Dynamic combination site optimization method and device for air traffic control multi-point positioning system

Abstract

The invention discloses a dynamic combination site optimization method and device for an air traffic control multi-point positioning system, and the method comprises the steps: building a site dynamic weight evaluation model, so as to determine a site weight; establishing a geometric accuracy factor lookup table for dynamically extracting geometric accuracy factors of the site combination; and determining an optimal site combination by adopting a self-adaptive combination optimization algorithm. According to the method, through a dynamic site optimization mechanism, site signal quality, geometric distribution and redundancy are evaluated in real time, site combinations participating in positioning are adjusted in a self-adaptive mode, and robustness in a complex scene is improved; according to the method, a multi-objective optimization model based on dynamic weight is adopted, and parameters such as a signal time difference of arrival error, a geometric accuracy factor and computing resource consumption are combined, so that rapid screening of an optimal site combination is realized, the computing load is effectively reduced, and the positioning reliability is guaranteed.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of air traffic control multi-point positioning, and particularly relates to a dynamic combination station optimization method and device for an air traffic control multi-point positioning system. BACKGROUND

[0002] With the rapid growth of global air transportation, the traditional air traffic management (ATM, referred to as air traffic control) system is facing great challenges. The limitations of traditional radar monitoring technology in coverage, positioning accuracy and operating cost are increasingly prominent. Especially in complex terrain areas, low-altitude flights and airport surface monitoring scenarios, radars are easily affected by line-of-sight blockage, multipath interference and other factors, resulting in monitoring blind areas. In addition, secondary surveillance radar (SSR) relies on the airborne transponder signal, which has a low update rate (usually 4-12 seconds) and is easily affected by signal conflicts, making it difficult to meet the safety needs of high-density airspace. These factors have promoted the development of new wide-area collaborative monitoring technologies, and multi-point positioning (MLAT) technology has emerged as the times require. MLAT is a collaborative monitoring technology based on time difference positioning (TDOA), which measures the time difference of aircraft transmitted signals arriving at each station through a network of distributed ground receiving stations, and calculates the target position by combining geometric algorithms. Its technical framework includes four core modules: Signal receiving network: composed of more than 4 high-precision synchronous receiving stations, with a deployment interval of usually 5-30 kilometers. Modern systems use software-defined radio technology to support 1090MHz and other frequency band signal reception, with a receiving sensitivity of up to -95dBm.

[0003] Time synchronization system: GPS / Beidou time service module and optical fiber transmission technology are used to achieve nanosecond-level (<50ns) time synchronization between stations, ensuring that the TDOA measurement error is controlled within 3 meters.

[0004] Central processing unit: through the extended Kalman filter (EKF) algorithm to fuse multi-station data, combined with the earth ellipsoid model (WGS84) for three-dimensional positioning solution. The typical system processing delay is less than 500ms, the horizontal positioning accuracy is 30 meters (95% confidence interval), and the vertical accuracy is better than 50 meters.

[0005] Station layout and station combination: before the system works, the stations of the system must be deployed, and the geometric layout of each station must meet certain requirements, so that the positioning accuracy of the system can be optimal, and when there are multiple receiving stations, dynamic station selection is needed to complete the optimal station layout of the dynamic target at the current time.

[0006] The station layout and dynamic combination technology is the core enabling element of the MLAT system, which directly affects the monitoring performance, reliability and economy. The future development trend will focus on intelligent dynamic optimization, heterogeneous network integration and deep integration of quantum precision measurement technology, providing key technical support for realizing global continuous seamless monitoring. Among them, the station layout has been completed before the system is built and cannot be changed during the system operation, so the dynamic combination of stations is particularly important when crossing different regions.

[0007] Currently, there are many station layout related technologies for air traffic control multi-point positioning systems, which describe the system station layout problem from various angles and methods. However, few people study the station selection problem for cross-region targets, and the existing station combination and selection strategies have the following shortcomings: (1) The existing MLAT system mostly uses a preset fixed station combination, which cannot dynamically respond to changes in the signal environment (such as weather interference, terrain obstruction, electromagnetic noise, etc.), resulting in a decrease in positioning accuracy; and the fixed number of stations can easily cause waste of computing resources (such as low-density airspace scenarios); and the influence factor of the geometric distribution of stations on positioning accuracy is not considered to change dynamically; (2) The existing method is difficult to balance the positioning accuracy, computational complexity and system redundancy demand when selecting stations; (3) When the coverage area of the MLAT system expands (such as airport group cooperative monitoring), the number of stations increases, leading to an explosive growth in combination, and the traditional exhaustive method cannot meet the real-time requirements; (4) After the existing MLAT system responds to station failure or temporary failure, it is easy to cause positioning interruption. SUMMARY

[0008] In order to solve the problems existing in the prior art, the present application proposes a dynamic combination station optimization method and device for an air traffic control multi-point positioning system. Through a dynamic station optimization mechanism, the station signal quality, geometric distribution and redundancy are evaluated in real time, and the station combination participating in positioning is adaptively adjusted to improve the robustness in complex scenarios.

[0009] The present application is implemented by the following technical solutions: A dynamic combination station optimization method for an air traffic control multi-point positioning system, comprising: establishing a station dynamic weight evaluation model to determine the station weight; establishing a geometric dilution of precision lookup table for dynamic extraction of the geometric dilution of precision of the station combination; using an adaptive combination optimization algorithm to determine the optimal station combination.

[0010] In some embodiments, the adaptive combination optimization algorithm for determining the optimal station combination includes: Dynamically extracting the geometric dilution of precision value of the basic site combination from the geometric dilution of precision lookup table; Dynamic programming generates initial candidate combinations; Generate an initial population with the initial candidate combination and use genetic algorithm to perform global optimization search; Based on the global optimization search results, the Pareto optimal solution of positioning accuracy and computational efficiency is achieved, that is, the optimal site combination is obtained.

[0011] In some embodiments, the dynamic programming to quickly generate initial candidate combinations includes: According to the state transition equation and the constraints, the first multiple candidate combinations are quickly generated; the state transition equation is: ; in, GDOP [ k ][ m ] indicates the front m Select from the base stations k The optimal GDOP value when there are 1 base station; is the GDOP improvement after adding the mth base station; GDOP [ k ][ m- 1] indicates the front m- Select one base station k The optimal GDOP value when there are 1 base station; GDOP [ k- 1][ m- 1] indicates the front m- Select one base station k- Optimal GDOP value when there is 1 base station; The constraints are: Effective combination ; in, β is the empirical coefficient; is the target height; For base stations i and base stations j The maximum distance.

[0012] In some embodiments, the global optimization search using a genetic algorithm includes: Chromosome encoding is used to encode base stations in multiple candidate combinations; Obtaining fitness functions corresponding to multiple candidate combinations, where the fitness function comprehensively considers geometric dilution of precision, computational overhead, and site weights; The genetic algorithm is used to obtain the optimal combination, including the optimal combination of multiple base stations, through multiple iterations of crossover and mutation.

[0013] In some embodiments, the Pareto optimal solution of positioning accuracy and computational efficiency comprises: a multi-objective optimization model is established by considering the time-consuming and geometric accuracy factor of calculation; the objective function of the multi-objective optimization model is that the geometric accuracy factor of the base station combination is minimum, and the time-consuming of base station combination calculation is minimum; the constraint condition is that the number of base station combination is greater than or equal to 4, and the geometric accuracy factor of base station combination is less than or equal to 3.0; The utility function is determined by the ratio of the geometric accuracy factor of the base station combination to the time-consuming of the base station combination calculation; The station combination with the maximum utility function is the optimal station combination.

[0014] In some embodiments, the station dynamic weight evaluation model is established, comprising: The weight of real-time signal signal-to-noise ratio is quantitatively evaluated; The weight of station historical availability is quantitatively evaluated; The station weight is comprehensively evaluated according to the weight of signal signal-to-noise ratio and the weight of availability.

[0015] In some embodiments, the weight of signal signal-to-noise ratio adopts a segmented weighting mechanism, which is expressed as: If the signal-to-noise ratio SNR is greater than or equal to 15 dB and less than 30 dB, the weight of signal signal-to-noise ratio is: 0.4×(SNR-15) / 15; If the signal-to-noise ratio SNR is greater than or equal to 30 dB and less than 50 dB, the weight of signal signal-to-noise ratio is: 0.4+0.2×(SNR-30) / 20; If the signal-to-noise ratio SNR is greater than or equal to 50 dB, the weight of signal signal-to-noise ratio is 0.6; And / or, the weight of availability is: 0.3 × (1 - failure times / total sampling times) + 0.7 × average signal quality; wherein the average signal quality refers to the arithmetic mean of the signal quality values of all sampling points in the sliding window.

[0016] In some embodiments, the geometric accuracy factor lookup table is established, comprising: Plane cutting: discretize the possible deployment position of each station into a preset size grid; Target height layering: divided into several layers according to the flight height layer; Typical combination pre-storage: store the geometric accuracy factor values of 4-8 station combinations; Geometric accuracy factor precalculation; Parallel calculation: use GPU cluster to precalculate all grid point combinations to generate a geometric accuracy factor matrix; Data compression storage: quantize the geometric accuracy factor value, convert the quantized floating point number to integer storage; Dictionary encoding: establish a hash mapping for repeated quantized values for compressed storage.

[0017] In some embodiments, the station combination geometric accuracy factor dynamic extraction includes: Input the current station combination; Coordinate grid mapping is performed on the current station combination; Generate combination encoding for the current station combination; High layer matching is performed on the current station combination; According to the combination encoding and the matched height layer, the geometric accuracy factor lookup table is matched, if there is a corresponding geometric accuracy factor value, the pre-stored geometric accuracy factor value is returned, otherwise the geometric accuracy factor value is calculated in real time, and the geometric accuracy factor lookup table is updated.

[0018] In another aspect, the application also provides a dynamic combination station optimization device for an air traffic control multi-point positioning system, comprising: The weight evaluation unit is used to establish a station dynamic weight evaluation model to determine the station weight; The dynamic extraction unit is used to establish a geometric accuracy factor lookup table for station combination geometric accuracy factor dynamic extraction; And, the adaptive optimization unit determines the optimal station combination by using an adaptive combination optimization algorithm.

[0019] The application provides a dynamic combination station optimization method for an air traffic control multi-point positioning system. The method uses a dynamic station optimization mechanism to real-time evaluate station signal quality, geometric distribution and redundancy, and adaptively adjusts the station combination participating in positioning to improve the robustness in complex scenarios. The method also uses a multi-objective optimization model based on dynamic weights, combines parameters such as signal arrival time difference error, geometric accuracy factor and computing resource consumption, and realizes fast screening of the optimal station combination to effectively reduce the computing load and guarantee the positioning reliability. In addition, the method also uses a hierarchical screening algorithm, which reduces the computing responsibility by pre-storing GDOP values, using parallel computing technology and a multi-level progressive strategy of adaptive combination algorithm, and realizes millisecond-level dynamic decision-making.

[0020] Correspondingly, the dynamic combination station optimization device for an air traffic control multi-point positioning system provided by the application also has the same technical effects as described above. BRIEF DESCRIPTION OF DRAWINGS

[0021] The drawings described herein are used to provide further understanding of the embodiments of the application, and form a part of the application, and do not constitute a limitation of the embodiments of the application. In the drawings: Figure 1Preferred method flowchart for embodiments of the present application Figure 2 GDOP lookup table workflow chart Figure 3 Global optimization algorithm flowchart using genetic algorithm Figure 4 Preferred device principle block diagram for embodiments of the present application Figure 5 Preferred system architecture schematic for embodiments of the present application Figure 6 Electronic device schematic for embodiments of the present application Figure 7 Computer readable storage medium schematic for embodiments of the present application Reference signs and corresponding component names 200 - preferred device, 201 - weight evaluation unit, 202 - dynamic extraction unit, 203 - adaptive optimization unit, 300 - preferred system, 301 - input device, 302 - output device, 303 - processor A, 304 - memory A, 400 - electronic device, 410 - memory B, 420 - processor B, 411 - computer program A, 500 - computer readable storage medium, 511 - computer program B. DETAILED DESCRIPTION

[0022] Hereinafter, the term "include" or "may include" used in various embodiments of the present application indicates the existence of the invented function, operation, or element, and does not limit one or more functions, operations, or elements to be added. Also, as used in various embodiments of the present application, the term "include", "have", and their conjugates merely indicate the presence of a certain feature, number, step, operation, element, component, or combination of the foregoing, and should not be construed as excluding the possibility of adding one or more features, numbers, steps, operations, elements, components, or combinations of the foregoing in advance.

[0023] In various embodiments of the present application, the expression "or" or "at least one of A or / and B" includes any combination of the listed terms or all combinations thereof. For example, the expression "A or B" or "at least one of A or / and B" can include A, can include B, or can include both A and B.

[0024] The expressions used in the various embodiments of the present application, such as "first", "second", etc., can modify various constituent elements in the various embodiments, but can not limit the corresponding constituent elements. For example, the above expressions do not limit the order and / or importance of the elements. The above expressions are only for the purpose of distinguishing one element from other elements. For example, the first user device and the second user device indicate different user devices, although both are user devices. For example, without departing from the scope of the various embodiments of the present application, a first element can be referred to as a second element, and likewise, a second element can be referred to as a first element.

[0025] It should be noted that if a description connects one constituent element to another constituent element, the first constituent element can be directly connected to the second constituent element, and a third constituent element can be "connected" between the first constituent element and the second constituent element. Conversely, when one constituent element is "directly connected" to another constituent element, it can be understood that there is no third constituent element between the first constituent element and the second constituent element.

[0026] The terms used in the various embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the various embodiments of the present application. As used herein, the singular form is intended to include the plural form, unless the context clearly indicates otherwise. Unless otherwise defined, all terms used herein, including technical terms and scientific terms, have the same meaning as commonly understood by those of ordinary skill in the art to which the various embodiments of the present application belong. The terms such as those defined in a generally used dictionary will be interpreted as having the same meaning as the contextual meaning in the relevant technical field and will not be interpreted as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of the present application.

[0027] To make the purposes, technical solutions and advantages of the present application clearer, further detailed description of the present application is made below in combination with embodiments and drawings, the illustrative embodiments of the present application and the description thereof are only for the purpose of explaining the present application and do not limit the present application.

[0028] Embodiment 1 The existing fixed site combination method cannot adapt to the signal quality fluctuation caused by the complex electromagnetic environment, and the number of fixed sites is easy to cause waste of computing resources, and the influence factor of site geometric distribution on positioning accuracy is not considered. Dynamic change, etc., resulting in decline in positioning accuracy, poor robustness and other problems. In view of this, the present embodiment proposes a dynamic combination site optimization method for air traffic control multi-point positioning system.

[0029] As shown in Figure 1 The method proposed in the present embodiment includes the following steps: Step 100, establish a dynamic site weight evaluation model to determine the site weight.

[0030] For each remote site, first establish a weight for its real-time dynamic availability, and then perform subsequent combination calculation based on the site weight. The site with higher weight is given priority to enter the dynamic combination preferred list. The embodiment adopts a multi-dimensional and adaptive site comprehensive evaluation system, breaking through the traditional single signal strength evaluation mode. The specific process is as follows: Step 111, quantitatively evaluate the weight of real-time signal SNR.

[0031] The core of TDOA positioning is to estimate the target position by measuring the time difference of signal arrival at different base stations. The accuracy of time difference directly depends on the accurate measurement (i.e. time stamping) of signal arrival time (TOA) at each base station. Time stamping error will directly pass to TDOA calculation, resulting in positioning error. Signals with high SNR are clear and have little noise interference, and the signal waveform (such as pulse front or correlation peak) is easy to accurately detect; low SNR signals, noise will mask the real waveform of the signal, causing the detected signal front or peak position to shift, for example, noise may cause the signal amplitude to fluctuate, causing threshold detection to trigger too early or too late, or the correlation peak becomes flat or not obvious after being disturbed by noise, causing the peak positioning to be ambiguous.

[0032] The theoretical lower limit of time stamping error is inversely proportional to SNR. The lower the SNR, the greater the mean square error of TOA estimation, which can be mathematically expressed as: where B is the signal bandwidth, is the mean square error of TOA estimation, so it can be seen that low SNR or narrow bandwidth signal will significantly increase the time stamping error.

[0033] Therefore, the embodiment needs to evaluate the SNR of each signal received by each site in real time as one of the considerations for site weight. For the target SNR weight value, a segmented weighting mechanism is introduced according to the SNR value:

[0034] The higher the SNR, the greater the SNR weight, and vice versa. At the same time, a dynamic noise baseline calibration is used: update the environmental noise baseline (P_noise_baseline) every preset time interval (for example, 5 minutes).

[0035] Step 112, quantitatively evaluate the weight of site historical availability.

[0036] The embodiment uses a sliding window (for example, window size = 1 hour) to statistically evaluate the weight of site historical availability, and the statistical method is as follows: T ar 权重= 0.3 x (1 - failure times / total sampling times) + 0.7 x average signal quality The average signal quality refers to the arithmetic mean of the signal quality values of all sampling points in the sliding window. The index values of the sampling points can be normalized to a value between 0 and 1.

[0037] Using the above two indicators (SNR weight and TAR weight), the station weight is comprehensively evaluated. The signal-to-noise ratio reflects the distance between the target and the station to some extent. The weight of the station close to the target should be higher.

[0038] Step 120, establish a geometric dilution of precision (GDOP) lookup table for dynamic extraction of station combination geometric dilution of precision.

[0039] This embodiment uses the lookup table method to complete the dynamic extraction of geometric dilution of precision (GDOP). Taking the wide-area multi-point positioning system as an example, the construction strategy and detailed steps of the lookup table are as follows: Step 121, plane cutting: discretize the possible deployment positions of each station into 100m x 100m grids (covering a range of 50km around the airport), and the grid coordinates are denoted as (xi, yi) ∈ Z2. For the field multi-point, the airport is divided into 1m x 1m grids, covering the airport runway, taxiway, and apron area.

[0040] Step 122, target height layering: divide by flight height layer (0~10km, every 500m as a layer), a total of 20 layers, denoted as hj ∈ {0, 500,..., 9500}; Step 123, prestore typical combination: store the GDOP values of 4~8 station combinations (covering 95% of common scenarios); Step 124, GDOP precalculation: the calculation formula is wherein represents the positioning error of the system in the direction; Step 125, parallel calculation: use the GPU cluster to precalculate all grid point combinations using the GDOP precalculation formula in step 124 to generate the GDOP matrix GDOP_Table[S][H], where: S is the station combination code (such as 16-bit binary for 4 stations), and H is the target height layer number; Step 126, data compression storage: quantize the GDOP values using a lossy compression method with a quantization precision of 0.1. The detailed process is as follows: Quantization, convert the GDOP value to decibel unit (dB) and round to the first digit after the decimal point: .

[0041] For example: the original , , rounding to 5.4, the quantized value Q = 5.4; storage optimization, converting the quantized float number (e.g. 5.4) to an integer storage (e.g. 54), saving bytes, each quantized value only needs 2 bytes (16 bits), instead of 4 bytes of the original float number; reverse calculation at decompression: , the error is about 0.02.

[0042] Step 127, dictionary encoding: Hash mapping is established for repeated GDOP values, and compressed storage is performed, and the algorithm process is as follows: Dictionary construction: statistics of all unique quantized values, and each value is assigned a unique short code. For example, quantized values [0, 30, 48, 70, 100]→dictionary {0:0x00, 30:0x01, 48:0x02, 70:0x03, 100:0x04}.

[0043] Compressed data: replace the original quantized value with a short code, and the compression rate depends on the repetition frequency of the quantized value.

[0044] The dynamic extraction process of the geometric dilution of precision (GDOP) is as shown in Figure 2 : Input the current site combination; Coordinate gridding mapping: project the actual coordinates to the nearest grid point; Combination encoding generation: use bit mask to represent the participating stations; Height layer matching: find the height layer where it is located; According to the site combination encoding and the matched height layer, match from the GDOP value database, if there is a corresponding GDOP value, return the pre-stored GDOP value, otherwise calculate the GDOP value in real time according to the above formula, and update the lookup table, and asynchronously write the new GDOP value to the GDOP value database.

[0045] Step 130, determine the optimal site combination by using the adaptive combination optimization algorithm.

[0046] This embodiment adopts a hybrid adaptive combination optimization algorithm (DP-GA) of dynamic programming-genetic algorithm, which combines the advantages of dynamic solidification and genetic algorithm, and solves the contradiction between efficiency and accuracy of traditional methods. The specific process is as follows: Step 131, find the GDOP value of the basic site combination (such as the basic site combination of five base stations is star-shaped station arrangement structure or inverted triangular station arrangement structure) from the lookup table.

[0047] According to Figure 2The flowchart shows that the GDOP value of the base station participating in the calculation is found in the stored GDOP table at the current target location. At this time, GDOP is a fixed value.

[0048] Step 132, dynamically planning to quickly generate an initial candidate combination.

[0049] Let GDOP[k][m] represent the optimal GDOP value when k base stations are selected from the first m base stations. It corresponds to the state transition equation:

[0050] wherein, is the GDOP improvement after adding the mth base station, which can be directly obtained using a lookup table. The screening of the newly added base station needs to satisfy the baseline length constraint combination (avoiding collinear base stations): effective combination wherein, β = 0.5 is an empirical coefficient, is the target height, is the maximum distance of two base stations (base station i and base station j), wherein Pi is the geometric position of station i, and Pj is the geometric position of station j.

[0051] According to the size of the GDOP value, the Top 50~Top 80 station candidate combinations are quickly generated in order from small to large, which takes <20 ms.

[0052] Step 133, global optimization search using genetic algorithm.

[0053] The embodiment uses genetic algorithm for global optimization search, and the specific process is as follows: First, use chromosome coding to encode the base station. The method is binary coding, and the gene length = the total number of base stations, 1 represents selecting the base station, and 0 represents not selecting the base station: Chromosome = [0, 1, 1, 0, 1, 0, …], which means that base stations 1, 2, and 4 are enabled.

[0054] Then get the fitness function, which comprehensively considers GDOP, calculation overhead, and station weight, and the fitness function is:

[0055] GDOP is the GDOP value of the current station combination; is the proportion factor when the genetic algorithm crossover operation is performed, which takes a value between 0 and 1, and the embodiment is preferably 0.7; is the calculation overhead; is the penalty function, which mainly considers the station weight problem, and the station weight obtained in step 110 is introduced to obtain:

[0056] wherein, M denotes the number of sites participating in the calculation; denotes the signal-to-noise ratio weight of the nth site; denotes the nth site availability statistics weight.

[0057] The above is the process of obtaining a complete population fitness function of a genetic algorithm, and the first 50 combinations obtained by step 132 are 50 populations , and the corresponding fitness function is Then, the optimal combination is obtained by using the genetic algorithm through multiple iterations such as crossover and mutation, and the optimal 5-10 combinations are retained. The global optimization search process of the genetic algorithm is as shown in Figure 3 . Since the genetic algorithm is a mature algorithm, it will not be described here.

[0058] Step 134, realize the Pareto optimal solution of positioning accuracy and calculation efficiency.

[0059] The objective function of the multi-objective optimization model is:

[0060] wherein, is the base station combination obtained by the genetic algorithm, is the time-consuming of single base station calculation, and the constraint condition is set as: .

[0061] Define the utility function, select the final solution, and the final site combination can be obtained, and the utility function is as follows:

[0062] wherein, is the GDOP value of the current site combination, is the time used for calculating GDOP of the current combination. When the utility function is the maximum, the final site combination is obtained.

[0063] The method proposed in the embodiment improves the robustness in complex scenarios by dynamically selecting the optimal combination of stations through a dynamic station selection mechanism, real-time evaluation of station signal quality and geometric distribution, and adaptive adjustment of the combination of stations involved in positioning. The method also proposes a multi-objective optimization model based on dynamic weights, which combines the time difference of arrival (TDOA) error, station geometric dilution of precision (GDOP), and computational resource consumption to quickly filter the optimal station combination, effectively reducing the computational load and ensuring positioning reliability. The method also uses a hierarchical filtering algorithm, which reduces the computational burden and achieves millisecond-level dynamic decision-making through pre-stored GDOP values, parallel computing techniques, and a multi-level progressive strategy of adaptive combination algorithms. In addition, the method introduces a dynamic redundancy threshold control mechanism, which allows the use of backup station combinations in case of abnormality in the optimal station combination.

[0064] In another embodiment, the embodiment also proposes a dynamic combination station selection device for an air traffic control multi-point positioning system, as shown in Figure 4 The selection device 200 includes: A weight evaluation unit 201 is configured to establish a station dynamic weight evaluation model to determine the station weight. The specific establishment process of the station dynamic weight evaluation model is described in step 110 above, and will not be repeated here.

[0065] A dynamic extraction unit 202 is configured to establish a geometric dilution of precision (GDOP) lookup table for dynamic extraction of station combination geometric dilution of precision. The specific establishment process of the GDOP lookup table is described in step 120 above, and will not be repeated here.

[0066] In addition, an adaptive selection unit 203 is configured to determine the optimal station combination using an adaptive combination optimization algorithm. The specific selection process is described in step 130 above, and will not be repeated here.

[0067] In another embodiment, the embodiment also proposes a dynamic combination station selection system for an air traffic control multi-point positioning system, as shown in Figure 5 The selection system 300 proposed in the embodiment includes: An input device 301, an output device 302, a processor A 303, and a memory A 304; wherein the number of the processor A 303 and the memory A 304 can be one or more, Figure 5 The input device 301, the output device 302, the processor A 303, and the memory A 304 can be connected through a bus or other means, Figure 5The bus connection is taken as an example.

[0068] By calling the operation instructions stored in the memory A304, the processor A303 is configured to perform the following steps: Establish a site dynamic weight evaluation model to determine the site weight; Establish a geometric dilution of precision (GDOP) lookup table for dynamic extraction of site combination geometric dilution of precision; Adaptive combination optimization algorithm is used to determine the optimal site combination.

[0069] Optionally, by calling the operation instructions stored in the memory A304, the processor A303 is also used to execute any implementation method in the corresponding embodiment of the above preferred method.

[0070] In another embodiment, this embodiment also provides an electronic device 400, such as Figure 6 As shown, the electronic device 400 includes: a memory B410, a processor B420, and a computer program A411 stored in the memory B410 and executable on the processor B420. When the processor B420 executes the computer program A411, the following steps are implemented: Establish a site dynamic weight evaluation model to determine the site weight; Establish a geometric dilution of precision (GDOP) lookup table for dynamic extraction of site combination geometric dilution of precision; Adaptive combination optimization algorithm is used to determine the optimal site combination.

[0071] Optionally, when the processor B420 executes the computer program A411, any implementation method corresponding to the embodiments of the above preferred method can be implemented.

[0072] It should be noted that the electronic device proposed in this embodiment is a device used to implement the above-mentioned preferred method. Therefore, based on the above-mentioned preferred method proposed in this embodiment, technical personnel in this field can understand the specific implementation method of the electronic device of this embodiment and its various variations. Therefore, how the electronic device specifically implements the above-mentioned preferred method will not be introduced in detail here. As long as the electronic device used by technical personnel in this field to implement the above-mentioned preferred method falls within the scope of protection to be protected by this application.

[0073] In another embodiment, this embodiment further provides a computer-readable storage medium 500, such as Figure 7 As shown, the computer readable storage medium 500 stores a computer program B511. When the computer program B511 is executed by the processor, the following steps are implemented: Establish a site dynamic weight evaluation model to determine the site weight; A geometric dilution of precision (GDOP) lookup table is established for dynamic extraction of site combination geometric dilution of precision factors; An adaptive combination optimization algorithm is used to determine the optimal site combination.

[0074] Optionally, the computer program B511, when executed by the processor, can implement any of the embodiments corresponding to the above-mentioned preferred method.

[0075] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0076] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) containing computer-usable program code.

[0077] The present application is described with reference to flowcharts and / or block diagrams according to the methods, devices (systems), and computer program products of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 The functions specified in one or more flows and / or blocks.

[0078] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction devices that implement the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks. Figure 1 The functions specified in one or more flows and / or blocks.

[0079] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a process for implementing the functions specified in the flowcharts and / or block diagrams.Figure 1 steps of a function specified in one or more flows and / or blocks. Figure 1 steps of a function specified in one or more flows and / or blocks.

[0080] The above detailed description has disclosed, for purposes of the application, the best understanding of the best mode of practicing the application, and will fully and completely show the application to others skilled in the art, to which qualifying the scope of the application, any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the application shall be included in the protection scope of the application.

Claims

1. A dynamic combination site selection method for a multilateration system of air traffic control, characterized in that, The application relates to a method for determining an optimal base station combination, and belongs to the field of positioning technology. The method comprises the following steps: a site dynamic weight evaluation model is established to determine site weights; a geometric dilution of precision lookup table is established to be used for dynamically extracting geometric dilution of precision of site combinations; 2. The dynamic combination site selection method of a multilateration system of air traffic control according to claim 1, characterized in that, an adaptive combination optimization algorithm is adopted to determine an optimal site combination. The adaptive combination optimization algorithm comprises the following steps: geometric dilution of precision values of a basic site combination are dynamically extracted from the geometric dilution of precision lookup table; an initial candidate combination is dynamically planned; an initial population is generated from the initial candidate combination, and a global optimization search is performed by adopting a genetic algorithm; 3. The dynamic combination site selection method of claim 2, wherein, based on a global optimization search result, a Pareto optimal solution of positioning precision and calculation efficiency is realized, that is, an optimal site combination is obtained. The dynamic planning fast generates the initial candidate combination, which comprises the following steps: ； wherein, a plurality of candidate combinations are quickly generated according to a state transition equation and a constraint condition; the state transition equation is as follows: [ k ][ m ] represents the optimal GDOP value when selecting m 1 base stations from the first k 1 base stations; is the GDOP improvement after adding the mth base station; GDOP [ k ][ m- 1] represents the optimal GDOP value when selecting m- 1 base stations from the first k 1 base stations; GDOP [ k- 1][ m- 1] represents the optimal GDOP value when selecting m- 1 base stations from the first k- 1 base stations; GDOP effective combination ; wherein, β is an empirical coefficient; is a target height; is a base station i and a maximum distance of the base station j and the base station.

4. The dynamic combination site preferred method of a air traffic control multi-point positioning system according to claim 3, wherein, The constraint condition is as follows: The global optimization search is performed by adopting the genetic algorithm, which comprises the following steps: a plurality of base stations in the candidate combinations are encoded by adopting a chromosome coding mode; an adaptability function corresponding to the candidate combinations is obtained, wherein the adaptability function comprehensively considers geometric dilution of precision, calculation cost and site weights; 5. The dynamic combination site preference method of claim 2, wherein, an optimal combination is obtained by adopting the genetic algorithm through a plurality of iteration processes of crossover and mutation, and the optimal combination comprises an optimal plurality of base station combinations. The Pareto optimal solution of positioning precision and calculation efficiency comprises the following steps: a multi-objective optimization model considering calculation time consumption and geometric dilution of precision is established; a target function of the multi-objective optimization model is that geometric dilution of precision of a base station combination is minimum, and calculation time consumption of the base station combination is minimum; a constraint condition is that a quantity of the base station combination is greater than or equal to 4, and geometric dilution of precision of the base station combination is less than or equal to 3.0; a utility function is determined by a ratio of geometric dilution of precision of the base station combination to calculation time consumption of the base station combination; 6. A dynamic combination site preferred method for an air traffic control multipoint positioning system according to any one of claims 1-5, characterized in that, a site combination when the utility function is maximum is obtained, and the site combination is the optimal site combination. The site dynamic weight evaluation model comprises the following steps: a weight of a real-time signal signal-to-noise ratio is quantitatively evaluated; a weight of site historical availability is quantitatively evaluated; 7. The dynamic combination site preference method of claim 6, wherein, site weights are comprehensively evaluated according to the weight of the signal signal-to-noise ratio and the weight of the availability. The weight of the signal signal-to-noise ratio adopts a segmented weighting mechanism and is expressed as follows: if a signal-to-noise ratio SNR is greater than or equal to 15 dB and less than 30 dB, the weight of the signal signal-to-noise ratio is 0.4 x (SNR-15) / 15; if the signal-to-noise ratio SNR is greater than or equal to 30 dB and less than 50 dB, the weight of the signal signal-to-noise ratio is 0.4+0.2 x (SNR-30) / 20; if the signal-to-noise ratio SNR is greater than or equal to 50 dB, the weight of the signal signal-to-noise ratio is 0.6; 8. A dynamic combination site preferred method for an air traffic control multipoint positioning system according to any one of claims 1-5, characterized in that, and / or, the weight of the availability is 0.3 x (1-failure times / total sampling times)+0.7 x average signal quality; wherein the average signal quality refers to an arithmetic mean value of signal quality values of all sampling points in a sliding window. The geometric dilution of precision lookup table comprises the following steps: plane cutting: positions where each site can be possibly deployed are discretized into preset size grids; Target height layering: divided into several layers according to flight height layer; Typical combination pre-storage: store geometric precision factor values of 4-8 station combinations; Geometric precision factor pre-computation; Parallel computation: use GPU cluster to pre-compute all grid point combinations to generate geometric precision factor matrix; Data compression storage: quantize geometric precision factor values, and convert quantized floating point numbers into integers for storage; Dictionary encoding: establish hash mapping for repeated quantized values for compressed storage.

9. The dynamic combination site preference method of claim 8, wherein, The station combination geometric precision factor dynamic extraction includes: Input current station combination; Coordinate gridding mapping for the current station combination; Generate combination code for the current station combination; Height layer matching for the current station combination; According to the combination code and the matched height layer, match in the geometric precision factor lookup table, if there is a corresponding geometric precision factor value, return the pre-stored geometric precision factor value, otherwise, calculate the geometric precision factor value in real time, and update the geometric precision factor lookup table.

10. A dynamic combination site preference apparatus for a multilateration air traffic control system, characterized by It includes: A weight evaluation unit for establishing a station dynamic weight evaluation model to determine the station weight; A dynamic extraction unit for establishing a geometric precision factor lookup table for station combination geometric precision factor dynamic extraction; And an adaptive optimization unit for determining the optimal station combination using an adaptive combination optimization algorithm.

Images