CN116931004A - GNSS slowly-varying deception detection method based on weighted Kalman gain - Google Patents

Abstract

The invention relates to the technical field of navigation system deception detection, and provides a GNSS slowly-varying deception detection method based on weighted Kalman gain. Comprising the following steps: acquiring and accumulating observation normalization information of the satellite in the measurement updating process through a sliding window to acquire accumulated normalization information; calculating a weighted Kalman gain according to the accumulated normalized information, and carrying out state update on the satellite through the weighted Kalman gain to obtain a filtering state vector; traversing all visible satellites at the measurement updating moment, and calculating the normalized innovation square sum of all satellites according to the filtering state vectors of a plurality of satellites to obtain test statistics; giving a false alarm rate, and calculating to obtain a detection threshold through the false alarm rate; and comparing the test statistic with the detection threshold, if the test statistic is larger than the detection threshold, then deception jamming exists, otherwise deception jamming does not exist. The invention suppresses the pollution of harmful news to the combined navigation filter, and improves the holding time of accurate news after deception invasion.

Description

A GNSS slowly changing spoofing detection method based on weighted Kalman gain

Technical field

The invention relates to the technical field of navigation system deception detection, and in particular to a GNSS slowly changing deception detection method based on weighted Kalman gain.

Background technique

Global Navigation Satellite System (GNSS) is a global navigation system that transmits satellite signals to enable ground receiving equipment to determine its position, speed and time. With the widespread application of GNSS in various fields, such as autonomous driving, smart agriculture, and disaster monitoring, it has become an indispensable position sensor in modern society.

However, spoofing detection and its improvement method based on GNSS/INS combined filter innovation use the traditional fixed-gain extended Kalman filter (EKF). When the initial induced deviation of slowly changing spoofing on the filter innovation is less than When the GNSS itself has measurement deviations, the system cannot distinguish and directly identify deception from the new information, thereby adding harmful new information to the filter measurement update to the maximum extent, causing the combined filter output to quickly deviate from the true value, making it impossible to implement effective deception detection.

Since the structure of GNSS signals is public, they are vulnerable to intentional spoofing interference, especially slow-variation spoofing. These spoofing signals can cause GNSS receivers to report incorrect position and time information, thus causing huge losses to users. .

Contents of the invention

The present invention aims to solve at least one of the technical problems existing in the related art. To this end, the present invention provides a GNSS slowly changing spoofing detection method based on weighted Kalman gain.

The present invention provides a GNSS slowly changing spoofing detection method based on weighted Kalman gain, which includes:

S1: During the measurement update process, obtain the satellite's observation normalized information through the sliding window, accumulate the observation normalized information, and obtain the accumulated normalized information;

S2: Calculate the weighted Kalman gain according to the accumulated normalized innovation information, update the status of the satellite through the weighted Kalman gain, and obtain the filtered state vector;

S3: Traverse all visible satellites at the measurement update time, calculate the normalized sum of squares of innovations of all satellites based on the filtered state vectors of multiple satellites, and obtain the test statistic;

S4: Set the false alarm rate, and obtain the detection threshold through the calculation of the false alarm rate;

S5: Compare the test statistic and the detection threshold. If the test statistic is greater than the detection threshold, there is deception interference. If the test statistic is less than or equal to the detection threshold, there is no deception interference. .

According to a GNSS slowly changing spoofing detection method based on weighted Kalman gain provided by the present invention, the expression of the accumulated normalized information is:

in, To accumulate normalized information,/> is the sliding window time length,/> is the satellite serial number,/> For the measurement update time,/> In order to obtain the observation normalized information time,/> For the first/> satellites/> Observation normalized innovation at time.

According to a GNSS slowly changing spoofing detection method based on weighted Kalman gain provided by the present invention, the acquisition period of the observation normalized new information in step S1 is Time to/> time.

According to a GNSS slowly changing spoofing detection method based on weighted Kalman gain provided by the present invention, the expression of the weighted Kalman gain in step S2 is:

in, is the weighted Kalman gain,/> is the traditional Kalman gain,/> is the upper bound of the confidence interval.

According to a GNSS slowly changing spoofing detection method based on weighted Kalman gain provided by the present invention, the expression of the test statistic in step S3 is:

in, is the test statistic,/> is the number of visible satellites within the altitude angle range during traversal.

According to a GNSS slowly changing spoofing detection method based on weighted Kalman gain provided by the present invention, the calculation formula of the detection threshold in step S4 is:

in, is the given false alarm rate,/> is the detection threshold,/> is the chi-square distribution degrees of freedom,/> is the first independent variable, is the second independent variable.

A GNSS slowly changing spoofing detection method based on weighted Kalman gain provided by the present invention also includes:

S6: When the determination result in step S5 is that there is no spoofing interference, move to the measurement update of the next detection cycle for detection.

According to a GNSS slowly changing spoofing detection method based on weighted Kalman gain provided by the present invention, step S1 also includes:

S11: Normalize the accumulated normalized information again.

The invention provides a GNSS slowly changing spoofing detection method based on weighted Kalman gain, which adopts effective spoofing detection means to ensure the reliability and accuracy of GNSS, so as to quickly discover the existence of spoofing signals and adopt countermeasures in a timely manner to achieve Efficient detection of slowly varying spoofing where the initial induced bias is smaller than the measurement bias of the GNSS itself.

Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.

Description of the drawings

In order to more clearly illustrate the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are the drawings of the present invention. For some embodiments, those of ordinary skill in the art can also obtain other drawings based on these drawings without exerting creative efforts.

Figure 1 is a flow chart of a GNSS slowly changing spoofing detection method based on weighted Kalman gain provided by an embodiment of the present invention.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present invention more clear, the technical solutions in the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention. , not all examples. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts fall within the scope of protection of the present invention. The following examples are used to illustrate the invention but are not intended to limit the scope of the invention.

In the description of the embodiments of the present invention, it should be noted that the terms "center", "longitudinal", "horizontal", "upper", "lower", "front", "back", "left" and "right" The orientations or positional relationships indicated by "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. are based on the orientations or positional relationships shown in the accompanying drawings and are only for the convenience of describing this document. The embodiments and simplified descriptions of the invention do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be construed as limiting the embodiments of the invention. Furthermore, the terms “first”, “second” and “third” are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.

In the description of the embodiments of the present invention, it should be noted that, unless otherwise clearly stated and limited, the terms "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection or a detachable connection. Or integrated connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present invention can be understood in specific situations.

In the embodiment of the present invention, unless otherwise expressly provided and limited, the first feature "on" or "below" the second feature may be that the first and second features are in direct contact, or the first and second features are in intermediate contact. Indirect media contact. Furthermore, the terms "above", "above" and "above" the first feature is above the second feature may mean that the first feature is directly above or diagonally above the second feature, or simply means that the first feature is higher in level than the second feature. "Below", "below" and "beneath" the first feature to the second feature may mean that the first feature is directly below or diagonally below the second feature, or simply means that the first feature has a smaller horizontal height than the second feature.

In the description of this specification, reference to the terms "one embodiment," "some embodiments," "an example," "specific examples," or "some examples" or the like means that specific features are described in connection with the embodiment or example. , structures, materials or features are included in at least one embodiment or example of embodiments of the present invention. In this specification, the schematic expressions of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and combine different embodiments or examples and features of different embodiments or examples described in this specification unless they are inconsistent with each other.

The embodiment of the present invention is described below with reference to FIG. 1 .

The present invention provides a GNSS slowly changing spoofing detection method based on weighted Kalman gain, which includes:

S1: During the measurement update process, obtain the satellite's observation normalized information through the sliding window, accumulate the observation normalized information, and obtain the accumulated normalized information;

Wherein, the expression of the accumulated normalized innovation is:

in, To accumulate normalized information,/> is the sliding window time length,/> is the satellite serial number,/> For the measurement update time,/> In order to obtain the observation normalized information time,/> For the first/> satellites/> Observation normalized innovation at time.

Among them, the acquisition period of the observation normalized information described in step S1 is Time to/> time.

Among them, step S1 also includes:

S11: Normalize the accumulated normalized information again.

S2: Calculate the weighted Kalman gain according to the accumulated normalized innovation information, update the status of the satellite through the weighted Kalman gain, and obtain the filtered state vector;

Further, in step S2, the weighted Kalman gain is calculated based on the re-normalized accumulated normalized information obtained in step S11.

Among them, the expression of the weighted Kalman gain described in step S2 is:

in, is the weighted Kalman gain,/> is the traditional Kalman gain,/> is the upper bound of the confidence interval.

S3: Traverse all visible satellites at the measurement update time, calculate the normalized sum of squares of innovations of all satellites based on the filtered state vectors of multiple satellites, and obtain the test statistic;

Among them, the expression of the test statistic described in step S3 is:

in, is the test statistic,/> is the number of visible satellites within the altitude angle range during traversal.

Furthermore, the standard for the number of visible satellites is based on an altitude angle greater than 1. In some embodiments, in order to make the traversal clearer, an altitude angle greater than 5 is selected for observation.

S4: Set the false alarm rate, and obtain the detection threshold through the calculation of the false alarm rate;

Among them, the calculation formula of the detection threshold described in step S4 is:

in, is the given false alarm rate,/> is the detection threshold,/> is the chi-square distribution degrees of freedom,/> is the first independent variable, is the second independent variable.

S5: Compare the test statistic and the detection threshold. If the test statistic is greater than the detection threshold, there is deception interference. If the test statistic is less than or equal to the detection threshold, there is no deception interference. .

Among them, it also includes:

S6: When the determination result in step S5 is that there is no spoofing interference, move to the measurement update of the next detection cycle for detection.

In some embodiments, a GNSS slow-variation spoofing detection simulation is performed for a certain drone's flight process. In the simulation, the starting point of the drone is 120°E and 30°N, and then moves horizontally at a speed of 100m/s. The heading angle is 40°, the simulation time is 200s, spoofing starts from the 100s, causing a position offset in the latitude direction, the speed offset of the spoofing is 0.1m/s, the GNSS update period is 0.1s, and the INS update period is 0.01s. , the simulation parameters are shown in Table 1.

Table 1 GNSS slowly changing spoofing detection simulation parameters for UAV flight process

Further, GNSS and INS data are collected through the following devices: a GNSS signal processing unit, used to receive real-time GNSS signals, solve to obtain GNSS measurement information, and output timing signals; an INS information output unit, receive timing signals, used to communicate with Synchronization of the GNSS signal processing unit, and outputs INS angular velocity and acceleration information; information fusion processing unit, used to fuse GNSS measurement information and INS angular velocity and acceleration information, perform status updates and measurement updates, and output new information; spoofing detection A unit configured to execute the weighted Kalman gain-based GNSS slowly varying spoofing detection method provided by the present invention based on the new information.

Further, first set the sliding window 10, and collect all the normalized information observation vectors of the first satellite within the sliding window at the measurement update time as:

Secondly, the normalized information observation vectors of the sliding window are accumulated and normalized again, and the accumulated normalized information is 0.6092.

Further, calculate the weighted Kalman gain of the first satellite participating in the measurement update at the measurement update time as:

Secondly, after obtaining the weighted Kalman gain, the measurement update is performed again to obtain the state vector filtered by the first satellite.

Further, perform the above steps in a loop, traverse all visible satellite observations at the measurement update time, and record the number of visible satellites as 10, and perform deception detection at the current time. The specific steps are: first, calculate the normalized new values of all satellites within the time window. The sum of squares of interest is obtained, and the test statistic is 36.3449. Secondly, the false alarm rate is given , the calculated detection threshold is 29.5883, the comparison test statistic 36.3449 is greater than the detection threshold 29.5883, deception interference is detected, and the process ends.

Furthermore, the weighted Kalman gain weights during the deception detection process show that during the filter update process, the filter weights of individual satellites are close to 0, and harmful innovations are suppressed. In addition, the simulation results of normalized innovations show the largest innovations. The absolute value of the information deviation reached 4.28965, which is conducive to the detection of deception. In addition, the final execution result of the slowly changing deception detection shows that at the 100s, the detection statistics are close to the test threshold, but do not exceed the threshold. By the 101s, the test statistics are rapidly When the detection threshold is exceeded, the slow-change deception detection method proposed by the present invention can detect slow-change deception in time, thereby ensuring the safety of navigation users.

The present invention provides a GNSS slowly changing spoofing detection method based on weighted Kalman gain. Through the weighted Kalman gain, the pollution of harmful new information to the integrated navigation filter is effectively suppressed, thereby improving the retention time of accurate new information after spoofing intrusion. , further improves the accumulation of new information, and further improves the sensitivity of spoofing detection. Especially for slowly changing spoofing where the initial induced deviation is smaller than the GNSS's own measurement deviation, reliable detection can be carried out.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that it can still be used Modifications are made to the technical solutions described in the foregoing embodiments, or equivalent substitutions are made to some of the technical features; however, these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (8)

1. A GNSS slowly changing spoofing detection method based on weighted Kalman gain, which is characterized by: S1: During the measurement update process, obtain the satellite's observation normalized information through the sliding window, accumulate the observation normalized information, and obtain the accumulated normalized information; S2: Calculate the weighted Kalman gain according to the accumulated normalized innovation information, update the status of the satellite through the weighted Kalman gain, and obtain the filtered state vector; S3: Traverse all visible satellites at the measurement update time, calculate the normalized sum of squares of innovations of all satellites based on the filtered state vectors of multiple satellites, and obtain the test statistic; S4: Set the false alarm rate, and obtain the detection threshold through the calculation of the false alarm rate; S5: Compare the test statistic and the detection threshold. If the test statistic is greater than the detection threshold, there is deception interference. If the test statistic is less than or equal to the detection threshold, there is no deception interference. . 2. A kind of GNSS slowly varying deception detection method based on weighted Kalman gain according to claim 1, characterized in that the expression of the accumulated normalized innovation is: in, To accumulate normalized information,/> is the sliding window time length,/> is the satellite serial number,/> For the measurement update time,/> In order to obtain the observation normalized information time,/> For the first/> satellites/> Observation normalized innovation at time. 3. A GNSS slowly changing spoofing detection method based on weighted Kalman gain according to claim 2, characterized in that the acquisition period of the observation normalized information in step S1 is Time to/> time. 4. A GNSS slowly changing deception detection method based on weighted Kalman gain according to claim 2, characterized in that the expression of the weighted Kalman gain in step S2 is: in, is the weighted Kalman gain,/> is the traditional Kalman gain,/> is the upper bound of the confidence interval. 5. A GNSS slowly varying deception detection method based on weighted Kalman gain according to claim 2, characterized in that the expression of the test statistic in step S3 is: in, is the test statistic,/> is the number of visible satellites within the altitude angle range during traversal. 6. A GNSS slowly changing spoofing detection method based on weighted Kalman gain according to claim 1, characterized in that the calculation formula of the detection threshold in step S4 is: in, is the given false alarm rate,/> is the detection threshold,/> is the chi-square distribution degrees of freedom,/> is the first independent variable,/> is the second independent variable. 7. A GNSS slowly changing spoofing detection method based on weighted Kalman gain according to claim 1, characterized in that it also includes: S6: When the determination result in step S5 is that there is no spoofing interference, move to the measurement update of the next detection cycle for detection. 8. A GNSS slowly changing spoofing detection method based on weighted Kalman gain according to claim 1, characterized in that step S1 also includes: S11: Normalize the accumulated normalized information again.