CN113960639A - Navigation source deployment position method based on deployment region iterative segmentation - Google Patents

Abstract

The invention provides a navigation source deployment position method based on deployment region iterative segmentation, which converts given rough deployable regions and service regions into a computable mathematical model, and obtains the deployment position of a reference station with optimal geometric distribution step by using an iterative computation method according to the requirements of station arrangement quantity, computation time and the like on the basis, thereby providing guarantee for the work of a regional navigation system and improving the service performance of the navigation system. The invention greatly increases the deployment speed of the regional navigation reference station and the station distribution rationality, and enables the computed deployment position to meet different geometric distribution requirements by changing the computation accuracy. The result can be rapidly calculated, the running time of the method is in the order of tens of seconds, and the real-time application is met; and the cloth station position with better geometric distribution is obtained. The problems that the number of the reference stations is fixed and the deployment space is limited are solved well.

Description

Navigation source deployment position method based on deployment region iterative segmentation

Technical Field

The invention relates to the field of radio navigation and positioning, in particular to a navigation source deployment position method.

Background

With the advancement of satellite-guided interference technology, satellite-guided systems are susceptible to interference and are unusable in battlefield environments. At the moment, the regional emergency navigation is a feasible navigation positioning means with remarkable effect, the navigation reference station can be carried on an aerial or ground platform for rapid deployment, and high-performance navigation positioning service can be provided for the defense rejection region.

Because the navigation reference station is very close to the user in regional emergency navigation, the geometric distribution of the reference station has a remarkable influence on the navigation performance.

Related researches on radio navigation geometric distribution at home and abroad are many, and some methods research a geometric layout scheme of 6 pseudolites, a geometric layout scheme of 4 pseudolites and a geometric layout scheme of 4, 5 or 9 pseudolites. But most of the reference stations are fixed in number, and the deployment space is not limited. In practical applications, a three-dimensional space region is often required to be served, the deployment space of the reference station is limited, and the number of navigation reference stations may vary. The method in the above document is therefore only applicable to a few of the above specific scenarios.

Disclosure of Invention

In order to overcome the defects of the prior art, the invention provides a navigation source deployment position method based on iterative segmentation of a deployment region. In order to solve the problem of determining the deployment position of a regional navigation system reference station in a combat environment, the technical problem to be solved by the invention is as follows: and converting the given rough deployable area and service area into a computable mathematical model, and gradually obtaining the deployment position of the reference station with optimal geometric distribution by using an iterative calculation method according to the requirements of station arrangement quantity, calculation time and the like on the basis, thereby providing guarantee for the work of the regional navigation system and improving the service performance of the navigation system.

In order to improve the positioning precision of regional emergency navigation, the invention utilizes the precision attenuation factor DOP to research the geometric distribution of the emergency navigation reference station. Aiming at the limitation in the practical application of regional emergency navigation, the invention provides a navigation source deployment position method based on DOP analysis and iterative segmentation of a deployment region. Firstly, a deployable area and a service area are defined by mathematical language, then the deployed area is subjected to iterative segmentation, and the optimal deployment position is rapidly calculated according to the station arrangement quantity and the DOP constraint rule (the precision attenuation factor of the service area is minimized). Finally, a typical application scene is designed for simulation experiments, and results show that the method provided by the invention can quickly obtain the optimal deployment position of the reference station according to the service area, the deployment area and the station distribution quantity. A good technical approach is provided for solving the problem of determining the deployment position of the regional emergency navigation system reference station.

The technical scheme adopted by the invention for solving the technical problem specifically comprises the following steps:

step 1: converting the deployment area and the service area into mathematical models;

1.1 region definition

The defined area is a spatial straight quadrangular prism, which comprises 6 parameters, which are respectively: four vertexes V1, V2, V3 and V4 of the bottom surface, height h, center point C and C are the midpoints of line segments formed by gravity centers of the upper bottom surface and the lower bottom surface, and when h is 0, the region is a quadrangle;

1.2 defining deployment and service areas

According to the given requirements of the limitation of the deployment Area and the range of the service Area, respectively determining parameters corresponding to the Sta _ Area and the service Area Served _ Area according to the Area defined by the step 1.1;

step 2: sampling a service area;

the service Area Served _ Area comprises countless points, so that direct calculation cannot be performed, and segmentation and sampling are required before calculation, and the method comprises the following specific steps of:

2.1 region segmentation method

R is a defined region, and the parameters include: vertexes V1, V2, V3 and V4 of the bottom surface quadrangle Quad are in the order of anticlockwise from the left top, and Half of the height of the area is Half of the height of the area; the coordinate format of any point involved in the area is (longitude, latitude, altitude); if the Half _ h is larger than 0, evenly dividing the three dimensions of longitude and latitude to obtain 8 sub-area sets R8; when Half _ h is equal to 0, 4 sub-region sets R4 are obtained;

2.2 service area sampling

The method for carrying out user point sampling on the service area by using the area segmentation method comprises the following specific steps:

inputting: a used _ Area, the number of sampling cycles is k;

and (3) outputting: a User coordinate point set User;

step 1: adding C coordinates of 8 top points and central points of the upper bottom surface and the lower bottom surface of the Served _ Area into a User;

step 2: the Server _ Area is firstly segmented by using a 2.1 region segmentation method to obtain a set Quadxi1 of regions to be segmented, the set Quadxi1 comprises a plurality of small regions, and the central point of each region in the Quadxi1 is added into a User;

step3: dividing each region in Quadxi1 by using a region division method, adding the obtained small region into a division result set Quadxi2, emptying Quadxi1, adding 1 to the cycle number, and entering Step 4;

step4: if the cycle number is equal to k, adding the central points of all elements in Quadxi2 into a User, if the cycle number is less than k, adding all elements in Quadxi2 into Quadxi1, and ending the cycle;

and step3: iteratively calculating the deployment position of the reference station;

the deployable Area is Sta _ Area, a User coordinate point set User is used, the number n of deployment reference sources and the number m of iteration are set, iterative segmentation is carried out on the deployable Area according to a geometric precision factor constraint rule, and the deployment position is calculated.

The region segmentation method comprises the following calculation steps:

inputting: region R

And (3) outputting: set of subregions R4 or R8

Step 1: calculating a gravity center point QuadC according to a convex polygon gravity center calculation method;

step 2: respectively calculating the coordinates of the middle points of four sides of the bottom surface quadrangle Quad: em12, em14, em23, em 34;

step3: dividing the Quad into 4 small quadrangles, taking V1, em12, Quad C and em14 as vertexes to obtain 1 small quadrangle Quad1, calculating a gravity center point Quad1C of the Quad1, obtaining Quad2, Quad3 and Quad4 in the same way, and calculating the gravity center points of the small quadrangles as shown in FIG. 2;

step4: judging the Half _ h, if the Half _ h is larger than 0, executing Step6, and if the Half _ h is equal to 0, executing Step 5;

step 5: dividing R into 4 sub-regions, collecting the sub-regions into R4, taking 4 vertexes of Quad1, gravity center points Quad1C and Half _ h of R as parameters, obtaining 1 small region R4_1 after R4 is divided again, and obtaining other 3 small regions R4_2, R4_3 and R4_4 in the same way;

step 6: dividing R into 8 subregions, wherein the subregions are set to be R8; setting height components of Quad1C coordinates as 1.5Half _ h and 0.5Half _ h respectively to obtain Quad1C1 and Quad1C2, wherein 1 small region R8_1 in R8 is obtained by taking 4 vertexes of Quad1, Half _ h/2 and Quad1C1 as central points, and 1 small region R8_2 in R8 is obtained by taking vertexes of Quad1, Half _ h/2 and Quad1C2 as parameters; similarly, the same steps are carried out on Quad2, Quad3 and Quad4 to obtain other 6 small regions in R8.

The step of calculating the deployment position of the reference station by iteration comprises the following steps:

inputting: sta _ Area, Served _ Area, reference source number n, User coordinate point set User and iteration number m;

and (3) outputting: deploying positions RefStation of n reference sources;

step 1: obtaining a small region set Rsta2Quadxism by Sta _ Area according to a region segmentation method, wherein the number of small regions is q, and when the Sta _ Area height is 0, q is 4; when the Sta _ Area height is greater than 0, q is 8;

step 2: dividing all small regions in the Rsta2Quadxism, dividing the small regions according to a region division method to obtain q x n small regions, and emptying the Rsta2 Quadxism;

step3: selecting n small regions from q x n, when n is equal to 4, selecting 1 small region from q small regions obtained by dividing each small region to obtain n regions; when n is larger than 4, traversing all combinations of n selected from q x n, and selecting 1 combination from the combinations to obtain n regions;

when the n regions satisfy: using a coordinate point set formed by the central points as deployment positions, calculating GDOP (vector graphics operator) for all points in the User by using the deployment positions, satisfying a geometric precision factor constraint rule, adding the n regions into Rsta2 quadrism, and adding 1 to the cycle times;

step4: and if the loop time is equal to m, the quasi-source deployment position RefStation is equal to a point set formed by the coordinates of the central point of each small area in the Rsta2 Quadrisms, and if the loop time is less than m, jumping to execute Step 2.

The value range of the deployment reference source number n is 3,4, … 10.

The iteration number m is in a range of 1,2 and … 5.

The geometric precision factor constraint rule is the minimum GDOP variance or mean minimum criterion of all points of the User.

The invention has the beneficial effects that:

(1) the method has stronger adaptability to complex application environments. In the practical application environment, compared with the existing method, the method defines the deployment area and the service area by using the mathematical language, takes the number of the reference stations as the variable parameter, and well solves the problems of fixed number of the reference stations and limited deployment space. The method can be applied to a complex navigation environment, and greatly improves the deployment speed of the regional navigation reference station and the station arrangement rationality.

(2) The method is suitable for different geometric distribution requirements. The calculated deployment position can meet different geometric distribution requirements by changing the calculation criterion. Such as the criterion of minimizing GDOP variance and mean of the area sampling points (the method employs the method of minimizing GDOP mean of the area sampling points).

(3) The method can be applied to real-time and non-real-time scenes. In a scene requiring real time, the number of the reference stations is set to be 4, the iterative computation times are low, the result can be rapidly computed, the running time of the method is in the order of tens of seconds, and the real-time application is met; in a scene with low real-time requirement, the number of the reference stations can be set to be more than 4, the iteration times are higher, and the station distribution position with better geometric distribution is obtained.

(4) The method is simple to use. Compared with the conventional method of judging according to experience, the method only needs to give the service area and the deployment area according to the definition, sets the calculation iteration times and the number of the reference stations according to the requirements of calculation time and precision, and is convenient to use in practical application.

(5) Under the background that satellite navigation rejects and navigation positioning service is provided by using regional navigation, the navigation source deployment position method scheme based on deployment region iterative segmentation provided by the invention defines deployment regions and service regions by using a mathematical language, and takes the number of reference stations as variable parameters, thereby well solving the problems of fixed number of reference stations and limited deployment space. The calculated deployment location can be made to meet different geometric distribution requirements by varying the calculation criteria. Simultaneously, the convertible parameters meet the requirements of real-time performance and precision, and the running time of the method can reach the second level at least. The method only needs to give a service area and a deployment area according to definition, sets the number of calculation iterations and the number of reference stations according to the requirements of calculation time and precision, and is convenient to use in practical application. Finally, the method can also be applied to the calculation of the deployment position of other radio navigation reference stations such as a cellular positioning system.

Drawings

FIG. 1 is a schematic view of the present invention.

FIG. 2 is a schematic view of the region segmentation of the present invention.

Fig. 3 is a diagram of a service area sampling result of the present invention.

Fig. 4 is a front view of the user area and deployment location of the present invention.

FIG. 5 is a top view of the user area and deployment location of the present invention.

FIG. 6 is the change of GDOP with the number of calculations according to the present invention.

Fig. 7 is a GDOP thermodynamic diagram of the service area of the present invention.

Fig. 8 is a flow chart of region segmentation in accordance with the present invention.

FIG. 9 is a flow chart of the reference station deployment location calculation of the present invention.

Detailed Description

The invention is further illustrated with reference to the following figures and examples.

Examples of the invention

Step 1: converting the deployment area and the service area into mathematical models;

1.1 region definition

The defined area is a spatial straight quadrangular prism, as shown in fig. 1, which includes 6 parameters, respectively: four vertexes V1, V2, V3 and V4 of the bottom surface, height h, center point C and C are the midpoints of line segments formed by gravity centers of the upper bottom surface and the lower bottom surface, and when h is 0, the region is a quadrangle;

1.2 defining deployment and service areas

According to the given requirements of the limitation of the deployment Area and the range of the service Area, respectively determining parameters corresponding to the Sta _ Area deployment Area and the service Area Served _ Area according to the Area mathematical model defined in the step 1.1;

step 2: sampling a service area;

the service Area Served _ Area comprises countless points, so that direct calculation cannot be performed, and segmentation and sampling are required before calculation, and the method comprises the following specific steps of:

2.1 region segmentation method

R is a defined region, as shown in fig. 2, and the parameters include: vertexes V1, V2, V3 and V4 of the bottom surface quadrangle Quad are in the order of anticlockwise from the left top, and Half of the height of the area is Half of the height of the area; the coordinate format of any point involved in the area is (longitude, latitude, altitude); if the Half _ h is larger than 0, evenly dividing the three dimensions of longitude and latitude to obtain 8 sub-area sets R8; when Half _ h is equal to 0, 4 sub-region sets R4 are obtained; the method is shown in fig. 8.

Inputting: region R

And (3) outputting: set of subregions R4 or R8

Step 1: calculating a gravity center point QuadC according to a convex polygon gravity center calculation method;

step 2: respectively calculating the coordinates of the middle points of four sides of the bottom surface quadrangle Quad: em12, em14, em23, em 34;

step3: dividing the Quad into 4 small quadrangles, taking V1, em12, Quad C and em14 as vertexes to obtain 1 small quadrangle Quad1, calculating a gravity center point Quad1C of the Quad1, obtaining Quad2, Quad3 and Quad4 in the same way, and calculating the gravity center points of the small quadrangles as shown in FIG. 2;

step4: judging the Half _ h, if the Half _ h is larger than 0, executing Step6, and if the Half _ h is equal to 0, executing Step 5;

step 5: dividing R into 4 sub-regions, collecting the sub-regions into R4, taking 4 vertexes of Quad1, gravity center points Quad1C and Half _ h of R as parameters, obtaining 1 small region R4_1 after R4 is divided again, and obtaining other 3 small regions R4_2, R4_3 and R4_4 in the same way;

step 6: dividing R into 8 subregions, wherein the subregions are set to be R8; setting height components of Quad1C coordinates as 1.5Half _ h and 0.5Half _ h respectively to obtain Quad1C1 and Quad1C2, wherein 1 small region R8_1 in R8 is obtained by taking 4 vertexes of Quad1, Half _ h/2 and Quad1C1 as central points, and 1 small region R8_2 in R8 is obtained by taking vertexes of Quad1, Half _ h/2 and Quad1C2 as parameters; similarly, the same steps are carried out on Quad2, Quad3 and Quad4 to obtain other 6 small regions in R8;

2.2 service area sampling

The method for carrying out user point sampling on the service area by using the area segmentation method comprises the following specific steps:

inputting: a used _ Area, the number of sampling cycles is k;

and (3) outputting: a User coordinate point set User;

step 1: adding C coordinates of 8 top points and central points of the upper bottom surface and the lower bottom surface of the Served _ Area into a User;

step 2: the Server _ Area is firstly segmented by using a 2.1 region segmentation method to obtain a set Quadxi1 of regions to be segmented, the set Quadxi1 comprises a plurality of small regions, and the central point of each region in the Quadxi1 is added into a User;

step3, dividing each region in Quadxi1 by using a region division method, adding the obtained small regions into a division result set Quadxi2, emptying Quadxi1, adding 1 to the cycle number, and entering Step 4;

and Step4, if the cycle number is equal to k, adding the central points of all elements in Quadxi2 into the User, if the cycle number is less than k, adding all elements in Quadxi2 into Quadxi1, and ending the cycle.

When k is 3, the number of sampling points obtained by sampling the service area is 4685, and the result is shown in fig. 3.

And step3: and (4) iteratively calculating the deployment position of the reference station.

The deployable Area is Sta _ Area, the User coordinate point set User sets the number n of deployment reference sources, the value range of n is 3,4, … 10, the iteration number m, the value range of m is 1,2, … 5, and the deployment Area is iteratively segmented according to the geometric precision factor constraint rule to calculate the deployment position, as shown in fig. 9.

A calculation step:

inputting: sta _ Area, Served _ Area, reference source number n, User coordinate point set User and iteration number m;

and (3) outputting: deploying positions RefStation of n reference sources;

step 1: obtaining a small region set Rsta2Quadxism by Sta _ Area according to a region segmentation method, wherein the number of small regions is q, and when the Sta _ Area height is 0, q is 4; when the Sta _ Area height is greater than 0, q is 8;

step 2: dividing all small regions in the Rsta2Quadxism, dividing the small regions according to a region division method to obtain q x n small regions, and emptying the Rsta2 Quadxism;

step3: selecting n small regions from q x n, when n is equal to 4, selecting 1 small region from q small regions obtained by dividing each small region to obtain n regions; when n is larger than 4, traversing all combinations of n selected from q x n, and selecting 1 combination from the combinations to obtain n regions;

when the n regions satisfy: using a coordinate point set formed by the central points as deployment positions, calculating GDOP (vector graphics operator) for all points in the User by using the deployment positions, satisfying a geometric precision factor constraint rule, adding the n regions into Rsta2 quadrism, and adding 1 to the cycle times;

step4: and if the loop time is equal to m, the quasi-source deployment position RefStation is equal to a point set formed by the coordinates of the central point of each small area in the Rsta2 Quadrisms, and if the loop time is less than m, jumping to execute Step 2.

The geometric precision factor constraint rule is the minimum GDOP variance or mean minimum criterion of all points of the User.

Simulation embodiment

The method provided by the invention is used for calculating the position of the deployed reference station, the number of the reference stations is set to be 4, the number of sampling layers of a user is 1 (the number of sampling points 77), and the iterative calculation is carried out for multiple times.

It can be seen from fig. 4 and 5 that the deployment locations are distributed substantially around the service area, two at high altitude and two at low altitude. As can be seen from fig. 6, after 5 iterations, the GDOP value of the service area calculated by the resulting deployment location is small and hardly changes any more. It can be seen from fig. 7 that the GDOP center area of the service area is smallest and gradually becomes larger toward the periphery.

From the above results, it can be obtained: when the deployment area comprises the service area, the calculated deployment position has very small geometric precision factor GDOP to the service area, and the high-precision navigation positioning requirement can be well met.

The simulation result fully shows the effectiveness and the correctness of the method, and the deployment position of the regional navigation reference source with better geometric distribution can be quickly calculated in practical application.

Claims (6)

1. A navigation source deployment position method based on deployment region iterative segmentation is characterized by comprising the following steps:

step 1: converting the deployment area and the service area into mathematical models;

1.1 region definition

The defined area is a spatial straight quadrangular prism, which comprises 6 parameters, which are respectively: four vertexes V1, V2, V3 and V4 of the bottom surface, height h, center point C and C are the midpoints of line segments formed by gravity centers of the upper bottom surface and the lower bottom surface, and when h is 0, the region is a quadrangle;

1.2 defining deployment and service areas

According to the given requirements of the limitation of the deployment Area and the range of the service Area, respectively determining parameters corresponding to the Sta _ Area and the service Area Served _ Area according to the Area defined by the step 1.1;

step 2: sampling a service area;

the service Area Served _ Area comprises countless points, so that direct calculation cannot be performed, and segmentation and sampling are required before calculation, and the method comprises the following specific steps of:

2.1 region segmentation method

R is a defined region, and the parameters include: vertexes V1, V2, V3 and V4 of the bottom surface quadrangle Quad are in the order of anticlockwise from the left top, and Half of the height of the area is Half of the height of the area; the coordinate format of any point involved in the area is (longitude, latitude, altitude); if the Half _ h is larger than 0, evenly dividing the three dimensions of longitude and latitude to obtain 8 sub-area sets R8; when Half _ h is equal to 0, 4 sub-region sets R4 are obtained;

2.2 service area sampling

The method for carrying out user point sampling on the service area by using the area segmentation method comprises the following specific steps:

inputting: a used _ Area, the number of sampling cycles is k;

and (3) outputting: a User coordinate point set User;

step 1: adding C coordinates of 8 top points and central points of the upper bottom surface and the lower bottom surface of the Served _ Area into a User;

step 2: the Server _ Area is firstly segmented by using a 2.1 region segmentation method to obtain a set Quadxi1 of regions to be segmented, the set Quadxi1 comprises a plurality of small regions, and the central point of each region in the Quadxi1 is added into a User;

step3: dividing each region in Quadxi1 by using a region division method, adding the obtained small region into a division result set Quadxi2, emptying Quadxi1, adding 1 to the cycle number, and entering Step 4;

step4: if the cycle number is equal to k, adding the central points of all elements in Quadxi2 into a User, if the cycle number is less than k, adding all elements in Quadxi2 into Quadxi1, and ending the cycle;

and step3: iteratively calculating the deployment position of the reference station;

the deployable Area is Sta _ Area, a User coordinate point set User is used, the number n of deployment reference sources and the number m of iteration are set, iterative segmentation is carried out on the deployable Area according to a geometric precision factor constraint rule, and the deployment position is calculated.

2. The navigation source deployment location method based on iterative segmentation of deployment regions according to claim 1, wherein:

the region segmentation method comprises the following calculation steps:

inputting: region R

And (3) outputting: set of subregions R4 or R8

Step 1: calculating a gravity center point QuadC according to a convex polygon gravity center calculation method;

step 2: respectively calculating the coordinates of the middle points of four sides of the bottom surface quadrangle Quad: em12, em14, em23, em 34; step3: dividing the Quad into 4 small quadrangles, taking V1, em12, Quad C and em14 as vertexes to obtain 1 small quadrangle Quad1, calculating a gravity center point Quad1C of the Quad1, obtaining Quad2, Quad3 and Quad4 in the same way, and calculating the gravity center points of the small quadrangles as shown in FIG. 2;

step4: judging the Half _ h, if the Half _ h is larger than 0, executing Step6, and if the Half _ h is equal to 0, executing Step 5;

step 5: dividing R into 4 sub-regions, collecting the sub-regions into R4, taking 4 vertexes of Quad1, gravity center points Quad1C and Half _ h of R as parameters, obtaining 1 small region R4_1 after R4 is divided again, and obtaining other 3 small regions R4_2, R4_3 and R4_4 in the same way;

step 6: dividing R into 8 subregions, wherein the subregions are set to be R8; setting height components of Quad1C coordinates as 1.5Half _ h and 0.5Half _ h respectively to obtain Quad1C1 and Quad1C2, wherein 1 small region R8_1 in R8 is obtained by taking 4 vertexes of Quad1, Half _ h/2 and Quad1C1 as central points, and 1 small region R8_2 in R8 is obtained by taking vertexes of Quad1, Half _ h/2 and Quad1C2 as parameters; similarly, the same steps are carried out on Quad2, Quad3 and Quad4 to obtain other 6 small regions in R8.

3. The navigation source deployment location method based on iterative segmentation of deployment regions according to claim 1, wherein:

the step of calculating the deployment position of the reference station by iteration comprises the following steps:

inputting: sta _ Area, Served _ Area, reference source number n, User coordinate point set User and iteration number m;

and (3) outputting: deploying positions RefStation of n reference sources;

step 1: obtaining a small region set Rsta2Quadxism by Sta _ Area according to a region segmentation method, wherein the number of small regions is q, and when the Sta _ Area height is 0, q is 4; when the Sta _ Area height is greater than 0, q is 8;

step 2: dividing all small regions in the Rsta2Quadxism, dividing the small regions according to a region division method to obtain q x n small regions, and emptying the Rsta2 Quadxism;

step3: selecting n small regions from q x n, when n is equal to 4, selecting 1 small region from q small regions obtained by dividing each small region to obtain n regions; when n is larger than 4, traversing all combinations of n selected from q x n, and selecting 1 combination from the combinations to obtain n regions;

when the n regions satisfy: using a coordinate point set formed by the central points as deployment positions, calculating GDOP (vector graphics operator) for all points in the User by using the deployment positions, satisfying a geometric precision factor constraint rule, adding the n regions into Rsta2 quadrism, and adding 1 to the cycle times;

step4: and if the loop time is equal to m, the quasi-source deployment position RefStation is equal to a point set formed by the coordinates of the central point of each small area in the Rsta2 Quadrisms, and if the loop time is less than m, jumping to execute Step 2.

4. The navigation source deployment location method based on iterative segmentation of deployment regions according to claim 3, wherein:

the value range of the deployment reference source number n is 3,4, … 10.

5. The navigation source deployment location method based on iterative segmentation of deployment regions according to claim 3, wherein:

the iteration number m is in a range of 1,2 and … 5.

6. The navigation source deployment location method based on iterative segmentation of deployment regions according to claim 3, wherein:

the geometric precision factor constraint rule is the minimum GDOP variance or mean minimum criterion of all points of the User.

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