A positioning satellite selection method, device and storage medium

Abstract

The present invention provides a positioning satellite selection method, device and storage medium. The method includes: determining a satellite selection area of ​​a current device from a satellite constellation that can be observed by the current device; obtaining an objective function of the satellite selection area; selecting a satellite that can minimize the objective function from the satellite selection area using an optimization algorithm; the objective function is obtained based on the positions of a preset number of satellites in the satellite selection area; and using the satellite that can minimize the objective function for positioning. Compared with a traditional uniformly distributed satellite selection algorithm and an optimization algorithm that uses geometric precision factor as an optimization target, the present method converts the traditional uniformly distributed satellite selection algorithm or geometric precision factor calculation solution into a simple function calculation of a geometric relationship, that is, minimizing the above objective function, thereby greatly improving the calculation efficiency, and in the case where the satellite signal is blocked, the optimal satellite combination can still be screened out based on the above objective function.

Description

Technical Field

[0001] The present invention relates to the field of satellite positioning technology, and in particular to a positioning satellite selection method, device and storage medium. Background Art

[0002] With the continuous construction and development of low-orbit satellite constellations, the number of visible satellites that receivers can obtain has increased significantly, providing objective conditions for improving navigation and positioning performance, but it also increases the signal processing burden of the receiver. Therefore, how to select a suitable satellite combination among a large number of visible satellites to meet positioning requirements while reducing the amount of calculation has become a research hotspot.

[0003] The existing satellite selection method mainly uses the geometric dilution of precision (GDOP) optimal star selection algorithm, which traverses all visible satellite combinations to find the combination with the smallest GDOP value. However, this star selection method has a large amount of calculation and affects real-time performance. Therefore, some scholars use recursive optimization algorithms to reduce the total number of selected satellites. However, the recursive algorithm has a large amount of calculation and will fall into the problem of local GDOP contribution, which leads to a significant decline in performance after excluding too many satellites. When there are too many visible satellites in a large-scale satellite constellation, the calculation efficiency of these traditional star selection methods will be significantly reduced, thereby affecting the real-time performance of positioning. In an occluded environment, the original satellite selection algorithm based on uniform distribution will also lead to a decrease in positioning accuracy.

[0004] It can be seen that in order to overcome the shortcomings of traditional star selection methods, research on satellite positioning for large-scale satellite constellations, especially positioning satellite selection methods for large-scale satellite constellations, can further improve the accuracy, real-time and reliability of navigation positioning, which is of vital importance to the further development of the domestic navigation and positioning field. Summary of the invention

[0005] The main technical problem solved by the present invention is how to select positioning satellites when facing a large-scale satellite constellation.

[0006] According to the first aspect, an embodiment provides a positioning satellite selection method, including:

[0007] Determine a satellite selection area of ​​the current device from the satellite constellations that the current device can observe, and obtain an objective function of the satellite selection area;

[0008] Selecting a satellite that can minimize the objective function from the selected star region by using an optimization algorithm; wherein the objective function is obtained based on the positions of a preset number of satellites in the selected star region;

[0009] The satellite that can minimize the objective function is used for positioning.

[0010] When there is no high elevation angle sub-star selection area and low elevation angle sub-star selection area in the star selection area, the expression of the objective function of the star selection area is:

[0011]

[0012] ;

[0013] in, x i 、y i 、z i They are respectively i Satellites on the coordinate axis X axis, Y Axis and Z The coordinate values ​​on the axis are normalized to the corresponding values. A 1. B 1. C 1. D 1. E 1 and F 1 is the weight coefficient of each item, n Indicates the preset quantity; m and l is the index of the corresponding item;

[0014] Wherein, the optimization algorithm includes a swarm intelligence optimization algorithm or an artificial neural network.

[0015] In one embodiment, A 1. B 1. C 1. D 1. E 1 and F 1 is equal to 1, m=4, l =2; the swarm intelligence optimization algorithm includes a genetic algorithm, a particle swarm algorithm, an ant colony algorithm, an immune algorithm, an artificial bee colony algorithm or a differential evolution algorithm.

[0016] In one embodiment, when there are a high elevation angle sub-star selection area and a low elevation angle sub-star selection area in the star selection area, the positioning satellite selection method further includes:

[0017] Obtaining a navigation message of a satellite constellation, calculating the elevation angle of each satellite in the satellite constellation that can be observed by the current device according to the navigation message, and taking the smallest of the elevation angles as the actual low elevation angle;

[0018] When the geometric precision factor meets a preset condition, a first functional relationship between the ratio of the number of satellites at the low elevation angle to the number of satellites at the high elevation angle and the actual low elevation angle is obtained, and the actual low elevation angle is substituted into the first functional relationship, and a maximum ratio of the number of satellites at the low elevation angle to the number of satellites at the high elevation angle is calculated by the first functional relationship;

[0019] According to the maximum ratio, a first number of satellites at low elevation angles and a second number of satellites at high elevation angles are selected, and a theoretical ratio between the first number and the second number is obtained, a second functional relationship between the theoretical high elevation angle, the actual low elevation angle and the theoretical ratio is obtained, and the actual low elevation angle and the theoretical ratio are substituted into the second functional relationship, and the theoretical high elevation angle is calculated by the second functional relationship;

[0020] The step of determining a satellite selection area of ​​the current device from the satellite constellation observable by the current device and obtaining an objective function of the satellite selection area includes:

[0021] Determining a low elevation angle sub-satellite selection area and a high elevation angle sub-satellite selection area for selecting a satellite according to the theoretical high elevation angle and the actual low elevation angle;

[0022] The objective function of the low elevation angle sub-star selection area and the objective function of the high elevation angle sub-star selection area are respectively obtained; wherein the star selection area includes the low elevation angle sub-star selection area and the high elevation angle sub-star selection area.

[0023] In one embodiment, the selecting a satellite capable of minimizing the objective function from the satellite selection region by using an optimization algorithm includes:

[0024] Selecting a first number of satellites capable of minimizing an objective function of the low elevation angle sub-selection region from the low elevation angle sub-selection region by using an optimization algorithm;

[0025] Selecting a second number of satellites capable of minimizing the objective function of the high elevation angle sub-selection region from the high elevation angle sub-selection region by using an optimization algorithm;

[0026] The objective function of the low elevation angle sub-selection area is obtained based on the positions of a first number of satellites in the low elevation angle sub-selection area, and the objective function of the high elevation angle sub-selection area is obtained based on the positions of a second number of satellites in the high elevation angle sub-selection area, and the sum of the first number and the second number is equal to the preset number.

[0027] In one embodiment, the objective function of the low elevation angle satellite selection area is expressed as:

[0028] ;

[0029] in, x i and yi They are the first i Satellites on the coordinate axis X axis, Y The corresponding value after normalization on the axis, A 2. B 2 and C 2 are the weight coefficients of each item, n 1 represents the first quantity; the expression of the objective function of the high elevation angle sub-selection area is:

[0030] ;

[0031] in, x j and y j They are the first j Satellites on the coordinate axis X axis, Y The corresponding value after normalization on the axis, D 2. E 2 and F 2 are weight coefficients of each item, n2 represents the second quantity; m and l is the index of the corresponding item; the optimization algorithm includes a swarm intelligence optimization algorithm or an artificial neural network.

[0032] In one embodiment, A 2. B 2. C 2. D 2. E 2 and F 2 is equal to 1, m=4, l =2; the swarm intelligence optimization algorithm includes a genetic algorithm, a particle swarm algorithm, an ant colony algorithm, an immune algorithm, an artificial bee colony algorithm or a differential evolution algorithm.

[0033] According to the second aspect, an embodiment further provides a positioning satellite selection device, including:

[0034] The objective function acquisition module is configured to determine a satellite selection area of ​​the current device from the satellite constellations that the current device can observe, and acquire an objective function of the satellite selection area;

[0035] The satellite selection module is configured to select a satellite that can minimize the objective function from the star selection area by using an optimization algorithm; wherein the objective function is obtained based on the positions of a preset number of satellites in the star selection area; when there is no high elevation angle sub-star selection area and low elevation angle sub-star selection area in the star selection area, the objective function of the star selection area is expressed as:

[0036]

[0037] ;

[0038] in, x i 、y i 、z i They are respectively i Satellites on the coordinate axis X axis, Y Axis and Z The coordinate values ​​on the axis are normalized to the corresponding values. A 1. B 1. C 1. D 1. E 1 and F 1 is the weight coefficient of each item, n Indicates the preset quantity; m and l is the index of the corresponding item; the optimization algorithm includes a swarm intelligence optimization algorithm or an artificial neural network;

[0039] The positioning module is configured to use the satellite that can minimize the objective function for positioning.

[0040] According to the third aspect, an embodiment further provides a computer-readable storage medium, comprising a program, wherein the program can be executed by a processor to implement the method as described in any embodiment herein.

[0041] According to the fourth aspect, an embodiment further provides a computer program product, comprising a computer program and / or instructions, wherein when the computer program and / or instructions are executed by a processor, the method described in any one of the embodiments herein is implemented.

[0042] The beneficial effects of this application are:

[0043] The present positioning satellite selection method comprises: determining a satellite selection area of ​​the current device from the satellite constellation observable by the current device, and obtaining an objective function of the satellite selection area; selecting a satellite capable of minimizing the objective function from the satellite selection area by using an optimization algorithm; wherein the objective function is obtained based on the positions of a preset number of satellites in the satellite selection area; and using the satellite capable of minimizing the objective function for positioning; compared with a traditional uniformly distributed satellite selection algorithm and an optimization algorithm that uses geometric precision factor as an optimization target, the positioning satellite selection method of the present application converts the traditional uniformly distributed satellite selection algorithm or geometric precision factor calculation solution into a simple function calculation of a geometric relationship, i.e., minimizing the above objective function, thereby greatly improving the calculation efficiency of the above optimization algorithm, and in the case where the satellite signal is blocked, the optimal satellite combination can still be screened out based on the objective function proposed in the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic diagram of a flow chart of a positioning satellite selection method according to an embodiment;

[0045] Figure 2 A schematic diagram of a flow chart of a positioning satellite selection method according to another embodiment;

[0046] Figure 3 A schematic diagram of a process for obtaining the objective function of the star selection area according to an embodiment;

[0047] Figure 4 A schematic diagram of a process for selecting a satellite capable of minimizing an objective function according to an embodiment;

[0048] Figure 5 A schematic diagram of a preset number of satellites selected in one embodiment;

[0049] Figure 6 The figure is a schematic diagram of the module structure of a positioning satellite selection device according to an embodiment. DETAILED DESCRIPTION

[0050] The present invention is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are for making the present application better understood. However, those skilled in the art can easily recognize that some features can be omitted in different situations, or can be replaced by other elements, materials, methods. In some cases, some operations related to the present application are not shown or described in the specification, this is to avoid the core part of the present application being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0051] In addition, the features, operations or characteristics described in the specification can be combined in any appropriate manner to form various implementations. At the same time, the steps or actions in the method description can also be interchanged or adjusted in a manner that is obvious to those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for the purpose of clearly describing a certain embodiment and are not meant to be a required sequence, unless otherwise specified that a certain sequence must be followed.

[0052] The serial numbers assigned to the components herein, such as "first", "second", etc., are only used to distinguish the objects described and do not have any order or technical meaning. The "connection" and "coupling" mentioned in this application, unless otherwise specified, include direct and indirect connections (couplings).

[0053] With the continuous development of low-orbit satellite constellations, there is reason to believe that high-precision and high-reliability positioning can be achieved using low-orbit satellite constellations. However, there are a large number of low-orbit satellites, which is both an opportunity and a challenge for positioning technology. A large number of low-orbit satellites has improved the geometric precision factor of positioning, but is limited by the performance of electronic equipment itself. It needs to select low-orbit satellites to observe from multiple low-orbit satellites according to its own needs. Due to the large number of low-orbit satellites and the large amount of satellite ephemeris data, the traditional algorithm calculation process has high power consumption and low calculation efficiency, which ultimately affects the positioning results.

[0054] The inventor investigated the existing literature and found that when selecting satellites using a low-orbit satellite constellation that can observe a large number of satellites, the traditional brute force search algorithm for the best geometric precision factor cannot be used, and the recursive algorithm can only be used when a small number of satellites are eliminated, and satellite selection cannot be performed efficiently.

[0055] For another example, in some existing positioning satellite selection methods, it is determined that the current device can observe the low elevation angle selection area and the high elevation angle selection area for selecting satellites in the satellite constellation, select the first number of satellites in the low elevation angle selection area, and select the second number of satellites in the high elevation angle selection area, and use the selected qualified satellites for positioning. In the existing method, "selecting the first number of qualified satellites from the satellite constellation" is achieved by "selecting the first number of satellites uniformly distributed on the horizontal plane from the satellite constellation as qualified satellites", and "selecting the second number of qualified satellites from the satellite constellation" is achieved by "selecting the second number of satellites uniformly distributed on the horizontal plane from the satellite constellation as qualified satellites". Since the current device may be in a variety of environments, some satellite signals are interfered by the environment, such as satellite signals being blocked by houses. In the case where the satellite signals are blocked, if the above-mentioned method of selecting satellites uniformly distributed on the horizontal plane is adopted, the optimal satellite combination cannot be screened out.

[0056] Therefore, the technical concept of the present application is to construct an objective function based on the position of each satellite in the satellite selection area, and to minimize the objective function as the optimization goal, and to execute the optimization algorithm to select satellites, and then convert the geometric dilution of precision (GDOP) calculation solution (such as using the geometric dilution of precision optimal satellite selection algorithm) into a simple function operation of geometric relationships (such as the above-mentioned objective function) to screen out the optimal satellite combination (especially when the satellite signal is blocked) and significantly improve the calculation efficiency of the optimization algorithm.

[0057] The technical solution of the present application will be described in detail below in conjunction with embodiments.

[0058] This application proposes a positioning satellite selection method, which is based on a device that can receive satellite signals, such as a receiver, so that a satellite for positioning can be selected in a low-orbit navigation satellite constellation, and then the positioning of the device can be achieved through the satellite for positioning. Figure 1 , the positioning satellite selection method comprises:

[0059] Step S100: Determine the star selection area of ​​the current device and obtain the target function;

[0060] Step S200: selecting a satellite that can minimize the objective function from the selected satellite region;

[0061] Step S300: Using the satellite that can minimize the objective function for positioning.

[0062] Specifically, step S100 includes: determining a satellite selection area of ​​the current device from the satellite constellation that the current device can observe, and obtaining an objective function of the satellite selection area; wherein the objective function is obtained based on the positions of a preset number of satellites in the satellite selection area;

[0063] Specifically, step S200 includes: using an optimization algorithm to select a satellite that can minimize the objective function from the satellite selection region.

[0064] It should be noted that, since "determining the satellite selection area of ​​the current device from the satellite constellation that the current device can observe" in step S100 belongs to the prior art in this field, those skilled in the art can determine the process according to actual needs, so it will not be repeated here.

[0065] In some embodiments, in step S100, the star selection area of ​​the current device is no longer further divided into the "high elevation angle star selection area" and the "low elevation angle star selection area" in the above-mentioned prior art, that is, there are no high elevation angle sub-star selection areas and low elevation angle sub-star selection areas in the star selection area of ​​this embodiment. In step S100: the expression of the objective function of the star selection area is:

[0066]

[0067] ;

[0068] in, x i 、y i 、z i They are respectively i Satellites on the coordinate axis X axis, Y Axis and Z The corresponding value after the normalization of the coordinate value on the axis, A 1. B 1. C 1. D 1. E 1 and F 1 is the weight coefficient of each item, n Indicates the preset quantity; m and l is the index of the corresponding item.

[0069] It should be noted that the above objective function J It is derived based on the condition of minimizing the geometric precision factor (such as the relationship between the selected satellites when the geometric precision factor takes the limit value as shown below).

[0070] It should be noted that the above objective function J The derivation depends on X axis,Y Axis and Z The coordinate value on the axis, so the above objective function J Is x , y and z In other embodiments, when the above objective function J It can rely on spherical coordinates, polar coordinates, etc., and those skilled in the art can J Just make adaptive adjustments.

[0071] In some embodiments, in step S100, those skilled in the art may further determine the above-mentioned A 1. B 1. C 1. D 1. E 1 and F The optimal value range of 1.

[0072] In some embodiments, in step S100, the above A 1. B 1. C 1. D 1. E 1 and F 1 is equal to 1, m=4, l =2.

[0073] In some embodiments, in step S200, the above-mentioned optimization algorithm includes a swarm intelligence optimization algorithm or an artificial neural network. Among them, the above-mentioned swarm intelligence optimization algorithm includes a simulated annealing algorithm, a genetic algorithm, a particle swarm algorithm, an ant colony algorithm, an immune algorithm, an artificial bee colony algorithm or a differential evolution algorithm. Among them, the genetic algorithm uses the principle of biological genetics to solve the optimization problem through gene coding and evolutionary operations. The particle swarm algorithm simulates the predation process of a flock of birds and solves the optimization problem by updating the particle position and velocity. The ant colony algorithm imitates the foraging behavior of ant colonies and solves the optimization problem through pheromones and heuristic search. The immune algorithm simulates the immune system mechanism and solves the optimization problem through antibody cloning and immune selection. The artificial bee colony algorithm imitates the food searching behavior of the bee colony and combines the local search strategy with the random search strategy. The differential evolution algorithm overcomes the disadvantage of the genetic algorithm that lacks local search and is used to solve complex optimization problems. Among them, the swarm intelligence optimization algorithm can also include: gravitational search algorithm, firefly algorithm, bat algorithm, cuckoo optimization algorithm, gray wolf optimization algorithm, whale optimization algorithm, Salp swarm algorithm, star bird optimization algorithm, spectrum optimization algorithm and spider bee optimization algorithm. Among them, the simulated annealing algorithm is a heuristic search algorithm that solves the optimization problem by simulating the annealing process in physics, can jump out of the local optimal solution, and find the global optimal solution, which is particularly suitable for solving complex optimization problems. Among them, artificial neural networks can be used to solve complex optimization problems (such as optimizing the objective function in this application). Artificial neural networks can handle complex nonlinear problems by simulating the working mode of the human brain and connecting a large number of neurons to transmit information. Neural networks have self-learning functions and can continuously optimize model parameters through training data to find the optimal solution to complex problems. Among them, point cloud segmentation is an important technology in the field of computer vision, which is used to extract useful information from point cloud data. Although point cloud segmentation itself does not directly solve the optimization problem, it can assist neural networks in feature extraction and target recognition in complex environments, thereby indirectly helping to solve complex optimization problems. The accuracy and efficiency of point cloud segmentation are crucial to improving the performance of neural networks in complex environments.

[0074] It should be noted that, in step S200, the swarm intelligence optimization algorithm is not limited to the algorithm exemplified above, and may also be an improved swarm intelligence optimization algorithm.

[0075] It should be noted that the optimization algorithm in step S200 also includes any other method that can be used to optimize the objective function in this application.

[0076] In some embodiments, when facing a large-scale satellite constellation, first based on the satellites that the current device can observe, and then when the geometric precision factor meets the preset conditions, obtain the first functional relationship between the ratio of the number of satellites at low elevation angles and high elevation angles and the actual low elevation angle, and the second functional relationship between the theoretical high elevation angle, the actual low elevation angle and the theoretical ratio, and obtain the maximum ratio from the actual low elevation angle, and then obtain the theoretical high elevation angle from the maximum ratio. While meeting the positioning accuracy requirements, determine the optimal geometric configuration of the required satellites. And it is based on the actual low elevation angles of satellites in the satellite constellation that the current device can observe, so the quasi-optimal configuration of three-dimensional positioning can be obtained in different observation-restricted environments, which greatly reduces the amount of data calculation and the calculation cost, while improving positioning accuracy.

[0077] In some embodiments, a positioning satellite selection method is provided, which is based on a device that can receive satellite signals, such as a receiver, so that a satellite for positioning can be selected in a low-orbit navigation satellite constellation, and then the positioning of the device is achieved through the satellite for positioning. Figure 2 , the positioning satellite selection method proposed in this application also includes:

[0078] Step S000: Obtain a theoretical high elevation angle and an actual low elevation angle.

[0079] Specifically, step S000 includes:

[0080] Step 010: Obtain the navigation message of the satellite constellation, calculate the elevation angle of each satellite in the satellite constellation that can be observed by the current device according to the navigation message, and take the smallest of the elevation angles as the actual low elevation angle;

[0081] Step 020: When the geometric precision factor meets the preset conditions, a first functional relationship between the ratio of the number of satellites at low elevation angles to that at high elevation angles and the actual low elevation angle is obtained, and the actual low elevation angle is substituted into the first functional relationship, and the maximum ratio of the number of satellites at low elevation angles to that at high elevation angles is calculated by the first functional relationship;

[0082] Step 030: According to the maximum ratio, select a first number of satellites at low elevation angles and a second number of satellites at high elevation angles, and obtain a theoretical ratio between the first number and the second number, obtain a second functional relationship between the theoretical high elevation angle, the actual low elevation angle and the theoretical ratio, substitute the actual low elevation angle and the theoretical ratio into the second functional relationship, and calculate the theoretical high elevation angle from the second functional relationship.

[0083] In some embodiments, in step 010, the current device receives navigation messages sent by the satellite constellation to obtain the ephemeris and pseudorange observations of all low-orbit navigation satellites, and determines all visible satellites in the low-orbit satellite constellation based on the receivable satellite signals. Then, the approximate position of the current device is obtained, for example, by coarse positioning based on WiFi signals or communication signals of the device, and the elevation angles and azimuth angles of all visible satellites in the low-orbit satellite constellation are calculated based on the approximate position of the current device, so that the elevation angle of each satellite in the satellite constellation that the current device can observe can be obtained, and the smallest one is used as the actual low elevation angle.

[0084] In some embodiments, since the current device may be in a variety of environments, some satellite signals may be interfered by the environment, such as satellite signals being blocked by houses, which may cause the theoretical lowest elevation angle of the current device to be not the actual low elevation angle. Therefore, in this embodiment, by calculating the actual low elevation angle of the current device and performing subsequent positioning satellite selection based on the actual low elevation angle, it is possible to provide positioning information with higher accuracy and reliability even when the current device observation is limited.

[0085] In some embodiments, in step 020, the geometric precision factor is an important coefficient for measuring positioning accuracy. Therefore, when the geometric precision factor meets the preset conditions, it can be ensured that the positioning accuracy of the selected satellites can meet the requirements when used for positioning. In some embodiments, when the geometric precision factor takes the limit value, the relationship between the selected satellites is as follows:

[0086] x 2 k +y 2 k +z 2 k ＝ 1, k =1, 2, …, n ;

[0087] x 1 y 1+ x 2 y 2+…+ x n y n =0;

[0088] x 1 z 1+ x 2 z 2+…+ x n zn =0;

[0089] y 1 z 1+ y 2 z 2+…+ y n z n =0;

[0090] x 2 1+ y 2 1+ x 2 2+ y 2 2+…+ x 2 n + y 2 n =2 n / 3;

[0091] z 2 1+ z 2 2+…+ z 2 n = n / 3;

[0092] x 1+ x 2+…+ x n =0;

[0093] y 1+ y 2+…+ y n =0;

[0094] z 1+ z 2+…+ z n →nz min ;

[0095] in, n is the total number of satellites, x k 、y k 、z k They are K Satellites on the coordinate axis X axis, Y Axis and ZThe coordinate value on the axis is normalized and corresponds to the value, z min For Z Theoretical minimum value after normalization on the axis.

[0096] In this embodiment, when selecting satellites from the low-orbit satellite constellation, satellites at low elevation angles and high elevation angles are selected, so that a functional relationship between the actual low elevation angle, the theoretical high elevation angle, and the ratio of the number of satellites at low elevation angles to the number of satellites at high elevation angles can be obtained to determine a better geometric configuration of required satellites. Specifically, when the geometric precision factor takes the limit value, it needs to satisfy the following formula:

[0097] x 2 k +y 2 k +z 2 k ＝ 1, k =1, 2, …, n ;

[0098] R * z 2 down / (1+ R ) + 1* z 2 up / (1+ R ) = 1 / 3;

[0099] R * z down / (1+ R ) + 1* z up / (1+ R ) → z down ;

[0100] in, R is the ratio of the number of satellites at low elevation angles to the number of satellites at high elevation angles, z down is the normalized height value corresponding to the actual low elevation angle, z up is the height value corresponding to the normalized theoretical elevation angle. z down and z up The normalized calculation of can be obtained by taking the sine of the actual low elevation angle and the theoretical high elevation angle respectively.

[0101] In some embodiments, since there is a limit value for the theoretical high elevation angle, a first functional relationship between the ratio of the number of satellites at the low elevation angle to the number of satellites at the high elevation angle and the actual low elevation angle can be obtained, which is specifically as follows:

[0102] ;

[0103] ;

[0104] From the above functional relationship, we can see that due to the theoretical high elevation angle Z The normalized height value on the axis is less than or equal to 1, that is, the observed elevation angle is less than or equal to ninety degrees, so that the first functional relationship between the ratio of the number of satellites at low elevation angles and high elevation angles and the actual low elevation angle can be obtained. And according to the actual low elevation angle and the first functional relationship, the maximum ratio of the number of satellites at low elevation angles to the number of satellites at high elevation angles can be calculated. In some embodiments, the actual low elevation angle is normalized and then inserted into the first functional relationship to calculate the maximum ratio, and when R takes the maximum value, the following formula can also be satisfied:

[0105] .

[0106] In this embodiment, the maximum ratio of the number of satellites at low elevation angles to the number of satellites at high elevation angles is calculated by the actual low elevation angle and the first functional relationship. Therefore, when observation is limited, the theoretical ratio of the number of satellites at low elevation angles to the number of satellites at high elevation angles can be dynamically selected according to the actual low elevation angle. Compared with the fixed ratio between high and low satellites in the prior art, it can be better applied to different environments, and the dynamic satellite geometry is obtained based on the actual low elevation angle, which can reduce the calculation cost and improve the positioning accuracy.

[0107] In some embodiments, in step 030, since the calculated maximum ratio may not be an integer or a rational number, or cannot directly satisfy the actual ratio of the number of satellites at low elevation angles to the number of satellites at high elevation angles, it is necessary to select a first number of satellites at low elevation angles and a second number of satellites at high elevation angles according to the maximum ratio, and obtain a theoretical ratio between the first number and the second number. In some embodiments, the first number and the second number can be selected according to demand. For example, if the accuracy requirement is high, the first number and the second number can be larger, otherwise they can be smaller. In some embodiments, the theoretical ratio needs to be as close to the maximum ratio as possible to meet the positioning accuracy requirement. In some embodiments, after obtaining the theoretical ratio between the first number and the second number, a second functional relationship between the theoretical high elevation angle, the actual low elevation angle and the theoretical ratio is obtained, wherein the second functional relationship satisfies:

[0108] ;

[0109] in, R 0 is the theoretical ratio, and then the theoretical high elevation angle can be calculated according to the theoretical ratio and the actual low elevation angle by a second functional relationship.

[0110] In some embodiments, the actual low elevation angle is i 0 , calculate its normalized height value z down = sin( i 0 ). Then the maximum ratio is calculated by the first functional relationship. R , through this maximum ratio R Determining a first number of satellites at low elevation angles n 1 and a second number of satellites at high elevation angles n 2 , and obtain the theoretical ratio between the first and second quantities: R 0 = n 1 / n 2 ; Then the height value normalized by the actual low elevation angle z down and theoretical ratio R 0 Substitute the second function relationship and calculate the normalized height value of the theoretical elevation angle z up , and then calculate the corresponding theoretical elevation angle i 1 =arcsin( z up ), and finally the actual low elevation angle is i 0 , Theoretical high elevation angle i 1 and theoretical ratio R 0 , thereby obtaining the optimal configuration for three-dimensional positioning.

[0111] For some examples, please refer to Figure 3 In step S100, a satellite selection area of ​​the current device is determined from the satellite constellations that the current device can observe, and an objective function of the satellite selection area is obtained, including:

[0112] Step S110b: determining a low elevation angle sub-selection area and a high elevation angle sub-selection area for selecting a satellite according to the theoretical high elevation angle and the actual low elevation angle;

[0113] Step S120b: respectively obtaining the objective function of the low elevation angle sub-satellite selection area and the objective function of the high elevation angle sub-satellite selection area.

[0114] The above-mentioned star selection area includes the low elevation angle sub-star selection area and the high elevation angle sub-star selection area.

[0115] In some embodiments, since there are limited satellites located exactly at the theoretical high elevation angle and the actual low elevation angle, it is necessary to determine the low elevation angle selection area and the high elevation angle selection area for selecting satellites according to the theoretical high elevation angle and the actual low elevation angle, so that a sufficient number of satellites can be selected from the low-orbit satellite constellation in the low elevation angle selection area and the high elevation angle selection area. In some embodiments, the first number of satellites and the second number of satellites selected from the satellite constellation in the low elevation angle selection area and the high elevation angle selection area need to meet conditions so that the selected satellites are close to their optimal geometric configuration, so that the satellites that meet the conditions can meet the positioning accuracy requirements when used for positioning.

[0116] In some embodiments, when determining the low elevation angle sub-selection area and the high elevation angle sub-selection area for selecting a satellite according to the theoretical high elevation angle and the actual low elevation angle, it specifically includes: taking a part between the theoretical high elevation angle and the actual low elevation angle as the low elevation angle selection area, the low elevation angle selection area includes the actual low elevation angle, taking a part between the low elevation angle sub-selection area and the maximum value of the elevation angle as the high elevation angle sub-selection area, the high elevation angle sub-selection area includes the theoretical high elevation angle. In this embodiment, the range of the elevation angle is [0°, 90°], so the maximum value of the elevation angle is ninety degrees.

[0117] In some embodiments, the low elevation angle sub-selection area is: [ i 0 , i 0 + Dth ]; The high elevation angle sub-selection area is: [ i 0 +2 Dth , i 0 +4 Dth ]; i 0 +4 Dth =min(90°, i 0 +4 Dth );in, Dth =( i 1 - i 0 )*1 / 3, i 1 is the theoretical high elevation angle, θ0 is the actual low elevation angle, and the value of the min function is an array (90°, i 0 +4 Dth ). As can be seen from the above embodiments, the portion between the theoretical high elevation angle and the actual low elevation angle can be evenly divided into three equal parts, wherein the portion close to the actual low elevation angle is used as the low elevation angle sub-star selection area, the portion close to the theoretical high elevation angle is used as part of the high elevation angle sub-star selection area, and the high elevation angle sub-star selection area also includes a portion greater than the theoretical high elevation angle, and if the portion exceeds ninety degrees, the value is ninety degrees. In some embodiments, the portion between the theoretical high elevation angle and the actual low elevation angle can be divided as needed, and the divided portion or all can also be used as the low elevation angle sub-star selection area or the high elevation angle sub-star selection area.

[0118] For some examples, please refer to Figure 4 In step S200, a satellite that can minimize the objective function is selected from the satellite selection region using an optimization algorithm, including:

[0119] Step S210b: selecting a first number of satellites from the low elevation angle sub-selection region by using an optimization algorithm that can minimize the objective function of the low elevation angle sub-selection region;

[0120] Step S220b: using an optimization algorithm to select a second number of satellites from the high elevation angle sub-selection region that can minimize the objective function of the high elevation angle sub-selection region.

[0121] Among them, the objective function of the low elevation angle sub-selection area is obtained based on the positions of a first number of satellites in the low elevation angle sub-selection area, and the objective function of the high elevation angle sub-selection area is obtained based on the positions of a second number of satellites in the high elevation angle sub-selection area, and the sum of the first number and the second number is equal to the preset number.

[0122] In some embodiments, in step S120b, the objective function of the low elevation angle sub-selection area is expressed as:

[0123] ;

[0124] in, x i and y i They are the first i Satellites on the coordinate axis X axis, Y The corresponding value after normalization on the axis, A 2. B 2 and C 2 are the weight coefficients of each item, n 1 represents the first quantity; m and l is the index of the corresponding item;

[0125] The expression of the objective function of the high elevation angle sub-selection area is:

[0126] ;

[0127] in, x j and y j They are the first j Satellites on the coordinate axis X axis, Y The corresponding value after normalization on the axis, D 2. E 2 and F 2 are weight coefficients of each item, n2 represents the second quantity; m and l is the index of the corresponding item.

[0128] It should be noted that when the above Z After the coordinate value z on the axis is determined, the aforementioned objective function J middle z m Just with J It doesn't matter, so J 1 and J 2 does not include z m This term, and at this time the objective function J middle( xz ) l and( yz ) l You can z l Extracted and turned back x l and y l , so in an ideal situation, we can compare x m and y m That is, the objective function in step S120b J 1 and the objective function J 2 is actually a special simplified case of the objective function J in the above embodiments.

[0129] In some embodiments, in step S120b, those skilled in the art may further determine the above-mentioned A 2 、B 2 、C 2 、D 2 、E 2 andF 2 The optimal value range of .

[0130] In some embodiments, in step S120b, the above A 2 、B 2 、C 2 、D 2 、E 2 and F 2 are all equal to 1, m=4, l =2.

[0131] In some embodiments, the specific process of "using satellites that can minimize the objective function for positioning" in step S300 belongs to the prior art in the field. For example, a multi-mode pseudo-range positioning solution method can be used to perform positioning solution of spatial position coordinates, thereby determining the spatial position coordinates of the user (such as the current device mentioned above) in the current observation epoch.

[0132] For some examples, please refer to Figure 5 , which illustrates the configuration of the satellites selected by this method. In the low elevation sub-selection area, the normalized altitude value is z down , the number of satellites selected is n 1 ; In the high elevation angle sub-selection area, the normalized height value is z up , the number of satellites selected is n 2 .

[0133] It can be seen that in some embodiments, the maximum ratio of the number of satellites at low elevation angles to the number of satellites at high elevation angles is calculated by the actual low elevation angle and the first functional relationship. Therefore, in the case of limited observation, the theoretical ratio of the number of satellites at low elevation angles to the number of satellites at high elevation angles can be dynamically selected according to the actual low elevation angle. Compared with the fixed ratio between high and low satellites in the prior art, it can be better applied to different environments, and the dynamic satellite geometry is obtained based on the actual low elevation angle, which can reduce the calculation cost and improve the positioning accuracy.

[0134] It can be seen that in some embodiments, since there are limited satellites located exactly at the theoretical high elevation angle and the actual low elevation angle, it is necessary to determine the low elevation angle sub-selection area and the high elevation angle sub-selection area for selecting satellites according to the theoretical high elevation angle and the actual low elevation angle, so that a sufficient number of satellites can be selected from the low-orbit satellite constellation in the low elevation angle sub-selection area and the high elevation angle sub-selection area. In some embodiments, the first number of satellites and the second number of satellites selected from the satellite constellation in the low elevation angle sub-selection area and the high elevation angle sub-selection area need to meet the conditions so that the selected satellites are close to their optimal geometric configuration, so that the satellites that meet the conditions can meet the positioning accuracy requirements when used for positioning.

[0135] It can be seen that in some embodiments, the portion between the theoretical high elevation angle and the actual low elevation angle can be evenly divided into three equal parts, wherein the portion close to the actual low elevation angle is used as the low elevation angle sub-star selection area, the portion close to the theoretical high elevation angle is used as part of the high elevation angle sub-star selection area, and the high elevation angle sub-star selection area also includes a portion greater than the theoretical high elevation angle, and if the portion exceeds ninety degrees, the value is ninety degrees. In some embodiments, the portion between the theoretical high elevation angle and the actual low elevation angle can be divided as needed, and the divided portion or all can also be used as the low elevation angle sub-star selection area or the high elevation angle sub-star selection area.

[0136] It can be seen that in some embodiments, when the geometric precision factor meets the preset conditions, the first functional relationship between the ratio of the number of satellites at low elevation angles and high elevation angles and the actual low elevation angle, and the second functional relationship between the theoretical high elevation angle, the actual low elevation angle and the theoretical ratio are obtained, so as to determine the optimal geometric configuration of the required satellites. Then, based on the actual low elevation angle of the current device, the theoretical ratio of satellites at low elevation angles and high elevation angles, as well as the theoretical high elevation angle, are calculated by the first functional relationship and the second functional relationship to obtain the optimal configuration of three-dimensional positioning, so that the positioning accuracy is improved, and the time and computational complexity required for selecting a large number of satellites are reduced, so that it can be applied to large-scale satellite constellations. Finally, the first number of satellites that can minimize the objective function of the low elevation sub-selection area and the second number of satellites that can minimize the objective function of the high elevation sub-selection area are selected in the low elevation sub-selection area and the high elevation sub-selection area respectively for positioning of the current device. Since it is based on the actual low elevation angle and the functional relationship, the theoretical ratio of the number of satellites at low elevation angles and high elevation angles is dynamically selected, which optimizes the ratio selection of high and low satellites in different observation environments, thereby adapting to different observation environments, while also reducing the amount of calculation and improving positioning accuracy. The positioning satellite selection method of the present application transforms the traditional uniformly distributed satellite selection algorithm or geometric dilution of precision (GDOP) calculation solution into a simple function calculation of geometric relationships, that is, minimizing the objective function of the present application, thereby greatly improving the calculation efficiency of the above-mentioned optimization algorithm, and even when the satellite signal is blocked, it can still screen out the optimal satellite combination based on the objective function proposed in the present application.

[0137] It can be seen that in some embodiments, the positioning satellite selection method of the present application, compared with the existing satellite selection method, can provide the satellite selection scheme that best suits the scene environment based on the minimum observation angle, provide higher precision GDOP results, and provide higher precision and higher reliability positioning information for a large number of satellite environmental conditions and observation restrictions for future autonomous driving / mobile communications and other fields.

[0138] It can be seen that in some embodiments, compared with other satellite selection algorithms, the positioning satellite selection method of the present application introduces a three-dimensional positioning optimal configuration model based on the particularity of large-scale satellite constellations, and uses the azimuth and elevation angles of the observed satellites as key information for satellite selection, thereby improving positioning accuracy and reducing the time and computational complexity required to select a large number of satellites.

[0139] It can be seen that in some embodiments, the traditional geometric satellite algorithm selects a high and low satellite ratio at a fixed ratio (such as 1:3), and the satellites are located at the top and the bottom. The positioning satellite selection method of the present application derives the construction process of the optimal configuration model in the algorithm, making it more suitable for satellite selection tasks of large-scale satellite constellations and reducing the amount of calculation.

[0140] It can be seen that in some embodiments, compared with other geometric satellite algorithms, the positioning satellite selection method of the present application is a dynamic optimization structure based on changes in observation angles, which can adapt to limited observation situations, and can obtain the high and low satellite ratios in a specific environment through the minimum observation elevation angle and the required number of satellites, thereby obtaining the elevation angle of the optimal satellite configuration and the number of satellites at the corresponding elevation angle. This dynamic optimization structure reduces the computational cost and improves positioning accuracy.

[0141] It can be seen that in some embodiments, compared with the traditional uniformly distributed satellite selection algorithm and the optimization algorithm that uses the geometric precision factor (GDOP) as the optimization target, the positioning satellite selection method of the present application converts the traditional uniformly distributed satellite selection algorithm or the geometric precision factor (GDOP) operation solution into a simple function operation of the geometric relationship, that is, minimizing the objective function of the present application, thereby greatly improving the operation efficiency of the above-mentioned optimization algorithm, and in the case where the satellite signal is blocked, it is still possible to screen out the optimal satellite combination based on the objective function proposed in the present application. In the case where the satellite signal is blocked, it is impossible to screen out the optimal satellite combination by directly using the traditional uniformly distributed satellite selection algorithm.

[0142] It should be noted that under unobstructed full visibility conditions, that is, when the satellite signal is not blocked, the positioning satellite selection method of the present application is basically equivalent to the traditional uniformly distributed satellite selection algorithm in selecting the optimal satellite combination; and the advantage of the positioning satellite selection method of the present application is that: when the satellite signal is blocked by obstacles (such as forests, buildings in cities, etc.), relatively ideal (such as open sky and other unobstructed situations) positioning satellites can be screened out from less than ideal environments.

[0143] The above is some explanation about the positioning satellite selection method. Figure 6 In some embodiments of the present application, a positioning satellite selection device is also disclosed, including:

[0144] The objective function acquisition module 100 is configured to determine a satellite selection area of ​​the current device from the satellite constellations that the current device can observe, and acquire an objective function of the satellite selection area;

[0145] The satellite selection module 200 is configured to select a satellite that can minimize the objective function from the satellite selection region using an optimization algorithm; wherein the objective function is obtained based on the positions of a preset number of satellites in the satellite selection region;

[0146] The positioning module 300 is configured to use the satellite that can minimize the objective function for positioning.

[0147] It should be noted that the processing procedures and technical effects specifically performed by the objective function acquisition module 100, the satellite selection module 200 and the positioning module 300 are basically the same as the processing procedures and technical effects of step S100, step S200 and step S300 in the aforementioned positioning satellite selection method, so they are not repeated here.

[0148] Some embodiments of the present application also disclose a computer-readable storage medium, including a program, which can be executed by a processor to implement the method described in any embodiment of the present invention.

[0149] In some embodiments of the present application, a computer program product is also disclosed, including a computer program and / or instructions, which, when executed by a processor, implement the method described in any one of the embodiments herein.

[0150] This document is described with reference to various exemplary embodiments. However, those skilled in the art will recognize that changes and modifications may be made to the exemplary embodiments without departing from the scope of this document. For example, various operating steps and components for performing the operating steps may be implemented in different ways (e.g., one or more steps may be deleted, modified, or combined into other steps) depending on the specific application or considering any number of cost functions associated with the operation of the system.

[0151] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. In addition, as understood by those skilled in the art, the principles of this article can be reflected in a computer program product on a computer-readable storage medium, which is pre-installed with a computer-readable program code. Any tangible, non-temporary computer-readable storage medium can be used, including magnetic storage devices (hard disks, floppy disks, etc.), optical storage devices (CD to ROM, DVD, Blu Ray disks, etc.), flash memory and / or the like. These computer program instructions can be loaded onto a general-purpose computer, a special-purpose computer, or other programmable data processing device to form a machine, so that these instructions executed on a computer or other programmable data processing device can generate a device that implements a specified function. These computer program instructions can also be stored in a computer-readable memory, which can instruct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory can form a manufactured product, including an implementation device that implements a specified function. Computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operating steps are performed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device can provide steps for implementing a specified function.

[0152] Although the principles of this invention have been shown in various embodiments, many modifications of structures, arrangements, proportions, elements, materials and components particularly suitable for specific environments and operational requirements can be used without departing from the principles and scope of this invention. The above modifications and other changes or amendments will be included in the scope of this invention.

[0153] The foregoing specific description has been described with reference to various embodiments. However, those skilled in the art will recognize that various modifications and changes can be made without departing from the scope of the present disclosure. Therefore, the consideration of the present disclosure will be illustrative rather than restrictive, and all these modifications will be included in its scope. Similarly, the advantages, other advantages and solutions to the problems of various embodiments have been described above. However, the benefits, advantages, solutions to the problems and any elements that can produce these, or make them more clear, should not be interpreted as critical, necessary or necessary. The term "include" and any other variants used in this article are all non-exclusive inclusions, so that the process, method, article or device including the list of elements not only includes these elements, but also includes other elements that are not explicitly listed or do not belong to the process, method, system, article or device. In addition, the term "coupled" and any other variants used in this article refer to physical connections, electrical connections, magnetic connections, optical connections, communication connections, functional connections and / or any other connections.

[0154] Those skilled in the art will appreciate that many changes may be made to the details of the above-described embodiments without departing from the basic principles of the invention. Therefore, the scope of the present invention should be determined solely by the claims.

Claims

1. A positioning satellite selection method, characterized in that: include: Determine a satellite selection area of ​​the current device from the satellite constellations that the current device can observe, and obtain an objective function of the satellite selection area; Selecting a preset number of satellites that can minimize the objective function from the selected star region by using an optimization algorithm; wherein the objective function is obtained based on the positions of the preset number of satellites in the selected star region; Using the satellite capable of minimizing the objective function for positioning; Wherein, when there is no high elevation angle sub-star selection area and low elevation angle sub-star selection area in the star selection area, the expression of the objective function of the star selection area is: ； in, x i 、y i 、z i They are respectively i Satellites on the coordinate axis X axis, Y Axis and Z The coordinate values ​​on the axis are normalized to the corresponding values. A 1. B 1. C 1. D 1. E 1 and F 1 is the weight coefficient of each item, n Indicates the preset quantity; m and l is the index of the corresponding item; Wherein, the optimization algorithm includes a swarm intelligence optimization algorithm or an artificial neural network.

2. The method according to claim 1, characterized in that A 1. B 1. C 1. D 1. E 1 and F 1 is equal to 1, m=4, l =2; the swarm intelligence optimization algorithm includes a genetic algorithm, a particle swarm algorithm, an ant colony algorithm, an immune algorithm, an artificial bee colony algorithm or a differential evolution algorithm.

3. The method according to claim 1, characterized in that When there are a high elevation angle sub-star selection area and a low elevation angle sub-star selection area in the star selection area, the method further includes: Obtaining a navigation message of a satellite constellation, calculating the elevation angle of each satellite in the satellite constellation that can be observed by the current device according to the navigation message, and taking the smallest of the elevation angles as the actual low elevation angle; When the geometric precision factor meets a preset condition, a first functional relationship between the ratio of the number of satellites at the low elevation angle to the number of satellites at the high elevation angle and the actual low elevation angle is obtained, and the actual low elevation angle is substituted into the first functional relationship, and a maximum ratio of the number of satellites at the low elevation angle to the number of satellites at the high elevation angle is calculated by the first functional relationship; According to the maximum ratio, a first number of satellites at low elevation angles and a second number of satellites at high elevation angles are selected, and a theoretical ratio between the first number and the second number is obtained, a second functional relationship between the theoretical high elevation angle, the actual low elevation angle and the theoretical ratio is obtained, and the actual low elevation angle and the theoretical ratio are substituted into the second functional relationship, and the theoretical high elevation angle is calculated by the second functional relationship; The step of determining a satellite selection area of ​​the current device from the satellite constellation observable by the current device and obtaining an objective function of the satellite selection area includes: Determining a low elevation angle sub-satellite selection area and a high elevation angle sub-satellite selection area for selecting a satellite according to the theoretical high elevation angle and the actual low elevation angle; The objective function of the low elevation angle sub-star selection area and the objective function of the high elevation angle sub-star selection area are respectively obtained; wherein the star selection area includes the low elevation angle sub-star selection area and the high elevation angle sub-star selection area.

4. The method according to claim 3, characterized in that The step of selecting a satellite capable of minimizing the objective function from the satellite selection region by using an optimization algorithm comprises: Selecting a first number of satellites capable of minimizing an objective function of the low elevation angle sub-selection region from the low elevation angle sub-selection region by using an optimization algorithm; An optimization algorithm is used to select a second number of satellites from the high elevation angle sub-selection region that can minimize the objective function of the high elevation angle sub-selection region; wherein the objective function of the low elevation angle sub-selection region is obtained based on the positions of a first number of satellites in the low elevation angle sub-selection region, and the objective function of the high elevation angle sub-selection region is obtained based on the positions of a second number of satellites in the high elevation angle sub-selection region, and the sum of the first number and the second number is equal to the preset number.

5. The method according to claim 4, characterized in that The expression of the objective function of the low elevation angle sub-selection area is: ； in, x i and y i They are the first i Satellites on the coordinate axis X axis, Y The corresponding value after normalization on the axis, A 2. B 2 and C 2 are the weight coefficients of each item, n 1 represents the first quantity; the expression of the objective function of the high elevation angle sub-selection area is: ； in, x j and y j They are the first j The corresponding value of each satellite after normalization on the X-axis and Y-axis. D 2. E 2 and F 2 are weight coefficients of each item, n2 represents the second quantity; m and l is the index of the corresponding item; the optimization algorithm includes a swarm intelligence optimization algorithm or an artificial neural network.

6. The method according to claim 5, characterized in that A 2. B 2. C 2. D 2. E 2 and F 2 is equal to 1, m=4, l =2; the swarm intelligence optimization algorithm includes a genetic algorithm, a particle swarm algorithm, an ant colony algorithm, an immune algorithm, an artificial bee colony algorithm or a differential evolution algorithm.

7. A positioning satellite selection device, characterized in that: include: The objective function acquisition module is configured to determine a satellite selection area of ​​the current device from the satellite constellations that the current device can observe, and acquire an objective function of the satellite selection area; The satellite selection module is configured to select a satellite that can minimize the objective function from the star selection area by using an optimization algorithm; wherein the objective function is obtained based on the positions of a preset number of satellites in the star selection area; when there is no high elevation angle sub-star selection area and low elevation angle sub-star selection area in the star selection area, the objective function of the star selection area is expressed as: ； in, x i 、y i 、z i They are respectively i Satellites on the coordinate axis X axis, Y Axis and Z The coordinate values ​​on the axis are normalized to the corresponding values. A 1. B 1. C 1. D 1. E 1 and F 1 is the weight coefficient of each item, n Indicates the preset quantity; m and l is the index of the corresponding item; the optimization algorithm includes a swarm intelligence optimization algorithm or an artificial neural network; The positioning module is configured to use the satellite that can minimize the objective function for positioning.

8. A computer-readable storage medium, characterized in that: The method comprises a program which can be executed by a processor to implement the method according to any one of claims 1 to 6.

9. A computer program product comprising a computer program and / or instructions, characterized in that: When the computer program and / or the instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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