High-precision lightweight integrated navigation data fusion method

Abstract

The invention relates to the technical field of data fusion, and particularly provides a high-precision lightweight integrated navigation data fusion method. The method comprises the following steps: constructing a logarithmic Gaussian kernel function about a measurement noise covariance; according to a logarithm Gaussian kernel function and a lightweight volume point updating strategy, estimating to obtain a measurement noise covariance; according to the measurement noise covariance, the filtering parameters are updated in real time so as to fuse the integrated navigation data, and the method improves the accuracy and real-time performance of data fusion while suppressing the influence of the measurement abnormal value on the data fusion.

Description

Technical Field

[0001] The present invention relates to the technical field of data fusion, and in particular to a high-precision and lightweight combined navigation data fusion method. Background Art

[0002] Traditional integrated navigation data fusion methods are generally based on the Kalman filter (KF). While this method can effectively process random signals and achieve optimal fusion of integrated navigation system data, its performance depends on accurate measurement noise statistics. In practical applications, due to internal sensor failures and external environmental interference, the measurement noise statistics are often unknown and time-varying. This can lead to deviations or even divergence in the KF solution, especially in the presence of measurement outliers. To address this issue, several robust integrated navigation data fusion methods have been proposed. While these methods can mitigate the influence of measurement outliers, they suffer from low fusion accuracy or a significant increase in computational load. Summary of the Invention

[0003] In view of this, the present invention provides a high-precision and lightweight integrated navigation data fusion method to improve the accuracy and real-time performance of data fusion while suppressing the influence of measurement outliers on data fusion.

[0004] In a first aspect, the present invention provides a high-precision and lightweight combined navigation data fusion method, the method comprising:

[0005] Step 1: Construct a logarithmic Gaussian kernel function about the measurement noise covariance;

[0006] Step 2: Estimate the measurement noise covariance based on the logarithmic Gaussian kernel function and the lightweight volume point update strategy;

[0007] Step 3: Update the filter parameters in real time according to the measurement noise covariance to fuse the integrated navigation data.

[0008] Optionally, step 1 includes:

[0009] The Gaussian kernel function is expressed as:

[0010]

[0011] Among them, α represents a variable, and its sample at time k is expressed as i represents the i-th element of the vector; m represents the dimension of the integrated navigation system measurement value; M represents a positive integer; H represents the kernel bandwidth and Represents ∑ k-j+1 The i-th diagonal element of , ∑ k-j+1 Represents the sample of R at time k-j+1.

[0012] definition and Where R represents the covariance of the measurement noise, R (ii) and Represent R and ∑ k The i-th diagonal element of , ∑ k represents the sample of R at time k, which is expressed as:

[0013]

[0014] Among them, z k represents the measurement value of the integrated navigation system, represents the predicted value of the integrated navigation system's measurement value at time k;

[0015] Perform the transformation steps on equation (1) to construct the logarithmic Gaussian kernel function of the measurement noise covariance R, which is expressed as:

[0016]

[0017] According to formula (3), the logarithmic Gaussian kernel function G of the measurement noise covariance is obtained k (R), which is expressed as:

[0018]

[0019] Optionally, step 2 includes:

[0020] Based on the logarithmic Gaussian kernel function and the lightweight volume point update strategy, the state quantity of the integrated navigation system is estimated, and the expression of the estimated value is:

[0021]

[0022] in, represents the estimated value of the state of the integrated navigation system at time k; represents the predicted value of the state quantity at time k; n represents the dimension of the state quantity of the integrated navigation system; represents the propagation volume point at time k-1, f(·) represents the state function of the nonlinear integrated navigation system; h(·) represents the measurement function of the nonlinear integrated navigation system; R k represents the covariance of the measurement noise at time k, and its expression is:

[0023]

[0024] in, m represents the dimension of the measurement value of the integrated navigation system; R d,k Represents R k Sigma sampling point; G k (Rd,k ) indicates the value of R d,k The logarithmic Gaussian kernel function; N(·) represents the Gaussian distribution function, z k represents the measurement value of the integrated navigation system, H represents the kernel bandwidth; Represents R d,k The i-th diagonal element of R (ii) and Denote the measurement noise covariance R and ∑ k The i-th diagonal element of , ∑ k represents the sample of R at time k, express The average value of M represents a positive integer, Represents ∑ k-j+1 The i-th diagonal element of , ∑ k-j+1 Represents the sample of R at time k-j+1.

[0025] Optionally, step 3 includes:

[0026] Given the state estimate of the integrated navigation system at time k-1 And the corresponding error covariance matrix P k-1|k-1 , using the cubature Kalman filter to initialize the posterior cubature point error matrix Then calculate the propagation volume point at time k-1 Prior state estimate of the state quantity and its covariance P k|k-1 , whose expressions are:

[0027]

[0028] in, express The i-th column element of Q k-1 represents the covariance of the process noise of the integrated navigation system; n represents the dimension of the state quantity of the integrated navigation system;

[0029] Calculate the error matrix of the predicted volume point and the prior weight matrix The expressions are:

[0030]

[0031] Where chol(·) represents the Cholesky decomposition operation; f(·) represents the state function of the nonlinear integrated navigation system; i=1,2,...,2n;

[0032] Calculate the predicted value of the integrated navigation system's k-time measurement value Its expression is:

[0033]

[0034] pass The gain K is given by equation (13) k To update, the expression is:

[0035]

[0036] Among them, R k represents the covariance of the measurement noise at time k; represents the predicted value of the integrated navigation system's measurement value at time k;

[0037] m represents the dimension of the measurement value of the integrated navigation system; R d,k Represents R k Sigma sampling point; G k (R d,k ) indicates the value of R d,k The logarithmic Gaussian kernel function; N(·) represents the Gaussian distribution function, z k represents the measurement value of the integrated navigation system, H represents the kernel bandwidth; Represents R d,k The i-th diagonal element of R (ii) and Denote the measurement noise covariance R and ∑ k The i-th diagonal element of , ∑ k represents the sample of R at time k, express The average value of M represents a positive integer, Represents ∑ k-j+1 The i-th diagonal element of , ∑ k-j+1 represents the sample of R at time k-j+1;

[0038] The navigation data at the current moment is fused by formula (14) to obtain the estimated value of the state of the integrated navigation system at time k: And update the state error covariance P k|k , whose expressions are:

[0039]

[0040] in, represents the predicted value of the state quantity at time k; z k Represents the measurement value of the integrated navigation system;

[0041] The posterior weight matrix is ​​calculated by formula (16): Its expression is:

[0042]

[0043] Wherein, Υ represents a weighted diagonal matrix;

[0044] Calculate the posterior volume point error matrix for the next navigation data fusion Its expression is:

[0045]

[0046] In a second aspect, an embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the high-precision and lightweight combined navigation data fusion method in the first aspect or any possible implementation of the first aspect.

[0047] In a third aspect, an embodiment of the present invention provides an electronic device comprising: one or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions that, when executed by the device, enable the device to execute the high-precision and lightweight combined navigation data fusion method in the first aspect or any possible implementation of the first aspect.

[0048] In the technical solution provided by the present invention, the method includes constructing a logarithmic Gaussian kernel function about the measurement noise covariance; estimating the measurement noise covariance based on the logarithmic Gaussian kernel function and a lightweight volume point update strategy; and updating the filter parameters in real time based on the measurement noise covariance to fuse the combined navigation data. This method improves the accuracy and real-time performance of data fusion while suppressing the influence of measurement outliers on data fusion. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 A flowchart of a combined navigation data fusion method provided by an embodiment of the present invention;

[0051] Figure 2A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0053] It should be understood that the embodiments described are only a portion of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative work are within the scope of protection of the present invention.

[0054] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "the" and "the" used in the embodiments of the present invention are also intended to include plural forms, unless the context clearly indicates other meanings.

[0055] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. Furthermore, the character " / " in this document generally indicates an "or" relationship between the associated objects.

[0056] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0057] Figure 1 The flowchart of the combined navigation data fusion method provided by the embodiment of the present invention is as follows: Figure 1 As shown, the method includes:

[0058] Step 1: Construct a logarithmic Gaussian kernel function about the measurement noise covariance;

[0059] In the embodiment of the present invention, step 1 includes:

[0060] The Gaussian kernel function is expressed as:

[0061]

[0062] Among them, α represents a variable, and its sample at time k is expressed as i represents the i-th element of the vector; m represents the dimension of the integrated navigation system measurement value; M represents a positive integer; H represents the kernel bandwidth and Represents ∑ k-j+1 The i-th diagonal element of , ∑ k-j+1 Indicates that in k - Sample of R at time j+1;

[0063] definition and Where R represents the covariance of the measurement noise, R (ii) and Represent R and ∑ k The i-th diagonal element of , ∑ k represents the sample of R at time k, which is expressed as:

[0064]

[0065] Among them, z k represents the measurement value of the integrated navigation system, represents the predicted value of the integrated navigation system's measurement value at time k;

[0066] Perform the transformation steps on equation (1) to construct the logarithmic Gaussian kernel function of the measurement noise covariance R, which is expressed as:

[0067]

[0068] According to formula (3), the logarithmic Gaussian kernel function G of the measurement noise covariance is obtained k (R), which is expressed as:

[0069]

[0070] Step 2: Estimate the measurement noise covariance based on the logarithmic Gaussian kernel function and the lightweight volume point update strategy;

[0071] In the embodiment of the present invention, step 2 includes:

[0072] Based on the logarithmic Gaussian kernel function and the lightweight volume point update strategy, the state quantity of the integrated navigation system is estimated, and the expression of the estimated value is:

[0073]

[0074] in, represents the estimated value of the state of the integrated navigation system at time k; represents the predicted value of the state quantity at time k; n represents the dimension of the state quantity of the integrated navigation system; represents the propagation volume point at time k-1, f(·) represents the state function of the nonlinear integrated navigation system; h(·) represents the measurement function of the nonlinear integrated navigation system; R k represents the covariance of the measurement noise at time k, and its expression is:

[0075]

[0076] in, m represents the dimension of the measurement value of the integrated navigation system; R d,k Represents R k Sigma sampling point; G k (R d,k ) indicates the value of R d,k The logarithmic Gaussian kernel function; N(·) represents the Gaussian distribution function, z k represents the measurement value of the integrated navigation system, H represents the kernel bandwidth; Represents R d,k The i-th diagonal element of R (ii) and Denote the measurement noise covariance R and ∑ k The i-th diagonal element of , ∑ k represents the sample of R at time k, express The average value of M represents a positive integer, Represents ∑ k-j+1 The i-th diagonal element of , ∑ k-j+1 Represents the sample of R at time k-j+1.

[0077] Step 3: Update the filter parameters in real time according to the measurement noise covariance to fuse the integrated navigation data.

[0078] In this embodiment of the present invention, step 3 includes:

[0079] Given the state estimate of the integrated navigation system at time k-1 And the corresponding error covariance matrix P k-1|k-1 , using the cubature Kalman filter to initialize the posterior cubature point error matrix Then calculate the propagation volume point at time k-1 Prior state estimate of the state quantity and its covariance P k|k-1 , whose expressions are:

[0080]

[0081] in, express The i-th column element of Q k-1 represents the covariance of the process noise of the integrated navigation system; n represents the dimension of the state quantity of the integrated navigation system;

[0082] Calculate the error matrix of the predicted volume point and the prior weight matrix The expressions are:

[0083]

[0084] Where chol(·) represents the Cholesky decomposition operation; f(·) represents the state function of the nonlinear integrated navigation system; i=1,2,...,2n;

[0085] Calculate the predicted value of the integrated navigation system's k-time measurement value Its expression is:

[0086]

[0087] pass The gain K is given by equation (13) k To update, the expression is:

[0088]

[0089] Among them, R k represents the covariance of the measurement noise at time k; represents the predicted value of the integrated navigation system's measurement value at time k;

[0090] m represents the dimension of the measurement value of the integrated navigation system; R d,k Represents R k Sigma sampling point; G k (R d,k ) indicates the value of R d,k The logarithmic Gaussian kernel function; N(·) represents the Gaussian distribution function, z k represents the measurement value of the integrated navigation system, H represents the kernel bandwidth; Represents R d,k The i-th diagonal element of R (ii) and Denote the measurement noise covariance R and ∑ k The i-th diagonal element of , ∑k represents the sample of R at time k, express The average value of M represents a positive integer, Represents ∑ k-j+1 The i-th diagonal element of , ∑ k-j+1 represents the sample of R at time k-j+1;

[0091] The navigation data at the current moment is fused by formula (14) to obtain the estimated value of the state of the integrated navigation system at time k: And update the state error covariance P k|k , whose expressions are:

[0092]

[0093] in, represents the predicted value of the state quantity at time k; z k Represents the measurement value of the integrated navigation system;

[0094] The posterior weight matrix is ​​calculated by formula (16): Its expression is:

[0095]

[0096] Wherein, Υ represents a weighted diagonal matrix;

[0097] Calculate the posterior volume point error matrix for the next navigation data fusion Its expression is:

[0098]

[0099] Steps 1, 2, and 3 are repeated until all navigation data are integrated.

[0100] In an embodiment of the present invention, the method exhibits excellent robustness in resisting interference from measurement outliers and can efficiently achieve high-precision fusion processing of combined navigation data. By constructing a logarithmic Gaussian kernel function and a lightweight volume point update strategy, the technical difficulties of the traditional method, which suffers from significantly reduced fusion accuracy and slow fusion speed under the interference of measurement outliers, are effectively resolved. In particular, while maintaining the existing hardware architecture unchanged, the method simultaneously improves the operating efficiency, system stability, and navigation accuracy of the combined navigation system, providing an innovative solution for optimizing navigation systems in complex environments and achieving high-precision and rapid fusion of navigation data in the presence of measurement outliers.

[0101] In the technical solution provided by the present invention, the method includes constructing a logarithmic Gaussian kernel function about the measurement noise covariance; estimating the measurement noise covariance based on the logarithmic Gaussian kernel function and a lightweight volume point update strategy; and updating the filter parameters in real time based on the measurement noise covariance to fuse the combined navigation data. This method improves the accuracy and real-time performance of data fusion while suppressing the influence of measurement outliers on data fusion.

[0102] Each step of the embodiment of the present invention may be performed by an electronic device, including but not limited to a tablet computer, a portable PC, a desktop computer, etc.

[0103] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program. When the program runs, the electronic device where the computer-readable storage medium is located is controlled to execute an embodiment of the above-mentioned high-precision lightweight combined navigation data fusion method.

[0104] Figure 2 A schematic diagram of an electronic device provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, the electronic device 21 includes: a processor 211, a memory 212, and a computer program 213 stored in the memory 212 and executable on the processor 211. When the computer program 213 is executed by the processor 211, the high-precision and lightweight combined navigation data fusion method in the embodiment is implemented. To avoid repetition, they are not described here one by one.

[0105] The electronic device 21 includes, but is not limited to, a processor 211 and a memory 212. Those skilled in the art will understand that Figure 2 It is only an example of the electronic device 21 and does not constitute a limitation of the electronic device 21. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0106] The processor 211 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0107] The memory 212 can be an internal storage unit of the electronic device 21, such as a hard disk or memory of the electronic device 21. The memory 212 can also be an external storage device of the electronic device 21, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash memory card, etc. equipped on the electronic device 21. Furthermore, the memory 212 can also include both an internal storage unit of the electronic device 21 and an external storage device. The memory 212 is used to store computer programs and other programs and data required by the network device. The memory 212 can also be used to temporarily store data that has been output or is about to be output.

[0108] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0109] The above description is only a preferred embodiment of the present invention and is 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 in the scope of protection of the present invention.

Claims

1. A high-precision and lightweight integrated navigation data fusion method, characterized in that: The method comprises: Step 1: Construct a logarithmic Gaussian kernel function about the measurement noise covariance; Step 2: Estimate the measurement noise covariance based on the logarithmic Gaussian kernel function and the lightweight volume point update strategy; Step 3: Update the filter parameters in real time according to the measurement noise covariance to fuse the integrated navigation data.

2. The method according to claim 1, characterized in that The step 1 comprises: The Gaussian kernel function is expressed as: Among them, α represents a variable, and its sample at time k is expressed as i represents the i-th element of the vector; m represents the dimension of the integrated navigation system measurement value; M represents a positive integer; H represents the kernel bandwidth and Represents ∑ k-j+1 The i-th diagonal element of , ∑ k-j+1 represents the sample of R at time k-j+1; definition and Where R represents the covariance of the measurement noise, R (ii) and Represent R and ∑ k The i-th diagonal element of , ∑ k represents the sample of R at time k, which is expressed as: Among them, z k represents the measurement value of the integrated navigation system, represents the predicted value of the integrated navigation system's measurement value at time k; Perform the transformation steps on equation (1) to construct the logarithmic Gaussian kernel function of the measurement noise covariance R, which is expressed as: According to formula (3), the logarithmic Gaussian kernel function G of the measurement noise covariance is obtained k (R), whose expression is:

3. The method according to claim 1, characterized in that The step 2 includes: Based on the logarithmic Gaussian kernel function and the lightweight volume point update strategy, the state quantity of the integrated navigation system is estimated, and the expression of the estimated value is: in, represents the estimated value of the state of the integrated navigation system at time k; represents the predicted value of the state quantity at time k; n represents the dimension of the state quantity of the integrated navigation system; represents the propagation volume point at time k-1, f(·) represents the state function of the nonlinear integrated navigation system; h(·) represents the measurement function of the nonlinear integrated navigation system; R k represents the covariance of the measurement noise at time k, and its expression is: in, m represents the dimension of the measurement value of the integrated navigation system; R d,k Represents R k Sigma sampling point; G k (R d,k ) indicates the value of R d,k The logarithmic Gaussian kernel function; N(·) represents the Gaussian distribution function, z k represents the measurement value of the integrated navigation system, H represents the kernel bandwidth; Represents R d,k The i-th diagonal element of R (ii) and Denote the measurement noise covariance R and ∑ k The i-th diagonal element of , ∑ k represents the sample of R at time k, express The average value of M represents a positive integer, Represents ∑ k-j+1 The i-th diagonal element of , ∑ k-j+1 Represents the sample of R at time k-j+1.

4. The method according to claim 1, wherein The step 3 includes: Given the state estimate of the integrated navigation system at time k-1 And the corresponding error covariance matrix P k-1|k-1 , using the cubature Kalman filter to initialize the posterior cubature point error matrix Then calculate the propagation volume point at time k-1 Prior state estimate of the state quantity and its covariance P k|k-1 , whose expressions are: in, express The i-th column element of ; Q k-1 represents the covariance of the process noise of the integrated navigation system; n represents the dimension of the state quantity of the integrated navigation system; Calculate the error matrix of the predicted volume point and the prior weight matrix The expressions are: Where chol(·) represents the Cholesky decomposition operation; f(·) represents the state function of the nonlinear integrated navigation system; i=1,2,...,2n; Calculate the predicted value of the integrated navigation system's k-time measurement value Its expression is: pass The gain K is given by equation (13) k To update, the expression is: Among them, R k represents the covariance of the measurement noise at time k; represents the predicted value of the integrated navigation system's measurement value at time k; m represents the dimension of the measurement value of the integrated navigation system; R d,k Represents R k Sigma sampling point; G k (R d,k ) indicates the value of R d,k The logarithmic Gaussian kernel function; N(·) represents the Gaussian distribution function, z k represents the measurement value of the integrated navigation system, H represents the kernel bandwidth; Represents R d,k The i-th diagonal element of R (ii) and Denote the measurement noise covariance R and ∑ k The i-th diagonal element of , ∑ k represents the sample of R at time k, express The average value of M represents a positive integer, Represents ∑ k-j+1 The i-th diagonal element of , ∑ k-j+1 Represents the sample of R at time k-j+1. The navigation data at the current moment is fused by formula (14) to obtain the estimated value of the state of the integrated navigation system at time k: And update the state error covariance P k|k , whose expressions are: in, represents the predicted value of the state quantity at time k; z k Represents the measurement value of the integrated navigation system; The posterior weight matrix is ​​calculated by formula (16): Its expression is: Wherein, Υ represents a weighted diagonal matrix; Calculate the posterior volume point error matrix for the next navigation data fusion Its expression is:

5. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the high-precision and lightweight combined navigation data fusion method according to any one of claims 1 to 4.

6. An electronic device, characterized in that: include: one or more processors; Memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions that, when executed by the device, enable the device to perform the high-precision lightweight combined navigation data fusion method according to any one of claims 1 to 4.

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