A method and apparatus for dynamic combination site preference for a multilateration system

Abstract

The application discloses a dynamic combination station site optimization method and device of an air traffic control multilateration system, and the method comprises the following steps: a station dynamic weight evaluation model is established to determine station weights; a geometric precision factor lookup table is established to be used for dynamically extracting a station combination geometric precision factor; and an adaptive combination optimization algorithm is used to determine an optimal station combination. Through a dynamic station optimization mechanism, the method can real-timely evaluate station signal quality, geometric distribution and redundancy, adaptively adjust a station combination participating in positioning, and improve robustness in a complex scene. In addition, the method also adopts a multi-target optimization model based on dynamic weights, combines parameters such as signal arrival time difference error, geometric precision factor and calculation resource consumption, realizes rapid screening of an optimal station combination, effectively reduces calculation load, and guarantees positioning reliability.

Description

Technical Field

[0001] This application relates to the field of air traffic control multi-point positioning technology, specifically to a method and apparatus for dynamic combination site selection in an air traffic control multi-point positioning system. Background Technology

[0002] With the rapid growth of global air traffic, traditional air traffic control (ATM) systems face significant challenges. The limitations of traditional radar surveillance technology in terms of coverage, positioning accuracy, and operating costs are becoming increasingly apparent. Especially in complex terrain areas, low-altitude flight, and airport surface surveillance scenarios, radar is susceptible to line-of-sight obstruction and multipath interference, leading to blind spots. Furthermore, secondary surveillance radar (SSR) relies on airborne transponder signals, has a low update rate (typically 4-12 seconds), and is easily affected by signal collisions, making it difficult to meet the security requirements of high-density airspace. These factors have driven the development of new wide-area cooperative surveillance technologies, giving rise to Multi-Point Positioning (MLAT) technology. MLAT is a cooperative surveillance technology based on Time Difference of Arrival (TDOA). It uses a distributed network of ground receiving stations to measure the time difference between the arrival times of aircraft-transmitted signals at each station, and combines this with geometric algorithms to calculate the target's position. Its technical framework comprises four core modules: Signal receiving network: Consists of four or more high-precision synchronous receiving stations, typically deployed at intervals of 5-30 kilometers. Modern systems employ software-defined radio technology, supporting signal reception in frequency bands such as 1090MHz, with a receiving sensitivity reaching -95dBm.

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

[0004] Central processing unit: It fuses multi-station data using the Extended Kalman Filter (EKF) algorithm and combines it with the Earth ellipsoid model (WGS84) to perform 3D positioning calculations. The typical system processing latency is less than 500ms, with a horizontal positioning accuracy of 30 meters (95% confidence interval) and a vertical accuracy better than 50 meters.

[0005] Site layout and site combination: Before the system can work, the layout of each site must be completed. The geometric layout of each site must meet certain requirements in order to achieve the optimal positioning accuracy of the system. When there are multiple receiving stations, dynamic site selection is required to complete the optimal site layout of the dynamic target at the current time.

[0006] Site layout and dynamic combination technology are core enabling elements of MLAT systems, directly impacting surveillance performance, reliability, and cost-effectiveness. Future development trends will focus on intelligent dynamic optimization, heterogeneous network fusion, and deep integration of quantum precision measurement technologies, providing key technical support for achieving continuous and seamless monitoring across the entire area. Since site layout is completed before system construction and cannot be changed during system operation, dynamic combination of sites becomes particularly important when targets traverse different areas.

[0007] Currently, there are many technologies related to site layout for air traffic control multipoint positioning systems, describing the system's site deployment problem from various perspectives and methods. However, few studies have been conducted on the site selection problem for cross-regional targets, and existing site combinations and selection strategies have the following drawbacks: (1) Existing MLAT systems mostly use a combination of pre-set fixed stations, 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 number of fixed stations can easily lead to a waste of computing resources (such as in low-density airspace scenarios); and the dynamic changes in the influence factors of the geometric distribution of stations on positioning accuracy are not considered. (2) Existing methods have difficulty balancing positioning accuracy, computational complexity and system redundancy requirements when selecting sites; (3) When the coverage area of ​​the MLAT system expands (such as airport cluster collaborative monitoring), the increase in the number of sites leads to an explosive growth in the combination, and the traditional exhaustive method cannot meet the real-time requirements. (4) The existing MLAT system is prone to location interruption after responding to site failures or temporary failures. Summary of the Invention

[0008] To address the problems existing in the prior art, this application proposes a dynamic combination site selection method and apparatus for an air traffic control multi-point positioning system. This application uses a dynamic site selection mechanism to evaluate the signal quality, geometric distribution and redundancy of sites in real time, and adaptively adjusts the combination of sites participating in positioning, thereby improving robustness in complex scenarios.

[0009] This application is achieved through the following technical solution: A method for dynamic combination site selection in an air traffic control multi-point positioning system includes: Establish a dynamic site weighting evaluation model to determine site weights; Establish a geometric precision factor lookup table for dynamic extraction of geometric precision factors for site combinations; An adaptive combinatorial optimization algorithm is used to determine the optimal combination of sites.

[0010] In some implementations, the method of using an adaptive combinatorial optimization algorithm to determine the optimal combination of sites includes: The geometric precision factor values ​​of the base station combination are dynamically extracted from the geometric precision factor lookup table. Dynamic programming generates initial candidate combinations; An initial population is generated from initial candidate combinations, and a genetic algorithm is used for global optimization search. Based on the globally optimized search results, the Pareto optimal solution for positioning accuracy and computational efficiency is achieved, that is, the optimal combination of sites is obtained.

[0011] In some implementations, the dynamic programming method rapidly generates initial candidate combinations, including: Based on the state transition equation and constraints, several candidate combinations are quickly generated; the state transition equation is: ; in, GDOP [ k ][ m ] indicates the preceding m Select from base stations k The optimal GDOP value for a single base station; This represents the GDOP improvement amount after adding the m-th base station; GDOP [ k ][ m- 1] indicates the preceding m- Select from 1 base station k The optimal GDOP value for a single base station; GDOP [ k- 1][ m- 1] indicates the preceding m- Select from 1 base station k- The optimal GDOP value with 1 base station; The constraints are as follows: Effective combination ; in, β This is an empirical coefficient; For target height; For base stations i and base stations j The maximum distance.

[0012] In some implementations, the use of a genetic algorithm for global optimization search includes: Chromosome-based encoding is used to encode base stations from multiple candidate combinations; Obtain fitness functions corresponding to multiple candidate combinations, wherein the fitness functions comprehensively consider geometric precision factor, computational cost and site weight; Using a genetic algorithm, through multiple iterations of crossover and mutation, the optimal combination is obtained, including the optimal combination of multiple base stations.

[0013] In some implementations, the Pareto optimal solution that achieves both positioning accuracy and computational efficiency includes: A multi-objective optimization model considering computation time and geometric precision factor is established. The objective function of the multi-objective optimization model is to minimize the geometric precision factor of the base station combination and minimize the computation time of the base station combination. The constraints are: the number of base station combinations is greater than or equal to 4, and the geometric precision factor of the base station combination is less than or equal to 3.0. The utility function is determined by the ratio of the geometric precision factor of the base station combination to the calculation time of the base station combination. The optimal site combination is the one that maximizes the utility function.

[0014] In some implementations, the establishment of a site dynamic weight evaluation model includes: Quantitatively evaluate the weights of the real-time signal-to-noise ratio; Quantify the weighting of a site's historical availability; The site weight is comprehensively evaluated based on the weights of signal-to-noise ratio and availability.

[0015] In some implementations, the signal-to-noise ratio (SNR) weights employ a segmented weighting mechanism, expressed as: If the signal-to-noise ratio (SNR) is greater than or equal to 15dB and less than 30dB, the weight of the signal-to-noise ratio is: 0.4×(SNR-15) / 15; If the signal-to-noise ratio (SNR) is greater than or equal to 30dB and less than 50dB, the weight of the signal SNR is: 0.4 + 0.2 × (SNR - 30) / 20; If the signal-to-noise ratio (SNR) is greater than or equal to 50 dB, then the weight of the signal-to-noise ratio is 0.6. And / or, the weight of availability is: 0.3 × (1 - number of failures / total number of samples) + 0.7 × average signal quality; where the average signal quality refers to the arithmetic mean of the signal quality values ​​of all sampling points within the sliding window.

[0016] In some implementations, establishing the geometric precision factor lookup table includes: Planar cutting: Discretizes the possible deployment locations of each site into a grid of preset size; Target altitude stratification: Divided into several layers according to flight altitude; Typical combination pre-stored: Stores the geometric precision factor values ​​for combinations of 4 to 8 stations; Pre-calculation of geometric accuracy factor; Parallel computation: All grid point combinations are pre-computed using a GPU cluster to generate a geometric accuracy factor matrix; Data compression and storage: The geometric precision factor value is logarithmically quantized, and the quantized floating-point number is converted into an integer for storage; Dictionary encoding: Establish a hash mapping for repeated quantized values ​​and compress and store them.

[0017] In some implementations, the dynamic extraction of the site combination geometric accuracy factor includes: Enter the current site combination; Perform coordinate grid mapping on the current site combination; Generate a combined code for the current site combination; Perform height layer matching on the current site combination; Based on the combined encoding and the matching height layer, a match is performed from the geometric precision factor lookup table. If a corresponding geometric precision factor value exists, the pre-stored geometric precision factor value is returned; otherwise, the geometric precision factor value is calculated in real time, and the geometric precision factor lookup table is updated.

[0018] On the other hand, this application also proposes a dynamic combined site selection device for an air traffic control multi-point positioning system, comprising: The weighting evaluation unit is used to establish a dynamic weighting evaluation model for sites in order to determine the weights of sites. The dynamic extraction unit is used to establish a geometric precision factor lookup table for dynamic extraction of geometric precision factors for site combinations. In addition, there is an adaptive optimization unit that uses an adaptive combinatorial optimization algorithm to determine the optimal combination of sites.

[0019] This application proposes a dynamic site selection method for air traffic control multipoint positioning systems. This method uses a dynamic site selection mechanism to evaluate the signal quality, geometric distribution, and redundancy of sites in real time, and adaptively adjusts the combination of sites participating in positioning to improve robustness in complex scenarios. The method also employs a multi-objective optimization model based on dynamic weights, combining parameters such as signal arrival time difference error, geometric accuracy factor, and computational resource consumption to achieve rapid screening of the optimal site combination, effectively reducing computational load and ensuring positioning reliability. In addition, the method also uses a hierarchical screening algorithm, which reduces computational burden and achieves millisecond-level dynamic decision-making through pre-storing GDOP values, parallel computing technology, and a multi-level progressive strategy of adaptive combination algorithms.

[0020] Correspondingly, the dynamic combination site selection device for an air traffic control multi-point positioning system proposed in this application also possesses the same technical effects as described above. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of the embodiments of this application and form part of this application, do not constitute a limitation on the embodiments of this application. In the drawings: Figure 1This is a flowchart of a preferred method proposed in an embodiment of this application; Figure 2 Workflow diagram for GDOP lookup table; Figure 3 The flowchart shows the global optimization algorithm using a genetic algorithm. Figure 4 This is a schematic diagram of the preferred device proposed in the embodiments of this application; Figure 5 This is a schematic diagram of a preferred system architecture proposed in an embodiment of this application; Figure 6 This is a schematic diagram of the electronic device proposed in the embodiments of this application; Figure 7 This is a schematic diagram of a computer-readable storage medium proposed in an embodiment of this application; Figure reference numerals and corresponding component names: 200 - Optimization device, 201 - Weight evaluation unit, 202 - Dynamic extraction unit, 203 - Adaptive optimization unit, 300 - Optimization 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 Implementation

[0022] In the following, the terms “comprising” or “may include” as used in the various embodiments of this application indicate the presence of a function, operation, or element of the invention and do not limit the addition of one or more functions, operations, or elements. Furthermore, as used in the various embodiments of this application, the terms “comprising,” “having,” and their cognates are intended only to indicate a specific feature, number, step, operation, element, component, or combination of the foregoing and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing, or adding one or more combinations of the foregoing.

[0023] In various embodiments of this application, the expression "or" or "at least one of A and / or B" includes any combination or all combinations of the words listed simultaneously. For example, the expression "A or B" or "at least one of A and / or B" may include A, may include B, or may include both A and B.

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

[0025] It should be noted that if a description is made of "connecting" one component to another, then the first component can be directly connected to the second component, and a third component can be "connected" between the first and second components. Conversely, when a component is "directly connected" to another component, it can be understood that there is no third component between the first and second components.

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

[0027] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the embodiments and accompanying drawings. The illustrative embodiments and descriptions of this application are only for explaining this application and are not intended to limit this application.

[0028] Example 1 Existing methods using fixed-site combinations cannot adapt to signal quality fluctuations caused by complex electromagnetic environments. Furthermore, the large number of fixed sites can lead to wasted computational resources, and the dynamic changes in factors affecting positioning accuracy due to the geometric distribution of sites are not considered, resulting in decreased positioning accuracy and poor robustness. To address these issues, this embodiment proposes a dynamic site selection method for air traffic control multi-point positioning systems.

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

[0030] For each remote site, a weight is first established for its real-time dynamic availability. Subsequent combination calculations are then performed based on these site weights, with higher weights prioritized for inclusion in the dynamic combination selection list. This embodiment employs a multi-dimensional, adaptive site comprehensive evaluation system, breaking through the traditional single signal strength assessment mode. The specific process is as follows: Step 111: Quantify the weight of the real-time signal-to-noise ratio (SNR).

[0031] The core of TDOA positioning is to estimate the target location by measuring the time difference of signal arrival at different base stations. The accuracy of the time difference directly depends on the precise measurement (time stamping) of the signal's Time of Arrival (TOA) by each base station. Timestamp errors are directly passed to TDOA calculations, leading to positioning errors. Signals with high SNR are clear, have less noise interference, and their waveforms (such as pulse leading edges or correlation peaks) are easy to detect accurately. With low SNR signals, noise can mask the true waveform, causing the detected signal leading edge or peak position to shift. For example, noise may cause signal amplitude fluctuations, leading to threshold detection being triggered too early or too late, or correlation peaks may become flat or indistinct after being interfered with by noise, resulting in ambiguous peak positioning.

[0032] The theoretical lower bound of timestamp error is inversely proportional to SNR. The lower the SNR, the larger the mean square error of the TOA estimate, which can be mathematically expressed as: Where B is the signal bandwidth. The mean square error is the TOA estimate. It can be seen that low SNR or narrow bandwidth signals will significantly increase the timestamp error.

[0033] Therefore, in this embodiment, the SNR of each signal received by each station needs to be evaluated in real time as one of the considerations for station weighting. For the target SNR weight value, a segmented weighting mechanism is introduced based on the SNR value:

[0034] The higher the signal-to-noise ratio (SNR), the greater the SNR weight, and vice versa; at the same time, dynamic noise baseline calibration is used: the ambient noise base (P_noise_baseline) is updated every preset time interval (e.g., 5 minutes).

[0035] Step 112: Quantify the weight of historical availability of the site.

[0036] This embodiment uses a sliding window (e.g., window size = 1 hour) to calculate the weight of a site's historical availability. The calculation method is as follows: T ar 权重= 0.3 × (1 - number of faults / total number of samples) + 0.7 × average signal quality Average signal quality refers to the arithmetic mean of the signal quality values ​​of all sampling points within a sliding window. It can normalize the index values ​​of the sampling points to a value between 0 and 1.

[0037] Using the two metrics mentioned above (SNR weight and TAR weight), the station weights are comprehensively evaluated. The signal-to-noise ratio reflects the distance between the target and the station to a certain extent, and the station that is closer to the target should have a higher weight.

[0038] Step 120: Establish a geometric precision factor (GDOP) lookup table for dynamic extraction of geometric precision factors for site combinations.

[0039] This embodiment uses a lookup table method to dynamically extract the geometrical factor of precision (GDOP). Taking a wide-area multi-point positioning system as an example, the strategy and detailed steps for constructing the lookup table are as follows: Step 121, Planar Cutting: Discretize the possible deployment locations of each site into a 100m×100m grid (covering a 50km radius around the airport), with grid coordinates (xi,yi)∈Z2. For multiple points on the field, the airport is divided into a 1m×1m grid, covering the airport runway, taxiway, and apron area.

[0040] Step 122, target altitude layering: divided according to flight altitude layers (0~10km, each layer is 500m), a total of 20 layers, denoted as hj∈{0,500,...,9500}; Step 123, Typical combination pre-storage: Store the GDOP values ​​of 4~8 site combinations (covering 95% of common scenarios); Step 124, GDOP pre-calculation: The calculation formula is as follows ,in These respectively represent the system in Positioning error in direction; Step 125, Parallel computation: Using a GPU cluster, pre-calculate all grid point combinations using the GDOP pre-calculation formula in step 124 to generate the GDOP matrix GDOP_Table[S][H], where: S is the site combination code (e.g., 4 sites are represented by 16-bit binary), and H is the target height layer number; Step 126, Data Compression and Storage: The GDOP value is logarithmically quantized using a lossy compression method with a quantization precision of 0.1. The detailed process is as follows: For quantification, the GDOP value is converted to decibels (dB) and then rounded to one decimal place. .

[0041] For example: original , Rounded to 5.4, the quantized value Q = 5.4; Storage optimization converts the quantized floating-point number (e.g., 5.4) into an integer for storage (e.g., 54), saving bytes. Each quantized value requires only 2 bytes (16 bits) instead of the original 4 bytes of the floating-point number. Reverse calculation during decompression: The error is approximately 0.02.

[0042] Step 127, Dictionary Encoding: Establish a hash mapping for duplicate GDOP values ​​and perform compressed storage. The algorithm process is as follows: Construct a dictionary: count all unique quantized values ​​and assign a unique short code to each value. For example, quantized values ​​[0,30,48,70,100] → dictionary {0:0x00,30:0x01,48:0x02,70:0x03,100:0x04}.

[0043] Data compression: The original quantized value is replaced with a short code. The compression ratio depends on the repetition frequency of the quantized value.

[0044] The dynamic extraction process of geometric precision factor (GDOP) is as follows: Figure 2 As shown: Enter the current site combination; Coordinate gridding mapping: Projecting actual coordinates onto the nearest grid point; Combined encoding generation: using bitmasks to represent participating sites; Height layer matching: Find the current height layer; Based on the site combination code and the matching height layer, a match is performed from the GDOP value database. If a corresponding GDOP value exists, the pre-stored GDOP value is returned; otherwise, the GDOP value is calculated in real time according to the above formula, the lookup table is updated, and a new GDOP value is asynchronously written to the GDOP value database.

[0045] Step 130: Use an adaptive combinatorial optimization algorithm to determine the optimal combination of sites.

[0046] This embodiment employs a hybrid adaptive combinatorial optimization algorithm (DP-GA) combining dynamic programming and genetic algorithms to resolve the inherent trade-off between efficiency and accuracy in traditional methods. The specific process is as follows: Step 131: Find the GDOP value of the basic site combination (e.g., the basic site combination of five base stations is a star-shaped site structure or an inverted triangle site structure) from the lookup table.

[0047] according to Figure 2The process shown retrieves the GDOP value of the current target location from the stored GDOP table, which is calculated by the base station. At this point, the GDOP is a fixed value.

[0048] Step 132: Dynamic programming is used to quickly generate initial candidate combinations.

[0049] Let GDOP[k][m] represent the optimal GDOP value when selecting k base stations from the first m base stations, and its corresponding state transition equation is:

[0050] in, The GDOP improvement after adding the m-th base station can be obtained directly using a lookup table. The selection of new base stations must satisfy the baseline length constraint (avoiding co-linear base stations): Valid combinations ,in, β =0.5 is an empirical coefficient. For the target height, Let Pi be the maximum distance between two base stations (base station i and base station j), where Pi is the geometric position of base station i and Pj is the geometric position of base station j.

[0051] Based on the GDOP value, the system quickly generates Top 50 to Top 80 candidate site combinations, sorted from smallest to largest, in less than 20ms.

[0052] Step 133: Use a genetic algorithm to perform a global optimization search.

[0053] This embodiment uses a genetic algorithm for global optimization search, and the specific process is as follows: First, the base stations are encoded using chromosome encoding. The method is binary encoding, where the gene length equals the total number of base stations; 1 indicates the base station is selected, and 0 indicates it is not selected. Chromosome = [0,1,1,0,1,0,…], which means that base stations 1, 2, and 4 are enabled.

[0054] Then, the fitness function is obtained. Taking into account GDOP, computational cost, and site weights, the fitness function is:

[0055] GDOP is the GDOP value of the current site combination; This is the scaling factor during the crossover operation in the genetic algorithm, and it takes a value between 0 and 1. In this embodiment, it is preferably 0.7. To calculate the cost; As the penalty function, it mainly considers the site weight problem. Introducing the site weights obtained in step 110, we get:

[0056] in, M Indicates the number of sites involved in the calculation; This represents the signal-to-noise ratio weight of the nth station; This represents the statistical weight of the availability of the nth site.

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

[0058] Step 134: Achieve the Pareto optimal solution for positioning accuracy and computational efficiency.

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

[0060] in, The base station combination obtained by the genetic algorithm. To calculate the time consumption for a single base station, the following constraints are set: .

[0061] By defining the utility function and selecting the final solution, we can obtain the final combination of sites. The utility function is as follows:

[0062] in, This represents the GDOP value of the current site combination. The time taken to calculate GDOP for the current combination. When the utility function... When the maximum value is reached, the final combination of sites is obtained.

[0063] The method proposed in this embodiment improves robustness in complex scenarios by dynamically evaluating site signal quality and geometric distribution in real time through a site selection mechanism, and adaptively adjusting the combination of sites participating in positioning. The method also proposes a multi-objective optimization model based on dynamic weights, combining parameters such as Time Difference of Arrival (TDOA) error, Geometric Precision Factor (GDOP), and computational resource consumption to quickly select the optimal site combination, effectively reducing computational load and ensuring positioning reliability. Furthermore, the method employs a hierarchical selection algorithm, using pre-stored GDOP values, parallel computing technology, and a multi-level progressive strategy of adaptive combination algorithms to reduce computational burden and achieve millisecond-level dynamic decision-making. In addition, the method introduces a dynamic redundancy threshold control mechanism (i.e., while selecting the optimal site combination, other site combinations remain as backup combinations; if the optimal site combination is abnormal, backup site combinations can be used). While selecting the primary site combination, a backup site queue is maintained in real time; if an abnormal signal is detected at a primary site, this method can be used to immediately switch to another combination.

[0064] In another embodiment, this embodiment also proposes a dynamic combined site selection device for an air traffic control multi-point positioning system, such as... Figure 4 As shown, the preferred device 200 includes: The weight evaluation unit 201 is used to establish a dynamic weight evaluation model for sites to determine site weights. The specific process of establishing the dynamic weight evaluation model for sites is as described in step 110 above, and will not be repeated here.

[0065] The dynamic extraction unit 202 is used to establish a geometric precision factor (GDOP) lookup table for dynamic extraction of geometric precision factors for site combinations. The specific process of establishing the set precision factor lookup table is as described in step 120 above, and will not be repeated here.

[0066] Furthermore, the adaptive optimization unit 203 uses an adaptive combinatorial optimization algorithm to determine the optimal combination of sites. The specific optimization process is as described in step 130 above, and will not be repeated here.

[0067] In another embodiment, this embodiment also proposes a dynamic combined site selection system for an air traffic control multi-point positioning system, such as... Figure 5 As shown, the preferred system 300 proposed in this embodiment includes: The system comprises an input device 301, an output device 302, a processor A303, and a memory A304; wherein the number of processors A303 and memory A304 can be one or more. Figure 5 The following description uses a processor A303 and a memory A304 as an example. The input device 301, output device 302, processor A303, and memory A304 can be connected via a bus or other means. Figure 5Taking the example of a connection between China and Israel via a bus.

[0068] Specifically, by calling the operation instructions stored in memory A304, processor A303 executes the following steps: Establish a dynamic site weighting evaluation model to determine site weights; Establish a geometric precision factor (GDOP) lookup table for dynamic extraction of geometric precision factors for site combinations; An adaptive combinatorial optimization algorithm is used to determine the optimal combination of sites.

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

[0070] In another embodiment, this embodiment also proposes 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, it performs the following steps: Establish a dynamic site weighting evaluation model to determine site weights; Establish a geometric precision factor (GDOP) lookup table for dynamic extraction of geometric precision factors for site combinations; An adaptive combinatorial optimization algorithm is used to determine the optimal combination of sites.

[0071] Optionally, when processor B420 executes computer program A411, it can implement any of the embodiments in the corresponding examples of the preferred method described above.

[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, those skilled in the art can understand the specific implementation method of the electronic device in this embodiment and its various variations. Therefore, the specific implementation method of the electronic device will not be described in detail here. Any electronic device used by those skilled in the art to implement the above-mentioned preferred method is within the scope of protection of this application.

[0073] In another embodiment, this embodiment also proposes a computer-readable storage medium 500, such as... Figure 7 As shown, the computer-readable storage medium 500 stores a computer program B511, which, when executed by a processor, performs the following steps: Establish a dynamic site weighting evaluation model to determine site weights; Establish a geometric precision factor (GDOP) lookup table for dynamic extraction of geometric precision factors for site combinations; An adaptive combinatorial optimization algorithm is used to determine the optimal combination of sites.

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

[0075] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0076] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0077] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0078] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0079] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0080] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above description is only a specific embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for dynamic combination site selection in an air traffic control multi-point positioning system, characterized in that, include: Establish a dynamic site weighting evaluation model to determine site weights; Establish a geometric precision factor lookup table for dynamic extraction of geometric precision factors for site combinations; An adaptive combinatorial optimization algorithm is used to determine the optimal site combination; the method of determining the optimal site combination using the adaptive combinatorial optimization algorithm includes: The geometric precision factor values ​​of the base station combination are dynamically extracted from the geometric precision factor lookup table. Dynamic programming generates initial candidate combinations; An initial population is generated from initial candidate combinations, and a genetic algorithm is used for global optimization search. Based on the globally optimized search results, a Pareto optimal solution is achieved that balances positioning accuracy and computational efficiency, i.e., the optimal combination of sites is obtained; the dynamic programming that generates the initial candidate combination includes: Based on the state transition equation and constraints, several candidate combinations are quickly generated; the state transition equation is: ； Wherein, GDOP[k][m] represents the optimal GDOP value when selecting k base stations from the first m base stations; GDOP is the improvement amount after adding the m-th base station; GDOP[k][m-1] represents the optimal GDOP value when selecting k base stations from the first m-1 base stations; GDOP[k-1][m-1] represents the optimal GDOP value when selecting k-1 base stations from the first m-1 base stations. The constraints are as follows: Effective combination ; Where β is an empirical coefficient; For target height; The maximum distance between base station i and base station j; the global optimization search using a genetic algorithm includes: Chromosome-based encoding is used to encode base stations from multiple candidate combinations; Obtain fitness functions corresponding to multiple candidate combinations, wherein the fitness functions comprehensively consider geometric precision factor, computational cost and site weight; A genetic algorithm is used to obtain the optimal combination, including the optimal combination of multiple base stations, through multiple iterations of crossover and mutation. The Pareto optimal solution that achieves both positioning accuracy and computational efficiency includes: A multi-objective optimization model considering computation time and geometric precision factor is established. The objective function of the multi-objective optimization model is to minimize the geometric precision factor of the base station combination and minimize the computation time of the base station combination. The constraints are: the number of base station combinations is greater than or equal to 4, and the geometric precision factor of the base station combination is less than or equal to 3.

0. The utility function is determined by the ratio of the geometric precision factor of the base station combination to the calculation time of the base station combination. The optimal site combination is the one that maximizes the utility function; the establishment of the dynamic site weight evaluation model includes: Quantitatively evaluate the weights of the real-time signal-to-noise ratio; Quantify the weighting of a site's historical availability; The site weight is comprehensively evaluated based on the weights of signal-to-noise ratio and availability.

2. The method for dynamic combination site selection in an air traffic control multi-point positioning system according to claim 1, characterized in that, The signal-to-noise ratio weights are calculated using a segmented weighting mechanism, expressed as follows: If the signal-to-noise ratio (SNR) is greater than or equal to 15dB and less than 30dB, the weight of the signal-to-noise ratio is: 0.4×(SNR-15) / 15; If the signal-to-noise ratio (SNR) is greater than or equal to 30dB and less than 50dB, the weight of the signal SNR is: 0.4 + 0.2 × (SNR - 30) / 20; If the signal-to-noise ratio (SNR) is greater than or equal to 50 dB, then the weight of the signal-to-noise ratio is 0.

6. And / or, the weight of availability is: 0.3 × (1 - number of failures / total number of samples) + 0.7 × average signal quality; where the average signal quality refers to the arithmetic mean of the signal quality values ​​of all sampling points within the sliding window.

3. The method for dynamic combination site selection in an air traffic control multi-point positioning system according to any one of claims 1-2, characterized in that, The establishment of the geometric precision factor lookup table includes: Planar cutting: Discretizes the possible deployment locations of each site into a grid of preset size; Target altitude stratification: Divided into several layers according to flight altitude; Typical combination pre-stored: Stores the geometric precision factor values ​​for combinations of 4 to 8 stations; Pre-calculation of geometric accuracy factor; Parallel computation: All grid point combinations are pre-computed using a GPU cluster to generate a geometric accuracy factor matrix; Data compression and storage: The geometric precision factor value is logarithmically quantized, and the quantized floating-point number is converted into an integer for storage; Dictionary encoding: Establish a hash mapping for repeated quantized values ​​and compress and store them.

4. The method for dynamic combination site selection in an air traffic control multi-point positioning system according to claim 3, characterized in that, The dynamic extraction of the geometric accuracy factor for the site combination includes: Enter the current site combination; Perform coordinate grid mapping on the current site combination; Generate a combined code for the current site combination; Perform height layer matching on the current site combination; Based on the combined encoding and the matching height layer, a match is performed from the geometric precision factor lookup table. If a corresponding geometric precision factor value exists, the pre-stored geometric precision factor value is returned; otherwise, the geometric precision factor value is calculated in real time, and the geometric precision factor lookup table is updated.

5. A dynamic combination site selection device for an air traffic control multi-point positioning system, characterized in that, include: The weighting evaluation unit is used to establish a dynamic weighting evaluation model for sites in order to determine the weights of sites. The dynamic extraction unit is used to establish a geometric precision factor lookup table for dynamic extraction of geometric precision factors for site combinations. In addition, an adaptive optimization unit uses an adaptive combinatorial optimization algorithm to determine the optimal combination of sites; The method of determining the optimal site combination using an adaptive combinatorial optimization algorithm includes: The geometric precision factor values ​​of the base station combination are dynamically extracted from the geometric precision factor lookup table. Dynamic programming generates initial candidate combinations; An initial population is generated from initial candidate combinations, and a genetic algorithm is used for global optimization search. Based on the globally optimized search results, a Pareto optimal solution is achieved that balances positioning accuracy and computational efficiency, i.e., the optimal combination of sites is obtained; the dynamic programming that generates the initial candidate combination includes: Based on the state transition equation and constraints, several candidate combinations are quickly generated; the state transition equation is: ； Wherein, GDOP[k][m] represents the optimal GDOP value when selecting k base stations from the first m base stations; GDOP is the improvement amount after adding the m-th base station; GDOP[k][m-1] represents the optimal GDOP value when selecting k base stations from the first m-1 base stations; GDOP[k-1][m-1] represents the optimal GDOP value when selecting k-1 base stations from the first m-1 base stations. The constraints are as follows: Effective combination ; Where β is an empirical coefficient; For target height; The maximum distance between base station i and base station j; the global optimization search using a genetic algorithm includes: Chromosome-based encoding is used to encode base stations from multiple candidate combinations; Obtain fitness functions corresponding to multiple candidate combinations, wherein the fitness functions comprehensively consider geometric precision factor, computational cost and site weight; A genetic algorithm is used to obtain the optimal combination, including the optimal combination of multiple base stations, through multiple iterations of crossover and mutation. The Pareto optimal solution that achieves both positioning accuracy and computational efficiency includes: A multi-objective optimization model considering computation time and geometric precision factor is established. The objective function of the multi-objective optimization model is to minimize the geometric precision factor of the base station combination and minimize the computation time of the base station combination. The constraints are: the number of base station combinations is greater than or equal to 4, and the geometric precision factor of the base station combination is less than or equal to 3.

0. The utility function is determined by the ratio of the geometric precision factor of the base station combination to the calculation time of the base station combination. The optimal site combination is the one that maximizes the utility function; the establishment of the dynamic site weight evaluation model includes: Quantitatively evaluate the weights of the real-time signal-to-noise ratio; Quantify the weighting of a site's historical availability; The site weight is comprehensively evaluated based on the weights of signal-to-noise ratio and availability.

Images