Multi-source track fusion method based on dynamic and static parameter normalization comparison

Abstract

This invention provides a multi-source track fusion method based on normalized comparison of dynamic and static parameters, comprising: reading static and dynamic parameters; receiving A / B channel data from an ADS-B data source according to the dynamic parameters; comparing the A and B channels through data quality statistics to select a usable channel; configuring a channel specified according to the dynamic parameters as the usable channel; performing anomaly processing on the data of the usable channel; and performing multi-source data fusion on the data of the usable channel after anomaly processing with usable channel data from other ADS-B data sources and / or internal format data from radar data sources. Furthermore, a server, storage medium, and system are also provided. The technical solution provided by this invention avoids problems such as unstable data, high packet loss rate, incomplete coverage, and a large amount of abnormal data from a single ADS-B ground station; ensures stable and reliable track data, and provides the highest quality surveillance data service for air traffic control.

Description

Technical Field

[0001] This invention relates to the field of civil aviation air traffic management technology, and in particular to a multi-source track fusion method, server, storage medium and system based on dynamic and static parameter normalization comparison. Background Technology

[0002] Automatic Dependent Surveillance Broadcast (ADS-B), as a flight surveillance technology, offers advantages over traditional radar surveillance, including faster update speeds, higher data accuracy, richer information transmission, and significantly lower installation and operating costs. ADS-B data is actively broadcast by airborne equipment to acquire aircraft-related information. Ground equipment receives this broadcast data, processes it, and outputs it in the standard ASTERIX CAT021 format. However, due to the limited coverage of a single ADS-B ground station and the reliance on the reliability of airborne equipment and the stability of the transmission environment, ADS-B data is susceptible to external interference, leading to data loss and anomalies during transmission. Therefore, data from a single ground station presents numerous problems in practical use, such as severe track data loss, inaccurate satellite positioning, or GPS signal interference resulting in numerous track position jumps, altitude jumps, and missing flight information, seriously impacting air traffic control and surveillance.

[0003] Although ADS-B data stations are used to receive data from one or more ground stations and perform fusion processing, stitching together the received ground station data and filtering out duplicate data to form a large ADS-B data coverage and monitoring area; however, due to ADS-B's dependence on airborne equipment, the stability and integrity of the fused data cannot be guaranteed when there is a lack or anomaly in the data sent by the onboard equipment of the flight. Summary of the Invention

[0004] The main objective of this invention is to provide a multi-source track fusion method, server, storage medium, and system based on dynamic and static parameter normalization comparison, aiming to solve the aforementioned problem of multi-source track data fusion.

[0005] To achieve the above objectives, this invention provides a multi-source track fusion method based on dynamic and static parameter normalization comparison, comprising:

[0006] Step S10: Read static and dynamic parameters;

[0007] Step S20: Receive A / B channel data from the ADS-B data source according to dynamic parameters; wherein, the A / B channel includes a primary A channel and a backup B channel;

[0008] Step S30: Determine whether to perform channel comparison between channel A and channel B; if yes, proceed to step S40; otherwise, proceed to step S50.

[0009] Step S40: Channel A and Channel B are compared and selected through data quality statistics to obtain the channel in use; the data quality statistics identify abnormal data in the channel through static parameters and aircraft flight characteristics and perform analysis and statistics.

[0010] Step S50: Configure the specified channel as the in-use channel according to the dynamic parameters;

[0011] Step S60: Perform abnormal data processing on the data of the channel in use;

[0012] Step S70: Perform multi-source data fusion on the data from the in-use channel that has completed the abnormal data processing, the data from the in-use channel of other ADS-B data sources, and / or the internal format data from the radar data source.

[0013] Furthermore, the static parameters are descriptive parameters used during program initialization and parameters defined internally by the system that cannot be arbitrarily modified; the dynamic parameters are parameters used during program execution to assist the program in receiving and processing data.

[0014] Further, step S40 includes:

[0015] Step S410: Count the number of data frames in channels A and B;

[0016] Step S420: Determine whether the data volume of the primary A channel is zero or exceeds the set upper limit; if yes, proceed to step S430; otherwise, proceed to step S460.

[0017] Step S430: Determine whether the data volume of the backup B channel is zero or exceeds the set upper limit; if yes, proceed to step S440; otherwise, proceed to step S450.

[0018] Step S440: Initialize channel A and end;

[0019] Step S450: Perform primary / standby switchover and determine that channel B is the channel in use;

[0020] Step S460: Analyze the data frames of channel A and channel B and perform data quality statistics; the data quality statistics include any one or more of the following: total number of data frames, number of erroneous data frames, data source delay statistics, number of low-quality data frames, number of position bounce data frames, number of data frames with large-scale position deviation, number of data frames with abnormal position swing, number of data frames with height jump, and number of data frames with speed jump.

[0021] Step S470: Calculate and compare the data quality of the two in-use channels according to the parameter configuration weights to obtain the channel selection result; the channel selection result is the selection of the in-use channel.

[0022] Furthermore, after selecting or specifying an in-use channel, the following is also included:

[0023] The data of the channels in use is parsed according to the dynamic parameter configuration or the default configuration to form internal format data.

[0024] Further, step S60 includes:

[0025] Step S610: Receive internal format data and obtain the values ​​of each data item;

[0026] Step S620: Determine if the required conditions are met. If yes, proceed to step S630; otherwise, the data filtering ends.

[0027] Step S630: Obtain the server time and the flight track time and compare them to determine if there is a data delay; if so, proceed to step S640; otherwise, the data filtering ends.

[0028] Step S640: Obtain historical information of a point on the track, and combine it with the information of the previous point to calculate track characteristics and identify position jump and altitude jump data;

[0029] Step S650: Determine whether the data conforms to the trajectory characteristics. If so, perform multi-source data fusion; otherwise, end the data filtering process.

[0030] Further, step S70 includes:

[0031] Step S701: Obtain the in-use channel data of the real-time ADS-B data source based on the dynamic parameters;

[0032] or,

[0033] Obtain the internal format data of the radar data source based on static parameters;

[0034] Step S702: Search the data item queue for flight information contained in the in-use channel data of the ADS-B data source or the internal format data of the radar data source;

[0035] Step S703: Determine whether the flight information is found in the data item queue; if not found, proceed to step S704; if found, proceed to step S705.

[0036] Step S704: Insert the flight information into the data item queue;

[0037] Step S705: Search for flight information in the ADS-B track queue;

[0038] Step S706: Determine whether the flight information is found in the ADS-B track queue; if not, proceed to step S707; otherwise, proceed to step S708.

[0039] Step S707: Insert the flight information into the ADS-B track queue and then end;

[0040] Step S708: Compare the timestamp of the flight data found in the ADS-B track queue with the timestamp of the real-time flight data and calculate the time interval;

[0041] Step S709: Determine the time interval and whether it is less than or equal to the minimum update cycle of the ADS-B data source set in the static parameters; if yes, proceed to step S710, otherwise proceed to step S711.

[0042] Step S710: Filter the real-time ADS-B data, end;

[0043] Step S711: Update the flight information in the ADS-B track queue.

[0044] Furthermore, the processing steps for the internal format data of the radar data source include:

[0045] Step S1: Analyze the data from each radar and confirm that the data is not abnormal;

[0046] Step S2: Unify the spatial coordinates and data units of the radar data;

[0047] Step S3: Determine if it is a multiple coverage; if yes, proceed to step S4; otherwise, proceed to step S5.

[0048] Step S4: Weighted integration of data items from multiple radar data sets;

[0049] Step S5: Set the radar data to altitude and add radar data tags to form comprehensive track data;

[0050] Step S6: Perform track filtering on the integrated track data and avoid sending a large number of track points at the same time to perform peak reduction processing to obtain the radar data source;

[0051] Step S7: Obtain the radar data source based on static parameters and perform internal format processing to obtain the internal format data of the radar data source.

[0052] Meanwhile, the present invention provides a server, including a memory and a processor. The memory stores a multi-source track fusion program based on dynamic and static parameter normalization comparison that can run on the processor. When the multi-source track fusion program based on dynamic and static parameter normalization comparison is executed by the processor, it implements the steps of the multi-source track fusion method based on dynamic and static parameter normalization comparison as described above.

[0053] The present invention provides a storage medium, which is a computer-readable storage medium, and stores a multi-source track fusion program based on dynamic and static parameter normalization comparison. The multi-source track fusion program based on dynamic and static parameter normalization comparison can be executed by one or more processors to implement the steps of the multi-source track fusion method based on dynamic and static parameter normalization comparison as described above.

[0054] Furthermore, this invention provides a multi-source track fusion system based on dynamic and static parameter normalization comparison, including a server, a web server, and a client as described above. The web server and client are respectively communicatively connected to the server. The server is used for data storage and distribution, data processing, and data collection and parsing. The web server is used for system monitoring, parameter configuration, and system management. The client is used for displaying track dynamics, track alarms, and track playback.

[0055] Compared with existing technologies, this invention provides a multi-source track fusion method, server, storage medium, and system based on dynamic and static parameter normalization comparison. Through static and dynamic parameter configuration, channel selection is performed according to ADS-B data characteristics to select the highest quality data before handling abnormal data. By fusing multi-source data, it avoids problems such as unstable data, high packet loss rate, incomplete coverage, and a large number of abnormal data from individual ADS-B ground stations. It ensures that the fastest-updating and most complete data is selected as the fusion data source. By combining multiple ADS-B data sources and multiple radar data sources, missing data items are supplemented, and conflicting data item values ​​are corrected, thereby forming stable, fast, and complete ADS-B fused data. This achieves stable and reliable track data, providing the highest quality surveillance data service for air traffic control. Attached Figure Description

[0056] Figure 1 This is a flowchart illustrating a multi-source track fusion method based on dynamic and static parameter normalization comparison provided in an embodiment of the present invention.

[0057] Figure 2 for Figure 1 A flowchart illustrating step S40;

[0058] Figure 3 for Figure 1 A flowchart illustrating step S60;

[0059] Figure 4 for Figure 1 A flowchart illustrating step S70;

[0060] Figure 5 for Figure 4 A schematic diagram illustrating the processing flow of internal formatted data from a radar data source.

[0061] Figure 6 This is a schematic diagram of the internal structure of a server provided in an embodiment of the present invention;

[0062] Figure 7 This is a schematic diagram of the structure of a multi-source track fusion system based on dynamic and static parameter normalization comparison according to an embodiment of the present invention;

[0063] Figure 8 This is a schematic diagram of a multi-source track fusion program module based on dynamic and static parameter normalization comparison in a server provided in an embodiment of the present invention.

[0064] In the diagram, 10 is the server; 20 is the web server; 30 is the client; 100 is the data storage and distribution module; 200 is the data processing module; 300 is the data collection and parsing module; 400 is the bypass module; and 500 is the configuration monitoring and management module. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0066] Please see Figure 1 An embodiment of the present invention provides a multi-source track fusion method based on dynamic and static parameter normalization comparison. Under the condition of multi-source surveillance data, each parameter is set as an operating parameter and dynamic and static parameters are classified. The normalization value is calculated by normalization method and compared to select the data source with the best data quality to participate in the fusion. It also supports comparison and verification with fused data of various radar formats. The radar fusion data is used to supplement and correct the ADS-B data, thereby obtaining the ADS-B fused track with the best data quality, the most stable data, and the most complete data items.

[0067] Specifically, the multi-source track fusion method based on dynamic and static parameter normalization comparison includes:

[0068] Step S10: Read static and dynamic parameters;

[0069] Specifically, based on the different functions and change frequencies of the parameters used in the data processing process, parameters are divided into static parameters and dynamic parameters, which are managed separately. Static parameters are descriptive parameters used during program initialization and parameters defined internally by the system that cannot be arbitrarily modified. Changes to static parameters require a program restart to take effect. Dynamic parameters are parameters used during program execution to assist the program in receiving and processing data. Dynamic parameters are parameters that may be adjusted or changed according to different requirements or actual situations. When dynamic parameters change, parameter change information must be sent, and the program re-initializes the dynamic parameters after receiving the change information. Changes to dynamic parameters can quickly affect the program's execution results. Users can modify dynamic parameters in real time according to the data processing status, thereby quickly meeting operational needs under specific conditions.

[0070] Step S20: Receive A / B channel data from the ADS-B data source according to dynamic parameters; wherein, the A / B channel includes a primary A channel and a backup B channel;

[0071] Specifically, when receiving A / B channel data from an ADS-B data source, the received data can be internally distributed. One path selects the primary A channel of each data source according to dynamic parameter configuration or default configuration to parse the data, forming internal format data, and then sends it to the data fusion processing flow. Simultaneously, after a certain period, it receives the return results from the data source channel selection process and, based on parameter configuration, selects whether to automatically switch the corresponding primary data source channel. The other path forwards raw data from all channels of each data source to the data quality statistics process in real time for channel comparison. The primary A channel and backup B channel mentioned here are not limited to two channels; they can be extended to multiple channels, i.e., one or more A channels and one or more B channels.

[0072] Step S30: Determine whether to perform channel comparison between channel A and channel B; if yes, proceed to step S40; otherwise, proceed to step S50.

[0073] You can choose whether to perform channel selection. If you do not participate in channel selection, you can directly use the default channel or set a specified channel as the channel in use. If you participate in channel selection, abnormal channel data is identified through static parameters and aircraft flight characteristics. Finally, based on the data quality judgment results, the channels of each data source are comprehensively evaluated according to the parameter weight configuration to obtain the channel with the best quality of each ADS-B data source.

[0074] Step S40: Channel A and Channel B are compared and selected through data quality statistics to obtain the channel in use; the data quality statistics identify abnormal data in the channel through static parameters and aircraft flight characteristics and perform analysis and statistics.

[0075] Please see Figure 2 Specifically, step S40 includes:

[0076] Step S410: Count the number of data frames in the A / B channels; specifically, count the total number of data frames received within one period.

[0077] Step S420: Determine whether the data volume of the primary A channel is zero or exceeds the set upper limit; if yes, proceed to step S430; otherwise, proceed to step S460; when the data volume is zero, it means that there is no data transmission in the channel; when it exceeds the set upper limit, it means that there is a data transmission abnormality in the channel, and the channel needs to be reinitialized.

[0078] Step S430: Determine whether the data volume of the backup B channel is zero or exceeds the set upper limit; if yes, proceed to step S440; otherwise, proceed to step S450; when the data volume of the A channel is zero or exceeds the set upper limit, the B channel is also statistically analyzed and determined to confirm the currently used channel.

[0079] Step S440: Initialize channel A and end;

[0080] Step S450: Perform primary / backup switchover and determine that channel B is the channel in use; when channel B is confirmed to be in use, determine channel B as the channel in use.

[0081] Step S460: Analyze the data frames of channel A and channel B and perform data quality statistics; the data quality statistics include any one or more of the following: total number of data frames, number of erroneous data frames, data source delay statistics, number of low-quality data frames, number of position bounce data frames, number of data frames with large-scale position deviation, number of data frames with abnormal position swing, number of data frames with height jump, and number of data frames with speed jump.

[0082] Specifically, data quality statistics include:

[0083] 1) Total number of data frames

[0084] Count the total number of data frames received within a period.

[0085] 2) Number of erroneous data frames

[0086] The number of abnormal data frames, such as those with abnormal frame format or missing required items, is counted.

[0087] 3) Statistics on data source latency

[0088] The number of data frames whose timestamps in the statistics frames exceed the maximum available latency time when compared with the server time.

[0089] 4) Number of low-quality data frames

[0090] According to the configuration statistics, the number of data frames in the ADS-B data source whose data quality factors such as NUC, NACp, NIC, and SIL are less than the values ​​that are unavailable in the static or dynamic parameter configurations.

[0091] 5) Number of position bounce data frames

[0092] The position bounce detection method is based on the fact that the rate of change of angles at three consecutive points P1, P2, and P3 on the same flight exceeds a set value, assuming the vector... and The angle between P2 and P3 is θ, and the time taken from P2 to P3 is T. 23 Then the rate of change of angle J:

[0093]

[0094] J = θ / T 23

[0095] If the rate of change of the heading angle of two vectors is greater than 16° / s and the included angle is greater than 170°, then it is judged as position bounce data and included in the position bounce data results.

[0096] 6) Number of data frames with large-scale positional deviation

[0097] The method for judging large-scale positional deviation is that the velocity changes of three adjacent points P1, P2, and P3 are greater than a set value. Assume the time taken for P1 to travel to P2 is T. 12 The distance is S 12 The time taken to travel from P2 to P3 is T. 23 The distance is S 23 Calculate the velocity change D:

[0098] D = |2 - V3| / T 23

[0099] in

[0100] V2 = S 12 / T 12

[0101] V3 = S 23 / T 23

[0102] If the rate of change of velocity D is greater than the parameter setting value, the trajectory data is considered to have a positional deviation, and this is included in the result of large-scale positional deviation of the data.

[0103] 7) Number of data frames with abnormal position swing

[0104] The calculation method is similar to that for position bounce. When the rate of change of heading angle of three consecutive points is greater than 16° / s and the included angle is less than 170°, it is considered a position swing flight and is included in the data position swing anomaly results.

[0105] 8) Number of height-hopping data frames

[0106] Compare the same track data, for two adjacent track points A and B, with altitudes h1 and h2 respectively, and a time difference of Δt between the two points, calculate their vertical acceleration:

[0107] g=(h1-h2) / Δt

[0108] If the vertical acceleration g is greater than the maximum vertical acceleration of the aircraft configured in the parameters, the flight path data is considered to have an abnormal altitude jump and is then statistically analyzed.

[0109] 9) Number of speed jump data frames

[0110] Compare the same track data, with two adjacent track points A and B, having velocities v1 and v2 respectively, and a time difference of Δt between the two points, calculate their horizontal acceleration:

[0111] a=(v1-v2) / Δt

[0112] If the horizontal acceleration 'a' is greater than the maximum horizontal acceleration of the aircraft configured in the parameters, then the flight path data is considered to have an abnormal altitude jump and is statistically analyzed.

[0113] Step S470: Calculate and compare the data quality of the two in-use channels according to the parameter configuration weights to obtain the channel selection result; the channel selection result is the selection of the in-use channel.

[0114] Based on the above statistical results, the following data sources were calculated: error rate s1 (number of erroneous data frames / total number of data frames), proportion of data frames with latency exceeding the configured unavailability time s2 (number of data frames with latency greater than the configured value / total number of data frames), track loss rate s3 (number of track points lost / total number of data frames), position jump rate s4 (number of position jump data frames / total number of data frames), altitude jump rate s5 (number of altitude jump data frames / total number of data frames), and speed jump rate s6 (number of speed jump data frames / total number of data frames).

[0115] Users assign different weights (p1, p2, p3, p4, p5, p6) to various indicators in the dynamic and static parameters according to their needs and the impact on the quality of the fused data. The final total score S of each channel of the data source can be obtained by the following formula:

[0116] s=s1*p1+s2*p2+s3*p3+s4*p4+s5*p5+s6*p6

[0117] Finally, the total data quality scores s obtained from each data source channel are compared, the channel with the best data quality for each data source is selected, and the comparison results are fed back to the ADS-B data processing flow through internal messages. During data processing, the data source channels participating in the fusion are automatically switched to the better quality channels based on the feedback results, thereby improving the quality of ADS-B fused data.

[0118] Step S50: Configure the specified channel as the in-use channel according to the dynamic parameters;

[0119] When no channel selection is performed, the channel specified according to the dynamic parameter configuration is the channel in use.

[0120] Step S60: Perform abnormal data processing on the data of the in-use channel; perform real-time data detection on the data of each ADS-B data source that participates in the fusion, i.e. the data of the in-use channel, and process the abnormal data.

[0121] The abnormal data processed includes:

[0122] 1) Missing required items. Required items include data source identifier, target report descriptor, address code, location, flight number, location marker time, and data quality factor. When any required data item is missing in an ADS-B data frame, its abnormal information is recorded, and the data frame is discarded and not used as a reference for fusion data.

[0123] 2) Data delay judgment: Based on the fact that the current domestic ADS-B ground station data update logic is all location-driven update, the timestamp marked when the ground station receives the location information sent by the airborne equipment in the ADS-B data frame is compared with the server time deployed by the method. The premise of this comparison is to determine that the synchronization error between the server time and the GPS clock is within an acceptable error range. When the difference between the two times exceeds the delay limit configured by the parameters, the data with excessive delay is filtered to ensure the real-time validity of the data and avoid interference with the fused data.

[0124] 3) Track characteristic detection: The fused data output from the previous few cycles of the detected track target is saved for a certain period. When the latest cycle data of the target is received, the position, heading angle, speed, altitude, etc. of the two track points are compared to determine whether the normal flight characteristics of the aircraft are met during its continuous flight. All track characteristics can be set in the dynamic and static parameters of the method, such as the normal flight speed range, altitude range, acceleration range, turning rate range, etc. When the calculation finds that the target violates the normal flight state of the aircraft and exhibits abnormalities such as position jump, large-scale jump, unreasonable swaying, speed jump, altitude jump, etc., the real-time data frame is filtered and the abnormality is recorded to ensure that the ADS-B data frames participating in the fusion are stable and reliable and conform to the normal flight characteristics of the aircraft.

[0125] Please see Figure 3 The specific steps S60 include:

[0126] Step S610: Receive internal format data and obtain the values ​​of each data item;

[0127] Step S620: Determine if the required conditions are met. If yes, proceed to step S630; otherwise, the data filtering ends.

[0128] Step S630: Obtain the server time and the flight track time and compare them to determine if there is a data delay; if so, proceed to step S640; otherwise, the data filtering ends.

[0129] Step S640: Obtain historical information of a point on the track, and combine it with the information of the previous point to calculate track characteristics and identify position jump and altitude jump data;

[0130] Step S650: Determine whether the data conforms to the trajectory characteristics. If so, perform multi-source data fusion; otherwise, end the data filtering process.

[0131] Step S70: Perform multi-source data fusion on the data from the in-use channel that has completed the abnormal data processing, the data from the in-use channel of other ADS-B data sources, and / or the internal format data from the radar data source.

[0132] Please refer to the following: Figure 4 and Figure 5 Specifically, step S70 includes:

[0133] Step S701: Obtain the in-use channel data of the real-time ADS-B data source based on the dynamic parameters;

[0134] or,

[0135] Obtain the internal format data of the radar data source based on static parameters;

[0136] The radar data source is provided to the ADS-B data fusion process for selecting, verifying, supplementing, and correcting ADS-B data items. The processing steps for the internal format data of the radar data source include:

[0137] Step S1: Analyze the data from each radar and confirm that the data is not abnormal; access the radar data, including primary and secondary radar data, S-mode radar data, integrated radar tracks, etc., and confirm that the data is normal.

[0138] Step S2: Unify the spatial coordinates and data units of the radar data; parse and unify the different units of position, altitude, velocity, etc. in each data format according to its data format definition.

[0139] Step S3: Determine if it is a multiple coverage; if yes, proceed to step S4; otherwise, proceed to step S5.

[0140] Step S4: Weighted integration of data items from multiple radar data sets;

[0141] Step S5: Set the radar data to altitude and add radar data tags to form comprehensive track data;

[0142] Step S6: Perform track filtering on the integrated track data and avoid sending a large number of track points at the same time to perform peak reduction processing to obtain the radar data source;

[0143] Step S7: Obtain the radar data source based on static parameters and perform internal format processing to obtain the internal format data of the radar data source.

[0144] Based on the weights of each radar source set by dynamic and static parameters, a weighted fusion mosaic algorithm is used to supplement the information items of the points in the multi-coverage area. A unified composite track is formed using information such as track position and altitude, and flight identification. Finally, radar composite track data in an internally unified format is generated and sent to the ADS-B data fusion module for fusion processing.

[0145] Step S702: Search the data item queue for flight information contained in the in-use channel data of the ADS-B data source or the internal format data of the radar data source;

[0146] Step S703: Determine whether the flight information is found in the data item queue; if not found, proceed to step S704; if found, proceed to step S705.

[0147] Step S704: Insert the flight information into the data item queue;

[0148] Step S705: Search for flight information in the ADS-B track queue;

[0149] Step S706: Determine whether the flight information is found in the ADS-B track queue; if not, proceed to step S707; otherwise, proceed to step S708.

[0150] Step S707: Insert the flight information into the ADS-B track queue and then end;

[0151] Step S708: Compare the timestamp of the flight data found in the ADS-B track queue with the timestamp of the real-time flight data and calculate the time interval;

[0152] Step S709: Determine the time interval and whether it is less than or equal to the minimum update cycle of the ADS-B data source set in the static parameters; if yes, proceed to step S710, otherwise proceed to step S711.

[0153] Step S710: Filter the real-time ADS-B data, end;

[0154] Step S711: Update the flight information in the ADS-B track queue.

[0155] Specifically, the data fusion between ADS-B data sources involves simultaneously receiving track data from multiple ADS-B data sources based on dynamic parameter configuration. The processing is tailored to the coverage of aircraft targets by ground stations. When a target is covered by a single ground station, to ensure the fused data includes the target's track data, the data from that single source is used as the ADS-B fusion data carrier and compared with subsequent radar data. When a target is repeatedly covered by multiple data sources, the same target will receive data from multiple ground stations. The method filters data with time intervals shorter than the minimum update cycle set in the dynamic and static parameters for ADS-B data, selecting the data with the update time closest to the minimum update cycle as the fusion data carrier for subsequent fusion with radar data, thus ensuring the timeliness of the ADS-B fused data.

[0156] Meanwhile, when there are inconsistencies in the ADS-B data versions output from different ADS-B data sources, the higher version data is converted into lower version data for fusion according to the definition of each version of ASTERIX CAT021. The final output of the fused data version is the lowest version of the ASTERIX CAT021 format data output from all ADS-B data sources.

[0157] The fusion of ADS-B and radar data sources takes into account the dependence of ADS-B data on airborne equipment. By receiving radar data, it supports the processing of surveillance source data such as primary and secondary radars, Mode S radar, and integrated radar tracks. It extracts data items from the ADS-B data, including 24-bit address codes, flight numbers, secondary codes, ground vectors, altitudes, and positions. It continuously updates the values ​​of these data items based on the received radar data source information to ensure that the stored data item values ​​are up-to-date in real time. When there are missing or abnormal data items in the fused track formed by the ADS-B data source, the information of these data items obtained from the radar data source is used to fill or update the ADS-B fused data. As a result, the ADS-B fused data output by the system is more complete, accurate, and reliable than any single ADS-B data source.

[0158] The multi-source track fusion method based on dynamic and static parameter normalization comparison provided by this invention, under the condition of multi-source surveillance data, sets each parameter as an operational parameter in a unified manner and classifies dynamic and static parameters. It uses a normalization method to calculate the normalized value and compares it to select the data source with the best data quality to participate in the fusion. It can also be compared and verified with fused data of various radar formats. The radar fused data is used to supplement and correct the ADS-B data, thereby obtaining ADS-B fused track data with the best data quality, the most stable data, and the most complete data items.

[0159] Simultaneously considering the generation and transmission characteristics of ADS-B data, ground stations, data stations, and other systems use different transmission links to output one or more channels of ADS-B data. A comprehensive data quality analysis is performed on all ADS-B data channels accessed by the system, and weight parameters are set for each statistical analysis data item according to the actual needs of users to adapt to different usage environments. The channel data quality is statistically analyzed regularly according to the parameter values ​​set by users, and the channel data with the best data quality in the same data source is selected for fusion.

[0160] By combining historical and latest aircraft track data with dynamic and static parameters for calculation and comparison, abnormal track data is identified. Confirmed abnormal track data is filtered out and excluded from the fusion process to ensure the stability of the fused data. Furthermore, considering the dependence of ADS-B data on onboard equipment, if the onboard equipment malfunctions, all ground systems will receive abnormal aircraft track data. Therefore, within radar coverage areas, radar data can be used to compare and verify ADS-B data. Missing ADS-B data items are supplemented, and if data item comparisons conflict, the accuracy of radar and ADS-B data can be verified through parameter verification. When radar data is accurate but ADS-B data is abnormal, the corresponding radar data value can replace the ADS-B data item value.

[0161] In addition, the present invention also provides a server.

[0162] Please see Figure 6 This is a schematic diagram of the internal structure of a server provided in an embodiment of the present invention. The server includes at least a memory 11, a processor 12, a communication bus 13, and a network interface 14.

[0163] The memory 11 includes at least one type of readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the server, such as the server's hard disk. In other embodiments, the memory 11 can be an external storage device of the server, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 11 can include both internal and external storage units of the server. The memory 11 can be used not only to store application software and various types of data installed on the server, such as alarm program code, but also to temporarily store data that has been output or will be output.

[0164] In some embodiments, processor 12 may be a central processing unit (CPU), controller, microcontroller, microprocessor or other data processing chip, used to run program code stored in memory 11 or process data, such as executing a multi-source track fusion program based on dynamic and static parameter normalization comparison.

[0165] Communication bus 13 is used to enable communication between these components.

[0166] Network interface 14 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface), which is typically used to establish communication connections between the server and other electronic devices.

[0167] Optionally, the server may also include a user interface, which may include a display, an input unit such as a keyboard, and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the server and to display a visual user interface.

[0168] Figure 6 Only a server with components 11-15 and a multi-source track fusion program based on dynamic and static parameter normalization comparison is shown. Those skilled in the art will understand that... Figure 6 The structure shown does not constitute a limitation on the server and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0169] exist Figure 6 In the server embodiment shown, the memory 11 stores a multi-source track fusion program based on dynamic and static parameter normalization comparison; when the processor 12 executes the multi-source track fusion program based on dynamic and static parameter normalization comparison stored in the memory 11, it performs the following steps:

[0170] Step S10: Read static and dynamic parameters;

[0171] Step S20: Receive A / B channel data from the ADS-B data source according to dynamic parameters; wherein, the A / B channel includes a primary A channel and a backup B channel;

[0172] Step S30: Determine whether to perform channel comparison between channel A and channel B; if yes, proceed to step S40; otherwise, proceed to step S50.

[0173] Step S40: Channel A and Channel B are compared and selected through data quality statistics to obtain the channel in use; the data quality statistics identify abnormal data in the channel through static parameters and aircraft flight characteristics and perform analysis and statistics.

[0174] Step S50: Configure the specified channel as the in-use channel according to the dynamic parameters;

[0175] Step S60: Perform abnormal data processing on the data of the channel in use;

[0176] Step S70: Perform multi-source data fusion on the data from the in-use channel that has completed the abnormal data processing, the data from the in-use channel of other ADS-B data sources, and / or the internal format data from the radar data source.

[0177] Please refer to the above as well. Figure 7 and Figure 8 As shown, an embodiment of the present invention also provides a multi-source track fusion system based on dynamic and static parameter normalization comparison, including a server 10, a web server 20, and a client 30 as described above. The web server 20 and the client 30 are respectively communicatively connected to the server 10. The server 10 is used for data storage and distribution, data processing, and data collection and parsing. The web server 20 is used for system monitoring, parameter configuration, and system management. The client 30 is used for displaying track dynamics, track alarms, and track playback. Figure 8 This is a schematic diagram of a multi-source track fusion program module based on dynamic and static parameter normalization comparison in one embodiment of the server 10 of the present invention. In this embodiment, the multi-source track fusion program based on dynamic and static parameter normalization comparison can be divided into a data storage and distribution module 100, a data processing module 200, a data collection and parsing module 300, a bypass module 400, and a configuration monitoring and management module 500. The data storage and distribution module 100 includes a recording and playback submodule and a data distribution and processing submodule; the data processing module 200 includes a data fusion verification submodule and a data quality statistics submodule; the data collection and parsing module 300 includes an ADS-B data collection submodule and a radar data collection submodule. The various system components and modules communicate with each other through a message processing program using system-defined message formats.

[0178] Specifically, the ADS-B data collection submodule receives ADS-B track data sent by the ADS-B data source, and analyzes the data quality results of each ADS-B data source based on the data reception status and the data quality statistics submodule. It then compares and selects the optimal channel for each ADS-B data source, parses the data from the optimal channel, and sends the data in an internal format that is easy for the system to read to the data fusion verification submodule for multi-data source fusion.

[0179] The radar data collection submodule receives radar data of various types, analyzes and fuses the received radar data to form a radar fusion track, assembles it into the system's internal data format, and sends it to the data fusion verification submodule for multi-source data fusion.

[0180] The data fusion verification submodule fuses multi-source ADS-B data, as well as received radar fusion data and ADS-B fusion data, ultimately forming a single ADS-B fusion data stream encompassing the ranges of each ADS-B data source. This module also performs cross-validation and TDOA algorithm verification using radar data and ADS-B data meeting certain conditions under multiple coverage areas to identify and mark false targets.

[0181] The recording and playback submodule is used to record the original input ADS-B data, the ADS-B data after fusion verification, and the data output by the publishing module; and to read and play back the recorded original ADS-B data, the data after fusion verification, and the data after system distribution according to the user configuration.

[0182] The data distribution and processing submodule customizes different data items, ranges, delays, and other parameters for sending data to different types of users.

[0183] The bypass module 400 accesses ADS-B data from critical sites and simultaneously monitors the operating status of each server in the A / B network. When both A / B servers fail simultaneously, resulting in no data output, it automatically selects one of the A / B channels of the critical site for output.

[0184] The configuration monitoring and management module 500 is used to configure system operating parameters, including online and offline parameters, monitor the data connection status of various data sources, monitor the operation status of various modules of the system, and perform remote shutdown, startup, and restart operations of modules.

[0185] Server 10 acquires ADS-B track data from ADS-B ground stations, data stations, and other data centers via IP network using UDP (single, group, and broadcast) methods. Data collection and parsing module 300 receives data from various ADS-B data sources via sockets according to system parameter configurations. It distributes and parses the received ADS-B data, forwarding the raw track data from each ADS-B data source to the data quality statistics submodule and the recording and playback submodule via UDP multicast for data quality analysis. The recording and playback submodule also records and plays back all received raw data. Simultaneously, the data distribution and processing submodule selects the primary channel data from each data source according to parameter configurations for parsing, forming data in the system's internal format. Based on the system's internal data transmission configuration, it then sends the data to other modules via UDP multicast for use. For ADS-B data sources that select automatic channel switching, the optimal channel information of the data source returned by the data quality statistics submodule will be received. If the two are inconsistent, the data collection and parsing module 300 will automatically switch the primary channel of the data source during the fusion data parsing process, select the channel data with better quality for parsing, and participate in the subsequent fusion processing.

[0186] The radar data collection submodule receives radar data in various formats via network IP protocol or serial port protocol, through protocol conversion and network transmission equipment module, including radar data of various primary and secondary radars, S-mode radar, integrated radar tracks, etc. The module parses and fuses the received radar data to form radar fusion data containing all radar targets, and transmits it internally within the system in an internal format according to parameter configuration for use by other modules.

[0187] The data fusion verification submodule is the core module of the system. This module receives ADS-B data and radar data, performs anomaly processing on the received ADS-B data, filtering out incomplete data items, excessive delays, low data quality, and data that does not conform to aircraft flight characteristics. For data that meets the requirements, it selects the data with the fastest update cycle for each track target as the ADS-B fusion data for that target. Other flight data with an update cycle shorter than the normal ADS-B data update cycle are filtered to ensure that only one data packet is output for each target within one cycle. This module also receives radar fusion data output from the radar collection and processing module. It associates the ADS-B fusion data with a series of parameters such as address code, flight number, secondary code, position, speed, and altitude, comparing the values ​​of corresponding data items in the two sets of data. If there are missing or conflicting ADS-B data items, the module extracts information such as flight number, secondary code, altitude, and speed from the radar data to supplement or replace the ADS-B data. This module also uses radar data. In areas covered by both radar and ADS-B, it compares ADS-B targets with radar targets. If an ADS-B target exists but the radar target does not, the target is considered a possible false target and marked in its output data packet. In areas covered by multiple ADS-B ground stations, if the data meets certain conditions, the actual target location information is calculated using the TDOA algorithm and compared with the location information in the target data packet. If the difference exceeds the parameter setting value, the target is considered a possible false target and marked in its output data packet.

[0188] The recording and playback submodule records the raw data from various ADS-B data sources, the fused data generated by the system, and the data output to external systems. The data is stored in binary files on the system's storage server. During playback, the system reads the data from the corresponding files according to the playback time period, the data source to be played back, the playback speed, and other information configured by the user, and outputs the data to the specified server or device via UDP protocol.

[0189] The data distribution and processing submodule distributes the fused ADS-B data, along with the received ADS-B and radar data, via UDP protocol according to user parameter configurations. It filters and customizes the output data based on different configurations. Simultaneous output of multiple data streams is supported, with each stream operating independently and without interference.

[0190] The bypass module 400 serves as an emergency module, accessing ADS-B data from critical sites and simultaneously monitoring the operational status of servers in the A / B networks. When both A / B servers with the same function fail simultaneously, causing data to be unable to be output normally from both networks, this module automatically selects one of the A / B channels of the critical site for output based on the parameter configuration, and provides the data to the downstream system using the UDP protocol.

[0191] The monitoring and management module 500 is configured as a web-based system, allowing users to monitor the system's operational status in real time. Monitored objects include ADS-B data sources, radar data sources, and all hardware devices in the system, such as servers, terminals, network devices, storage devices, protocol conversion devices, and clock servers. The module determines the operational status of ADS-B data sources by statistically analyzing data packet counts, track counts, and CAT023 and CAT247 information. For radar data sources, it assesses their operational status based on data packet counts and track counts. The module also monitors the operational information of various hardware devices and module processes via SNMP. Hardware device monitoring includes CPU usage, memory usage, disk usage, and network port traffic. Application module monitoring includes CPU usage, memory usage, and operational information reported by the application module itself via UDP. This module integrates system parameter configuration and log collection and query functions. Users can configure and manage ADS-B and radar data sources via a web page, manage various operating parameters required by different system modules, and manage user accounts, permissions, and basic parameter configurations. The module also collects and records log information from various servers through the syslog service, displaying it on the web page for user viewing and querying.

[0192] Depending on their functions, each module can be deployed on a single server or on different servers. Modules and servers operate in a primary / backup mode, with primary and backup modules monitoring each other's operating status and heartbeat packets. When the primary module malfunctions or fails, the system can quickly and automatically switch to backup, ensuring the stability of the system.

[0193] The functions or operation steps implemented by the above-mentioned data storage and distribution module 100, data processing module 200, data collection and parsing module 300, bypass module 400 and configuration monitoring and management module 500 when they are executed are largely the same as those in the above embodiments, and will not be repeated here.

[0194] Furthermore, this embodiment of the invention also proposes a storage medium, which is a computer-readable storage medium, storing a multi-source track fusion program based on dynamic and static parameter normalization comparison. This multi-source track fusion program based on dynamic and static parameter normalization comparison can be executed by one or more processors to achieve the following operations:

[0195] Step S10: Read static and dynamic parameters;

[0196] Step S20: Receive A / B channel data from the ADS-B data source according to dynamic parameters; wherein, the A / B channel includes a primary A channel and a backup B channel;

[0197] Step S30: Determine whether to perform channel comparison between channel A and channel B; if yes, proceed to step S40; otherwise, proceed to step S50.

[0198] Step S40: Channel A and Channel B are compared and selected through data quality statistics to obtain the channel in use; the data quality statistics identify abnormal data in the channel through static parameters and aircraft flight characteristics and perform analysis and statistics.

[0199] Step S50: Configure the specified channel as the in-use channel according to the dynamic parameters;

[0200] Step S60: Perform abnormal data processing on the data of the channel in use;

[0201] Step S70: Perform multi-source data fusion on the data from the in-use channel that has completed the abnormal data processing, the data from the in-use channel of other ADS-B data sources, and / or the internal format data from the radar data source.

[0202] The specific implementation of the storage medium of the present invention is basically the same as the embodiments of the multi-source track fusion method and apparatus based on dynamic and static parameter normalization comparison described above, and will not be repeated here.

[0203] It should be noted that the sequence numbers of the above embodiments of the present invention are merely for descriptive purposes and do not represent the superiority or inferiority of the embodiments. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, apparatus, article, or method. Without further limitations, an element defined by the phrase "comprising a…" does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0204] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a drone, mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0205] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A multi-source track fusion method based on dynamic and static parameter normalization comparison, characterized in that, include: Step S10: Read static and dynamic parameters; Step S20: Receive A / B channel data from the ADS-B data source according to dynamic parameters; wherein, the A / B channel includes a primary A channel and a backup B channel; Step S30: Determine whether to perform channel comparison between channel A and channel B; if yes, proceed to step S40; otherwise, proceed to step S50. Step S40: Channel A and Channel B are compared and selected through data quality statistics to obtain the channel in use; the data quality statistics identify abnormal data in the channel through static parameters and aircraft flight characteristics and perform analysis and statistics. Step S50: Configure the specified channel as the in-use channel according to the dynamic parameters; Step S60: Perform abnormal data processing on the data of the channel in use; Step S70: Perform multi-source data fusion on the data from the in-use channel that has completed the abnormal data processing, the data from the in-use channel of other ADS-B data sources, and / or the internal format data from the radar data source.

2. The multi-source track fusion method based on dynamic and static parameter normalization comparison according to claim 1, characterized in that, The static parameters are descriptive parameters used during program initialization and parameters defined internally by the system that cannot be arbitrarily modified; the dynamic parameters are parameters used during program execution to assist the program in receiving and processing data.

3. The multi-source track fusion method based on dynamic and static parameter normalization comparison according to claim 1, characterized in that, Step S40 includes: Step S410: Count the number of data frames in channels A and B; Step S420: Determine whether the data volume of the primary A channel is zero or exceeds the set upper limit; if yes, proceed to step S430; otherwise, proceed to step S460. Step S430: Determine whether the data volume of the backup B channel is zero or exceeds the set upper limit; if yes, proceed to step S440; otherwise, proceed to step S450. Step S440: Initialize channel A and end; Step S450: Perform primary / standby switchover and determine that channel B is the active channel; Step S460: Analyze the data frames of channel A and channel B and perform data quality statistics; the data quality statistics include any one or more of the following: total number of data frames, number of erroneous data frames, data source delay statistics, number of low-quality data frames, number of position bounce data frames, number of data frames with large-scale position deviation, number of data frames with abnormal position swing, number of data frames with height jump, and number of data frames with speed jump. Step S470: Calculate and compare the data quality of the two in-use channels according to the parameter configuration weights to obtain the channel selection result; the channel selection result is used to select the in-use channel.

4. The multi-source track fusion method based on dynamic and static parameter normalization comparison according to claim 1, characterized in that, After selecting or specifying an in-use channel, the following also applies: The data of the channels in use is parsed according to the dynamic parameter configuration or the default configuration to form internal format data.

5. The multi-source track fusion method based on dynamic and static parameter normalization comparison according to claim 4, characterized in that, Step S60 includes: Step S610: Receive internal format data and obtain the values ​​of each data item; Step S620: Determine if the required conditions are met. If yes, proceed to step S630; otherwise, the data filtering ends. Step S630: Obtain the server time and the flight track time and compare them to determine if there is a data delay; if so, proceed to step S640; otherwise, the data filtering ends. Step S640: Obtain historical information of a point on the track, and combine it with the information of the previous point to calculate track characteristics and identify position jump and altitude jump data; Step S650: Determine whether the data conforms to the trajectory characteristics. If so, perform multi-source data fusion; otherwise, end the data filtering process.

6. The multi-source track fusion method based on dynamic and static parameter normalization comparison according to claim 1, characterized in that, Step S70 includes: Step S701: Obtain the in-use channel data of the real-time ADS-B data source based on the dynamic parameters; or, Obtain the internal format data of the radar data source based on static parameters; Step S702: Search the data item queue for flight information contained in the in-use channel data of the ADS-B data source or the internal format data of the radar data source; Step S703: Determine whether the flight information is found in the data item queue; if not found, proceed to step S704; if found, proceed to step S705. Step S704: Insert the flight information into the data item queue; Step S705: Search for flight information in the ADS-B track queue; Step S706: Determine whether the flight information is found in the ADS-B track queue; if not, proceed to step S707; otherwise, proceed to step S708. Step S707: Insert the flight information into the ADS-B track queue and then end; Step S708: Compare the timestamp of the flight data found in the ADS-B track queue with the timestamp of the real-time flight data and calculate the time interval; Step S709: Determine the time interval and whether it is less than or equal to the minimum update cycle of the ADS-B data source set in the static parameters; if yes, proceed to step S710, otherwise proceed to step S711. Step S710: Filter the real-time ADS-B data, end; Step S711: Update the flight information in the ADS-B track queue.

7. The multi-source track fusion method based on dynamic and static parameter normalization comparison according to claim 6, characterized in that, The processing steps for the internal format data of the radar data source include: Step S1: Analyze the data from each radar and confirm that the data is not abnormal; Step S2: Unify the spatial coordinates and data units of the radar data; Step S3: Determine if it is a multiple coverage; if yes, proceed to step S4; otherwise, proceed to step S5. Step S4: Weighted integration of data items from multiple radar data sets; Step S5: Set the radar data to altitude and label the radar data to form comprehensive track data; Step S6: Perform track filtering on the integrated track data and avoid sending a large number of track points at the same time to perform peak reduction processing to obtain the radar data source; Step S7: Obtain the radar data source based on static parameters and perform internal format processing to obtain the internal format data of the radar data source.

8. A server, characterized in that, The system includes a memory and a processor. The memory stores a multi-source track fusion program based on dynamic and static parameter normalization comparison that can run on the processor. When the multi-source track fusion program based on dynamic and static parameter normalization comparison is executed by the processor, it implements the steps of the multi-source track fusion method based on dynamic and static parameter normalization comparison as described in any one of claims 1 to 7.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and the storage medium stores a multi-source track fusion program based on dynamic and static parameter normalization comparison. The multi-source track fusion program based on dynamic and static parameter normalization comparison can be executed by one or more processors to implement the steps of the multi-source track fusion method based on dynamic and static parameter normalization comparison as described in any one of claims 1 to 7.

10. A multi-source track fusion system based on dynamic and static parameter normalization comparison, characterized in that, It includes the server, web server, and client as described in claim 8, wherein the web server and client are respectively communicatively connected to the server; the server is used for data storage and distribution, data processing, and data collection and parsing; the web server is used for system monitoring, parameter configuration, and system management; and the client is used for displaying track dynamics, track alarms, and track playback.

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