An Adaptive Selection Method for Networked Localization Nodes under Multiple Constraints

Abstract

The present invention relates to a method for adaptively selecting networked positioning nodes under multiple constraints, belonging to the technical field of spectrum monitoring. Enter the site traversal loop. First, calculate the signal-to-noise ratio of the received signals at the selected sites respectively. After replacing the sites that do not meet the signal-to-noise ratio requirements, calculate the signal-to-noise ratio optimization factor. Then, calculate the straight-line distances between the three stations and the baseline optimization factor. Subsequently, calculate the included angles between the master station and the two slave stations, the included angles between the target and the two slave stations, and the configuration optimization factor. Finally, calculate the weighted sum of each optimization factor to obtain the optimization value of the current site combination. When the traversal condition is met, end the loop. Obtain the site selection scheme by positioning the globally maximum site combination optimization value, and complete the adaptive optimization of networked positioning nodes. The present invention improves the environmental adaptability of the system and the target positioning performance, and realizes the dynamic optimal allocation of resources in the application environment.

Description

Technical Field

[0001] The present invention relates to an adaptive selection method for networked positioning nodes based on multiple constraints in the field of array spectrum monitoring. Background Art

[0002] The positioning of electromagnetic targets is an extremely important branch in the field of spectrum monitoring, and the distributed cooperative positioning based on networked nodes is regarded as the most potential technical direction to break through the bottleneck of traditional positioning technology. However, when the positioning system is working, it is often under the constraints of various environmental backgrounds, resulting in the positioning accuracy not meeting the design requirements. Moreover, in the networked detection system, the factors affecting the positioning performance are more complex, making it difficult to fully exert the cooperative advantages of distributed nodes. Therefore, how to adaptively select positioning nodes according to the target characteristics and environmental characteristics, while improving the positioning ability of networked nodes and the adaptability and stability of the system, will become one of the effective ways to further release the working energy efficiency of distributed systems. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide an adaptive selection method for networked positioning nodes based on multiple constraints in the field of spectrum monitoring.

[0004] The technical problem to be solved by the present invention is realized by the following technical solutions:

[0005] An adaptive selection method for networked positioning nodes based on multiple constraints, comprising the following steps:

[0006] Step 1, arbitrarily select a point D in the target area as the target reference position;

[0007] Step 2, enter the i-th traversal of the site location, and select three stations from the available stations as the main station A i and the secondary stations B i and C i ; where the initial value of i is 1;

[0008] Step 3, calculate the signal-to-noise ratios S Ai 、S Bi and S Ci of the three stations, and determine whether the minimum value of the signal-to-noise ratio requirements for the main station and the secondary stations is satisfied. If the judgment condition is satisfied, proceed to the next step; otherwise, replace the stations that do not meet the signal-to-noise ratio requirements;

[0009] Step 4, calculate the straight-line distances and between the three stations, and use the cosine theorem to calculate the included angle Φ Ai =∠B i A i C iAnd the included angle Φ between the target and the two slave stations D = ∠B i DC i ;

[0010] Step 5, calculate the signal-to-noise ratio optimization factor S i , the baseline optimization factor L i and the configuration optimization factor T i ;

[0011] S i = S Ai + S Bi + S Ci , T i = (Φ Ai + Φ D ) / 180

[0012] Step 6, construct the site optimization function J, and calculate the optimization value J of the current iteration's site combination i :

[0013] J i = λ1·S i + λ2·L i + λ3·T i

[0014] Wherein, λ1, λ2, and λ3 are the weighting coefficients of each optimization factor and are constant real numbers;

[0015] Step 7, determine whether the condition for ending the traversal is satisfied. If the condition is not satisfied, return to Step 2 for the next iteration. If the condition is satisfied, search for the maximum optimization value J opt = J max , and obtain the optimal combination of sites under the constraint conditions.

[0016] Furthermore, the judgment condition in Step 3 is:

[0017] (S Ai ≥ SNR1) && (S Bi ≥ SNR2) && (S Ci ≥ SNR2)

[0018] Wherein, SNR1 and SNR2 are respectively the minimum values required for the received signal-to-noise ratios of the master station and the slave station, and SNR1 ≥ SNR2.

[0019] Furthermore, the weighting coefficients λ1, λ2, and λ3 in Step 6 are set according to the environmental conditions and the target characteristics respectively.

[0020] The present invention has the following advantages compared with the prior art:

[0021] 1. Introduce multiple constraints to improve the adaptability and stability of the positioning algorithm to different environmental conditions and target characteristics;

[0022] 2. For networked receiving nodes, be able to adaptively select the optimal positioning site configuration;

[0023] 3. Compared with the traditional method for site selection optimization based on the GDOP cost function, the site selection optimization function selected in the present invention requires less computational effort. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is the overall flowchart of the present invention.

[0025] Figure 2 is the site selection optimization result obtained in an embodiment of the present invention when the target position changes.

[0026] Figure 3 is the site selection optimization result obtained in another embodiment of the present invention when the target position changes. DETAILED DESCRIPTION OF THE INVENTION

[0027] Refer to Figure 1 , enter the site traversal loop, first calculate the signal-to-noise ratio of the received signals at the selected sites respectively, calculate the signal-to-noise ratio optimization factor after replacing the sites that do not meet the signal-to-noise ratio requirements, then calculate the straight-line distance between the three sites and the baseline optimization factor, then calculate the included angle between the master station and the two slave stations and the included angle between the target and the two slave stations and the configuration optimization factor, and finally calculate the weighted sum of each optimization factor to obtain the optimization value of the current site combination. When the traversal condition is met, end the loop, and obtain the site selection scheme by positioning the global maximum site combination optimization value, thus completing the adaptive optimization of the networked positioning nodes.

[0028] An adaptive selection method for networked positioning nodes based on multiple constraint conditions, comprising the following steps:

[0029] Step 1: Arbitrarily select a point D(x D , y D ) in the target area as the target reference position, where x D and y D are the longitude and latitude of the target reference position respectively;

[0030] Step 2: Enter the i-th traversal of the sites, and select three sites from the available sites as the master station A i (x Ai , y Ai ) and the slave stations B i (x Bi , y Bi ) and C i (x Ci , y Ci ) respectively;

[0031] Step 3: Calculate the signal-to-noise ratios S Ai , S Bi and S Ci , and judge the following conditions:

[0032] (S Ai ≥ SNR1) && (S Bi ≥ SNR2) && (S Ci ≥ SNR2)

[0033] In the above formula, SNR1 and SNR2 are respectively the minimum values required for the received signal-to-noise ratios of the master station and the slave stations, SNR1 ≥ SNR2. Only when the above conditions are met, proceed with the subsequent step processing; otherwise, replace the stations that do not meet the signal-to-noise ratio requirements.

[0034] Step 4: Calculate the straight-line distances between the three stations and and use the cosine theorem to calculate the included angle Φ between the master station and the line connecting the two slave stations Ai = ∠B i A i C i and the included angle Φ between the target and the line connecting the two slave stations D = ∠B i DC i ;

[0035]

[0036]

[0037]

[0038]

[0039]

[0040] Step 5: Calculate the signal-to-noise ratio optimization factor S i , the baseline optimization factor L i and the configuration optimization factor T i ;

[0041] S i = S Ai + S Bi + S Ci , T i = (Φ Ai + Φ D ) / 180

[0042] Step 6: Construct the site optimization function J and calculate the optimization value J of the current iteration's site combination i :

[0043] J i = λ1·S i + λ2·L i + λ3·T i

[0044] In the above formula, λ1, λ2, and λ3 are the weighting coefficients of each preferred factor and are constant real numbers;

[0045] Step 7: Determine whether the condition for ending the traversal is satisfied. If the condition is not satisfied, return to Step 2 for the next iteration. If the condition is satisfied, determine the optimal combination of station locations under this constraint by searching for the maximum preferred value J opt = J max , and determine the optimal combination of station locations under this constraint:

[0046] A opt (x Aopt , y Aopt ), B opt (x Bopt , y Bopt ), C opt (x Copt , y Copt ).

[0047] Simulation verification: Figure 2 、 Figure 3 The simulation conditions of and are as follows: The number of networked receiving stations is 20. Keep the coordinates of the receiving stations fixed. When the longitude and latitude of the target positions are (114.53°, 38.78°) and (114.55°, 38.82°) respectively, the station location preference results are as shown in Figure 2 、 Figure 3 respectively. Among them, the pentagon (☆) is marked as the target, the rhombus (◇) is marked as the main station after algorithm preference, the triangle (△) is marked as the two secondary stations after algorithm preference, and the circle (○) is marked as all positioning alternative nodes (including the one main station and two secondary stations selected by preference).

[0048] The following conclusions can be obtained from the simulation results: The present invention can complete the adaptive selection of optimal station locations of networked nodes under multiple constraints when the environment and target state change. Therefore, the algorithm adaptability and algorithm stability of the networked positioning technology can be improved.

Claims

1. A method for adaptively selecting networked positioning nodes under multiple constraints, characterized in that, It includes the following steps: Step 1, arbitrarily select a point D within the target area as the target reference position; Step 2: Enter the i-th traversal of the site address. Select three stations from the available stations as the main station A i and the secondary stations B i and C i ; where the initial value of i is 1; Step 3, calculate the signal-to-noise ratios S Ai , S Bi and S Ci , and determine whether the minimum values required for the signal-to-noise ratios received by the master station and the slave station are satisfied. If the judgment condition is satisfied, proceed to the next step; otherwise, replace the stations that do not meet the signal-to-noise ratio requirements. Step 4, calculate the straight-line distances between the three stations and and use the cosine theorem to calculate the included angle Φ between the main station and the connection line of the two slave stations Ai = ∠B i A i C i and the included angle Φ between the target and the connection line of the two slave stations D = ∠B i DC i ; Step 5, calculate the signal-to-noise ratio optimization factor S i , baseline optimization factor L i and configuration optimization factor T i ; S i = S Ai + S Bi + S Ci , T i = (Φ Ai + Φ D ) / 180 Step 6: Construct the site selection optimization function J and calculate the optimization value J of the site combination at the current iteration i : J i = λ1·S i + λ2·L i + λ3·T i where λ1, λ2, and λ3 are the weighting coefficients of each preferred factor and are constant real numbers; Step 7: Determine whether the condition for ending the traversal is met. If the condition is not met, return to Step 2 for the next iteration. If the condition is met, search for the maximum optimal value J opt = J max , and obtain the optimal combination of site addresses under the constraint conditions.

2. The method for adaptively selecting networked positioning nodes under multiple constraints according to claim 1, characterized in that, The judgment condition in Step 3 is: (S Ai ≥ SNR1) && (S Bi ≥ SNR2) && (S Ci ≥ SNR2) where SNR1 and SNR2 are respectively the minimum values of the received signal-to-noise ratio requirements of the master station and the slave station, and SNR1 ≥ SNR2.

3. The method for adaptively selecting networked positioning nodes under multiple constraints according to claim 1, characterized in that, The weighting coefficients λ1, λ2, and λ3 in Step 6 are set respectively according to the environmental conditions and target characteristics.

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