Fusion positioning method and device based on inertial navigation system and global navigation satellite system

Abstract

The invention relates to the technical field of intelligent driving, and discloses a fusion positioning method and device based on an inertial navigation system and a global navigation satellite system, and the method comprises the steps: obtaining first positioning data through the global navigation satellite system; acquiring second positioning data through the inertial navigation system; performing data filtering processing on the first positioning data by adopting second positioning data to obtain third positioning data; and carrying out fusion processing on the second positioning data and the third positioning data by adopting a Kalman filter to obtain target positioning data. According to the invention, the first positioning data is screened to eliminate interference data, and the Kalman filter is combined to carry out data fusion on the second positioning data and the first positioning data, so that the positioning precision of the system is improved; therefore, the problem of inaccurate positioning of the inertial navigation system and the global navigation satellite system under the condition that the signal of the global navigation satellite system is weakly interfered is solved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent driving technology, and in particular to a fusion positioning method and device based on an inertial navigation system and a global navigation satellite system. Background Art

[0002] The integrated navigation system of INS (Inertial Navigation System) and GNSS (Global Navigation Satellite System) has become the core technology in the fields of intelligent driving, vehicle tracking and pedestrian navigation due to its advantages of low cost and high integration. However, when used alone, the INS system faces the problem of the increase of the integral cumulative error over time, while the GNSS signal may be interrupted due to bad weather, urban canyon effect or obstructions, affecting the positioning accuracy.

[0003] In order to overcome these limitations, the industry widely uses Kalman filters as the core algorithm for INS and GNSS data fusion. When GNSS signals are stable and available, Kalman filters can efficiently fuse the data of the two, significantly improving the positioning accuracy and robustness of the system through complementary advantages.

[0004] However, when GNSS signals become unreliable due to external interference, judging the signal quality based solely on the internal state of the sensor or the number of satellites is often not accurate enough, which may cause the system to mistakenly regard the interfered GNSS data as valid input, thereby generating positioning errors. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention proposes a fusion positioning method and device based on an inertial navigation system and a global navigation satellite system to solve the above technical problems.

[0006] In a first aspect, a fusion positioning method based on an inertial navigation system and a global navigation satellite system is provided, which is applied to a vehicle-mounted positioning system, including:

[0007] acquiring first positioning data through a global navigation satellite system;

[0008] Acquiring second positioning data through an inertial navigation system;

[0009] Using the second positioning data to perform data filtering processing on the first positioning data to obtain third positioning data;

[0010] A Kalman filter is used to fuse the second positioning data and the third positioning data to obtain target positioning data.

[0011] Further, the first positioning data is filtered using the second positioning data to obtain third positioning data, including:

[0012] Calculating a first credibility corresponding to the first positioning data by using a mass function, and calculating a second credibility corresponding to the second positioning data by using the mass function;

[0013] Calculating a conflict factor between the first positioning data and the second positioning data using the first credibility and the second credibility;

[0014] The second positioning data whose conflict factor is lower than a preset conflict factor threshold is determined as the third positioning data.

[0015] Further, a Kalman filter is used to fuse the second positioning data and the third positioning data to obtain target positioning data, including:

[0016] According to the dynamic characteristics of the positioning system, the state transfer equation is constructed;

[0017] According to the state transfer equation, a measurement equation for the second positioning data and a measurement equation for the third positioning data are respectively constructed;

[0018] The measurement value of the measurement equation of the second positioning data and the measurement value of the measurement equation of the third positioning data are combined to obtain a combined measurement vector.

[0019] Adjusting the measurement matrix and the measurement noise covariance matrix of the Kalman filter to obtain an adjusted Kalman filter;

[0020] The adjusted Kalman filter is used to update the combined measurement vector to obtain the target positioning data.

[0021] Furthermore, before the positioning system enters the tunnel, a Kalman filter algorithm is used to fuse the second positioning data with the first positioning data before entering the tunnel to obtain a positioning result in the tunnel.

[0022] Furthermore, it also includes real-time monitoring of the dynamic characteristics and environmental factors of the positioning system to obtain monitoring results;

[0023] According to the monitoring results, the values ​​of the noise covariance matrix and the measurement noise covariance matrix in the Kalman filter process are dynamically adjusted.

[0024] In a second aspect, a fusion positioning device based on an inertial navigation system and a global navigation satellite system is provided, characterized in that it includes:

[0025] A first positioning data module is configured to obtain first positioning data through a global navigation satellite system;

[0026] A second positioning data module is configured to obtain second positioning data through an inertial navigation system;

[0027] A third positioning data module is configured to perform data filtering processing on the first positioning data using the second positioning data to obtain third positioning data;

[0028] The target positioning data module is configured to use a Kalman filter to perform fusion processing on the second positioning data and the third positioning data to obtain target positioning data.

[0029] Furthermore, it includes a processor and a memory storing program instructions, and the processor is configured to execute the fusion positioning method based on the inertial navigation system and the global navigation satellite system as described in the above item when running the program instructions.

[0030] According to a third aspect, an electronic device is provided, comprising the aforementioned fusion positioning device based on an inertial navigation system and a global navigation satellite system.

[0031] In a fourth aspect, a storage medium is provided, storing program instructions, which, when run, execute the fusion positioning method based on the inertial navigation system and the global navigation satellite system as described in any of the above items.

[0032] The invention adopting the above technical solution has the following advantages:

[0033] The present invention screens the first positioning data obtained by the global navigation satellite system to eliminate interference data, and then combines the Kalman filter to fuse the second positioning data obtained by the inertial navigation system with the first positioning data obtained by the global navigation satellite system, thereby improving the positioning accuracy of the system, thereby solving the problem of inaccurate positioning of the inertial navigation system and the global navigation satellite system when the global navigation satellite system signal is subject to weak interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the specific implementation of the present invention, the following will briefly introduce the drawings required for use in the specific implementation. In all the drawings, each element or part is not necessarily drawn according to the actual scale.

[0035] Figure 1 This is a flow chart of a fusion positioning method based on an inertial navigation system and a global navigation satellite system according to the present invention;

[0036] Figure 2 The present invention is a flow chart of a fusion positioning device based on an inertial navigation system and a global navigation satellite system. DETAILED DESCRIPTION

[0037] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0038] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should be the common meanings understood by technicians in the field to which the present invention belongs. The terms "first", "second", etc. in the specification and claims of the embodiments of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so as to implement the embodiments of the present disclosure described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. Unless otherwise specified, the term "multiple" means two or more. In the embodiments of the present disclosure, the character " / " indicates that the objects before and after are in an "or" relationship. For example, A / B means: A or B. The term "and / or" is a description of the association relationship of objects, indicating that there can be three relationships. For example, A and / or B means: A or B, or, A and B. The term "corresponding" can refer to an association relationship or a binding relationship, and A and B correspondingly refer to an association relationship or a binding relationship between A and B.

[0039] like Figure 1-2 As shown, the fusion positioning method based on the inertial navigation system and the global navigation satellite system of the present invention is applied to the vehicle-mounted positioning system, including:

[0040] Step S01: obtaining first positioning data through a global navigation satellite system (such as GPS, Galieo, etc.);

[0041] Step S02: obtaining second positioning data through an inertial navigation system (such as a gyroscope, an accelerometer, etc.);

[0042] Step S03: using the second positioning data to perform data filtering processing on the first positioning data to obtain third positioning data;

[0043] Step S04: Use a Kalman filter to fuse the second positioning data and the third positioning data to obtain target positioning data.

[0044] In some embodiments, using the second positioning data to perform data filtering processing on the first positioning data to obtain third positioning data includes:

[0045] Calculating a first credibility corresponding to the first positioning data by using a mass function, and calculating a second credibility corresponding to the second positioning data by using a mass function;

[0046] Calculate the conflict factor between the first positioning data and the second positioning data using the first credibility and the second credibility;

[0047] The second positioning data whose conflict factor is lower than a preset conflict factor threshold is determined as the third positioning data.

[0048] Specifically, the process of DS evidence theory is as follows:

[0049] Θ is a hypothesis space, Ω represents the set of all subsets of the hypothesis space,

[0050] Θ={GPS,INS}

[0051]

[0052] If the mapping function m satisfies m()=0, When , it is called a mass function. The mass function is actually the evaluation weight of various hypotheses.

[0053] m(A) represents the credibility of A, and the mass functions of GPS and INS are obtained as follows:

[0054]

[0055] Finally, calculate the conflict factor K

[0056]

[0057] The coefficient 1 / (1-K) is a normalization factor that avoids assigning non-zero probabilities to the empty set in the combination.

[0058] Assume K≠1 and set a threshold Kth. If K exceeds the threshold, it is considered that the two pieces of evidence are incompatible and there may be erroneous data.

[0059] By judging the mass function, the one with high credibility is selected as the final result, thus eliminating the wrong data.

[0060] In some embodiments, a Kalman filter is used to fuse the second positioning data and the third positioning data to obtain target positioning data, including:

[0061] According to the dynamic characteristics of the positioning system, the state transfer equation is constructed;

[0062] According to the state transfer equation, a measurement equation for the second positioning data and a measurement equation for the third positioning data are respectively constructed;

[0063] The measurement value of the measurement equation of the second positioning data and the measurement value of the measurement equation of the third positioning data are combined to obtain a combined measurement vector.

[0064] Adjusting the measurement matrix and the measurement noise covariance matrix of the Kalman filter to obtain an adjusted Kalman filter;

[0065] The adjusted Kalman filter is used to update the combined measurement vector to obtain the target positioning data.

[0066] In some embodiments, before the positioning system enters the tunnel, a Kalman filter algorithm is used to fuse the second positioning data with the first positioning data before entering the tunnel to obtain a positioning result inside the tunnel.

[0067] In some embodiments, it also includes real-time monitoring of dynamic characteristics and environmental factors of the positioning system to obtain monitoring results;

[0068] According to the monitoring results, the values ​​of the noise covariance matrix and the measurement noise covariance matrix in the Kalman filter process are dynamically adjusted.

[0069] Specifically, after removing the erroneous data, the Kalman fusion algorithm is needed to fuse the INS data with the GPS data. In the Kalman fusion algorithm, it is assumed that the initial sampling rate of the sensor is uniform, and the dynamic model is expressed as:

[0070] x k+1 =F k x k +B k u k +ν k

[0071] x k+1 is the state vector at time k+1, x k is the state vector at time k, F k is the state transfer matrix, B k is the control input matrix, u k is the input vector at time k, ν k is the state noise. The measurement values ​​corresponding to the INS and GPS sensors are:

[0072]

[0073] n=(INS,GPS)

[0074] is the measurement value of sensor n at time k, is the observation matrix of sensor N at time k, and the measurement noise sequence Follows a distribution with mean zero.

[0075] Combining the measurement vectors from two sensors requires the following main steps:

[0076]

[0077] Finally, the new measurement equation is as follows:

[0078] z k =H k x k +w k

[0079] Since the two sensors are independent of each other, the covariance matrix of the measurement noise is:

[0080]

[0081] These fused measurements can then be tracked to obtain a fused state vector estimate via a traditional Kalman filter.

[0082] In some other embodiments, a fusion positioning device based on an inertial navigation system and a global navigation satellite system is provided, including:

[0083] A first positioning data module is configured to obtain first positioning data through a global navigation satellite system;

[0084] A second positioning data module is configured to obtain second positioning data through an inertial navigation system;

[0085] A third positioning data module is configured to perform data filtering processing on the first positioning data using the second positioning data to obtain third positioning data;

[0086] The target positioning data module is configured to use a Kalman filter to perform fusion processing on the second positioning data and the third positioning data to obtain target positioning data.

[0087] In some embodiments, a processor and a memory storing program instructions are included, and the processor is configured to execute the fusion positioning method based on the inertial navigation system and the global navigation satellite system as described above when running the program instructions.

[0088] In other embodiments, an electronic device (including a mobile phone, a computer, etc.) is provided, comprising the aforementioned fusion positioning device based on an inertial navigation system and a global navigation satellite system.

[0089] In other embodiments, a readable storage medium is provided, storing program instructions, and when the program instructions are run, they execute any of the above-mentioned methods for fusion positioning based on an inertial navigation system and a global navigation satellite system.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A fusion positioning method based on an inertial navigation system and a global navigation satellite system, characterized in that: Applied to vehicle positioning systems, including: acquiring first positioning data through a global navigation satellite system; Acquiring second positioning data through an inertial navigation system; Using the second positioning data to perform data filtering processing on the first positioning data to obtain third positioning data; A Kalman filter is used to fuse the second positioning data and the third positioning data to obtain target positioning data.

2. The fusion positioning method based on the inertial navigation system and the global navigation satellite system according to claim 1 is characterized in that: Using the second positioning data to perform data filtering processing on the first positioning data to obtain third positioning data includes: Calculating a first credibility corresponding to the first positioning data by using a mass function, and calculating a second credibility corresponding to the second positioning data by using the mass function; Calculating a conflict factor between the first positioning data and the second positioning data using the first credibility and the second credibility; The second positioning data whose conflict factor is lower than a preset conflict factor threshold is determined as the third positioning data.

3. The fusion positioning method based on the inertial navigation system and the global navigation satellite system according to claim 1 is characterized in that: The Kalman filter is used to fuse the second positioning data and the third positioning data to obtain target positioning data, including: According to the dynamic characteristics of the positioning system, the state transfer equation is constructed; According to the state transfer equation, a measurement equation for the second positioning data and a measurement equation for the third positioning data are respectively constructed; The measurement value of the measurement equation of the second positioning data and the measurement value of the measurement equation of the third positioning data are combined to obtain a combined measurement vector. Adjusting the measurement matrix and the measurement noise covariance matrix of the Kalman filter to obtain an adjusted Kalman filter; The adjusted Kalman filter is used to update the combined measurement vector to obtain the target positioning data.

4. The fusion positioning method based on the inertial navigation system and the global navigation satellite system according to claim 1 is characterized in that: Also includes: Before the positioning system enters the tunnel, the Kalman filter algorithm is used to fuse the second positioning data with the first positioning data before entering the tunnel to obtain the positioning result in the tunnel.

5. The fusion positioning method based on inertial navigation system and global navigation satellite system according to claim 3 is characterized in that: Also includes: Monitor the dynamic characteristics and environmental factors of the positioning system in real time and obtain monitoring results; According to the monitoring results, the values ​​of the noise covariance matrix and the measurement noise covariance matrix in the Kalman filter process are dynamically adjusted.

6. A fusion positioning device based on an inertial navigation system and a global navigation satellite system, characterized in that: include: A first positioning data module is configured to obtain first positioning data through a global navigation satellite system; A second positioning data module is configured to obtain second positioning data through an inertial navigation system; A third positioning data module is configured to perform data filtering processing on the first positioning data using the second positioning data to obtain third positioning data; The target positioning data module is configured to use a Kalman filter to perform fusion processing on the second positioning data and the third positioning data to obtain target positioning data.

7. A fusion positioning device based on an inertial navigation system and a global navigation satellite system, comprising a processor and a memory storing program instructions, characterized in that: The processor is configured to execute the fusion positioning method based on the inertial navigation system and the global navigation satellite system as described in any one of claims 1 to 5 when running the program instructions.

8. An electronic device, characterized in that: It includes the fusion positioning device based on the inertial navigation system and the global navigation satellite system as described in claim 6 or 7.

9. A storage medium storing program instructions, characterized in that: When the program instructions are executed, the fusion positioning method based on the inertial navigation system and the global navigation satellite system as described in any one of claims 1 to 5 is executed.

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