power, N is noise power, k is Boltzmann constant, T is noise temperature, B_nIs the noise bandwidth;

(2.2) calculating the Carrier to noise ratio C/N by the Signal to noise ratio₀：

C/N₀＝SNR×B_n

Wherein the content of the first and second substances,

N₀＝kT

in the formula, N₀Is white noise power spectral density;

setting a fixed-length sliding window, calculating a data set variance MV, and creating a variance sequence;

the method specifically comprises the following steps:

(3.1) setting a sliding window of length w, dividing the subset square and mean square by this

Calculating the variance of the data subsets in the window by the difference between the subsets;

(3.2) moving the window forward by a fixed sliding interval, and calculating the variance of the new data subset;

(3.3) repeating the above process over the entire data set, creating a sequence of variances:

the MV expression for the nth sliding window is:

wherein x (i) is the ith C/N in the data₀The value of the sample, w is the length of the MV window, k is the sliding interval, and N is the total number of sliding windows.

Setting a detection threshold and detecting a variance sequence;

and step five, after the semi-physical simulation experiment, the method can be used for detecting whether the deception jamming occurs.

For example, the MV model is built as shown in fig. 2:

the MV expression for the nth sliding window is:

wherein x (i) is the ith C/N in the data₀The value of the sample, w is the length of the MV window, k is the sliding interval, and N is the total number of sliding windows.

When the length w of the MV window is 200 (sampling point with time length of 0.4s and sampling frequency of 500Hz) and the sliding interval k is 1, the advantages of different deception powers are set to be 3dB, 5dB and 7dB, and the distribution setting detection threshold value is 5.4(dB-Hz)²、5.7(dB-Hz)²And 7.2(dB-Hz)²A spoof signal may be detected.

The above description is only one of the preferred embodiments of the present invention, and is not intended to limit the present invention in any way, but any modifications or equivalent variations made in accordance with the technical spirit of the present invention are within the scope of the present invention as claimed.