Multi-objective base station layout optimization method

Abstract

The invention discloses a multi-objective optimization base station layout method, which comprises the following steps: firstly, determining a base station layout area boundary, and determining a system coverage area boundary; then determining a precision factor function and a signal field intensity function used in the optimization process; setting initial parameters of a multi-objective optimization algorithm; the precision factor calculation equation and the signal field intensity calculation equation are used as fitness functions of a multi-objective optimization algorithm for iterative circulation; and obtaining a final Pareto frontier solution set, and selecting a layout scheme from the Pareto solution set according to an actual terrain environment to arrange the navigation base station, so that the layout of the base station is completed. According to the method, the system signal coverage range is introduced, so that the coverage range of the land-based navigation system can be expanded while the precision factor distribution is optimized.

Description

Technical Field

[0001] The invention belongs to the field of communication technology, and in particular relates to a multi-objective optimization method for base station layout. Background Art

[0002] Since the land-based radio navigation system can not only independently provide services to users in a satellite-denied environment, but also integrate positioning with satellite navigation to improve the positioning accuracy of satellite navigation, in order to meet the navigation and positioning needs when satellite navigation systems are unavailable, the land-based radio navigation system has developed rapidly in recent years.

[0003] In actual applications, in order to improve the positioning accuracy of land-based navigation systems, it is necessary to optimize the layout of base stations to reduce the precision factor of the system within the service range. In addition, since there is an upper limit on the transmission power of base stations, the signal propagation range of each base station is limited. However, traditional base station layout optimization methods often only optimize the base station layout for a single optimization goal, the precision factor, without considering the signal coverage range of the system at the same time, which is not suitable for actual application scenarios. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the present invention provides a multi-objective optimization base station layout method, first determine the base station layout area boundary, determine the system coverage area boundary; then determine the precision factor function and signal field strength function used in the optimization process; then set the initial parameters of the multi-objective optimization algorithm; then use the precision factor calculation equation and the signal field strength calculation equation as the fitness value function of the multi-objective optimization algorithm for iterative cycles; obtain the final Pareto front solution set, select the layout scheme from the Pareto solution set according to the actual terrain environment to arrange the navigation base station, and thus complete the base station layout. By introducing the system signal coverage range, the present invention can improve the coverage range of the land-based navigation system while optimizing the precision factor distribution.

[0005] The technical solution adopted by the present invention to solve the technical problem is as follows:

[0006] Step 1: Determine the boundaries of the base station deployment area and the system coverage area boundaries;

[0007] Step 2: Determine the precision factor function f used in the optimization process DOP and signal strength function f E ;

[0008] Step 3: Set the initial parameters of the multi-objective optimization algorithm;

[0009] Step 4: The precision factor calculation equation and the signal field strength calculation equation are used as the fitness function of the multi-objective optimization algorithm for iterative circulation;

[0010] Step 5: Get the final Pareto front solution set, and select a layout plan from the Pareto solution set to arrange the navigation base stations according to the actual terrain environment. This completes the base station layout.

[0011] Furthermore, the step 1 is specifically as follows:

[0012] Step 1-1: Measure the range area where base station layout can be carried out to determine the longitude and latitude coordinates of the boundary points Then we get the boundary point matrix of the layout area

[0013] Step 1-2: Set the boundary points of system function coverage Then we get the coverage area boundary point matrix

[0014] Furthermore, the step 2 is specifically as follows:

[0015] Step 2-1: The precision factor function based on the pseudo-range positioning algorithm is:

[0016]

[0017] Among them, h 11 、h 22 、h 33 are the diagonal elements of the matrix H;

[0018] H=(G T G) -1

[0019]

[0020] Among them, (x i ,y i ,z i ) is the coordinate of the i-th base station among the total number of n navigation base stations in the Earth-centered Earth-fixed coordinate system, and the obtained f DOP That is the precision factor at (x, y, z); r i I represents the length of the observation vector of base station i at (x, y, z); x ,I y ,I z Respectively represent r i The value of the partial derivative of x, y, and z at (x, y, z);

[0021] Step 2-2: The signal field strength calculation function of medium and long wave electromagnetic signals during transmission is:

[0022]

[0023] Where E R and E TThey represent the field strength obtained by forward calculation and the field strength obtained by reverse calculation using the Millington method respectively.

[0024] Furthermore, the initial parameters in step 3 include: the number of optimization iterations T, the number of population individuals N, the number of optimization targets m, the number of base stations dim in each layout solution, and the number of sampling points sp in the coverage area.

[0025] Furthermore, the step 4 is specifically as follows:

[0026] Step 4-1: The representation of the i-th individual in the population is Where k represents the number of base stations to be deployed this time; the entire population at the tth iteration is expressed as Then, the offspring population is generated through the optimization algorithm

[0027] Step 4-2: After obtaining the offspring population, check whether each offspring individual is inside the base station deployment area. If it is outside the boundary, a new individual is subsequently generated inside the base station deployment area;

[0028] Step 4-3: Evenly select sp calculation locations u within the coverage area j =(x i ,y i ,z i ), 1≤j≤sp, the offspring population X t+1 and each calculation location is substituted with the precision factor function f DOP and signal strength function f E Get the fitness value of each individual and

[0029] Step 4-3-1: Calculate the precision factor adaptation value of the i-th individual When calculating, traverse and calculate each calculation location u j =(x i ,y i ,z i ) For each individual x i The precision factor Finally find the average

[0030] Step 4-3-2: Calculate the field strength adaptation value of the i-th individual When calculating, traverse and calculate each calculation location u j =(x i ,y i ,z i ) For each individual x i Signal strength The signal strength at each calculation location Greater than the field strength threshold E limit ,but minus 1, The initial value is zero;

[0031] Step 4-4: The offspring individual X t+1 and the optimal Pareto solution set X p Perform non-dominated sorting to obtain Pareto frontiers of different levels, and update the optimal Pareto solution set according to the Pareto levels of different individuals;

[0032] Step 4-5: When the number of iterations reaches T, stop iterating.

[0033] The beneficial effects of the present invention are as follows:

[0034] Compared with the traditional single-target base station layout optimization method based on precision factor, the present invention can improve the coverage of the land-based navigation system while optimizing the precision factor distribution by introducing the system signal coverage range. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flow chart of the base station layout method of the present invention;

[0036] Figure 2 This is a schematic diagram of the station deployment area and coverage range of an embodiment of the present invention;

[0037] Figure 3 A schematic diagram of a hybrid path according to an embodiment of the present invention;

[0038] Figure 4 This is a schematic diagram of the optimal Pareto solution set according to an embodiment of the present invention; DETAILED DESCRIPTION

[0039] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0040] Different navigation base station layouts have different precision factor distributions. At the same time, due to the limitation of base station power, the navigation signal coverage ranges sent by navigation base stations with different layouts are different. Based on this problem, the present invention proposes a multi-objective optimization base station layout method that considers both precision factor and signal coverage range for a land-based navigation system, so that the land-based navigation system can provide a larger coverage range while having a lower precision factor distribution.

[0041] The main processes include:

[0042] S1: Determine the boundaries of the base station deployment area and the system coverage area boundaries;

[0043] S2: Determine the precision factor function f used in the optimization process DOPand signal strength function f E ;

[0044] S3: Set the initial parameters of the multi-objective optimization algorithm;

[0045] S4: The precision factor calculation equation and the signal field strength calculation equation are used as the fitness function of the multi-objective optimization algorithm for iterative circulation;

[0046] S5: Get the final Pareto front solution set, and select a layout plan from the Pareto solution set to arrange the navigation base stations according to the actual terrain environment, thus completing the base station layout.

[0047] The purpose of step S1 is to determine the range in which the base station can be deployed, and then limit the range of generation of the offspring population in the subsequent algorithm iteration optimization process; determining the boundary of the system coverage area is to determine the range for subsequent precision factor and signal field strength calculation. The specific process of this step is:

[0048] S11: Measure the latitude and longitude coordinates of the boundary points in the area where base station layout can be carried out Then we get the boundary point matrix of the layout area

[0049] S12: Set the boundary points of system function coverage Then we get the coverage area boundary point matrix

[0050] Step S2 is to determine the precision factor calculation method and signal field strength calculation method at different locations within the system coverage area. Since different positioning algorithms have different precision factor calculation methods, and electromagnetic waves of different frequencies have different field strength calculation functions during transmission, it is necessary to determine specific precision factor functions and signal field strength functions. The specific functions that can be used in this step include but are not limited to:

[0051] S21: The precision factor function based on the pseudo-range positioning algorithm is:

[0052] Among them, h 11 、h 22 、h 33 are the diagonal elements of the matrix H.

[0053] H=(G T G) -1

[0054]

[0055] Among them, (x i ,y i ,z i) is the coordinate of the i-th base station among the total number of n navigation base stations in the Earth-centered Earth-fixed coordinate system, and the obtained f DOP This is the precision factor at (x,y,z).

[0056] S22: The signal field strength calculation function of medium and long wave electromagnetic signals during transmission is:

[0057]

[0058] Where E R and E T is the magnitude of the electric field obtained using the Millington method.

[0059] The initial parameters in step S3 include: the number of optimization iterations T, the number of population individuals N, the number of optimization targets m, the number of base stations dim in each layout solution, and the number of sampling points sp in the coverage area.

[0060] Step S4 is to iterate the individuals in the population. In each iteration, the offspring generation method in the optimization algorithm is first used to generate the offspring population from the parent population. Then, the fitness value of each individual with respect to the two optimization objectives is calculated. Then, each individual is sorted based on the crowding degree. The historical optimal Pareto solution set of the population is updated according to the non-dominated sorting result. The above process is repeated until the number of iterations reaches T. The specific process of this step is as follows:

[0061] S41: The representation of the i-th individual in the population is Where k represents the number of base stations to be deployed this time. Then the entire population at the tth iteration can be expressed as Then, the offspring population is generated through the optimization algorithm

[0062] S42: After obtaining the offspring population, check whether each offspring individual is within the base station deployment area. If it is outside the boundary, a new individual is subsequently generated within the base station deployment area.

[0063] S43: uniformly select sp calculation locations u within the coverage area j =(x i ,y i ,z i ), 1≤j≤sp, the offspring population X t+1 and each calculation location is substituted with the precision factor function f DOP and signal strength function f E Get the fitness value of each individual and

[0064] S431: Calculating the precision factor adaptation value of the i-th individual When calculating, traverse and calculate each calculation location u j =(x i ,y i ,z i ) For each individual x i The precision factor Finally find the average

[0065] S432: Performing field strength adaptation of the i-th individual When calculating, traverse and calculate each calculation location u j =(x i ,y i ,z i ) For each individual x i Signal strength The signal strength at each calculation location Greater than the field strength threshold E limit ,but Minus one, The initial value is zero.

[0066] S44: The offspring individual X t+1 and the optimal Pareto solution set X p Perform non-dominated sorting to obtain Pareto frontiers of different levels, and update the optimal Pareto solution set according to the Pareto levels of different individuals.

[0067] S45: When the number of iterations reaches T, stop the iteration.

[0068] In step S5, since some of the station layout solutions in the most Pareto solution set may not be implemented due to actual terrain reasons, it is necessary to select a suitable layout solution according to the terrain environment to arrange the navigation system base stations, thereby completing the base station layout.

[0069] Example:

[0070] like Figure 1 The figure shows a flow chart of a base station layout method under multi-objective optimization based on precision factor and signal coverage proposed by the present invention, and the specific implementation case is as follows:

[0071] S1: Base station deployment area and system coverage area boundaries such as Figure 2 As shown, we can get:

[0072]

[0073] S2: Determine the precision factor function and signal strength function

[0074] S21: The precision factor function based on the pseudo-range positioning algorithm is:

[0075] Among them, h 11 、h 22 、h 33 are the diagonal elements of the matrix H.

[0076] H=(G T G) -1

[0077]

[0078] Among them, (x i ,y i ,z i ) is the coordinate of the i-th base station among the total number of n navigation base stations in the Earth-centered Earth-fixed coordinate system, and the obtained f DOP This is the precision factor at (x,y,z).

[0079] S22: The signal field strength calculation function of medium and long wave electromagnetic signals during transmission is:

[0080]

[0081] When the signal transmission path contains three different media, such as Figure 3 As shown, E R and E T The calculation method is as follows:

[0082] E R =E1(d1)-E2(d1)+E2(d1+d2)-E3(d1+d2)+E3(d1+d2+d3)

[0083] E T =E3(d3)-E2(d3)+E2(d3+d2)-E1(d3+d2)+E1(d3+d2+d1)

[0084] Where E1(d) represents the field strength value when the medium and long wave signal propagates dkm in a medium with conductivity σ1 and relative dielectric constant ε1. The signal propagation field strength curves for different conductivity and relative dielectric constant can be found on the official website of the International Telecommunication Union.

[0085] S3: Set the number of optimization iterations T = 500, the number of individuals in the population N = 50, the number of optimization targets m = 2, and the number of base stations dim = 4.

[0086] S4: start the iteration cycle;

[0087] S41: The i-th individual in the population is represented by Then the entire population at the tth iteration can be expressed as The resulting offspring population is

[0088] S42: Check whether each individual is within the base station deployment area. If it is outside the boundary, a new individual is subsequently generated within the base station deployment area.

[0089] S43: uniformly select sp calculation locations u within the coverage area j =(x i ,y i ,z i ), 1≤j≤sp, the offspring population X t+1 and each calculation location is substituted with the precision factor function f DOP and signal strength function f E Get the fitness value of each individual and

[0090] S431: Calculating the precision factor adaptation value of the i-th individual When calculating, traverse and calculate each calculation location u j =(x i ,y i ,z i ) For each individual x i The precision factor Finally, we can get the average:

[0091]

[0092] S432: Performing signal field strength adaptation value of the i-th individual When calculating, traverse and calculate each calculation location u j =(x i ,y i ,z i ) For each individual x i Signal strength The signal strength at each calculation location Greater than the field strength threshold E limit ,but Minus one, The initial value is zero.

[0093] S44: According to the precision factor adaptation value of each individual and signal strength adaptation value Population X t+1 and the optimal Pareto solution set X p Perform non-dominated sorting to obtain Pareto frontiers of different levels, and update the optimal Pareto solution set according to the Pareto levels of different individuals.

[0094] S441: For individual x iand individual x j The non-dominated sorting process between is as follows:

[0095] if and Then individual x i Dominant individual x j ;

[0096] if and Then individual x i By individual x j dominate; dominate;

[0097] Otherwise, individual x i and individual x j Complementary domination between them.

[0098] S442: If population X t+1 Individual x in i If it is not dominated by any other individual, then this individual is the most Pareto solution and can be included in the optimal Pareto solution set X p ; If the optimal Pareto solution set X p Individual x in j is dominated by at least one other individual, then individual x j From the optimal Pareto solution set X p Remove from.

[0099] S45: When the number of iterations reaches T = 500, stop the iteration and output the optimal Pareto solution set as follows Figure 4 As shown, at this time, from the optimal Pareto solution set X p By selecting the appropriate solution set, you can get the appropriate layout solution.

[0100] Select the individual x=(25.62, 118.01; 26.10, 119.12; 26.11, 117.69; 24.85, 117.72) in the Pareto solution set closest to the coordinate point (-1400, 5) in the figure to demonstrate the station layout effect.

Claims

1. A multi-objective optimization method for base station layout, characterized in that: The steps include: Step 1: Determine the boundaries of the base station deployment area and the system coverage area boundaries; Step 2: Determine the precision factor function f used in the optimization process DOP And the signal field strength function f E ; Step 3: Set the initial parameters of the multi-objective optimization algorithm; Step 4: The precision factor calculation equation and the signal field strength calculation equation are used as the fitness function of the multi-objective optimization algorithm for iterative circulation; Step 5: Get the final Pareto front solution set, and select a layout plan from the Pareto solution set to arrange the navigation base stations according to the actual terrain environment. This completes the base station layout.

2. A multi-objective optimization base station layout method according to claim 1, characterized in that: The step 1 is specifically as follows: Step 1-1: Measure the range area where base station layout can be carried out to determine the longitude and latitude coordinates of the boundary points Then we get the boundary point matrix of the layout area Step 1-2: Set the boundary points of system function coverage Then we get the coverage area boundary point matrix 3. A multi-objective optimization base station layout method according to claim 2, characterized in that: The step 2 is specifically as follows: Step 2-1: The precision factor function based on the pseudo-range positioning algorithm is: Among them, h 11 、h 22 、h 33 are the diagonal elements of the matrix H; Among them, (x i ,y i ,z i ) is the coordinate of the i-th base station among the total number of n navigation base stations in the Earth-centered Earth-fixed coordinate system, and the obtained f DOP That is the precision factor at (x, y, z); r i I represents the length of the observation vector of base station i at (x, y, z); x ,I y ,I z Respectively represent r i The value of the partial derivative of x, y, and z at (x, y, z); Step 2-2: The signal field strength calculation function of medium and long wave electromagnetic signals during transmission is: Where E R and E T They represent the field strength obtained by forward calculation and the field strength obtained by reverse calculation using the Millington method respectively.

4. A multi-objective optimization base station layout method according to claim 3, characterized in that: The initial parameters in step 3 include: the number of optimization iterations T, the number of population individuals N, the number of optimization targets m, the number of base stations dim in each layout solution, and the number of sampling points sp in the coverage area.

5. A multi-objective optimization base station layout method according to claim 4, characterized in that: The step 4 is specifically as follows: Step 4-1: The representation of the i-th individual in the population is Where k represents the number of base stations to be deployed this time; the entire population at the tth iteration is expressed as Then, the offspring population is generated through the optimization algorithm Step 4-2: After obtaining the offspring population, check whether each offspring individual is inside the base station deployment area. If it is outside the boundary, a new individual is subsequently generated inside the base station deployment area; Step 4-3: Evenly select sp calculation locations u within the coverage area j =(x i ,y i ,z i ), 1≤j≤sp, the offspring population X t+1 and each calculation location is substituted with the precision factor function f DOP And the signal field strength function f E Get the fitness value of each individual and Step 4-3-1: Calculate the precision factor adaptation value of the i-th individual When calculating, traverse and calculate each calculation location u j =(x i ,y i ,z i ) For each individual x i The precision factor Finally find the average Step 4-3-2: Calculate the field strength adaptation value of the i-th individual When calculating, traverse and calculate each calculation location u j =(x i ,y i ,z i ) For each individual x i Signal strength The signal strength at each calculation location Greater than the field strength threshold E limit ,but minus 1, The initial value is zero; Step 4-4: Set the offspring individual X t+1 and the optimal Pareto solution set X p Perform non-dominated sorting to obtain Pareto frontiers of different levels, and update the optimal Pareto solution set according to the Pareto levels of different individuals; Step 4-5: When the number of iterations reaches T, stop iterating.

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