Geomagnetic contour matching method and device based on adaptive spatial constraint, and medium

Abstract

The invention discloses a geomagnetic contour matching method and device based on adaptive spatial constraint, and a medium. The method comprises the following steps: obtaining influence parameters of a to-be-matched point and a current weight coefficient; the weight coefficients are weights used for calculating combination similarity scores of the to-be-matched points, and a plurality of weight coefficients are provided; adjusting the current weight coefficient through the influence parameter to obtain a target weight coefficient; determining a combination similarity score according to the target weight coefficient; and taking the to-be-matched point corresponding to the minimum score as a candidate point. Taking the candidate point as a center, and narrowing a search range to carry out cyclic matching until an iteration condition is met; and taking the candidate point obtained by the last iteration as a target matching position. Therefore, by dynamically adjusting and calculating the weight coefficients of the combined similarity scores, contributions of the similarity constraint terms corresponding to different weight coefficients in the similarity scores are adjusted, so that the method continuously adapts to complex and multi-change navigation environments, and accurate matching of carrier positions is realized.

Description

Technical Field

[0001] This application relates to the field of navigation technology, and particularly to a geomagnetic contour matching method, device and medium based on adaptive spatial constraints. Background Art

[0002] The geomagnetic contour matching technology is a technology that uses the spatial distribution characteristics of the earth's magnetic field for navigation and positioning. This technology has made remarkable progress in some specific application scenarios, such as underwater navigation, aviation navigation, and precise positioning of ground vehicles. Especially in the underwater environment, since GPS signals cannot penetrate the water surface, geomagnetic navigation has become an important navigation method.

[0003] Although the current geomagnetic navigation technology has made certain progress, during the geomagnetic matching process, due to the complex and changeable navigation environment, the navigation system has poor adaptability to the changing environment, thus reducing the navigation accuracy and reliability.

[0004] Therefore, how to improve the adaptability of the navigation system in a complex environment and improve the accuracy and reliability of geomagnetic matching is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, one aspect of this application provides a geomagnetic contour matching method based on adaptive spatial constraints, and the method includes: S10: Search the area to be matched with the inertial navigation system as the center to obtain the points to be matched; S11: Obtain the influence parameters of the current weight coefficient; the weight coefficient is the weight used to calculate the combined similarity score of the points to be matched, and there are multiple weight coefficients; S12: After adjusting the current weight coefficient through the influence parameters, obtain the target weight coefficient; S13: Determine the combined similarity score according to the target weight coefficient; and use the point to be matched corresponding to the minimum value of the combined similarity score as the candidate point; S14: With the candidate point as the center, narrow the search range, and loop to execute steps S11 to S13 until the iteration condition is reached; and use the candidate point obtained in the last iteration as the target matching position.

[0006] Optionally, the weight coefficients include a magnetic field difference weight, a spatial continuity weight, and an inertial navigation trajectory difference weight; the influence parameters include a measured magnetic field vector, carrier motion information, and inertial navigation time information; adjusting the current weight coefficient through the influence parameters includes: Determine the magnetic field gradient eigenvalue according to the measured magnetic field vector; and adjust the magnetic field difference weight according to the magnetic field gradient eigenvalue; Determine a motion state index value for reflecting the stability of the carrier motion state based on the carrier motion information; adjust the spatial continuity weight according to the motion state index value. Determine the error accumulation time of the inertial navigation system according to the inertial navigation time information; and adjust the inertial navigation trajectory difference weight according to the error accumulation time.

[0007] Optionally, the carrier motion information includes the motion speed and the heading angle of the carrier; the determining of the motion state index value according to the carrier motion information includes: Determine the speed change increment between the current moment and the previous moment through the motion speed; Determine the heading change increment between the current moment and the previous moment through the heading angle; Use the current motion speed of the carrier as the weight of the heading change increment; Calculate the weighted sum of the speed change increment and the heading change increment based on the weight of the heading change increment to obtain the motion state index value.

[0008] Optionally, the adjusting of the spatial continuity weight according to the motion state index value includes: When the motion state index value is greater than the index threshold, obtain a preset weight adjustment rate and spatial continuity weight extreme value; Adjust the spatial continuity weight within the range of the spatial continuity weight extreme value based on the weight adjustment rate and the motion state index value; wherein, the larger the motion state index value, the larger the spatial continuity weight.

[0009] Optionally, the determining of the magnetic field gradient eigenvalue according to the measured magnetic field vector includes: Obtain a preset gradient feature extreme value and magnetic field weight extreme value; Determine the gradient vector between adjacent measured magnetic field vectors; Determine an initial gradient feature according to the gradient feature extreme value, the magnetic field weight extreme value and the gradient vector; Normalize the initial gradient feature to obtain the magnetic field gradient eigenvalue.

[0010] Optionally, the larger the magnetic field gradient eigenvalue, the larger the magnetic field difference weight; The larger the error accumulation time, the smaller the inertial navigation trajectory difference weight; The sum of the magnetic field difference weight, the spatial continuity weight and the inertial navigation trajectory difference weight is equal to 1.

[0011] Optionally, the components of the combined similarity score include at least two of a magnetic field difference penalty term, a spatial continuity penalty term, and an inertial navigation trajectory difference penalty term; Among them, the magnetic field difference penalty term is calculated through non-linear mapping based on the measured magnetic field vector of the point to be matched; the spatial continuity penalty term is calculated through normalized distance based on the position change amount of the carrier; the inertial navigation trajectory difference penalty term is calculated through normalized distance based on the position information of the inertial navigation system.

[0012] Another aspect of the present application provides a geomagnetic contour matching device based on adaptive spatial constraints, and the device includes: A point-to-be-matched acquisition module, configured to search for a region to be matched centered on an inertial navigation system to obtain points to be matched; An influence parameter acquisition module, configured to acquire influence parameters of a current weight coefficient; the weight coefficient is a weight used to calculate the combined similarity score of the point to be matched, and there are multiple weight coefficients; A weight adjustment module, configured to obtain a target weight coefficient after adjusting the current weight coefficient through the influence parameter; A candidate point determination module, configured to determine the combined similarity score according to the target weight coefficient; and use the point to be matched corresponding to the minimum value of the combined similarity score as a candidate point; A loop control module, configured to narrow the search range centered on the candidate point, and loop to control the influence parameter acquisition module, the weight adjustment module, and the candidate point determination module until the iteration condition is met; and use the candidate point obtained in the last iteration as the target matching position.

[0013] Another aspect of the present application provides a geomagnetic contour matching device based on adaptive spatial constraints, including a memory and a processor. A computer program that can run on the processor is stored on the memory, and when the processor executes the program, the steps of the geomagnetic contour matching method based on adaptive spatial constraints are implemented.

[0014] Another aspect of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the geomagnetic contour matching method based on adaptive spatial constraints are implemented.

[0015] The beneficial effects of a geomagnetic contour matching method, device, and medium based on adaptive space constraints provided by this application are as follows: During the process of geomagnetic contour matching, the weight coefficients for calculating the combined similarity score are dynamically adjusted, thereby adjusting the contributions of the similarity constraint terms corresponding to different weight coefficients to the similarity score, continuously adapting to complex and diverse navigation environments, and achieving precise matching of the carrier's position. That is, by dynamically adjusting the weights of multi-source information, the accuracy and reliability of geomagnetic matching navigation are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic flowchart of a geomagnetic contour matching method based on adaptive space constraints provided by an embodiment of this application; Figure 2 It is a schematic diagram of the principle of a geomagnetic contour matching method based on adaptive space constraints provided by an embodiment of this application; Figure 3 It is a schematic flowchart of a geomagnetic contour matching method based on adaptive space constraints provided by another embodiment of this application; Figure 4 It is an intention diagram of dynamic adjustment of weight coefficients provided by an embodiment of this application; Figure 5 It is a schematic structural diagram of a geomagnetic contour matching device based on adaptive space constraints provided by an embodiment of this application; Figure 6 It is a schematic structural diagram of a geomagnetic contour matching device based on adaptive space constraints provided by another embodiment of this application.

[0017] The reference numerals are as follows: 60 is a memory, 61 is a processor, 62 is a display screen, 63 is an input / output interface, 64 is a communication interface, 65 is a power supply, 66 is a communication bus, 601 is a computer program, 602 is an operating system, and 603 is data. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms "a", "the", and "said" used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0019] It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to a determination".

[0020] Figure 1 The flowchart of a geomagnetic contour matching method based on adaptive spatial constraints provided by an embodiment of this application is shown as Figure 1 shown, and this method includes: S10: Centering on the inertial navigation system, search the area to be matched to obtain points to be matched; In a specific embodiment, when performing geomagnetic contour matching on the movement position of the carrier, centering on the inertial navigation system on the carrier, a grid search is established for the area to be matched. Under the initial search parameters, the area to be matched is searched to obtain points to be matched.

[0021] Among them, the initial search parameters include but are not limited to the initial search area, the initial search step size, the maximum search radius, and the maximum search step size. After performing the grid search, multiple points to be matched that may be the actual position of the carrier at the current moment can be obtained.

[0022] It should be noted that the carrier in the embodiments of this application may include but are not limited to airplanes, intelligent driving vehicles, submersibles, ships, and unmanned aerial vehicles. In an optional embodiment, the maximum search radius can be set to 1000 meters (m), the initial search step size can be set to 50 m, the search grid point number can be set to a 21*21 grid, and the obtained grid point number is 441.

[0023] S11: Obtain the influence parameters of the current weight coefficient; the weight coefficient is the weight used to calculate the combined similarity score of the points to be matched, and there are multiple weight coefficients; In a specific embodiment of matching the carrier position, the combined similarity score of the points to be matched can be calculated, and based on the combined similarity score, the point closest to the actual position of the carrier among all the current points to be matched can be determined, that is, the best matching point.

[0024] However, when calculating the combined similarity score, it is often affected by many constraint conditions. For example, the magnetic field difference between the measured magnetic field data and the reference magnetic field data, and the spatial continuity between the current matching position and the matching position at the previous moment. And in different navigation environments, the magnetic field difference constraint and the spatial continuity constraint show different degrees of influence.

[0025] Therefore, in order to improve the matching accuracy, when calculating the combined similarity score, the weight coefficients of different penalty terms in the combined similarity score can be adjusted. Specifically, obtain the influence parameters of the current weight coefficients, which include but are not limited to the measured magnetic field vector, the reference magnetic field vector, and the carrier motion information. The weight coefficients include but are not limited to the magnetic field difference weight and the spatial continuity weight.

[0026] S12: After adjusting the current weight coefficients through the influence parameters, obtain the target weight coefficients; S13: Determine the combined similarity score according to the target weight coefficients; and use the candidate point corresponding to the minimum value of the combined similarity score as the candidate point; Furthermore, adjust the current weight coefficients through the influence parameters to obtain the target weight coefficients. It can be understood that during the continuous movement of the carrier, the navigation environment is constantly changing. In order to better adapt to the complex and changing navigation environment, when performing matching, obtain the influence parameters of the weight coefficients in real time. During the screening process of the current candidate points, adjust the current weight coefficients through the current influence parameters so as to determine the target weight coefficients used when calculating the combined similarity score next time.

[0027] It should be noted that if the screening of the candidate points is the first screening, that is, the screening of the candidate points under the initial search parameters, the weight coefficients to be adjusted currently are the initially set weight coefficients. If the current screening of the candidate points is not the first screening, the weight coefficients to be adjusted currently are the weight coefficients used in the previous round of screening.

[0028] After obtaining the target weight coefficients, determine the combined similarity score under the target weight coefficients, that is, determine the combined similarity scores of each candidate point in the current navigation environment, and use the candidate point corresponding to the minimum value of the combined similarity score as the candidate point. That is, the candidate point is the best matching point in the current matching round, that is, the candidate point is the point closest to the actual position of the carrier among the current candidate points.

[0029] S14: With the candidate point as the center, narrow the search range, and loop through steps S11 to S13 until the iteration condition is met; and use the candidate point obtained in the last iteration as the target matching position.

[0030] Furthermore, after obtaining a candidate point in each round of matching, with the candidate point as the center, continuously narrow the search range, and loop through steps S11 to S13, that is, perform circular search and matching of candidate points. Until the iteration condition is met. The candidate point obtained in the last iteration is recorded as the target matching position. Connect the target matching positions obtained at each moment to obtain the movement trajectory of the carrier.

[0031] Among them, narrowing the search scope may include, but is not limited to, narrowing the search area and narrowing the search step size. The iteration condition may include, but is not limited to, reaching the number of iterations.

[0032] In an alternative embodiment, in order to further improve the matching accuracy, after obtaining the target matching position, the target matching position can be optimized by Kalman filtering to obtain a more accurate navigation position.

[0033] Thus, in the geomagnetic contour matching method based on adaptive spatial constraints provided by the embodiments of the present application, during the process of geomagnetic contour matching, the weight coefficients for calculating the combined similarity score are dynamically adjusted, so as to adjust the contributions of the similarity constraint terms corresponding to different weight coefficients to the similarity score, thereby continuously adapting to the complex and changing navigation environment and achieving accurate matching of the carrier position. That is, by dynamically adjusting the weights of multi-source information, the accuracy and reliability of geomagnetic matching navigation are improved.

[0034] As an alternative embodiment, the weight coefficients include a magnetic field difference weight, a spatial continuity weight, and an inertial navigation trajectory difference weight; the influencing parameters include the measured magnetic field vector, the carrier motion information, and the inertial navigation time information.

[0035] It can be understood that the present application dynamically adjusts different weight coefficients in the combined similarity score to adapt to the complex and changing navigation environment. Different weight relationships are used to adjust the contributions of different penalty terms in the combined similarity score when calculating the combined similarity score.

[0036] In an alternative embodiment, different penalty terms in the combined similarity score include a magnetic field difference penalty term, a spatial continuity penalty term, and an inertial navigation trajectory difference penalty term. Among them, the magnetic field difference penalty term is used to reflect the degree of magnetic field difference between the measured magnetic field vector of the point to be matched and the reference magnetic field vector, the spatial continuity penalty term is used to reflect the degree of spatial continuity between the current point to be matched and the target matching position at the previous moment, and the inertial navigation trajectory difference penalty term is used to reflect the degree of difference between the current matching trajectory and the inertial navigation trajectory.

[0037] Correspondingly, different weight coefficients are set for different penalty terms, specifically including a magnetic field difference weight, a spatial continuity weight, and an inertial navigation trajectory difference weight. Among them, the magnetic field difference weight is the weight of the magnetic field difference penalty term, the spatial continuity weight is the weight of the spatial continuity penalty term, and the inertial navigation trajectory difference weight is the weight of the inertial navigation trajectory difference penalty term.

[0038] In the present application, an adaptive weight mechanism is adopted. At time , let the carrier position (i.e., the current position of the point to be matched) be , the measured magnetic field vector be , and the reference magnetic field vector be , the current position of the inertial navigation is . Then the calculation formula of the combined similarity score is Formula (1): (1) Wherein, is the combined similarity score, is the magnetic field difference weight, is the spatial continuity weight, is the inertial navigation trajectory difference weight, is the magnetic field difference penalty term, is the spatial continuity penalty term, is the inertial navigation trajectory difference penalty term.

[0039] In an optional embodiment, the magnetic field difference weight can be adjusted by the gradient feature generated by the magnetic field. Therefore, the influencing parameters may include the measured magnetic field vector. In another optional embodiment, the spatial continuity weight can be adjusted by observing the motion state of the carrier. Therefore, the influencing parameters may include the carrier motion information. In still another optional embodiment, the inertial navigation trajectory difference weight can be adjusted by the error accumulation time generated by the inertial navigation. Therefore, the influencing parameters may include the inertial navigation time information.

[0040] Figure 2 is a schematic diagram of the principle of a geomagnetic profile matching method based on adaptive space constraint provided by an embodiment of the present application. For ease of understanding, the following will be combined with Figure 2 to further illustrate.

[0041] On the basis of the above embodiment, when the weight coefficients include the magnetic field difference weight, the spatial continuity weight, and the inertial navigation trajectory difference weight, and the influencing parameters include the measured magnetic field vector, the carrier motion information, and the inertial navigation time information, the position of the carrier is matched. As Figure 2 shown, the navigation system first performs initialization. The system initialization includes setting initial search parameters, setting initial weight coefficients, and setting weight adjustment parameters. Among them, the weight adjustment parameters include, but are not limited to, the weight adjustment rate and the spatial continuity weight extreme value.

[0042] It should be noted that when performing an initial match on the point to be matched, that is, when performing the first round of loop iteration, an initial weight coefficient needs to be set in advance, that is, the initial weight coefficient is set in the system initialization. For example, in an optional embodiment, the initial magnetic field difference weight can be set to 0.6, the initial spatial continuity weight can be set to 0.3, and the initial inertial navigation trajectory difference weight can be set to 0.1.

[0043] After the system initialization, the system enters the matching loop. As Figure 2 shown, under the initial search parameter conditions, grid search is performed to generate points to be matched, and the combined similarity score is calculated according to the initial weight coefficients. The point to be matched corresponding to the minimum value of the combined similarity score is used as the candidate point, that is, the candidate point is generated according to the combined similarity score.

[0044] Furthermore, it is judged whether to continue the loop matching according to the preset iteration conditions. If the loop matching is not performed, that is, the iteration conditions are satisfied, the candidate point obtained from the current round of loop iteration is used as the target matching position at this time.

[0045] If it is determined to continue the loop matching, at this time, the influence parameter of the current weight coefficient is obtained, and the current weight coefficient is adjusted according to the image parameters to obtain the target weight coefficient. Furthermore, in the next round of loop matching, the combined similarity score is calculated through the target coefficient, and such loop matching is performed until the iteration conditions are met.

[0046] Figure 3 FIG. is a schematic flow chart of a geomagnetic contour matching method based on adaptive space constraint provided by another embodiment of the present application. On the basis of the above embodiment, as Figure 2 shown, the current weight coefficient is adjusted through the influence parameter, including: S30: Determine the magnetic field gradient eigenvalue according to the measured magnetic field vector; and adjust the magnetic field difference weight according to the magnetic field gradient eigenvalue; As Figure 2 shown, when the current weight coefficient is adjusted according to the influence parameter, in an optional embodiment, the magnetic field difference weight can be adjusted by the measured magnetic field vector. Specifically, since the magnetic field gradient can reflect the spatial change rate of the geomagnetic field, therefore, by measuring the magnetic field vector, the magnetic field gradient eigenvalue of the point to be matched can be calculated, and then the magnetic field difference weight can be adjusted according to the magnetic field gradient eigenvalue.

[0047] S31: Determine the motion state index value for reflecting the stability of the carrier motion state according to the carrier motion information; adjust the spatial continuity weight according to the motion state index value; In another optional embodiment, as Figure 2 shown, the spatial continuity weight can be adjusted by the carrier motion information. Specifically, the motion state index value is calculated through the carrier motion information, and the motion state index value refers to a value that can be used to reflect the stability of the carrier motion state. Furthermore, the spatial continuity weight can be adjusted according to the motion state index value. That is, the spatial continuity weight is adjusted according to the stability of the carrier motion state.

[0048] S32: Determine the error accumulation time of the inertial navigation system according to the inertial navigation time information; and adjust the inertial navigation trajectory difference weight according to the error accumulation time.

[0049] As Figure 2 shown, in an alternative embodiment, the error accumulation time of the inertial navigation system can be calculated according to the inertial navigation time information, and further, the inertial navigation trajectory difference weight can be adjusted according to the error accumulation time. Specifically, the inertial navigation time information obtained in real time includes the current time and the last reset time of the inertial navigation system. Thus, the error accumulation time can be determined according to formula (2): (2) where is the error accumulation time, is the current time, is the last reset time of the inertial navigation system.

[0050] When adjusting the inertial navigation trajectory difference weight according to the error accumulation time, obtain the preset maximum error accumulation time and the maximum value of the inertial navigation trajectory difference weight, and then the inertial navigation trajectory difference weight adjustment function formula (3): (3) where is the maximum value of the inertial navigation trajectory difference weight, is the maximum error accumulation time.

[0051] In an alternative embodiment, the preset maximum error accumulation time can be set to 300 seconds (s), and the maximum value of the inertial navigation trajectory difference weight can be set to 0.3. In addition, it should be noted that when obtaining the current time in real time, a sampling time interval can be preset. For example, the sampling time interval can be set to 0.1 s.

[0052] Thus, based on formula (3), through the maximum error accumulation time and the error accumulation time the adjustment of the inertial navigation trajectory difference weight can be achieved.

[0053] It should be noted that in order to improve the matching efficiency, in an alternative embodiment, multi-threading can be called to execute steps S30 to S32 in parallel, that is, synchronously adjust all weight coefficients. In addition, it should also be noted that the obtained target weight coefficients after adjustment correspondingly include the target magnetic field difference weight, the target spatial continuity weight, and the target inertial navigation trajectory difference weight.

[0054] Therefore, the geomagnetic contour matching method based on adaptive spatial constraints provided by the embodiments of the present application realizes dynamic adjustment of multiple weight coefficients in the combined similarity score through an adaptive weight mechanism, enhances the adaptability of the system in complex environments, realizes dynamic fusion of multi-source information, ensures the universality of the algorithm, and improves the adaptability in complex environments. Moreover, each design link cooperates with each other to form a complete adaptive matching framework.

[0055] As an alternative embodiment, the carrier motion information includes the motion speed and heading angle of the carrier; according to the carrier motion information, determining the motion state index value includes: Determining the speed change increment between the current moment and the previous moment through the motion speed; Determining the heading change increment between the current moment and the previous moment through the heading angle; Using the current motion speed of the carrier as the weight of the heading change increment; Calculating the weighted sum of the speed change increment and the heading change increment based on the weight of the heading change increment to obtain the motion state index value.

[0056] The motion speed and heading angle of the carrier can reflect the stability of the carrier's motion, and the carrier stability is crucial for the spatial continuity weight. Therefore, the spatial continuity weight can be adjusted by the motion state index value used to reflect the stability of the carrier's motion state. That is, the adaptive adjustment of the spatial continuity weight is based on the motion state index value.

[0057] Specifically, the motion state index value is calculated according to formula (4) : (4) Wherein, is the motion state index value, is the speed change increment, is the heading change increment. In a specific embodiment, based on the motion speed obtained in real time, the speed change increment between the current moment and the previous moment is determined, that is, , wherein, is the motion speed at the current moment, is the motion speed at the previous moment.

[0058] In addition, based on the heading angle obtained in real time, the heading change increment between the current moment and the previous moment can be determined, that is, , wherein, is the heading angle at the current moment, is the heading angle at the previous moment.

[0059] Considering that the heading angle has a greater impact on the position change, that is, the heading change has a greater impact on the motion stability. Therefore, in an alternative embodiment, as shown in formula (4), the motion speed at the current moment is introduced into the heading change increment as a weight. Thus, based on the weight of the heading change increment, the weighted sum of the speed change increment and the heading change increment is calculated, and the motion state index value can be obtained.

[0060] Based on the above embodiments, as an alternative embodiment, according to the motion state index value, the spatial continuity weight is adjusted, including: When the motion state index value is greater than the index threshold, obtain the preset weight adjustment rate and the extreme value of the spatial continuity weight; Based on the weight adjustment rate and the motion state index value, adjust the spatial continuity weight within the range of the extreme value of the spatial continuity weight; wherein, the greater the motion state index value, the greater the spatial continuity weight.

[0061] It can be understood that the motion state index value comprehensively considers the speed change and steering change of the carrier, and can comprehensively reflect the motion characteristics of the carrier. Among them, the speed change increment can reflect the acceleration and deceleration degree of the carrier's motion, and the heading change increment can reflect the severity of the steering.

[0062] Specifically, in a specific embodiment of adjusting the spatial continuity weight according to the motion state index value , when it is determined that the motion state index value is greater than the index threshold, obtain the preset weight adjustment rate and the extreme value of the spatial continuity weight. Further, the spatial continuity weight can be adjusted according to formula (5): (5) Wherein, is the minimum value of the spatial continuity weight, is the maximum value of the spatial continuity weight, and constitute the extreme value of the spatial continuity weight, is the weight adjustment rate. In formula (5), an exponential decay form of the spatial continuity weight adjustment function is adopted, which can achieve a smooth weight transition and avoid sudden changes.

[0063] In an alternative embodiment, the minimum value of the spatial continuity weight can be set to 0.1, the maximum value of the spatial continuity weight can be set to 0.4, and the weight adjustment rate It can be set to 0.3. In an alternative embodiment, when adjusting the spatial continuity weight , it is adjusted within the extreme value range of the spatial continuity weight. In fact, the weight adjustment rate is a parameter that can be dynamically adjusted and can be adjusted according to different application scenarios.

[0064] In addition, it should be noted that the index threshold for starting to adjust the spatial continuity weight , in an alternative embodiment, can be set to . In a specific embodiment, when the motion state index value is greater than the index threshold , the spatial continuity weight will be adjusted towards the maximum value of the spatial continuity weight at the weight adjustment rate . That is, the larger the motion state index value , the larger the spatial continuity weight .

[0065] If the motion state index value is not greater than the index threshold , it will decay towards the minimum value of the spatial continuity weight . In fact, the index threshold is used to distinguish between a stable motion state and a violent motion state, triggering different weight adjustment directions.

[0066] Figure 4 This is the dynamic adjustment intention of a weight coefficient provided by the embodiments of the present application. As Figure 4 shown, when the motion state of the carrier changes, the spatial continuity weight will be adjusted accordingly.

[0067] Thus, the matching method provided by the embodiments of the present application can improve the matching performance under the condition of violent movement of the carrier by adjusting the spatial continuity weight through the motion state index value.

[0068] In an alternative embodiment, according to the measured magnetic field vector, determining the magnetic field gradient eigenvalue includes: obtaining the preset gradient feature extreme value and magnetic field weight extreme value; determining the gradient vector between adjacent measured magnetic field vectors; determining the initial gradient feature according to the gradient feature extreme value, magnetic field weight extreme value and gradient vector; normalizing the initial gradient feature to obtain the magnetic field gradient eigenvalue.

[0069] In a specific embodiment, an initial gradient feature can be calculated based on the gradient feature extreme value, the magnetic field weight extreme value, and the gradient vector. Specifically, refer to formula (6): (6) Wherein, is the initial gradient feature, is the gradient vector between adjacent measured magnetic field vectors, and the gradient vector can be expressed as: . represents the Frobenius norm.

[0070] Furthermore, the initial gradient feature is normalized to obtain the magnetic field gradient feature value. Specifically, the initial gradient feature is normalized to the range [0, 1]. Refer to formula (7): (7) Wherein, is the magnetic field gradient feature value, is the minimum gradient feature value, is the maximum gradient feature value, and constitute the gradient feature extreme value. In an alternative embodiment, the minimum gradient feature value can be set to 0.1, and the maximum gradient feature value can be set to 0.5.

[0071] Furthermore, a magnetic field difference weight adjustment function can be generated based on the magnetic field gradient feature value . Specifically, refer to formula (8): (8) Wherein, is the minimum magnetic field weight, is the maximum magnetic field weight, and constitute the magnetic field weight extreme value. In an alternative embodiment, the minimum magnetic field weight can be set to 0.3, and the maximum magnetic field weight can be set to 0.7. As Figure 4 shown, the magnetic field difference weight will be dynamically adjusted with the change of the magnetic field gradient feature. In formula (8), the Frobenius norm is used to comprehensively consider the gradient information in three directions and provide a scalar measure of the gradient intensity.

[0072] Based on the above embodiments, as an alternative embodiment, the larger the magnetic field gradient eigenvalue, the larger the magnetic field difference weight; the larger the error accumulation time, the smaller the inertial navigation trajectory difference weight; the sum of the magnetic field difference weight, the spatial continuity weight, and the inertial navigation trajectory difference weight is equal to 1.

[0073] The magnetic field gradient can reflect the spatial change rate of the geomagnetic field. In the region with a larger gradient, the magnetic field characteristics are more significant. Increasing the magnetic field difference weight in the region with significant gradients can make full use of the significant geomagnetic characteristic information. Therefore, in an alternative embodiment, the larger the magnetic field gradient eigenvalue , the larger the magnetic field difference weight . While in the region with a gentle gradient, appropriately reducing the magnetic field difference weight can reduce the influence of magnetic field measurement noise, that is, the smaller the magnetic field gradient eigenvalue , the smaller the magnetic field difference weight .

[0074] In another alternative embodiment, according to formula (3), it can be seen that the error of the inertial navigation system increases with time accumulation, and it is necessary to dynamically adjust the inertial navigation trajectory difference weight .

[0075] In formula (3), using the exponential decay model conforms to the general law of the growth of inertial navigation error, and the decay rate of the inertial navigation trajectory difference weight can be adjusted according to the specific characteristics of the inertial navigation system through the maximum error accumulation time . Specifically, according to , it can be known that the decay rate is inversely proportional to the maximum error accumulation time Figure 4 . The larger the maximum error accumulation time , the slower the decay; the smaller the maximum error accumulation time , the faster the decay.

[0076] Therefore, in an alternative embodiment, when the system design requires quickly reducing the inertial navigation trajectory difference weight (such as when the inertial navigation error accumulates rapidly), a smaller maximum error accumulation time can be set, and correspondingly, the decay rate at this time is larger.

[0077] It can be understood that the geomagnetic contour matching method based on adaptive space constraint provided by this application can flexibly adjust the contribution degrees of different information sources in a weighted form, improving the adaptability of the algorithm. To overcome the limitations of a single information source, in an alternative embodiment, the sum of the magnetic field difference weight, the spatial continuity weight, and the inertial navigation trajectory difference weight is equal to 1. That is, .

[0078] Thus, through the constraint that the sum of weights is 1, the normalization of the combined similarity score calculation is ensured, which facilitates comparison in different scenarios. At the same time, it ensures that the relative contribution degrees of different information sources in matching and positioning always maintain a reasonable proportional relationship. The combination of the three constraints can complement each other, thus overcoming the limitations of a single information source. In an optional embodiment, assigning a relatively large value to the initial weight coefficient can make full use of the short-term high-precision characteristics of inertial navigation.

[0079] As Figure 4 shown, it intuitively shows the dynamic change characteristics of the three weights. The magnetic field difference weight changes with the magnetic field gradient eigenvalue, the spatial continuity weight changes with the motion state index value, and the inertial navigation trajectory difference weight decays exponentially with the error accumulation time.

[0080] In an optional embodiment, as Figure 4 shown, the periodic change of the magnetic field gradient eigenvalue is simulated by a sine function, and the magnetic field difference weight is dynamically adjusted within the range of the maximum magnetic field difference weight and the minimum magnetic field difference weight. Among them, the maximum magnetic field difference weight can be set to 0.7, and the minimum magnetic field difference weight can be set to 0.3.

[0081] The fluctuation period is about 50 seconds, reflecting the periodic change of the magnetic field gradient eigenvalue. When the magnetic field gradient eigenvalue is large, the magnetic field difference weight increases. For example, Figure 4 as shown in it reaches about 0.7 during 100 - 200 seconds. When the magnetic field gradient eigenvalue is small, the magnetic field difference weight Figure 4 decreases. For example,

[0082] except for the period of 100 - 200 seconds, it is about 0.45 - 0.55 at other times. Figure 4 In , for the spatial continuity weight , the change of the three-stage motion state is clearly shown. During the uniform motion segments from 0 to 100 seconds and from 300 to 500 seconds, the carrier motion state is stable, and the credibility of the spatial continuity constraint is high. At this time, the spatial continuity weight

[0083] is maintained at a relatively high level (about 0.35 - 0.45). During the maneuvering segment from 100 to 300 seconds, the carrier makes large maneuvers, the credibility of the spatial constraint decreases, and the spatial continuity weight

[0084] significantly decreases (down to about 0.2 at the lowest), showing periodic fluctuations, reflecting the dynamic changes during the maneuvering process. For the inertial navigation trajectory difference weight Figure 4As shown, starting from the initial value, the characteristic of the inertial navigation error accumulating over time is simulated by an exponential function. As time goes by, the weight of the inertial navigation trajectory difference continually decreases and decays to approximately 0.05 at 500 seconds. This indicates that the credibility of the inertial navigation information significantly decreases over time, and the decay curve is smooth, conforming to the characteristics of the error accumulation of the inertial navigation system.

[0085] Based on the above embodiments, as an alternative embodiment, the constituent elements of the combined similarity score include a magnetic field difference penalty term, a spatial continuity penalty term, and an inertial navigation trajectory difference penalty term. Among them, the magnetic field difference penalty term is calculated through a non - linear mapping according to the measured magnetic field vector of the point to be matched; the spatial continuity penalty term is calculated through a normalized distance according to the position change amount of the carrier; the inertial navigation trajectory difference penalty term is calculated through a normalized distance according to the position information of the inertial navigation system.

[0086] Specifically, the magnetic field difference penalty term can adopt a non - linear mapping, see formula (10): (10) Where, is the measured magnetic field vector, is the reference magnetic field vector, is the magnetic field difference penalty term, is the magnetic field difference sensitivity parameter, is the vector norm between the measured magnetic field vector and the reference magnetic field vector, is the norm of the reference magnetic field vector. In an alternative embodiment, the magnetic field difference sensitivity parameter can be set to 2.

[0087] In the embodiments of the present application, using the ratio of vector norms can eliminate the scale influence of the magnetic field intensity, and the Sigmoid - type non - linear mapping can suppress the influence of outliers and improve the robustness of the algorithm. In addition, the sensitivity of the difference metric can be adjusted through the magnetic field difference sensitivity parameter .

[0088] In an alternative embodiment, the normalized distance can be used to measure the spatial continuity penalty term. Specifically, it is calculated through formula (11): (11) Where, is the point to be matched, is the target matching position at the previous moment, is the carrier position change amount, is the maximum search radius. In an alternative embodiment, the maximum search radius can be set to 1000m.

[0089] In the embodiments of the present application, distance normalization can ensure the adaptability of the constraint strength to the search range. The minimum value limit prevents over-constraint and maintains a certain degree of search flexibility.

[0090] In another alternative embodiment, the normalized distance can also be used to measure the inertial navigation trajectory difference penalty term. Specifically, it is calculated by formula (12): (12) Where, is the current position of inertial navigation, is the Euclidean distance between the current position of the point to be matched and the current position of inertial navigation, The function is used to ensure that the constraint value does not exceed 1.

[0091] In the embodiments of the present application, using the normalized distance can limit the constraint value within the range of [0, 1]. Using the maximum search radius as the normalization factor can make the constraint adapt to the scale of the search space. In addition, the constraint strength increases linearly with the deviation from the inertial navigation position until it reaches the maximum value of 1.

[0092] Furthermore, based on formula (1), according to the magnetic field difference weight , the spatial continuity weight , is the inertial navigation trajectory difference weight, the magnetic field difference penalty term , the spatial continuity penalty term , the inertial navigation trajectory difference penalty term , the combined similarity score can be calculated.

[0093] Therefore, the geomagnetic contour matching method based on adaptive space constraint provided by the embodiments of the present application can improve the navigation adaptability of the navigation system in complex and changeable environments through the adaptive weight mechanism and reasonable metric design, improve the accuracy and reliability of geomagnetic matching navigation by dynamically adjusting the weights of multi-source information, and effectively suppress the influence of inertial navigation errors.

[0094] In the above embodiments, the geomagnetic contour matching method based on adaptive space constraint is described in detail. The present application also provides an embodiment corresponding to a geomagnetic contour matching device based on adaptive space constraint.

[0095] Figure 5 is a schematic structural diagram of a geomagnetic contour matching device based on adaptive space constraint provided by the embodiments of the present application. As Figure 5 shown, the device includes: A point-to-be-matched acquisition module 50, configured to search the area to be matched centered on the inertial navigation system to obtain the point to be matched; An influence parameter acquisition module 51, configured to acquire influence parameters of the current weight coefficients; the weight coefficients are weights used for calculating the combined similarity score of the points to be matched, and there are multiple weight coefficients. A weight adjustment module 52, configured to obtain target weight coefficients after adjusting the current weight coefficients through the influence parameters. A candidate point determination module 53, configured to determine the combined similarity score according to the target weight coefficients; and use the point to be matched corresponding to the minimum value of the combined similarity score as the candidate point. A loop control module 54, configured to take the candidate point as the center, narrow the search range, and loop to control the influence parameter acquisition module, the weight adjustment module, and the candidate point determination module until the iteration condition is met; and use the candidate point obtained in the last iteration as the target matching position.

[0096] In addition, the geomagnetic contour matching device based on adaptive space constraint provided by the embodiment of the present application further includes: A magnetic field difference weight adjustment module, configured to determine the magnetic field gradient eigenvalue according to the measured magnetic field vector; and adjust the magnetic field difference weight according to the magnetic field gradient eigenvalue. A space continuity weight adjustment module, configured to determine a motion state index value reflecting the stability of the carrier motion state according to the carrier motion information; and adjust the space continuity weight according to the motion state index value. An inertial navigation trajectory difference weight adjustment module, configured to determine the error accumulation time of the inertial navigation system according to the inertial navigation time information; and adjust the inertial navigation trajectory difference weight according to the error accumulation time.

[0097] A speed change increment determination module, configured to determine the speed change increment between the current moment and the previous moment through the motion speed. A heading change increment determination module, configured to determine the heading change increment between the current moment and the previous moment through the heading angle. A weight assignment module, configured to use the current motion speed of the carrier as the weight of the heading change increment. A motion state index value calculation module, configured to calculate the weighted sum of the speed change increment and the heading change increment based on the weight of the heading change increment to obtain the motion state index value.

[0098] The space continuity weight adjustment module is further configured to, when the motion state index value is greater than the index threshold, obtain a preset weight adjustment rate and the extreme value of the space continuity weight; and adjust the space continuity weight within the range of the extreme value of the space continuity weight based on the weight adjustment rate and the motion state index value; wherein, the greater the motion state index value, the greater the space continuity weight.

[0099] An extreme value acquisition module, configured to acquire preset extreme values of gradient features and magnetic field weights; A gradient vector determination module, configured to determine a gradient vector between adjacent measured magnetic field vectors; An initial gradient feature determination module, configured to determine an initial gradient feature according to the extreme values of gradient features, magnetic field weights, and the gradient vector; A magnetic field gradient feature value calculation module, configured to normalize the initial gradient feature to obtain a magnetic field gradient feature value.

[0100] Figure 6 The following is a schematic structural diagram of a geomagnetic contour matching device based on adaptive spatial constraints provided in another embodiment of the present application. As Figure 6 shown, the geomagnetic contour matching device based on adaptive spatial constraints includes: a memory 60, configured to store a computer program; A processor 61, configured to implement the steps of the geomagnetic contour matching method based on adaptive spatial constraints mentioned in the above embodiment when executing the computer program.

[0101] The geomagnetic contour matching device provided in this embodiment may include, but is not limited to, a laptop computer or a desktop computer, etc.

[0102] Among them, the processor 61 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 61 may be implemented in at least one hardware form of a digital signal processor (DSP for short), a field programmable gate array (FPGA for short), or a programmable logic array (PLA for short). The processor 61 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the wake state, also known as a central processing unit (CPU for short); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 61 may be integrated with a graphics processing unit (GPU for short), and the GPU is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 61 may also include an artificial intelligence (AI for short) processor, and the AI processor is used to process computing operations related to machine learning.

[0103] The memory 60 may include one or more computer-readable storage media, which may be non-transitory. The memory 60 may also include high-speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices and flash storage devices. In this embodiment, the memory 60 is at least used to store the following computer program 601. After the computer program is loaded and executed by the processor 61, it can implement the related steps of the geomagnetic profile matching method based on adaptive space constraints disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 60 may also include an operating system 602 and data 603, etc., and the storage method may be temporary storage or permanent storage. Among them, the operating system 602 may include Windows, Unix, Linux, etc. The data 603 may include, but is not limited to, the related data involved in the geomagnetic profile matching method based on adaptive space constraints.

[0104] In some embodiments, the geomagnetic profile matching device based on adaptive space constraints may further include a display screen 62, an input / output interface 63, a communication interface 64, a power supply 65, and a communication bus 66.

[0105] Those skilled in the art can understand that Figure 6 the structure shown in does not constitute a limitation on the geomagnetic profile matching device based on adaptive space constraints, and may include more or fewer components than shown in the figure.

[0106] The geomagnetic profile matching device based on adaptive space constraints provided by the embodiments of the present application includes a memory and a processor. When the processor executes the program stored in the memory, it can implement the geomagnetic profile matching method based on adaptive space constraints in the above embodiments.

[0107] It should be noted that although the operations are depicted in a specific order in the drawings, this should not be construed as requiring the operations to be performed in the specific order shown or sequentially, or requiring all of the illustrated operations to be performed to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. In addition, the separation of the various system modules and components in the above embodiments should not be understood as required in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

Claims

1. A geomagnetic contour matching method based on adaptive spatial constraints, characterized in that: The method comprises: S10: Searching the area to be matched with the inertial navigation system as the center to obtain the points to be matched; S11: Obtaining an influencing parameter of a current weight coefficient; the weight coefficient is a weight used to calculate a combined similarity score of the to-be-matched points, and there are multiple weight coefficients; S12: After adjusting the current weight coefficient by the influencing parameter, a target weight coefficient is obtained; S13: determining the combined similarity score according to the target weight coefficient; and taking the to-be-matched point corresponding to the minimum value of the combined similarity score as a candidate point; S14: With the candidate point as the center, narrow the search range, and execute steps S11 to S13 in a loop until the iteration condition is met; and use the candidate point obtained in the last iteration as the target matching position.

2. The geomagnetic contour matching method based on adaptive spatial constraints according to claim 1, characterized in that: The weight coefficients include magnetic field difference weight, spatial continuity weight and inertial navigation trajectory difference weight; The influencing parameters include a measured magnetic field vector, carrier motion information, and inertial navigation time information; adjusting the current weight coefficient by using the influencing parameters includes: Determining a magnetic field gradient characteristic value according to the measured magnetic field vector; and adjusting the magnetic field difference weight according to the magnetic field gradient characteristic value; Determining a motion state index value for reflecting the stability of the motion state of the carrier according to the carrier motion information; and adjusting the spatial continuity weight according to the motion state index value; The error accumulation time of the inertial navigation system is determined according to the inertial navigation time information; and the inertial navigation trajectory difference weight is adjusted according to the error accumulation time.

3. The geomagnetic contour matching method based on adaptive spatial constraints as claimed in claim 2, characterized in that: The carrier motion information includes the motion speed and heading angle of the carrier; and determining the motion state index value according to the carrier motion information includes: Determine the speed increment between the current moment and the previous moment through the movement speed; Determine the heading change increment between the current moment and the previous moment through the heading angle; Using the current moving speed of the carrier as the weight of the heading change increment; Based on the weight of the heading change increment, a weighted sum of the speed change increment and the heading change increment is calculated to obtain the motion state index value.

4. The geomagnetic contour matching method based on adaptive spatial constraints as claimed in claim 2, characterized in that: Adjusting the spatial continuity weight according to the motion state index value includes: When the motion state index value is greater than the index threshold, obtaining a preset weight adjustment rate and a spatial continuity weight extreme value; Based on the weight adjustment rate and the motion state index value, the spatial continuity weight is adjusted within the range of the spatial continuity weight extreme value; wherein, the larger the motion state index value is, the larger the spatial continuity weight is.

5. The geomagnetic contour matching method based on adaptive spatial constraints as claimed in claim 2, characterized in that: Determining the magnetic field gradient characteristic value according to the measured magnetic field vector includes: Obtaining preset gradient characteristic extreme values ​​and magnetic field weight extreme values; determining a gradient vector between adjacent measured magnetic field vectors; Determining an initial gradient feature according to the gradient feature extreme value, the magnetic field weight extreme value and the gradient vector; The initial gradient characteristics are normalized to obtain the magnetic field gradient characteristic value.

6. The geomagnetic contour matching method based on adaptive spatial constraints according to claim 2, characterized in that: The larger the magnetic field gradient characteristic value is, the larger the magnetic field difference weight is; The longer the error accumulation time is, the smaller the inertial navigation trajectory difference weight is; The sum of the magnetic field difference weight, the spatial continuity weight and the inertial navigation trajectory difference weight is equal to 1.

7. The geomagnetic contour matching method based on adaptive spatial constraints according to claim 1, characterized in that: The constituent elements of the combined similarity score include at least two of a magnetic field difference penalty term, a spatial continuity penalty term, and an inertial navigation trajectory difference penalty term; Among them, the magnetic field difference penalty term is calculated through nonlinear mapping according to the measured magnetic field vector of the point to be matched; the spatial continuity penalty term is calculated through normalized distance according to the position change of the carrier; and the inertial navigation trajectory difference penalty term is calculated through normalized distance according to the position information of the inertial navigation system.

8. A geomagnetic profile matching device based on adaptive spatial constraints, characterized in that: The device comprises: The module for obtaining points to be matched is used to search the area to be matched with the inertial navigation system as the center to obtain points to be matched; An influencing parameter acquisition module, used to acquire an influencing parameter of a current weight coefficient; the weight coefficient is a weight used to calculate a combined similarity score of the to-be-matched points, and there are multiple weight coefficients; A weight adjustment module, used to adjust the current weight coefficient by using the influencing parameter to obtain a target weight coefficient; A candidate point determination module is used to determine the combined similarity score according to the target weight coefficient; and use the to-be-matched point corresponding to the minimum value of the combined similarity score as a candidate point; The loop control module is used to narrow the search range with the candidate point as the center, and loop control the influence parameter acquisition module, the weight adjustment module and the candidate point determination module until the iteration condition is met; and the candidate point obtained in the last iteration is used as the target matching position.

9. A geomagnetic contour matching device based on adaptive spatial constraints, comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the program, the steps of the geomagnetic contour matching method based on adaptive spatial constraints as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the geomagnetic contour matching method based on adaptive spatial constraints as described in any one of claims 1 to 7 are implemented.

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