Ambiguity deviation vector classification and probability decoupling collaborative protection level construction method and device

Abstract

The invention discloses an ambiguity deviation vector classification and probability decoupling collaborative protection level construction method and device, and belongs to the technical field of integrity monitoring. The method comprises the following steps: firstly, constructing a model of a positioning error under a fixed solution, then constructing an initial protection level model based on a Bayesian formula, then performing protection level parameter decoupling by utilizing an ambiguity deviation vector probability, classifying deviation vectors according to a modulus length, calculating a modulus length boundary value, and finally obtaining a protection level parameter; and finally, calculating a final protection level based on decoupling and classification results. According to the method, the influence of ambiguity error fixation on the protection level can be quantified, and the requirements of safety critical applications on reliability and interpretability are met.

Description

Technical Field

[0001] This invention belongs to the field of integrity monitoring technology, specifically relating to a method and apparatus for constructing protection levels through ambiguity deviation vector classification and probability decoupling coordination. Background Technology

[0002] High-precision positioning technology based on Global Navigation Satellite Systems (GNSS) has been widely used in intelligent transportation, autonomous driving, and aerospace. Common high-precision GNSS positioning modes include Real-Time Kinematic (RTK), Precise Point Positioning (PPP), and PPP-RTK, which combines the advantages of both. These technologies can improve positioning accuracy to the centimeter level by solving for fixed integer ambiguities in the carrier phase. However, as high-precision positioning technology gradually expands into safety-critical applications, ensuring the reliability and integrity of positioning results has become an urgent problem to be solved.

[0003] For traditional code-observation GNSS positioning, the calculation framework for protection levels is relatively mature. However, for high-precision positioning primarily based on carrier phase, especially when ambiguity fixation is involved, existing protection level calculation models face several challenges. One of the key steps in high-precision positioning is constraining floating-point ambiguity estimates (floating-point solutions) to integer values ​​(fixed solutions). However, fixed solutions introduce a new failure mode—fixing errors. Despite the existence of many ambiguity verification methods, incorrect ambiguity fixation can still occur due to various random errors or faults in measurements. Once ambiguity is incorrectly fixed, it will lead to significant positioning errors. Most existing protection level models only consider gross errors in pseudorange observations or known measurement failure modes, lacking modeling and quantification of the systematic biases caused by incorrect integer ambiguity fixation.

[0004] Current high-precision GNSS positioning systems still have significant shortcomings in supporting integrity assurance. Existing methods lack protective boundary modeling for specific positional deviations caused by different ambiguity candidate solutions, making it difficult to meet the reliability and interpretability requirements of safety-critical applications. Therefore, there is an urgent need to develop a protection level calculation method for fixed solutions of high-precision GNSS ambiguities, which can effectively identify and quantify the risk of error fixation, providing a fundamental guarantee for the deployment and application of high-precision positioning systems in safety-critical scenarios. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method and apparatus for constructing protection levels through ambiguity deviation vector classification and probability decoupling. Addressing the problem of calculating protection levels in high-precision positioning, this invention utilizes the probabilities corresponding to ambiguity deviation vectors to decouple the parameters constituting the protection level, and achieves accurate construction of protection levels under fixed ambiguity conditions by classifying and considering the ambiguity deviation vectors.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for constructing a protection level that combines ambiguity bias vector classification with probabilistic decoupling, the method comprising:

[0008] Step 1: Construct a positioning error model that considers a fixed solution;

[0009] Step 2: Combine the positioning error model with Bayes' theorem to construct an initial protection level model;

[0010] Step 3: Decouple the parameters of the initial protection level model based on the ambiguity deviation vector probability, and separate the independent contributions of different deviation vectors to the protection level;

[0011] Step 4: Classify the ambiguity deviation vector based on the modulus length and calculate the modulus length boundary value;

[0012] Step 5: Calculate the final protection level based on the decoupling and classification results.

[0013] Furthermore, in step 1, the baseline solution deviation is considered when there is a fixed ambiguity deviation. for:

[0014] (1)

[0015] in, and These are the fixed ambiguity vector and the correct ambiguity vector, respectively. Represents the true value of the baseline vector. Represents the ambiguity floating-point solution. Represents the baseline floating-point solution. Indicates that the ambiguity is fixed to The baseline vector, express and The difference, express and The difference, Represents the ambiguity deviation vector. express The variance-covariance matrix, express and The variance-covariance matrix.

[0016] Furthermore, step 2 includes: under the condition of unbiased observations, using Bayes' theorem, the corresponding integrity risk IR is expanded as follows:

[0017] (2)

[0018] in, express The vertical component in and These represent events where the ambiguity is correctly fixed and events where the ambiguity is incorrectly fixed, respectively. Corresponding to the k-th ambiguity deviation vector event, , and They are respectively , and The corresponding probability, P{} represents the probability of the event, and VPL represents the protection level.

[0019] Furthermore, step 3 includes: the sum of the probabilities of all events with fixed ambiguity is known to be 1, expressed as:

[0020] (3)

[0021] Based on equation (3), the integrity risk described by equation (2) is allocated to each case according to the probability of fixed ambiguity:

[0022] (4)

[0023] Simplified to:

[0024] (5)

[0025] Therefore, the protection level is determined accordingly:

[0026] (6)

[0027] in, and Respectively and The level of protection obtained under the event and They are respectively The vertical deviation and standard deviation are respectively. and Respectively and The protection level coefficient obtained under the event.

[0028] Furthermore, step 4 includes: applying the Cauchy-Schwarz inequality to... Scaling;

[0029] (8)

[0030] in, This represents the magnitude of the row vector corresponding to the vertical component of the matrix. express and The product matrix corresponds to the row vectors of the vertical components. express The i-th element, express The i-th element, the variable ρ represents The modulus length determines the level of protection, and is classified according to formula (9):

[0031] (9)

[0032] in, Represents all moduli of length ρ The probabilities corresponding to the events are summed to determine a boundary value p, and the following judgment conditions are applied:

[0033] (10)

[0034] Set the initial value of p to 0 and the minimum value of ρ to 1. When p = 0, the third term on the left side of equation (10) is 0. Gradually increase the size of p until equation (10) is no longer valid. The resulting protection level meets the integrity risk requirements.

[0035] Furthermore, in step 5, the final protection level calculation expression is:

[0036] (11).

[0037] Furthermore, the method is applicable to high-precision positioning scenarios in global satellite navigation systems, including real-time dynamic positioning and precise point positioning.

[0038] On the other hand, the present invention provides a protection level construction device that combines ambiguity deviation vector classification and probability decoupling, comprising:

[0039] The positioning error module is used to construct a positioning error model that considers a fixed solution.

[0040] The protection level module is used to combine the positioning error model and construct an initial protection level model based on Bayes' theorem.

[0041] The decoupling module is used to decouple the parameters of the initial protection level model based on the ambiguity deviation vector probability, and separate the independent contributions of different deviation vectors to the protection level.

[0042] The classification module is used to classify the ambiguity deviation vector based on the modulus and calculate the modulus boundary value;

[0043] The calculation module is used to calculate the final protection level based on the decoupling and classification results.

[0044] Thirdly, the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned method for constructing a protection level that combines ambiguity deviation vector classification and probability decoupling.

[0045] Fourthly, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enable the processor to implement the aforementioned method for constructing a protection level that combines ambiguity deviation vector classification with probabilistic decoupling.

[0046] The beneficial effects of this invention are as follows:

[0047] The impact of quantified ambiguity fixation errors: By probabilistically decoupling and classifying the ambiguity deviation vector, this invention can effectively quantify the impact of integer ambiguity fixation errors on the protection level. This compensates for the lack of modeling and quantification of systematic deviations caused by ambiguity fixation errors in existing technologies, providing accurate quantitative basis for integrity assurance in high-precision positioning systems.

[0048] Achieving accurate calculation of protection level: This invention constructs a positioning error model and an initial protection level model based on Bayes' theorem, and performs probabilistic decoupling and classification processing on the ambiguity deviation vector, ultimately achieving accurate calculation of the protection level under fixed ambiguity conditions. This method effectively considers the impact of fixed error faults and meets the stringent requirements of safety-critical applications for the reliability and interpretability of protection levels.

[0049] Simplifying the calculation process and improving efficiency: By classifying deviation vectors based on their modulus length and calculating modulus length boundary values, this invention significantly simplifies the calculation process for protection levels. When dealing with a large number of ambiguity deviation vectors, it avoids calculating the protection level for each case individually, significantly reducing computational complexity and improving computational efficiency. This makes the method applicable to high-precision positioning scenarios with high real-time requirements.

[0050] Enhancing System Reliability and Security: This invention effectively identifies and quantifies the risk of ambiguity fixation errors, providing reliable technical support for the deployment and application of high-precision positioning systems in safety-critical scenarios. By accurately calculating the protection level, the system can take proactive measures when potential ambiguity fixation errors occur, reducing the impact of positioning errors on safety and thus improving the overall system reliability and security. Attached Figure Description

[0051] Figure 1This is a flowchart of a protection level construction method for ambiguity deviation vector classification and probability decoupling coordination according to the present invention. Detailed Implementation

[0052] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0053] like Figure 1 The diagram shown is a flowchart of a protection level construction method based on ambiguity deviation vector classification and probability decoupling coordination according to the present invention. The method specifically includes:

[0054] Step 1: Construct a positioning error model that considers a fixed solution;

[0055] Considering baseline solution bias when there is a fixed ambiguity bias (i.e., the positioning error model) is:

[0056] (1)

[0057] in, and These are the fixed ambiguity vector and the correct ambiguity vector, respectively. Represents the true value of the baseline vector. Represents the ambiguity floating-point solution. Represents the baseline floating-point solution. Indicates that the ambiguity is fixed to The baseline vector, express and The difference, express and The difference, Represents the ambiguity deviation vector. express The variance-covariance matrix, express and The variance-covariance matrix. The first two terms are random variables that both follow a normal distribution with a mean of zero, and the third term is the ambiguity fixed to... The baseline solution offset generated at that time.

[0058] Step 2: Construct an initial protection level model based on Bayes' theorem;

[0059] Under the condition of unbiased observations, using Bayes' theorem, the corresponding integrity risk (IR) can be expanded as follows:

[0060] (2)

[0061] in, express The vertical component in and These represent events where the ambiguity is correctly fixed and events where the ambiguity is incorrectly fixed, respectively. Corresponding to the k-th ambiguity deviation vector event, , and They are respectively , and The corresponding probability, P{} represents the probability of the event, and VPL represents the protection level. This formula shows that the protection level is influenced by both the fixed probability of ambiguity and the deviation vector, making direct calculation of the protection level difficult and requiring simplification.

[0062] Step 3: Decouple the parameters of the initial protection level model based on the ambiguity deviation vector probability;

[0063] Given that the sum of the probabilities of all events with fixed ambiguity is 1:

[0064] (3)

[0065] Based on equation (3), the integrity risk described by equation (2) is allocated to each case according to the probability of fixed ambiguity:

[0066] (4)

[0067] Simplified, we get:

[0068] (5)

[0069] Therefore, the protection level is determined accordingly:

[0070] (6)

[0071] in, and Respectively and The level of protection obtained under the event and They are respectively The vertical deviation and standard deviation are respectively. and Representing CF and The protection level coefficient obtained under the event.

[0072] (7)

[0073] This represents the 1-x quantile of a Gaussian distribution with zero mean and unit variance. The difference in protection level is only due to Determine the ambiguity deviation vector Decided each The size of the parameter decouples the protection level parameters.

[0074] Step 4: Classify the ambiguity deviation vector based on the modulus length and calculate the modulus length boundary value;

[0075] because The large number of items necessitates individual calculations. The method is not feasible; it is necessary to... The system categorizes elements and calculates the corresponding protection level for each category to simplify the computation. Unconsidered elements are then limited by IR (approximately 0.01 times the IR). The sum of fixed failure probabilities for a vector. Using Cauchy-Schwarz inequality... Scaling:

[0076] (8)

[0077] in, This represents the magnitude of the row vector corresponding to the vertical component of the matrix. express and The product matrix corresponds to the row vectors of the vertical components. express The i-th element, express The i-th element, the variable ρ represents The modulus length. Where ρ determines the level of protection, and then the following classification is performed.

[0078] (9)

[0079] in, Represents all moduli of length ρ The sum of probabilities corresponding to the events, according to the expression (9), can determine a boundary value p, and the following judgment condition:

[0080] (10)

[0081] For ease of representation, the initial value of p is set to 0, and the minimum value of ρ is set to 1. When p = 0, the third term on the left side of equation (10) is 0. Gradually increase the value of p until equation (10) no longer holds, then it can be considered that the final protection level meets the integrity risk requirement.

[0082] Step 5: Calculate the protection level based on the decoupling and classification results.

[0083] The final protection level is obtained by taking the maximum value among all protection levels using equation (11):

[0084] (11)

[0085] On the other hand, the present invention provides a protection level construction device that combines ambiguity deviation vector classification and probability decoupling, the various modules of which can implement the various steps of the aforementioned method, specifically including:

[0086] The positioning error module is used to construct a positioning error model that considers a fixed solution.

[0087] The protection level module is used to combine the positioning error model and construct an initial protection level model based on Bayes' theorem.

[0088] The decoupling module is used to decouple the parameters of the initial protection level model based on the ambiguity deviation vector probability, and separate the independent contributions of different deviation vectors to the protection level.

[0089] The classification module is used to classify the ambiguity deviation vector based on the modulus and calculate the modulus boundary value;

[0090] The calculation module is used to calculate the final protection level based on the decoupling and classification results.

[0091] Thirdly, the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned method for constructing a protection level that combines ambiguity deviation vector classification and probability decoupling.

[0092] Fourthly, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enable the processor to implement the aforementioned method for constructing a protection level that combines ambiguity deviation vector classification with probabilistic decoupling.

[0093] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for constructing a protection level that combines ambiguity deviation vector classification with probabilistic decoupling, characterized in that, The method includes: Step 1: Construct a positioning error model that considers a fixed solution; Step 2: Combine the positioning error model with Bayes' theorem to construct an initial protection level model; Step 3: Decouple the parameters of the initial protection level model based on the ambiguity deviation vector probability, and separate the independent contributions of different deviation vectors to the protection level; Step 4: Classify the ambiguity deviation vector based on the modulus length and calculate the modulus length boundary value; Step 5: Calculate the final protection level based on the decoupling and classification results.

2. The protection level construction method based on ambiguity deviation vector classification and probability decoupling collaboration according to claim 1, characterized in that, In step 1, the baseline solution deviation is considered when there is a fixed ambiguity deviation. for: （1） in, and These are the fixed ambiguity vector and the correct ambiguity vector, respectively. Represents the true value of the baseline vector. Represents the ambiguity floating-point solution. Represents the baseline floating-point solution. Indicates that the ambiguity is fixed to The baseline vector, express and The difference, express and The difference, Represents the ambiguity deviation vector. express The variance-covariance matrix, express and The variance-covariance matrix.

3. The protection level construction method based on ambiguity deviation vector classification and probability decoupling collaboration according to claim 2, characterized in that, Step 2 includes: under the condition of unbiased observations, using Bayes' theorem, the corresponding integrity risk IR is expanded as follows: （2） in, express The vertical component in and These represent events where the ambiguity is correctly fixed and events where the ambiguity is incorrectly fixed, respectively. Corresponding to the k-th ambiguity deviation vector event, , and They are respectively , and The corresponding probability, P{} represents the probability of the event, and VPL represents the protection level.

4. The protection level construction method based on ambiguity deviation vector classification and probability decoupling collaboration according to claim 3, characterized in that, Step 3 includes: Given that the sum of the probabilities of all events with fixed ambiguity is 1, expressed as: （3） Based on equation (3), the integrity risk described by equation (2) is allocated to each case according to the probability of fixed ambiguity: （4） Simplified to: （5） Therefore, the level of protection is determined accordingly; （6） in, and Representing CF and The level of protection obtained under the event and They are respectively The vertical deviation and standard deviation are respectively. and Respectively and The protection level coefficient obtained under the event.

5. The protection level construction method based on ambiguity deviation vector classification and probability decoupling collaboration according to claim 4, characterized in that, Step 4 includes: using the Cauchy-Schwarz inequality to... Scaling; （8） in, This represents the magnitude of the row vector corresponding to the vertical component of the matrix. express and The product matrix corresponds to the row vectors of the vertical components. express The i-th element, express The i-th element, the variable ρ represents The modulus length determines the level of protection, and is classified according to formula (9): （9） in, Represents all moduli of length ρ The probabilities corresponding to the events are summed to determine a boundary value p, and the following judgment conditions are applied: （10） Set the initial value of p to 0 and the minimum value of ρ to 1. When p = 0, the third term on the left side of equation (10) is 0. Gradually increase the size of p until equation (10) is no longer valid. The resulting protection level meets the integrity risk requirements.

6. The protection level construction method based on ambiguity deviation vector classification and probability decoupling collaboration according to claim 5, characterized in that, In step 5, the final protection level calculation expression is: （11）。 7. The protection level construction method based on ambiguity deviation vector classification and probability decoupling collaboration according to claim 1, characterized in that, The method is applicable to high-precision positioning scenarios in global satellite navigation systems, including real-time dynamic positioning and precise point positioning.

8. A protection level construction device that combines ambiguity deviation vector classification and probability decoupling, characterized in that, include: The positioning error module is used to construct a positioning error model that considers a fixed solution. The protection level module is used to combine the positioning error model and construct an initial protection level model based on Bayes' theorem. The decoupling module is used to decouple the parameters of the initial protection level model based on the ambiguity deviation vector probability, and separate the independent contributions of different deviation vectors to the protection level. The classification module is used to classify the ambiguity deviation vector based on the modulus and calculate the modulus boundary value; The calculation module is used to calculate the final protection level based on the decoupling and classification results.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When one or more programs are executed by the one or more processors, the one or more processors implement the protection level construction method of ambiguity deviation vector classification and probability decoupling collaboration as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores executable instructions that, when executed by a processor, enable the processor to implement the protection level construction method of ambiguity deviation vector classification and probability decoupling collaboration as described in any one of claims 1-7.

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