A satellite navigation fault detection method based on motion state prediction

Abstract

The present invention provides a satellite navigation fault detection method based on motion state prediction, which obtains historical GNSS navigation information and performs smoothing to obtain smoothed navigation information, establishes an n-order motion state function model, brings the smoothed navigation information into the n-order motion state function model, and solves the motion state model parameters by the least square method; performs extrapolation of the navigation information at the current moment to obtain the extrapolated navigation information, and uses the extrapolated navigation information to determine whether the navigation information output by the GNSS at the current moment fails. The present invention uses a smoothing algorithm to smooth the historical motion state information, uses the smoothed motion state information and time to solve the parameters of the established motion state function model, and further extrapolates the motion information at the current moment through the solved motion state function, which can be used as a benchmark to realize fault detection of the navigation information output by the GNSS, and the method can realize fault detection of the GNSS without other auxiliary information.

Description

Technical Field

[0001] The invention relates to the technical field of measurement and testing, and in particular to a satellite navigation fault detection method based on motion state prediction. Background Art

[0002] The Global Navigation Satellite System (GNSS) uses orbiting satellites in space to provide real-time speed and position information for vehicles. It is widely used in various types of vehicles such as sea, land, and air, as well as in many fields such as surveying, mapping, and monitoring. However, satellite navigation requires the receiver to receive the signals transmitted by the navigation satellite for positioning and speed calculation, which makes the system susceptible to various interferences. In addition to the fault information contained in the satellite itself, the GNSS signal will also be affected by the atmospheric ionosphere, troposphere, and human interference during the transmission from the satellite to the receiver. In addition, the receiver will also be affected by the local environment, such as urban canyons and tunnels, in different environments, resulting in the output of fault information. In order to ensure the reliability of satellite navigation information, real-time fault detection of the navigation information output by GNSS is required.

[0003] For the vehicle, the motion law satisfies Newton's law of motion, and its motion state is gradual. Therefore, it can be considered that the motion state of the vehicle in the future is related to the motion state at the current moment and in the previous period, and has a weak correlation or even no correlation with the earlier motion state. Based on this neighborhood correlation of the motion state, the historical motion state information of the vehicle can be used to fit the motion state data sequence using the corresponding motion state function model, so as to extrapolate and predict the motion state information of the aircraft in the future. Using the extrapolated motion information as a benchmark, fault detection of GNSS output navigation information can be achieved.

[0004] Patent CN202211050352.9 discloses a dual-redundancy satellite navigation receiver, fault detection and processing method and device. The dual-redundancy satellite navigation receiver includes a navigation signal processing module A, a navigation signal processing module B, and a redundancy judgment module. The navigation signal processing module A and the navigation signal processing module B receive data and complete data synchronization, and send the comparison result to the redundancy judgment module after data consistency comparison; the redundancy judgment module is used to receive the processing results of the navigation signal processing module A and the navigation signal processing module B, and perform a three-redundancy judgment with the state estimation model output to determine the judgment result. The present invention establishes a three-redundancy judgment mechanism by constructing an adaptive state estimation model inside the receiver, which solves the problem that dual-redundancy faults are difficult to judge. However, the fault detection method requires a dual-redundancy satellite navigation receiver to realize fault detection, which is equivalent to introducing external auxiliary information for a single receiver, and has high hardware requirements; this method cannot perform fault detection in the scenario of a single-redundancy receiver and its navigation.

[0005] Therefore, a method for detecting a fault in GNSS output navigation information without other auxiliary information is needed. Summary of the invention

[0006] The present invention is to solve the problem of GNSS fault detection, and provides a satellite navigation fault detection method based on motion state prediction. The method uses the fault-free navigation information in the previous period of time and adopts a smoothing algorithm to smooth the historical motion state information; the smoothed motion state information and time are used to solve the parameters of the established motion state function model, and the motion information at the current moment is further extrapolated through the solved motion state function. Based on this, the fault detection of GNSS output navigation information can be realized. The method can realize GNSS fault detection without other auxiliary information.

[0007] The present invention provides a satellite navigation fault detection method based on motion state prediction, comprising the following steps:

[0008] S1. Obtain historical GNSS navigation information g x , x=1,2,…,i, where g i is the navigation information of the last moment, g i-1 Provide navigation information for the upper moments;

[0009] S2. Historical GNSS navigation information g i Perform smoothing to obtain smoothed navigation information g x ;

[0010] S3, establish n-order motion state function model;

[0011] f(t)=b 0 +b 1 t+b 2 t 2 +…+b n-1 t n-1 ;

[0012] Where t is time, b is y is the motion state model parameter, y=0,1,2,…,n-1, f(t) is the extrapolated navigation information at time t;

[0013] S4, smoothing the navigation information g x Bring in the n-order motion state function model and use the least squares method to calculate the motion state model parameter b y The solution of

[0014] S5. Use motion state model parameter b y , the n-order motion state function model is used for the current time t kThe navigation information is extrapolated to obtain the extrapolated navigation information f(t k );

[0015] S6. Using extrapolated navigation information f(t k ) Determine the navigation information g output by GNSS at the current time k Whether a fault has occurred;

[0016] When |f(t k )-g k |>4σ GNSS , it is judged that the navigation information output by GNSS is faulty, otherwise, GNSS is not faulty; where σ GNSS is the accuracy of GNSS navigation information, σ GNSS is a constant, a satellite navigation fault detection method based on motion state prediction is completed.

[0017] The satellite navigation fault detection method based on motion state prediction described in the present invention is, as a preferred embodiment, in step S1, the GNSS navigation information includes GNSS speed information and GNSS position information.

[0018] In the satellite navigation fault detection method based on motion state prediction described in the present invention, as a preferred embodiment, the GNSS speed information includes: eastward speed, northward speed and celestial speed;

[0019] GNSS location information includes longitude information, latitude information, and altitude information.

[0020] In the satellite navigation fault detection method based on motion state prediction described in the present invention, as a preferred embodiment, the smoothing method in step S2 is:

[0021]

[0022] Wherein, 2m+1 is the smoothing window length, m≥0, i>2m+1.

[0023] The satellite navigation fault detection method based on motion state prediction described in the present invention is, as a preferred embodiment, in step S3, n≤2m+1.

[0024] The satellite navigation fault detection method based on motion state prediction described in the present invention is, as a preferred embodiment, in step S4, TB=G;

[0025] in,

[0026]

[0027] B=(b 0 b 1 b2 …b n-1 ) T .

[0028] The satellite navigation fault detection method based on motion state prediction described in the present invention, as a preferred embodiment, uses the least squares method to obtain the parameter matrix as follows:

[0029] B=(T T T) -1 T T G.

[0030] The satellite navigation fault detection method based on motion state prediction described in the present invention is, as a preferred embodiment, in step S5,

[0031] f(t k )=b 0 +b 1 t k +b 2 t k 2 +…+b n-1 t k n-1 .

[0032] The present invention has the following advantages:

[0033] The present invention uses the fault-free navigation information in the previous period of time and adopts a smoothing algorithm to smooth the historical motion state information; the smoothed motion state information and time are used to solve the parameters of the established motion state function model, and the motion information at the current moment is further extrapolated through the solved motion state function, which can be used as a reference to realize the fault detection of GNSS output navigation information. This method can realize the fault detection of GNSS without other auxiliary information. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a flow chart of a satellite navigation fault detection method based on motion state prediction;

[0035] Figure 2a The GNSS eastward velocity output including the fault generated in Embodiment 1 of a satellite navigation fault detection method based on motion state prediction;

[0036] Figure 2b is the GNSS north velocity output generated in Embodiment 1 of a satellite navigation fault detection method based on motion state prediction;

[0037] Figure 2c The GNSS celestial velocity output including the fault generated in Embodiment 1 of a satellite navigation fault detection method based on motion state prediction;

[0038] Figure 3a The GNSS east position output generated in Embodiment 1 of a satellite navigation fault detection method based on motion state prediction;

[0039] Figure 3b The GNSS north position output containing the fault generated in Embodiment 1 of a satellite navigation fault detection method based on motion state prediction;

[0040] Figure 3c The GNSS celestial position output generated in Embodiment 1 of a satellite navigation fault detection method based on motion state prediction;

[0041] Figure 4 This is a fault detection result of Example 1 of a satellite navigation fault detection method based on motion state prediction. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0043] Example 1

[0044] like Figure 1 As shown, a satellite navigation fault detection method based on motion state prediction includes the following steps:

[0045] Step 1: Save the GNSS navigation information at the historical moment x (x=1,2,…,i); where g i Indicates the navigation information at the last moment, g i-1 Indicates the navigation information at the last moment, and so on. GNSS navigation information includes 6 parameters: east speed, north speed, celestial speed, longitude, latitude and altitude.

[0046] Step 2: According to Figure 1 , refer to formula (1) for the historical GNSS navigation information g x (x=1,2,…,i) is smoothed;

[0047]

[0048] represents the smoothed navigation information; 2m+1 (m=0, 1, 2, ...) is the smoothing window length. Historical navigation information g i The data length should be greater than 2m+1, but not too long, otherwise the extrapolated information will be interfered by the earlier navigation information, resulting in reduced accuracy.

[0049] Step 3: Establish an n-order motion state function model;

[0050] f(t)=b 0 +b 1 t+b 2 t 2 +…+b n-1 t n-1 (2)

[0051] Where t represents time, b y (y=0,1,2,…,n-1) are motion state model parameters, and f(t) represents the extrapolated navigation information corresponding to time t. In addition, n must satisfy n≤2m+1.

[0052] Step 4: According to Figure 1 , the motion state model parameters are solved using the smoothed historical navigation information;

[0053] The smoothed navigation information g in equation (1) is x Substituting (x=1,2,…,i) into equation (2), transforming and combining into a matrix form, we can obtain:

[0054]

[0055] t i Indicates navigation information i The corresponding time is B=(b 0 b 1 b 2 …b n-1 ) T , the time correlation matrix is

[0056]

[0057] At this time, equation (3) can be expressed as TB = G, and the parameter matrix can be calculated using the least squares method as follows:

[0058] B=(T T T) -1 T T G (5)

[0059] Step 5: Extrapolate navigation information using the solved motion state model;

[0060] The parameter b calculated by equation (5) 0 ,b 1 ,b 2 ,…,b n-1 Substituting into formula (2), we can calculate the current time t k The extrapolated navigation information is

[0061] f(tk )=b 0 +b 1 t k +b 2 t k 2 +…+b n-1 t k n-1 (6)

[0062] Step 6: According to Figure 1 , using the extrapolated navigation information f(t k ) Determine the navigation information g output by GNSS at the current time k Is there a fault? If

[0063] |f(t k )-g k |>4σ GNSS (7)

[0064] If the navigation information output by GNSS fails, it is considered that the GNSS is faulty. Otherwise, it is considered that the GNSS is not faulty. GNSS Indicates the accuracy of GNSS corresponding navigation information, which is determined in advance by the performance of the GNSS system itself.

[0065] The specific implementation manner and implementation effect of the present invention are further illustrated below through simulation experiments.

[0066] The simulation conditions are set as follows: the initial position is [108.83°; 34.67°; 400m]; the vehicle travels eastward at a constant speed of 20m / s, and the movement duration is 2000s; the satellite navigation system update period is 1s, the satellite velocity error is 0.3m / s (1σ), the horizontal position error is 10m (1σ), and the height error is 20m (1σ); the smoothing window length is set to 5 (m=2), the motion state model order is set to n=5, and the data length of the historical navigation information is set to 9.

[0067] Figure 2a to 2c is the GNSS velocity error information containing faults generated in an embodiment of the present invention; wherein Figure 2a The display shows that the eastward speed has a constant deviation fault at 500s. Figure 2b Displays northbound speed without any faults, Figure 2c It shows that the azimuth speed has a jump fault at 1000s.

[0068] Figure 3a to 3c is the GNSS position error information containing faults generated in an embodiment of the present invention; wherein Figure 3a , 3c Displays the east position and the celestial position without any faults. Figure 3bIt shows that a continuous jump fault occurred at 1500s in the north position.

[0069] Figure 4 is a fault detection result in an embodiment of the present invention.

[0070] The effects of the embodiments show that the method of GNSS fault detection using motion state extrapolation in the technology of the present invention can effectively identify the fault information output by GNSS without the need for other auxiliary information.

[0071] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A satellite navigation fault detection method based on motion state prediction, characterized in that: The following steps are involved: S1. Obtain historical GNSS navigation information g x , x=1,2,…,i, where g i is the navigation information of the last moment, g i-1 Provide navigation information for the upper moments; S2. Analyzing the historical GNSS navigation information g i Perform smoothing to obtain smoothed navigation information The smoothing method is: Where 2m+1 is the smoothing window length, m≥0, i>2m+1; S3, establish n-order motion state function model; f(t)=b0+b1t+b2t 2 +…+b n-1 t n-1 ; Where t is time, b is y is the motion state model parameter, y=0,1,2,…,n-1, f(t) is the extrapolated navigation information at time t; S4, smoothing the navigation information Bring the n-order motion state function model into the equation, and use the least squares method to calculate the motion state model parameter b y The solution of S5, using the motion state model parameter b y , the n-order motion state function model is performed at the current time t k The navigation information is extrapolated to obtain the extrapolated navigation information f(t k ); S6. Using the extrapolated navigation information f(t k ) Determine the navigation information g output by GNSS at the current time k Whether a fault has occurred; When |f(t k )-g k |>4σ GNSS , it is judged that the navigation information output by GNSS is faulty, otherwise, GNSS is not faulty; where σ GNSS is the accuracy of GNSS navigation information, σ GNSS is a constant, a satellite navigation fault detection method based on motion state prediction is completed.

2. A satellite navigation fault detection method based on motion state prediction according to claim 1, characterized in that: In step S1, the GNSS navigation information includes GNSS speed information and GNSS position information.

3. A satellite navigation fault detection method based on motion state prediction according to claim 2, characterized in that: The GNSS speed information includes: eastward speed, northward speed and celestial speed; The GNSS location information includes longitude information, latitude information and altitude information.

4. The satellite navigation fault detection method based on motion state prediction according to claim 1 is characterized in that: In step S3, n≤2m+1.

5. The satellite navigation fault detection method based on motion state prediction according to claim 1, characterized in that: In step S4, TB=G; in, 6. A satellite navigation fault detection method based on motion state prediction according to claim 5, characterized in that: The parameter matrix obtained by using the least squares method is: B=(T T T) -1 T T G。 7. The satellite navigation fault detection method based on motion state prediction according to claim 1, characterized in that: In step S5, f(t k )=b0+b1t k +b2t k 2 +…+b n-1 t k n-1 。

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