Navigation management method and device based on deception perception

Abstract

The embodiment of the invention discloses a deception perception-based navigation management method, which comprises the following steps of: acquiring interested information which comprises signal power, attitude and direction of a GNSS (Global Navigation Satellite System) satellite; calculating a deception offset vector according to the interested information, and mapping the deception offset vector into eccentricity of non-central chi-square distribution; calculating a detection statistical magnitude, and calculating a missed detection probability according to the detection statistical magnitude; performing statistics on the detected deception signals according to a spatial direction to form a probability quality function, calculating spatial entropy according to the probability quality function, and calculating integrity weight loss according to the spatial entropy; calculating a position error expectation, and obtaining an over-limit probability according to the position error expectation; and determining a minimum protection level satisfying a constraint according to the integrity weight loss, the missed detection probability and the over-limit probability, and determining a navigation processing strategy according to the minimum protection level satisfying the constraint.

Description

Technical Field

[0001] This application relates to the field of signal security and integrity protection of global satellite navigation systems, and in particular to a navigation management method and apparatus based on deception perception. Background Technology

[0002] GNSS systems are widely used in aviation, maritime, and land navigation, but GNSS signals are vulnerable to spoofing attacks, which seriously affect navigation safety. Currently, most GNSS protection level calculation schemes still use fixed protection levels or are based solely on geometric / noise models under spoofing conditions, failing to dynamically couple the spoofing intensity (i.e., the non-centrality of measurement offset) with the protection level, making it difficult to effectively control the risk of integrity failure.

[0003] Furthermore, most existing technical solutions employ the calculation of the phase difference between dual-antenna carriers for spoofing detection, such as CN117075150A "A Dual-Antenna Carrier Phase Anti-Spoofing Detection Method Based on DBSCAN Clustering Algorithm" and CN113031021A "A Spoofing Interference Detection Method for Satellite Navigation Orientation Equipment Based on Carrier Mutual Difference," without linking spoofing signal detection to the GNSS protection level. Moreover, these existing technical solutions only apply to spoofing signal detection and do not dynamically couple the detection results to the GNSS protection level. Even if signal anomalies are detected, there is a lack of a reverse-engineering mechanism to systematically map the detection statistics to the position tolerance (PL), resulting in a disconnect between alarms and navigation decisions. Summary of the Invention

[0004] This specification provides a navigation management method and apparatus based on deception perception to solve the technical problem of real-time assessment and calculation of navigation safety limits and navigation management under the condition of deception signal attacks during aviation operations.

[0005] To address the aforementioned technical problems, the embodiments in this specification provide the following technical solutions: This specification provides an embodiment of a navigation management method based on deception perception, the method comprising: Acquire information of interest, including the signal power, attitude, and orientation of the GNSS satellite; Calculate the deception offset vector based on the information of interest, and map the deception offset vector to the eccentricity of a non-central chi-square distribution; Calculate the detection statistic, and calculate the false negative probability based on the detection statistic; The detected deception signals are statistically analyzed according to spatial direction to form a probability mass function, and the spatial entropy is calculated based on the probability mass function. The integrity weight loss is then calculated based on the spatial entropy. Calculate the expected position error, and obtain the probability of exceeding the limit based on the expected position error; The minimum protection level that satisfies the constraints is determined based on the integrity weight loss, the probability of missed detection, and the probability of exceeding the limit. The navigation processing strategy is then determined based on the minimum protection level that satisfies the constraints.

[0006] Preferably, the minimum protection level that satisfies the constraints is determined based on the integrity weight loss, the probability of missed detection, and the probability of exceeding the limit, including: The integrity weight loss, the probability of missed detection, and the probability of exceeding the limit are input into the effective integrity loss upper limit model to solve for the minimum protection level that satisfies the constraints.

[0007] As a preferred option, the minimum protection level that satisfies the constraints includes: Under the preset integrity risk limit, the minimum protection level that satisfies the constraints is solved by the effective integrity loss limit calculation model.

[0008] Preferably, determining the navigation processing strategy based on the minimum protection level that satisfies the constraints includes: If the minimum protection level that satisfies the constraints does not exceed the threshold, then the current navigation solution is maintained.

[0009] Preferably, determining the navigation processing strategy based on the minimum protection level that satisfies the constraints includes: If the minimum protection level that satisfies the constraints exceeds the threshold, a navigation alarm will be issued.

[0010] As a preferred option, navigation alerts include: Output alarm information to the flight management system.

[0011] Preferably, the method further includes: The detection statistics are used to determine whether a deception signal has been received.

[0012] Preferably, the GNSS satellite signal power, GNSS satellite attitude, and GNSS satellite orientation are obtained from the GNSS receiver, inertial measurement unit, and multi-antenna array, respectively.

[0013] Preferably, the deception offset vector is calculated using a pre-constructed deception offset vector model; And / or, The detection statistics are calculated using a pre-constructed detection statistics model.

[0014] This specification provides an embodiment of a navigation management device based on deception perception, the device comprising: A data acquisition unit is used to acquire information of interest, including the signal power, attitude, and orientation of GNSS satellites. The fusion computing unit is used to calculate the deception offset vector based on the information of interest, and map the deception offset vector into an eccentricity of a non-central chi-square distribution; Calculate the detection statistic, and calculate the false negative probability based on the detection statistic; The detected deception signals are statistically analyzed according to spatial direction to form a probability mass function, and the spatial entropy is calculated based on the probability mass function. The integrity weight loss is then calculated based on the spatial entropy. Calculate the expected position error, and obtain the probability of exceeding the limit based on the expected position error; The integrity decision and alarm output unit is used to determine the minimum protection level that satisfies the constraints based on the integrity weight loss, the probability of missed detection, and the probability of exceeding the limit, and to determine the navigation processing strategy based on the minimum protection level that satisfies the constraints.

[0015] The above-described at least one technical solution adopted in the embodiments of this specification can achieve the following beneficial effects: The above technical solution does not require the use of multiple antennas for attitude determination. It relies solely on the parallel calculation of power / direction characteristics of a single antenna for deception detection. At the same time, it dynamically couples the non-center parameter λ under deception conditions with the protection level to ensure that the integrity risk level can be dynamically adjusted under deception scenarios, thus ensuring that the integrity risk is controllable. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the drawings used in the description of the embodiments of this specification or the prior art will be briefly described below. Obviously, the drawings used in some embodiments of this application are only described below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the architecture of the navigation management method based on deception perception provided in the first embodiment of this specification. Detailed Implementation

[0018] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments involved in the specific implementation are only a part of the embodiments of this application, and not all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments in the specific implementation without creative effort should fall within the protection scope of this application.

[0019] The first embodiment of this specification (hereinafter referred to as "Embodiment 1") provides a navigation management method based on deception perception. The execution subject of Embodiment 1 includes, but is not limited to, a terminal, a server, an operating system, or an application. That is, the execution subject can be diverse and can be set, used, or changed as needed. In addition, a third-party application can also assist the execution subject in executing Embodiment 1. For example, the method in Embodiment 1 can be executed by a server, and a corresponding application can be installed on a terminal (which can be held by a user). Data can be transmitted between the terminal or the application and the server, thereby assisting the server in executing the method in Embodiment 1.

[0020] The navigation management method based on deception perception provided in Example 1 includes: S101: Obtain information of interest, which includes the signal power, attitude, and orientation of the GNSS satellite; In Example 1, information of interest can be acquired, including the signal power, attitude, and orientation (i.e., incident direction) of the GNSS satellite. Specifically, the signal power of each GNSS satellite can be acquired from the GNSS receiver, the attitude of each GNSS satellite can be acquired from the inertial measurement unit, and the orientation of each GNSS satellite can be acquired from the multi-antenna array.

[0021] S103: Calculate the deception offset vector based on the information of interest, and map the deception offset vector to the eccentricity of a non-central chi-square distribution; After obtaining the above information of interest, the deception offset vector (or expected residual vector) can be calculated based on the above information of interest. The deception offset vector is used to characterize the theoretical offset caused by the deception signal.

[0022] Among them, a deception offset vector model can be pre-constructed, and the deception offset vector model is represented as follows: ; In the formula Represents the deception offset vector; This represents the actual signal power received by the i-th satellite at the current observation time; This represents the received signal power of the i-th satellite under "nominal conditions"; This represents the directional consistency angle corresponding to the i-th satellite, which is the angle between the measured direction and the theoretical line-of-sight (LOS) direction of the satellite. directional consistency is mapped to a weight / projection coefficient of [−1, 1]; the closer to 1, the more consistent the direction; the smaller the coefficient, the less consistent the direction.

[0023] The aforementioned deception offset vector model is used to construct a template for predictable offsets caused by deception. Wherein, the power ratio... Used to detect power anomalies or trends of enhancement. The two are used to indicate whether the capture direction is consistent, and the two are fused to obtain the deception offset vector.

[0024] By inputting the information of interest mentioned above into the deception offset vector model, the deception offset vector can be obtained.

[0025] Furthermore, the deception offset vector is mapped to an eccentricity of a non-central chi-square distribution, as shown in the following equation: ; This formula is used to calculate the effect of deception bias under weighted measures. The larger the non-centrality parameter, the more significant the bias. In this formula, λ is the non-centrality parameter, which determines how much the statistical distribution deviates from the non-deception case when deception bias exists. W The weight matrix is ​​calculated by taking the inverse of the observation noise covariance.

[0026] S105: Calculate the detection statistic and calculate the false detection probability based on the detection statistic; In Example 1, a detection statistic can be calculated. This can be achieved by pre-constructing a detection statistic model, which is then used to calculate the detection statistic. The detection statistic model is represented as follows: ; In this formula, τ is the detection statistic, representing the weighted sum of squared residuals between the actual and expected observations; R is the actual observation combination, which is an observation vector composed of the power ratio / directional consistency of each satellite, etc. exp Represents the expected combination of observations, and represents the expected value vector under the condition of no deception / nominality; The central chi-square distribution (the statistical distribution under the no-deception assumption) is represented by k; k represents... The degrees of freedom are related to the dimension of the observation vector / number of constraints; This indicates a non-central chi-square distribution.

[0027] After calculating the detection statistic, its distribution can be analyzed. Example 1 proposes that, in the absence of deception, the detection statistic follows a central chi-square distribution, i.e. When deception exists (i.e., a deception offset vector exists), the detection statistic follows a non-central chi-square distribution with an offset of λ. .

[0028] Furthermore, in the event of deception, the probability of a missed detection can be calculated based on the detection statistic, as shown in the following formula: ; In this formula, Indicates the probability of a missed detection; T represents the detection threshold; This represents the cumulative distribution function (CDF) value of the non-central chi-square distribution at point T.

[0029] The aforementioned false negative probability represents the probability that, by calculating the value of the non-central chi-square distribution at the detection threshold T, a detection is not triggered even when spoofing exists (not triggering detection means the detection statistic still falls within the threshold). Based on the detection statistic, it can be determined whether a spoofing signal has been received, including whether the detection statistic τ is greater than the detection threshold T. If τ > T, then a spoofing signal is determined to have been received.

[0030] If a deception signal is detected, it means that the power of the previously received signal was actually the superposition of the power of the real navigation signal and the potential deception signal.

[0031] S107: The detected deception signals are statistically analyzed according to spatial direction to form a probability mass function, and the spatial entropy is calculated based on the probability mass function. The integrity weight loss is calculated based on the spatial entropy. In Example 1, the detected deception signals are statistically analyzed according to spatial direction to form a probability mass function (the probability mass function is expressed as...). The specific implementation process is as follows: First, divide the entire visible space into M non-overlapping grids, and denote the i-th grid as d. i The value of i ranges from 1 to M. The number of values ​​falling into each spatial grid d is statistically analyzed. i The number of deceptive signals N i Then, the probability mass function is calculated by normalizing it using the following formula. : ; In this formula, N i For the detected falling into the i-th spatial grid d i The number of spoofing signal samples within; N is the total number of spoofing signals detected, i.e. .

[0032] Furthermore, spatial entropy can be calculated based on the probability mass function. Spatial entropy is used to characterize the distributional complexity of the deception signal. The calculation of spatial entropy is expressed as follows: ; In this formula, H ( D ) represents spatial entropy.

[0033] Furthermore, the integrity weight loss can be calculated based on spatial entropy (the integrity weight loss is denoted as ). P IL ), which is represented as follows: .

[0034] S109: Calculate the expected position error, and obtain the probability of exceeding the limit based on the expected position error; In Example 1, the expected position error can be calculated, as shown below: ; In this formula, This represents the expected position error. G represents the standard geometric matrix used in navigation calculations to map the error in the measurement domain to the position domain; μ represents the expected value of the position error.

[0035] The position error expectation represents the systematic offset of the navigation and positioning system caused by a spoofing attack, and is used to accurately assess the degree to which the current positioning result deviates from the true position.

[0036] Furthermore, the probability of exceeding the limit (or the probability of exceeding the limit at location) can be obtained based on the expected position error, as follows: ; In this formula, This indicates the probability of exceeding the limit. Q() represents the right-tail probability function (Q function) of the standard normal distribution, used to calculate the probability that a random variable falls outside a specific interval; PL represents the protection level, which is the maximum position error tolerance allowed in the current flight phase as required by civil aviation regulations or mission requirements. This represents the expected position error. The standard deviation of the position error includes the measurement noise inherent in the system.

[0037] The probability of exceeding the threshold represents the probability that the actual positioning error exceeds a threshold under the condition of a deception attack. It is used to assess whether the error generated by the navigation system is still within a safe and controllable range.

[0038] S111: Determine the minimum protection level that satisfies the constraints based on the integrity weight loss, the probability of missed detection, and the probability of exceeding the limit; determine the navigation processing strategy based on the minimum protection level that satisfies the constraints.

[0039] In Example 1, the minimum protection level that satisfies the constraints can be determined based on the above-mentioned integrity weight loss, missed detection probability, and over-limit probability.

[0040] Preferably, the minimum protection level that satisfies the constraints is determined based on the integrity weight loss, the probability of missed detection, and the probability of exceeding the limit, including: The integrity weight loss, the probability of missed detection, and the probability of exceeding the limit are input into the effective integrity loss upper limit model to solve for the minimum protection level that satisfies the constraints.

[0041] As a preferred option, the minimum protection level that satisfies the constraints includes: Under the preset integrity risk limit, the minimum protection level that satisfies the constraints is solved by the effective integrity loss limit calculation model.

[0042] The following explains in detail how to calculate the minimum protection level that satisfies the constraints: An effective integrity loss upper bound model can be pre-constructed, as follows: ; In this formula, This indicates the upper limit of effective integrity loss.

[0043] The above obtained P IL , as well as Input the effective integrity loss upper limit model, perform the above multiplication operation on the effective integrity loss upper limit model to obtain the effective integrity loss upper limit. Under the pre-set integrity risk upper limit (denoted as PHMI), solve for the minimum protection level that satisfies the constraints, expressed as follows: ; In this formula, This represents the minimum protection level that satisfies the constraints (the protection level is denoted as PL). This formula represents the dynamic protection level calculation model.

[0044] In Example 1, the navigation processing strategy is determined based on the minimum protection level that satisfies the constraints. Preferably, determining the navigation processing strategy based on the minimum protection level that satisfies the constraints includes: If the minimum protection level that satisfies the constraints does not exceed the threshold (which serves as an alarm threshold), the current navigation calculation is maintained; and / or, if the minimum protection level that satisfies the constraints exceeds the threshold, a navigation alarm is issued, which may include outputting alarm information to the flight management system.

[0045] The following is combined Figure 1 The architecture of the execution entity in Embodiment 1 is illustrated by way of example: In this example, the execution entity of Embodiment 1 may include a data acquisition unit, a communication bus interface, a fusion computing unit, and an integrity decision and alarm output unit.

[0046] The data acquisition unit is used to acquire satellite signal power, attitude and direction information from the GNSS receiver, inertial measurement unit and multi-antenna array respectively. This includes receiving GNSS signals and spoofing signals from each satellite, extracting the power of the "spoofing signal" and "real signal" in each satellite channel, and providing the elevation / azimuth angle of each satellite.

[0047] The communication bus interface can be an ARINC 429 bus, used to transmit the signal power, direction, attitude and other characteristic values ​​(which are information of interest) collected in the data acquisition module to the upper-layer fusion computing unit.

[0048] The fusion computing unit calculates parameters such as the spoofing offset vector, the eccentricity of the non-central chi-square distribution, detection statistics, missed detection probability, probability quality function, spatial entropy, integrity weight loss, error expectation, and out-of-limit probability based on the received satellite power ratio and orientation consistency data. It then transmits these calculated parameters to the integrity decision and alarm output unit.

[0049] The integrity decision and alarm output unit is used to solve for the minimum protection level under the constraint of a preset integrity risk upper limit (PHMI). It assesses the current effective integrity loss probability. If the PHMI is exceeded, an integrity risk decision is made, and a navigation processing strategy is determined. If an integrity risk exists, an alarm message is output and transmitted to the flight management system.

[0050] Example 1 can achieve the following beneficial effects: Example 1 calculates the navigation integrity loss probability P by combining the non-centrality parameter λ caused by the deception signal with the deception spatial distribution entropy H(D). LOI Furthermore, under a given integrity risk upper limit PHMI, a dynamic protection level calculation model for the adaptive protection level PL is derived through inverse solving. This model dynamically couples deception detection, risk quantification, and tolerance assessment to achieve real-time calculation of deception signal strength, distribution, and position error tolerance, thereby calculating the minimum protection level and achieving dynamic risk assessment. Example 1 can achieve real-time assessment and calculation of navigation safety limits under conditions of deception signal attacks during aviation operations, and can be applied in various fields, including civil aviation.

[0051] In Example 1, the expected residual introduced by the deception signal is constructed as a non-central chi-square eccentricity λ through power ratio and direction consistency. Combined with the integrity weight loss derived from spatial entropy H(D), an effective integrity loss upper limit model is constructed, which includes the probability of missed detection, integrity weight loss, and location out-of-limit probability. (This model belongs to the comprehensive tail probability model), and then calculates the protection level under the premise of deception under the given integrity risk upper limit PHMI, and determines whether the integrity risk upper limit is exceeded and outputs alarm information, thereby improving the real-time assessment calculation of navigation safety limits and the effectiveness of navigation management.

[0052] Example 1 constructs a lightweight deception feature as an expected residual vector and maps it to a non-central parameter to calculate the false negative probability. This is then coupled with spatial entropy weights to form the effective integrity loss probability. Finally, under the constraint of a preset integrity risk upper limit, the protection level is deduced, realizing a closed loop of "detection-risk-protection level-decision". This overcomes the problem that existing technologies only support detection and do not provide a deduction mechanism from detection statistics to PL.

[0053] In Example 1, spoofing detection is performed using single-antenna power / direction characteristics instead of the traditional multi-antenna carrier phase orientation / double-difference residual detection, which helps to further improve the efficiency of spoofing detection.

[0054] The second embodiment of this specification provides a navigation management device based on deception perception, corresponding to the method described in Embodiment 1. The device includes: A data acquisition unit is used to acquire information of interest, including the signal power, attitude, and orientation of GNSS satellites. The fusion computing unit is used to calculate the deception offset vector based on the information of interest, and map the deception offset vector into an eccentricity of a non-central chi-square distribution; Calculate the detection statistic, and calculate the false negative probability based on the detection statistic; The detected deception signals are statistically analyzed according to spatial direction to form a probability mass function, and the spatial entropy is calculated based on the probability mass function. The integrity weight loss is then calculated based on the spatial entropy. Calculate the expected position error, and obtain the probability of exceeding the limit based on the expected position error; The integrity decision and alarm output unit is used to determine the minimum protection level that satisfies the constraints based on the integrity weight loss, the probability of missed detection, and the probability of exceeding the limit, and to determine the navigation processing strategy based on the minimum protection level that satisfies the constraints.

[0055] Preferably, the minimum protection level that satisfies the constraints is determined based on the integrity weight loss, the probability of missed detection, and the probability of exceeding the limit, including: The integrity weight loss, the probability of missed detection, and the probability of exceeding the limit are input into the effective integrity loss upper limit model to solve for the minimum protection level that satisfies the constraints.

[0056] As a preferred option, the minimum protection level that satisfies the constraints includes: Under the preset integrity risk limit, the minimum protection level that satisfies the constraints is solved by the effective integrity loss limit calculation model.

[0057] Preferably, determining the navigation processing strategy based on the minimum protection level that satisfies the constraints includes: If the minimum protection level that satisfies the constraints does not exceed the threshold, then the current navigation solution is maintained.

[0058] Preferably, determining the navigation processing strategy based on the minimum protection level that satisfies the constraints includes: If the minimum protection level that satisfies the constraints exceeds the threshold, a navigation alarm will be issued.

[0059] As a preferred option, navigation alerts include: Output alarm information to the flight management system.

[0060] Preferably, the fusion computing unit is also used to: determine whether a deception signal has been received based on the detection statistics.

[0061] Preferably, the GNSS satellite signal power, GNSS satellite attitude, and GNSS satellite orientation are obtained from the GNSS receiver, inertial measurement unit, and multi-antenna array, respectively.

[0062] Preferably, the deception offset vector is calculated using a pre-constructed deception offset vector model; And / or, The detection statistics are calculated using a pre-constructed detection statistics model.

[0063] Example 2 can serve as the execution subject of Example 1.

[0064] The contents not described in detail in Embodiment 1 and Embodiment 2 can be referred to each other. Embodiment 2 can achieve the same beneficial effects as Embodiment 1. The above embodiments can be used in combination.

[0065] The above description is merely an embodiment of this specification and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of this application should be included within the scope of the claims of this application.

Claims

1. A navigation management method based on deception perception, characterized in that, The method includes: Acquire information of interest, including the signal power, attitude, and orientation of the GNSS satellite; Calculate the deception offset vector based on the information of interest, and map the deception offset vector to the eccentricity of a non-central chi-square distribution; Calculate the detection statistic, and calculate the false negative probability based on the detection statistic; The detected deception signals are statistically analyzed according to spatial direction to form a probability mass function, and the spatial entropy is calculated based on the probability mass function. The integrity weight loss is then calculated based on the spatial entropy. Calculate the expected position error, and obtain the probability of exceeding the limit based on the expected position error; The minimum protection level that satisfies the constraints is determined based on the integrity weight loss, the probability of missed detection, and the probability of exceeding the limit. The navigation processing strategy is then determined based on the minimum protection level that satisfies the constraints.

2. The method as described in claim 1, characterized in that, The minimum protection level that satisfies the constraints is determined based on the integrity weight loss, the probability of missed detection, and the probability of exceeding the limit, including: The integrity weight loss, the probability of missed detection, and the probability of exceeding the limit are input into the effective integrity loss upper limit model to solve for the minimum protection level that satisfies the constraints.

3. The method as described in claim 1, characterized in that, Solving for the minimum protection level that satisfies the constraints includes: Under the preset integrity risk limit, the minimum protection level that satisfies the constraints is solved by the effective integrity loss limit calculation model.

4. The method according to any one of claims 1 to 3, characterized in that, Determining the navigation processing strategy based on the minimum protection level that satisfies the constraints includes: If the minimum protection level that satisfies the constraints does not exceed the threshold, the current navigation solution is maintained.

5. The method according to any one of claims 1 to 3, characterized in that, Determining the navigation processing strategy based on the minimum protection level that satisfies the constraints includes: If the minimum protection level that satisfies the constraints exceeds the threshold, a navigation alarm will be issued.

6. The method as described in claim 5, characterized in that, Navigation alerts include: Output alarm information to the flight management system.

7. The method as described in claim 1, characterized in that, The method further includes: The detection statistics are used to determine whether a deception signal has been received.

8. The method as described in claim 1, characterized in that, The GNSS satellite signal power, GNSS satellite attitude, and GNSS satellite orientation are obtained from the GNSS receiver, inertial measurement unit, and multi-antenna array, respectively.

9. The method as described in claim 1, characterized in that, The deception offset vector is calculated using a pre-constructed deception offset vector model; And / or, The detection statistics are calculated using a pre-constructed detection statistics model.

10. A navigation management device based on deception perception, characterized in that, The device includes: A data acquisition unit is used to acquire information of interest, including the signal power, attitude, and orientation of GNSS satellites. The fusion computing unit is used to calculate the deception offset vector based on the information of interest, and map the deception offset vector into an eccentricity of a non-central chi-square distribution; Calculate the detection statistic, and calculate the false negative probability based on the detection statistic; The detected deception signals are statistically analyzed according to spatial direction to form a probability mass function, and the spatial entropy is calculated based on the probability mass function. The integrity weight loss is then calculated based on the spatial entropy. Calculate the expected position error, and obtain the probability of exceeding the limit based on the expected position error; The integrity decision and alarm output unit is used to determine the minimum protection level that satisfies the constraints based on the integrity weight loss, the probability of missed detection, and the probability of exceeding the limit, and to determine the navigation processing strategy based on the minimum protection level that satisfies the constraints.

Images