Distributed spectrum red shift / starlight refraction combined navigation method and related product

Abstract

The invention discloses a distributed spectrum red shift / starlight refraction combined navigation method and related products, and belongs to the technical field of spacecraft combined navigation. According to the distributed spectrum red shift / starlight refraction combined navigation method provided by the invention, a distributed spectrum red shift / starlight refraction combined navigation system is constructed, so that all navigation sources can work in parallel and do not interfere with one another, and even if a certain navigation source breaks down or has a relatively large error, the navigation performance of the whole system cannot be seriously influenced; therefore, the fault tolerance and reliability of the navigation system are enhanced; a federated filtering framework is adopted, and adaptive information factors are combined to carry out iterative information fusion, so that a navigation system can dynamically adjust the adaptive information factors, reflect differences among state variables of sub-filters in real time and track changes of the differences, the navigation estimation precision is improved, and the navigation accuracy is improved. And the risk that the state error and the fault are transmitted along with the resetting of the sub-filter is also reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of spacecraft combined navigation, and in particular to a distributed spectral redshift / starlight refraction combined navigation method and related products. Background Art

[0002] As a key support for the automation and intelligent operation of spacecraft, the core position of autonomous navigation technology is self-evident. As a cutting-edge autonomous astronomical navigation method, the spectral redshift navigation system can accurately solve the radial velocity of the spacecraft by analyzing the spectral redshift information of celestial bodies, and then solve the navigation information. With the intuitive principle, high autonomy and excellent anti-interference ability, this technology has shown great potential in the field of autonomous navigation of spacecraft.

[0003] However, the development of spectral redshift navigation technology has not been smooth sailing. Although the early spectral redshift navigation method can directly measure the speed, the acquisition of position information depends on the integration of the speed, which leads to the significant accumulation of position errors after long-term navigation. To solve this problem, subsequent studies proposed a spectral redshift positioning navigation system, but the system introduced more navigation parameters and required the simultaneous acquisition of redshift measurement information of more than six non-collinear celestial bodies to ensure the accuracy of navigation solution. This requirement is often difficult to achieve in practical applications, especially in interference environments, thus limiting the widespread application of spectral redshift navigation systems. In order to overcome the limitations of the spectral redshift navigation system, researchers began to explore the combination with other navigation technologies, aiming to build an autonomous combined navigation system. In this process, the optimization of the spacecraft combined navigation structure is also crucial. The spacecraft combined navigation structure needs to adopt a distributed filtering structure. Compared with centralized filtering, distributed filtering divides the entire filter into a two-level structure with good fault tolerance. Among many distributed filtering, the simplest federated filtering algorithm estimates the state of the navigation subsystem, and then obtains the final navigation state estimate by information fusion of the sub-filter state estimates, which has high fault tolerance. However, the accuracy of this federal filtering algorithm is not high. In order to further improve the filtering accuracy, experts have proposed a federal filtering algorithm based on the information distribution principle. After the filtering is completed, the federal filtering algorithm based on the information distribution principle uses the global state estimate of the main filter to replace the local state estimate of the sub-filter, and sets the corresponding information factor based on the error upper bound technology to eliminate the sub-filter cross-correlation caused by replacing the sub-filter state estimate, thereby achieving higher navigation estimation accuracy.

[0004] However, in actual integrated navigation, there are certain differences between the sub-filters constructed by different sensors. The fixed information factors selected based on experience cannot reflect the differences between the state variables of each sub-filter in real time and track their changes. Instead, the state estimation error of the main filter will affect the estimation accuracy of all sub-filters, thus weakening the fault tolerance of the integrated navigation system.

[0005] Therefore, how to design a new distributed spectral redshift / starlight refraction combined navigation method to ensure the accuracy and reliability of spacecraft navigation has become a technical problem that technical personnel in this field urgently need to overcome. Summary of the invention

[0006] The purpose of the present invention is to provide a distributed spectral redshift / starlight refraction combined navigation method and related products to overcome the problem of low navigation accuracy and reliability due to fixed information factors in the prior art.

[0007] The present invention solves the above technical problems through the following technical solutions:

[0008] The present invention provides a distributed spectral redshift / starlight refraction combined navigation method, comprising the following steps:

[0009] S1. Construct the mathematical model of the spectral redshift / starlight refraction combined navigation system;

[0010] S2. Based on the mathematical model, a distributed spectral redshift / starlight refraction combined navigation system is constructed;

[0011] S3. A federated filtering framework is used to perform information fusion on the adaptive information factor iteration of the distributed spectral redshift / starlight refraction combined navigation system to achieve navigation estimation of the distributed spectral redshift / starlight refraction combined navigation system.

[0012] A further improvement of the present invention is that S1 specifically comprises the following steps:

[0013] Based on the orbital dynamics equation, the state equation of the spectral redshift / starlight refraction combined navigation system is constructed; based on the solution equation of the spectral redshift positioning navigation system, the first measurement equation of the spectral redshift / starlight refraction combined navigation system is constructed; based on the solution equation of the starlight refraction navigation system, the second measurement equation of the spectral redshift / starlight refraction combined navigation system is constructed.

[0014] A further improvement of the present invention is that S2 specifically includes the following steps: constructing a spectral redshift navigation subsystem based on the state equation and the first measurement equation; constructing a starlight refraction sub-navigation system based on the state equation and the second measurement equation.

[0015] A further improvement of the present invention is that the federal filtering framework includes a main filter, a first sub-filter and a second sub-filter.

[0016] A further improvement of the present invention is that: the main filter adopts a federal filter; and the first sub-filter and the second sub-filter both adopt a cubature Kalman filter.

[0017] A further improvement of the present invention is that the information fusion regarding the iteration of the adaptive information factor specifically comprises the following steps:

[0018] S31, performing local state estimation on the spectral redshift navigation subsystem through the first subfilter to obtain a local state estimation value of the spectral redshift navigation subsystem; performing local state estimation on the starlight refraction sub-navigation system through the second subfilter to obtain a local state estimation value of the starlight refraction sub-navigation system;

[0019] S32, fusing the local state estimation value of the spectral redshift navigation subsystem and the local state estimation value of the starlight refraction navigation subsystem through the main filter to obtain the global state estimation value of the distributed spectral redshift / starlight refraction combined navigation system;

[0020] S33. Based on the local state estimate of the spectral redshift navigation subsystem, the local state estimate of the starlight refraction navigation subsystem and the global state estimate, an adaptive information factor is constructed in combination with the probability density function and the Bayesian estimation principle. The first sub-filter and the second sub-filter are reset based on the adaptive information factor, and steps S31 to S33 are repeated until the navigation is completed.

[0021] The present invention also provides a distributed spectral redshift / starlight refraction combined navigation system, comprising:

[0022] The first module is used to construct a mathematical model of the spectral redshift / starlight refraction combined navigation system;

[0023] The second module is used to construct a distributed spectral redshift / starlight refraction combined navigation system based on the mathematical model;

[0024] The third module is used to adopt a federated filtering framework to perform information fusion on the adaptive information factor iteration of the distributed spectral redshift / starlight refraction combined navigation system, so as to realize the navigation estimation of the distributed spectral redshift / starlight refraction combined navigation system.

[0025] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned distributed spectral redshift / starlight refraction combined navigation method when executing the computer program.

[0026] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned distributed spectral redshift / starlight refraction combined navigation method are implemented.

[0027] The present invention also provides a computer program product, comprising a computer program, which implements the steps of the above-mentioned distributed spectral redshift / starlight refraction combined navigation method when executed by a processor.

[0028] Compared with the prior art, the positive and progressive effects of the present invention are:

[0029] The invention provides a distributed spectral redshift / starlight refraction combined navigation method, and constructs a distributed spectral redshift / starlight refraction combined navigation system, so that various navigation sources (spectral redshift navigation and starlight refraction navigation) can work in parallel without interfering with each other, and even if a certain navigation source fails or has a large error, it will not cause a serious impact on the navigation performance of the entire system, thereby enhancing the fault tolerance and reliability of the navigation system; a federal filtering framework is adopted, and iterative information fusion is performed in combination with an adaptive information factor, so that the navigation system can dynamically adjust the adaptive information factor, reflect the difference between the state variables of each sub-filter in real time and track the change thereof, which not only improves the accuracy of navigation estimation, but also reduces the risk of state errors and failures being transmitted with the resetting of the sub-filters. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The drawings in the specification are used to provide further understanding of the present invention and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0031] Figure 1 It is a schematic diagram of the principle of the federation framework based on the adaptive information factor;

[0032] Figure 2 It is a structural schematic diagram of a distributed spectral redshift / starlight refraction combined navigation system of the present invention;

[0033] Figure 3 The present invention is a schematic flow chart of a distributed spectral redshift / starlight refraction combined navigation method. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0035] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0036] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0037] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0038] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0039] 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 determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0040] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0041] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments, which are intended to explain the present invention rather than to limit it.

[0042] See also Figure 3 The present invention provides a distributed spectral redshift / starlight refraction combined navigation method, comprising the following steps:

[0043] S1. Construct the mathematical model of the spectral redshift / starlight refraction combined navigation system;

[0044] S2. Based on the mathematical model, a distributed spectral redshift / starlight refraction combined navigation system is constructed;

[0045] S3. A federated filtering framework is used to perform information fusion on the adaptive information factor iteration of the distributed spectral redshift / starlight refraction combined navigation system to achieve navigation estimation of the distributed spectral redshift / starlight refraction combined navigation system.

[0046] The invention provides a distributed spectral redshift / starlight refraction combined navigation method, and constructs a distributed spectral redshift / starlight refraction combined navigation system, so that various navigation sources (spectral redshift navigation and starlight refraction navigation) can work in parallel without interfering with each other, and even if a certain navigation source fails or has a large error, it will not cause a serious impact on the navigation performance of the entire system, thereby enhancing the fault tolerance and reliability of the navigation system; a federal filtering framework is adopted, and iterative information fusion is performed in combination with an adaptive information factor, so that the navigation system can dynamically adjust the adaptive information factor, reflect the difference between the state variables of each sub-filter in real time and track the change thereof, which not only improves the accuracy of navigation estimation, but also reduces the risk of state errors and failures being transmitted with the resetting of the sub-filters.

[0047] Preferably, S1 specifically comprises the following steps:

[0048] Based on the orbital dynamics equation, the state equation of the spectral redshift / starlight refraction combined navigation system is constructed; based on the solution equation of the spectral redshift positioning navigation system, the first measurement equation of the spectral redshift / starlight refraction combined navigation system is constructed; based on the solution equation of the starlight refraction navigation system, the second measurement equation of the spectral redshift / starlight refraction combined navigation system is constructed.

[0049] Preferably, S2 specifically includes the following steps: constructing a spectral redshift navigation subsystem based on the state equation and the first measurement equation; constructing a starlight refraction sub-navigation system based on the state equation and the second measurement equation.

[0050] Preferably, the federated filtering framework includes a main filter, a first sub-filter and a second sub-filter.

[0051] Preferably, the main filter adopts a federal filter; and the first sub-filter and the second sub-filter both adopt a cubature Kalman filter.

[0052] Preferably, see Figure 1 and Figure 2 The information fusion about the adaptive information factor iteration specifically includes the following steps:

[0053] S31, performing local state estimation on the spectral redshift navigation subsystem through the first subfilter to obtain a local state estimation value of the spectral redshift navigation subsystem; performing local state estimation on the starlight refraction sub-navigation system through the second subfilter to obtain a local state estimation value of the starlight refraction sub-navigation system;

[0054] S32, fusing the local state estimation value of the spectral redshift navigation subsystem and the local state estimation value of the starlight refraction navigation subsystem through the main filter to obtain the global state estimation value of the distributed spectral redshift / starlight refraction combined navigation system;

[0055] S33. Based on the local state estimate of the spectral redshift navigation subsystem, the local state estimate of the starlight refraction navigation subsystem and the global state estimate, an adaptive information factor is constructed in combination with the probability density function and the Bayesian estimation principle. The first sub-filter and the second sub-filter are reset based on the adaptive information factor, and steps S31 to S33 are repeated until the navigation is completed.

[0056] Based on the same inventive concept, the present invention also provides a distributed spectral redshift / starlight refraction combined navigation system, comprising:

[0057] The first module is used to construct a mathematical model of the spectral redshift / starlight refraction combined navigation system;

[0058] The second module is used to construct a distributed spectral redshift / starlight refraction combined navigation system based on the mathematical model;

[0059] The third module is used to adopt a federated filtering framework to perform information fusion on the adaptive information factor iteration of the distributed spectral redshift / starlight refraction combined navigation system, so as to realize the navigation estimation of the distributed spectral redshift / starlight refraction combined navigation system.

[0060] Embodiment 1

[0061] A distributed spectral redshift / starlight refraction combined navigation method comprises the following steps:

[0062] S1. Construct a mathematical model of the spectral redshift / starlight refraction combined navigation system; construct the state equation of the spectral redshift / starlight refraction combined navigation system based on the orbital dynamics equation; construct the first measurement equation of the spectral redshift / starlight refraction combined navigation system based on the solution equation of the spectral redshift positioning navigation system; construct the second measurement equation of the spectral redshift / starlight refraction combined navigation system based on the solution equation of the starlight refraction navigation system.

[0063] Based on the orbital dynamics equation, the state equation can be obtained as:

[0064]

[0065] In the formula, μ represents the gravitational constant of the sun, μ i represents the gravitational constant of the i-th planet, p represents the position of the spacecraft, v represents the speed of the spacecraft, P represents the number of planets, and p si represents the position of the spacecraft to the i-th planet, p i represents the position of the i-th planet, represents the differential of p, represents the calculus of v, F() represents the state transfer function obtained according to the dynamic equation, represents the state quantity in continuous form, is the process noise.

[0066] After discretization, we can get:

[0067] X k =F k (X k-1 )+w k (2)

[0068] In the formula, F k () represents the state transfer function at time k after discretization, X k represents the state information at time k after discretization, w k represents the process noise after discretization.

[0069] The velocity navigation equation of known spectral redshift is:

[0070]

[0071] Where u is the direction vector between the celestial body and the spacecraft in the inertial coordinate system, r represents the spectral redshift of the observed celestial body, and v c represents the speed of the observed celestial body, and c represents the speed of light.

[0072] in,

[0073]

[0074] In the formula, p c Indicates the position of the observed celestial body.

[0075] Combining equations (3) and (4), the spectral redshift positioning and navigation solution equation can be obtained as follows:

[0076]

[0077] Assuming that the spacecraft can observe the spectral redshift of m celestial bodies, the first measurement equation of the spectral redshift / starlight refraction combined navigation system is constructed based on the spectral redshift positioning and navigation solution equation, which is:

[0078]

[0079] In the formula, Z r is the spectral redshift measurement vector, H r () is the spectrum redshift measurement function, Δr is the spectrum redshift measurement error vector, r 1 ,…,r m represents the spectral redshift of m celestial bodies, Δr 1 ,…,Δr m represents the spectral redshift error of m celestial bodies, h r1 (X),…,h rm (X) represents the independent measurement function of the redshift of the spectra of m celestial bodies.

[0080] Discretizing formula (6) yields:

[0081] Z r，k =H r，k (X k )+v r，k (7)

[0082] In the formula, H r,k () represents the spectral redshift measurement function at time k after discretization, Z r,k represents the spectral redshift measurement at time k, v r,k =Δr k Represents the measurement error of the spectral redshift at time k after discretization.

[0083] At the same time, it is known that the starlight refraction navigation solution equation is:

[0084]

[0085] Where a is the altitude angle of the refracted starlight observed by the star sensor relative to the earth, u a Refracted starlight unit vector, R e is the radius of the earth, Δa is the refraction angle measurement error, and the superscript T represents the transpose operation of a vector or matrix.

[0086] Assuming that the spacecraft can observe the starlight refraction angles of m celestial bodies, the second measurement equation of the spectral redshift / starlight refraction combined navigation system is constructed based on the solution equation of the starlight refraction navigation system, which is:

[0087]

[0088] In the formula, Z a is the measurement vector of the refraction angle of celestial starlight, H a () is the measurement function of the celestial body starlight refraction angle, Δa is the measurement error vector of the celestial body starlight refraction angle, a 1 ,…,a mrepresents the refraction angle of starlight from m celestial bodies, Δa 1 ,…,Δa m represents the starlight refraction angle error of m celestial bodies, h a1 (X),…,h an (X) represents the measurement function of the refraction angle of starlight from m celestial bodies.

[0089] Discretizing formula (9) yields:

[0090] Z a，k =H a，k (X k )+v a，k (10)

[0091] In the formula, H a,k () represents the measurement function of the celestial body starlight refraction angle at time k after discretization, Z a,k represents the measurement of the refraction angle of celestial starlight at time k, v a,k =Δa k Represents the measurement error of the refraction angle of celestial starlight at time k after discretization.

[0092] S2. Based on the mathematical model, a distributed spectral redshift / starlight refraction combined navigation system is constructed; based on the state equation and the first measurement equation, a spectral redshift navigation subsystem is constructed; based on the state equation and the second measurement equation, a starlight refraction sub-navigation system is constructed.

[0093] S3, adopting a federated filtering framework to perform information fusion on the distributed spectral redshift / starlight refraction combined navigation system about the adaptive information factor iteration, so as to realize the navigation estimation of the distributed spectral redshift / starlight refraction combined navigation system; the federated filtering framework comprises a main filter, a first sub-filter and a second sub-filter, the main filter adopts a federated filter; the first sub-filter and the second sub-filter both adopt a volumetric Kalman filter;

[0094] S31, performing local state estimation on the spectral redshift navigation subsystem through the first subfilter to obtain a local state estimation value of the spectral redshift navigation subsystem; performing local state estimation on the starlight refraction sub-navigation system through the second subfilter to obtain a local state estimation value of the starlight refraction sub-navigation system;

[0095] First, the spectral redshift navigation subsystem state prediction at time k is calculated as:

[0096]

[0097] Among them, w r,i is the weight of the state volume point of the spectral redshift navigation subsystem, x r,i,k|k-1 is the volume point predicted for the i-th spectral redshift navigation subsystem state, η r,i,k-1is the i-th spectral redshift navigation subsystem state volume point at time k-1, which can be expressed as:

[0098]

[0099] Among them, X k-1 is the global state estimate of the spectral redshift / starlight refraction integrated navigation system at time k-1, P r,k-1 is the local state covariance matrix of the spectral redshift navigation subsystem at time k-1, ξ i Yes n×2n The i-th column of n×2n It is the initial volume point selected according to the volume rule.

[0100] Then, calculate the measurement volume points:

[0101]

[0102] in, is the measurement volume point of the spectral redshift navigation subsystem, P r,k|k-1 One-step prediction of the local state covariance matrix of the spectral redshift navigation subsystem is:

[0103]

[0104] Among them, Q r,k is the process noise covariance matrix in the spectral redshift navigation subsystem filter at time k.

[0105] Further, the spectral redshift navigation subsystem measurement prediction point can be obtained as:

[0106]

[0107] In the formula, Z r,k|k-1 is the prediction of the spectral redshift at time k-1, z r,i,k|k-1 Predict volume point for the i-th spectral redshift measurement.

[0108] Then we can get:

[0109]

[0110] in, is the measurement covariance matrix in the subfilter of the spectral redshift navigation subsystem, is the local state-measurement covariance matrix of the spectral redshift navigation subsystem.

[0111] Furthermore, from equations (16) and (17), the filter gain of the spectral redshift navigation subsystem filter can be obtained as:

[0112]

[0113] Finally, the local state estimation and covariance matrix of the spectral redshift navigation subsystem are obtained as follows:

[0114]

[0115] In the formula, is the local state estimate of the spectral redshift navigation subsystem, P r,k Local state covariance matrix of the spectral redshift navigation subsystem.

[0116] Similar to the spectral redshift navigation subsystem, the starlight refraction navigation subsystem mainly uses the volumetric Kalman filter as a subfilter of the spectral redshift navigation subsystem. Based on the second measurement equation, the state equation and its related parameters, the local state of the starlight refraction navigation subsystem is estimated. The specific process is similar to that of the spectral redshift navigation subsystem, as follows:

[0117] First, the state prediction of the starlight refraction navigation subsystem at time k is calculated as:

[0118]

[0119] Among them, w a,i is the weight of the state volume point of the starlight refraction navigation subsystem, x a,i,k|k-1 is the state prediction volume point of the i-th starlight refraction navigation subsystem, η a,i,k-1 is the i-th state volume point of the starlight refraction navigation subsystem at time k, expressed as:

[0120]

[0121] Among them, P a,k-1 is the local state covariance matrix of the starlight refraction navigation subsystem at time k-1.

[0122] Then, calculate the measurement volume points:

[0123]

[0124] in, is the volume point measured by the starlight refraction navigation subsystem, P a,k|k-1 One-step prediction of the local state covariance matrix of the starlight refraction navigation subsystem is:

[0125]

[0126] Among them, Q a,k is the process noise covariance matrix in the starlight refraction navigation subsystem filter at time k.

[0127] Further, the measurement prediction points of the starlight refraction navigation subsystem are:

[0128]

[0129] In the formula, Z a,k|k-1 is the predicted point of starlight refraction angle measurement at time k-1, z a,i,k|k-1 is the predicted volume point for the i-th starlight refraction angle measurement. Then we can get:

[0130]

[0131] in, is the measurement covariance matrix in the sub-filter of the starlight refraction navigation subsystem, is the state-measurement covariance matrix in the sub-filter of the starlight refraction navigation subsystem.

[0132] Furthermore, from equations (26) and (27), the filter gain of the sub-filter of the starlight refraction navigation subsystem can be obtained as:

[0133]

[0134] Finally, the local state estimation and covariance matrix of the starlight refraction navigation subsystem can be obtained as follows:

[0135]

[0136] In the formula, is the local state estimate of the starlight refraction navigation subsystem, P a,k is the local state covariance matrix of the starlight refraction navigation subsystem.

[0137] S32, fusing the local state estimation value of the spectral redshift navigation subsystem and the local state estimation value of the starlight refraction navigation subsystem through the main filter to obtain the global state estimation value of the distributed spectral redshift / starlight refraction combined navigation system;

[0138] The local states of the starlight refraction navigation subsystem and the spectral redshift navigation subsystem are fused through the information fusion formula in the federated filtering architecture, as follows:

[0139]

[0140] In the formula, is the global state estimation of the spectral redshift / starlight refraction integrated navigation system, P k is the global state covariance matrix of the spectral redshift / starlight refraction combined navigation system.

[0141] S33. Based on the local state estimate of the spectral redshift navigation subsystem, the local state estimate of the starlight refraction navigation subsystem and the global state estimate, an adaptive information factor is constructed in combination with the probability density function and the Bayesian estimation principle. The first sub-filter and the second sub-filter are reset based on the adaptive information factor, and steps S31 to S33 are repeated until the navigation is completed.

[0142] In order to adaptively allocate information, the adaptive information factor based on the probability density function is designed as follows:

[0143] According to the Bayesian estimation principle, we can get:

[0144]

[0145] Where s represents the spectral redshift navigation subsystem or the starlight refraction navigation subsystem, X k is the navigation state actually output at time k, is the probability density of the local state estimate of the s navigation subsystem, is the conditional probability density of the local state estimation of the s navigation subsystem when the navigation state quantity is known at time k, and

[0146]

[0147] According to the definition of probability density function, the larger the conditional probability density corresponding to the local state estimate of the sub-filter corresponding to the navigation subsystem, the closer the local state estimate of the sub-filter corresponding to the navigation subsystem is to the output navigation state quantity, and the more relevant information it contains about the navigation state. Therefore, based on the conditional probability density of formula (34), the adaptive information factor can be constructed as follows:

[0148] Assume that the navigation state quantity output at time k is the global state estimate of the main filter:

[0149]

[0150] Assuming that all state estimates and measurements of the navigation system obey Gaussian distribution, we can get:

[0151]

[0152] From equations (33), (36) and (37), we can get the adaptive information factor:

[0153]

[0154] In the formula is the adaptive information factor corresponding to the s navigation subsystem.

[0155] Based on the probability density adaptive information factor, the navigation subsystem subfilter can be adaptively reset to further improve the accuracy and reliability of the navigation system. The specific working steps are as follows:

[0156]

[0157] As shown in formula (26) to formula (29), the information allocation factor It is no longer a fixed value, but is adaptively adjusted according to the changes in the probability density of local state estimation and global state estimation.

[0158] Based on the same inventive concept, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the distributed spectral redshift / starlight refraction combined navigation method are implemented, wherein the memory may include a memory, such as a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk memory, etc.; the processor, the network interface, and the memory are interconnected through an internal bus, and the internal bus may be an industrial standard architecture bus, a peripheral component interconnection standard bus, an extended industrial standard structure bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory is used to store programs. Specifically, the program may include a program code, and the program code includes computer operation instructions. The memory may include a memory and a non-volatile memory, and provide instructions and data to the processor.

[0159] Based on the same inventive concept, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the distributed spectral redshift / starlight refraction combined navigation method are implemented. Specifically, the computer-readable storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. The volatile memory may include RAM (Random Access Memory) and / or cache memory, etc. The non-volatile memory may include ROM (Read-Only Memory), hard disk, flash memory, optical disk, magnetic disk, etc.

[0160] Based on the same inventive concept, an embodiment of the present application provides a computer program product, which includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer device, the computer device executes the steps of the above-mentioned distributed spectral redshift / starlight refraction combined navigation method.

[0161] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM (Compact Disc Read-Only Memory), optical storage, etc.) containing computer-usable program codes.

[0162] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer device or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0163] These computer program instructions may also be stored in a computer readable memory capable of directing a computer device or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0164] These computer program instructions can also be loaded onto a computer device or other programmable data processing device so that a series of operation steps are executed on the computer device or other programmable device to produce a process implemented by the computer device, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0165] Finally, it should be noted that the above-listed embodiments exist only as one or more specific forms of expression of the technical solution of the present invention. Their purpose is to clearly explain the concept, principle and application of the present invention through specific examples, and it is by no means intended to limit the protection scope of the present invention to these specific embodiments. In fact, the real value of the present invention lies in the technical ideas and innovations it proposes, rather than its form of expression or means of implementation.

[0166] For ordinary technicians in the relevant technical field, after in-depth reading and understanding of the technical solution of the present invention, they are fully capable of making various forms of changes, modifications or equivalent substitutions to the specific implementation methods of the invention based on their own professional knowledge and skills. These changes may include but are not limited to: adjusting the value range of technical parameters, optimizing algorithm processes to improve efficiency, replacing some technical components to achieve better compatibility or reduce costs, etc. As long as these changed technical solutions still substantially maintain the technical features claimed for protection by the original invention, that is, they can still achieve the core functions and effects of the present invention, then these changes should be deemed to fall within the scope of protection of the pending claims of the present invention.

[0167] In addition, with the continuous progress and development of technology, new technical means and methods continue to emerge, which also provides broad space for further improvement and perfection of the present invention. Therefore, the protection scope of the present invention should also include those reasonably foreseeable improvements and extensions based on the existing technology. As long as these improvements and extensions do not deviate from the basic principles and core concepts of the present invention, they should be regarded as equivalents of the present invention and are also protected by patent rights.

Claims

1. A distributed spectral redshift / starlight refraction combined navigation method, characterized in that: The following steps are involved: S1. Construct the mathematical model of the spectral redshift / starlight refraction combined navigation system; S2. Based on the mathematical model, a distributed spectral redshift / starlight refraction combined navigation system is constructed; S3. A federated filtering framework is used to perform information fusion on the adaptive information factor iteration of the distributed spectral redshift / starlight refraction combined navigation system to achieve navigation estimation of the distributed spectral redshift / starlight refraction combined navigation system.

2. A distributed spectral redshift / starlight refraction combined navigation method according to claim 1, characterized in that: S1 specifically includes the following steps: Based on the orbital dynamics equation, the state equation of the spectral redshift / starlight refraction combined navigation system is constructed; based on the solution equation of the spectral redshift positioning navigation system, the first measurement equation of the spectral redshift / starlight refraction combined navigation system is constructed; based on the solution equation of the starlight refraction navigation system, the second measurement equation of the spectral redshift / starlight refraction combined navigation system is constructed.

3. A distributed spectral redshift / starlight refraction combined navigation method according to claim 2, characterized in that: S2 specifically includes the following steps: constructing a spectral redshift navigation subsystem based on the state equation and the first measurement equation; constructing a starlight refraction sub-navigation system based on the state equation and the second measurement equation.

4. A distributed spectral redshift / starlight refraction combined navigation method according to claim 3, characterized in that: The federated filtering framework includes a main filter, a first sub-filter and a second sub-filter.

5. A distributed spectral redshift / starlight refraction combined navigation method according to claim 4, characterized in that: The main filter adopts a federated filter; the first sub-filter and the second sub-filter both adopt a cubature Kalman filter.

6. A distributed spectral redshift / starlight refraction combined navigation method according to claim 5, characterized in that: The information fusion about the adaptive information factor iteration specifically includes the following steps: S31, performing local state estimation on the spectral redshift navigation subsystem through the first subfilter to obtain a local state estimation value of the spectral redshift navigation subsystem; performing local state estimation on the starlight refraction sub-navigation system through the second subfilter to obtain a local state estimation value of the starlight refraction sub-navigation system; S32, fusing the local state estimation value of the spectral redshift navigation subsystem and the local state estimation value of the starlight refraction navigation subsystem through the main filter to obtain the global state estimation value of the distributed spectral redshift / starlight refraction combined navigation system; S33. Based on the local state estimate of the spectral redshift navigation subsystem, the local state estimate of the starlight refraction navigation subsystem and the global state estimate, an adaptive information factor is constructed in combination with the probability density function and the Bayesian estimation principle. The first sub-filter and the second sub-filter are reset based on the adaptive information factor, and steps S31 to S33 are repeated until the navigation is completed.

7. A distributed spectral redshift / starlight refraction combined navigation system, characterized in that: include: The first module is used to construct a mathematical model of the spectral redshift / starlight refraction combined navigation system; The second module is used to construct a distributed spectral redshift / starlight refraction combined navigation system based on the mathematical model; The third module is used to adopt a federated filtering framework to perform information fusion on the adaptive information factor iteration of the distributed spectral redshift / starlight refraction combined navigation system, so as to realize the navigation estimation of the distributed spectral redshift / starlight refraction combined navigation system.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the distributed spectral redshift / starlight refraction combined navigation method described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the distributed spectral redshift / starlight refraction combined navigation method described in any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the distributed spectral redshift / starlight refraction combined navigation method described in any one of claims 1 to 6 are implemented.

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