Multi-source aircraft track data fusion method and device

Abstract

The invention relates to a multi-source aircraft track data fusion method. The method comprises the following steps: receiving a plurality of pieces of track data about a target from a plurality of sources; based on the respective data formats of the multiple pieces of track data, analyzing the multiple pieces of track data and repackaging the multiple pieces of track data into the same data format; unifying data references of the multiple pieces of track data; performing correlation processing on the multiple pieces of track data to determine track data subsets with high correlation; and performing weighted average fusion on the subset of track data to determine a fused track, wherein weights for the weighted average fusion include static weights based on covariance of the subset of track data and dynamic weights based on dynamic extrapolation of the subset of track data. The invention further discloses a corresponding multi-source track data fusion device.

Description

Technical Field

[0001] The present application generally relates to the field of dynamic monitoring of aircraft test flight tracks, and more particularly to a method for fusing multi-source aircraft track data. Background Art

[0002] During the flight of an aircraft, ground monitoring or control personnel need to monitor the aircraft's speed, altitude, longitude and latitude and other track data in real time in order to keep abreast of the aircraft's flight dynamics. Existing track monitoring methods include radar, ADS-B, navigation satellite systems (for example, Beidou), etc. Track data comes from various radar stations or ADS-B ground stations and other equipment, and there are many data sources. At the same time, there are also large differences in data format, data content, etc. in track data such as radar, ADS-B, and navigation satellite systems (for example, Beidou). Different types of track data can generally only be used separately, and are limited by equipment performance and equipment distribution, and cannot fully utilize the advantages of multiple types of track data. This results in a waste of data resources.

[0003] Therefore, there is a need in the art for improved technology for implementing dynamic real-time monitoring of aircraft flight based on track data. Summary of the invention

[0004] One aspect of the present disclosure relates to a multi-source track data fusion method, comprising receiving multiple track data about a target from multiple sources; parsing the multiple track data based on their respective data formats and repackaging them into the same data format; unifying the data references of the multiple track data; and performing weighted average fusion on subsets of the track data to determine a fused track, wherein the weights used for the weighted average fusion include static weights based on the covariance of the track data subsets and dynamic weights based on dynamic extrapolation of the track data subsets.

[0005] According to some exemplary embodiments, unifying data references of the plurality of track data includes unifying one or more of coordinate systems, time, and altitude references of the plurality of track data.

[0006] According to some exemplary embodiments, correlating the multiple track data to determine a track data subset with high correlation includes determining the credibility of each track data in the multiple track data based on a multi-source fusion track credibility criterion, and determining the track data subset with high correlation based on the credibility.

[0007] According to some exemplary embodiments, the multi-source track data fusion method further includes determining the credibility of the track data by comparing each of the multiple track data with one or more of the position, speed, flight number, and altitude information of the unified integrated track.

[0008] According to some exemplary embodiments, the plurality of sources include a BeiDou navigation satellite system, and a data format of the track data of the BeiDou navigation satellite system includes a short message.

[0009] According to some exemplary embodiments, the plurality of sources further include one or more of radar and ADS-B.

[0010] According to some exemplary embodiments, the multi-source track data fusion method further includes dividing the flight area of ​​the target into multiple mosaic windows; in each mosaic window, determining the measurement covariance of each of the multiple sources; and determining the static weight based on the measurement covariance, wherein determining the static weight includes calculating the equivalent covariance of each source according to the error ellipse contour curve as the covariance weight of the track data of the source in the mosaic window, wherein the static weight includes the covariance weight.

[0011] According to some exemplary embodiments, the multi-source track data fusion method further includes assigning mosaic window weights corresponding to the track data of the multiple sources in each mosaic window based on the accuracy of the multiple sources' monitoring of the target, wherein the static weight also includes the mosaic window weight.

[0012] According to some example embodiments, the static weight is further based on normalization of the mosaic window weight and the covariance weight.

[0013] According to some exemplary embodiments, the multi-source track data fusion method further includes dynamically shifting the track using a kinematic equation based on the difference between the timestamp of the track and the current time to obtain the dynamic weight. Dynamically shifting the track using a kinematic equation can make the shifted position closer to the actual position of the aircraft.

[0014] Another aspect of the present disclosure includes a multi-source track data fusion device, including a receiving unit for receiving multiple track data about a target from multiple sources; a data processing unit for parsing the multiple track data based on their respective data formats and repackaging them into the same data format; unifying the data baselines of the multiple track data; performing correlation processing on the multiple track data to determine a highly correlated track data subset; and performing weighted average fusion on the track data subset to determine a fused track, wherein the weights used for the weighted average fusion include static weights based on the accuracy of the multiple sources' monitoring of the target and dynamic weights based on the covariance of the track data subsets; and an output unit for outputting the fused track data to a back-end device for display or call.

[0015] Other aspects of the present disclosure also include other corresponding devices, equipment, computer-readable media, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a multi-source aircraft track data fusion system according to an aspect of the present disclosure is shown.

[0017] Figure 2 A schematic diagram of a track data processing unit according to an aspect of the present disclosure is shown.

[0018] Figure 3 A flow chart of a multi-source aircraft track data fusion method according to one aspect of the present disclosure is shown.

[0019] Figure 4 A flow chart of a data access processing method for multi-source aircraft track data fusion according to one aspect of the present disclosure is shown.

[0020] Figure 5 A flow chart of a data decoding / parsing processing method for multi-source aircraft track data fusion according to one aspect of the present disclosure is shown.

[0021] Figure 6 A flow chart of a multi-source aircraft track data fusion method according to one aspect of the present disclosure is shown. DETAILED DESCRIPTION

[0022] Figure 1 FIG. 1 is a schematic diagram of a multi-source aircraft track data fusion system 100 according to an aspect of the present disclosure. Figure 1 As shown in , the receiving unit 102 can receive different types of track data from multiple sources 120. The multiple sources may include, but are not limited to, for example, radar, ADS-B, navigation satellite systems (e.g., Beidou), etc. Each source has its own data type and format, and is generally not universal. For example, with the promotion and application of Beidou technology, the technical solution for obtaining the track data of an aircraft through Beidou's unique short message function will be promoted, and the track monitoring means based on Beidou's short message function will also be gradually developed in the future. As can be seen, although receiving track data from multiple sources 120 is described in this application as receiving track data directly from each source 120, the present disclosure is not limited to this, but the track data can be received from one or more intermediate devices.

[0023] According to an exemplary embodiment, the data processing unit 104 may perform multi-source track data fusion processing on different types of track data received by the receiving unit 102 from multiple sources 120. For example, performing multi-source track data fusion processing on different types of track data may include decoding and / or parsing different types of track data, and repackaging the decoded and / or parsed data content into the same data format. Decoding and / or parsing different types of track data and repackaging can make it easy for the solution according to the present disclosure to accommodate track data in a new format / encoded and / or track data from a new source.

[0024] According to an exemplary embodiment, the data processing unit 104 may convert the repackaged data content to unify the data reference. The unified data reference may include but is not limited to unifying the coordinate system, time, and / or altitude reference of different types of track data.

[0025] According to an exemplary embodiment, the data processing unit 104 may perform track correlation processing on the unified data content of the data reference. The track correlation processing may include determining the correlation between the track data. According to an exemplary embodiment, determining the correlation between the track data may include comparing the credibility values ​​of each track data to determine the correlation between the track data, and determining whether the multi-source original track data is related to the unified integrated track. According to an exemplary embodiment, the unified integrated track includes the track data obtained after the fusion processing at the previous moment.

[0026] According to an exemplary embodiment, the credibility value of the track data may be determined based on a multi-source fused track credibility criterion. According to a further exemplary embodiment, the credibility value of the track data may be determined based on a multi-source fused track credibility criterion by comparing and checking the position, speed, flight number, altitude and other information of the multi-source original track data with the unified integrated track.

[0027] According to an exemplary embodiment, the data processing unit 104 may perform track fusion on a highly correlated data content subset selected after track correlation processing. According to some exemplary embodiments, track fusion may adopt an average fusion method. According to some exemplary embodiments, track fusion may adopt a weighted average fusion method. According to further embodiments, the weight coefficient of the weighted average may include, for example, a static weight based on the accuracy of target monitoring by track monitoring equipment from multiple sources and a dynamic weight based on the covariance of the track data subset.

[0028] According to an exemplary embodiment, the data processing unit 104 may provide the fused track data to the output unit 106. According to an exemplary embodiment, the output unit 106 may output the fused track data to a backend device for display and / or calling.

[0029] Figure 2 A schematic diagram of a track data processing unit 200 according to an aspect of the present disclosure is shown. Figure 2 The track data processing unit 200 may include, implement, or constitute the above combination Figure 1 The described part of the data processing unit 104 for the multi-source aircraft track data fusion system 100 and the like.

[0030] According to an exemplary embodiment, the track data processing unit 200 may include at least one or more of a data decoding / parsing unit 202 , a data encapsulation unit 204 , a data reference conversion unit 206 , a track data correlation unit 208 , and a track fusion unit 210 , or any combination thereof.

[0031] According to an exemplary embodiment, the data decoding / parsing unit 202 can decode and / or parse different types of track data (if necessary). Track data from different sources (including but not limited to radar, ADS-B, navigation satellite system (e.g., Beidou), etc.) may have their own data types and formats, and generally cannot be universal. For example, Beidou technology can provide track data based on Beidou short message function.

[0032] According to an exemplary embodiment, the data encapsulation unit 204 may obtain the decoded / parsed data content from the data decoding / parsing unit 202 , re-encapsulate the decoded / parsed data content into the same data format, and provide the re-encapsulated data content to the data reference conversion unit 206 .

[0033] According to an exemplary embodiment, the data reference conversion unit 206 may obtain the repackaged data content from the data encapsulation unit 204 , convert the repackaged data content to unify the data reference, and provide the data content with the unified data reference to the track data correlation unit 208 .

[0034] According to an exemplary embodiment, unifying the data reference may include, but is not limited to, unifying the coordinate system, time, and / or altitude reference of different types of track data. For example, radar, ADS-B, and Beidou short message track data may be based on various different coordinate systems, such as rectangular coordinate system, WGS-84 geodetic coordinate system, etc. According to an exemplary embodiment, converting the repackaged data content to unify the data reference may include unifying the track data reference coordinates according to the conversion formula between coordinate systems.

[0035] According to an exemplary embodiment, the following altitude information may be commonly used in multi-source fusion tracks: geographic altitude, pressure altitude, and flight altitude layer. If the geographic altitude exists, the geographic altitude is filtered in the receiving thread and the geographic altitude is extrapolated. For the pressure altitude or flight altitude layer data items, if the QNH (corrected sea level pressure) value of the flight area is obtained, the pressure altitude and flight altitude are corrected, and altitude filtering and altitude extrapolation are also performed; otherwise, no processing may be performed on the pressure altitude and flight altitude layer data items.

[0036] According to exemplary embodiments, converting the repackaged data content to unify the data reference may include performing data calculation unification on the time reference and / or altitude reference of different types of track data. For example, according to at least some exemplary embodiments, in order to solve the problem of inconsistent timestamps of track information caused by communication delays and differences in data transmission speeds, the motion equation fitting may be used to unify the data timestamps and complete the unified processing of the time reference of the data.

[0037] According to an exemplary embodiment, the track data correlation unit 208 may obtain the unified data content of the data reference from the data reference conversion unit 206, perform track correlation processing on the unified data content of the data reference, and provide a highly correlated data content subset selected after the track correlation processing to the track fusion unit 210. The track correlation processing may include determining the correlation between the track data.

[0038] According to an exemplary embodiment, determining the correlation between track data may include comparing the magnitude of the credibility values ​​of each track data to determine the correlation between the track data, and judging whether the multi-source original track data is related to the unified integrated track. According to an exemplary embodiment, the credibility value of the track data may be determined based on the credibility criterion of the multi-source fusion track. According to a further exemplary embodiment, the credibility value of the track data may be determined based on the credibility criterion of the multi-source fusion track by comparing the position, speed, flight number, altitude and other information of the multi-source original track data with the unified integrated track. For example, the credibility of the track data that meets the set corresponding inspection standards may be increased by a certain value accordingly.

[0039] According to an exemplary embodiment, the track fusion unit 210 may obtain a highly correlated data content subset selected after track correlation processing from the track data correlation unit 208, and perform track fusion on the highly correlated data content subset selected after track correlation processing. According to some exemplary embodiments, track fusion may adopt an average fusion method. According to some exemplary embodiments, track fusion may adopt a weighted average fusion method. According to a further embodiment, the weight coefficient of the weighted average may include, for example, a static weight based on the accuracy of target monitoring by track monitoring equipment from multiple sources and a dynamic weight based on the covariance of the track data subset. According to an exemplary embodiment, during the track fusion processing, these weight parameters are combined to select the track data with the timestamp closest to the current moment in the track data subset, thereby outputting the final fused track data. Track fusion will be further described below.

[0040] Although the above combination Figure 2 The various units 202-210 are described as separate and mutually cooperating independent units, but it will be understood by those skilled in the art that the track data processing unit 200 may also be implemented or executed using a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device (PLD), discrete gate or transistor logic, discrete hardware components, or any combination thereof. The track data processing unit 200 may be implemented directly in hardware, in a software module executed by a processor, or in a combination of the two. The software module may include a single instruction, or many instructions, and may be distributed over several different code segments, between different programs, and across multiple storage media. The storage medium may be coupled to the processor so that the processor can read and write information from / to the storage medium. Alternatively, the storage medium may be integrated into the processor.

[0041] Figure 3 A flow chart of a multi-source aircraft track data fusion method 300 according to an aspect of the present disclosure is shown. Figure 3 The multi-source aircraft track data fusion method 300 can be used, for example, to combine the above Figure 1 A multi-source aircraft track data fusion system 100 is described.

[0042] According to an exemplary embodiment, Figure 3 The multi-source aircraft track data fusion method 300 may include, for example, receiving track data at block 302. Receiving track data at block 302 may include receiving different types of track data from multiple sources 120. The multiple sources may include, but are not limited to, for example, radar, ADS-B, navigation satellite systems (e.g., BeiDou), etc. For example, BeiDou technology may provide track data based on BeiDou short message function. Future technologies may provide track data in other formats / encodings.

[0043] According to an exemplary embodiment, Figure 3 The multi-source aircraft track data fusion method 300 may include, for example, decoding the track data at block 304. According to an exemplary embodiment, different types of track data may be decoded and / or parsed (if necessary). Track data from different sources (including but not limited to, for example, radar, ADS-B, navigation satellite system (e.g., Beidou), etc.) may have their own data types and formats, and are generally not universal, so they need to be decoded / parsed separately.

[0044] According to an exemplary embodiment, Figure 3 The multi-source aircraft track data fusion method 300 may include, for example, performing data repackaging at block 306. Data repackaging may provide the same data format for different types of track data from various sources.

[0045] According to an exemplary embodiment, Figure 3 The multi-source aircraft track data fusion method 300 may include, for example, benchmarking track data from different sources at block 308. According to an exemplary embodiment, benchmarking data may include, but is not limited to, benchmarking different types of track data in coordinate systems, time, and / or altitude.

[0046] According to an exemplary embodiment, Figure 3 The multi-source aircraft track data fusion method 300 may include, for example, performing track correlation processing on the data content of the data baseline after unification in box 310. Track correlation processing may include determining the correlation between track data. Track data from different sources cover different information content. For example, ADS-B tracks contain flight number information, which can determine which aircraft the data belongs to; while radar data usually only has aircraft codes, and aircraft codes may be repeated across the country. In actual algorithm design, problems such as ADS-B signal deception and interference, as well as problems such as bit errors in data transmission, must also be considered. Therefore, correlation processing of all track data can significantly eliminate interference from irrelevant data and improve the accuracy of the final fused track.

[0047] According to an exemplary embodiment, determining the correlation between track data may include comparing the magnitude of the credibility values ​​of each track data to determine the correlation between the track data, and determining whether the multi-source original track data is related to the unified integrated track. According to an exemplary embodiment, the credibility value may be used to determine which track data is related to the unified integrated track, so as to use a track subset with high correlation to support subsequent data fusion processing.

[0048] According to an exemplary embodiment, the credibility value of the track data may be determined based on a multi-source fused track credibility criterion. According to a further exemplary embodiment, the credibility value of the track data may be determined based on a multi-source fused track credibility criterion by comparing and checking the position, speed, flight number, altitude and other information of the multi-source original track data with the unified integrated track.

[0049] According to an exemplary embodiment, tracks with high correlation may be determined based on a correlation threshold, for example. For example, a track subset with correlation greater than or equal to the correlation threshold may be used for subsequent track fusion.

[0050] According to an exemplary embodiment, Figure 3 The multi-source aircraft track data fusion method 300 may include, for example, performing track fusion on a highly correlated track data content subset selected after track correlation processing in box 312. According to some exemplary embodiments, track fusion may adopt an average fusion method. According to some exemplary embodiments, track fusion may adopt a weighted average fusion method. According to further embodiments, the weight coefficient of the weighted average may include, for example, a static weight based on the accuracy of target monitoring by track monitoring equipment from multiple sources and a dynamic weight based on the covariance of the track data subset.

[0051] According to an exemplary embodiment, Figure 3 The multi-source aircraft track data fusion method 300 may include, for example, sending the fused track data at block 314. According to some exemplary embodiments, sending the fused track data may include outputting the fused track data to a backend device for display and / or calling.

[0052] Figure 4 FIG. 4 is a flow chart of a data access processing method 400 for multi-source aircraft track data fusion according to an aspect of the present disclosure. According to an exemplary embodiment, the data access processing method 400 for multi-source aircraft track data fusion may be, for example, a combination of the above Figure 1 The receiving unit 102 described above performs the above steps.

[0053] According to an exemplary embodiment, the data access processing method 400 for multi-source aircraft track data fusion may include obtaining port configuration information at block 402. Obtaining port configuration information may include, for example, combining the above Figure 1 The described receiving unit 102 etc. obtains configuration information of ports communicating with multiple sources and / or intermediate devices for receiving track data.

[0054] According to an exemplary embodiment, the data access processing method 400 for multi-source aircraft track data fusion may include creating a data receiving buffer at block 404 , and creating a data receiving thread at block 406 .

[0055] According to an exemplary embodiment, the data access processing method 400 for multi-source aircraft track data fusion may include monitoring data access ports associated with a plurality of sources and / or intermediate devices at block 408 .

[0056] According to an exemplary embodiment, the data access processing method 400 for multi-source aircraft track data fusion may include determining whether data is received on the data access port at block 410. If not, the method 400 returns to block 408 to continue monitoring the data access port. If yes, the method 400 goes to block 412.

[0057] According to an exemplary embodiment, the data access processing method 400 for multi-source aircraft track data fusion may include, at block 412 , storing the data received at the data access port into the buffer created at block 404 .

[0058] According to an exemplary embodiment, the data access processing method 400 for multi-source aircraft track data fusion may include sending a notification message at block 414. According to an exemplary embodiment, sending a notification message at block 414 may include sending a message to a subsequent data processing module to notify it to perform track data processing.

[0059] According to an exemplary embodiment, the data access processing method 400 for multi-source aircraft track data fusion may include determining whether to exit the program at block 416. If not, the method 400 goes to block 408 to continue monitoring the data access port. If yes, the method 400 goes to block 418.

[0060] According to an exemplary embodiment, the data access processing method 400 for multi-source aircraft track data fusion may include releasing system resources to exit the program at block 418 .

[0061] Figure 5 FIG. 5 is a flow chart of a data decoding / analysis processing method 500 for multi-source aircraft track data fusion according to an aspect of the present disclosure. According to an exemplary embodiment, the data decoding / analysis processing method 500 for multi-source aircraft track data fusion can be, for example, combined with the above Figure 1 The data processing unit 104 described herein or in combination with Figure 2 The data decoding / parsing unit 202 described above performs the above.

[0062] According to an exemplary embodiment, the data decoding / parsing processing method 500 for multi-source aircraft track data fusion may include creating a thread at block 502 . The method 500 then proceeds to block 504 .

[0063] According to an exemplary embodiment, the data decoding / parsing process 500 for multi-source aircraft track data fusion may include determining whether to exit the system at block 504. If so, the method 500 proceeds to block 524. If not, the method 500 proceeds to block 506.

[0064] According to an exemplary embodiment, the data decoding / parsing processing method 500 for multi-source aircraft track data fusion may include determining whether a notification message has been received at block 506. According to an exemplary embodiment, the notification message may come from, for example, the above combination Figure 1 According to an exemplary embodiment, the notification message may be sent by, for example, the above combination Figure 1 The receiving unit 102 described above sends the Figure 1 The data processing unit 104 described herein or in combination with Figure 2 If not, the method 500 returns to block 504. If yes, the method 500 goes to block 508.

[0065] According to an exemplary embodiment, the data decoding / parsing processing method 500 for multi-source aircraft track data fusion may include reading data at block 508. Reading data may include reading a data receiving buffer and clearing the data receiving buffer. According to an exemplary embodiment, the data receiving buffer may include the above-mentioned combination of Figure 1 According to an exemplary embodiment, the data receiving buffer may include the above-mentioned Figure 1 The receiving unit 102 described above is combined with Figure 4 The described method 400 creates (eg, block 404 ) and stores track data received on a data port from various sources (eg, block 412 ).

[0066] According to an exemplary embodiment, the data decoding / parsing process 500 for multi-source aircraft track data fusion may include extracting the data length at block 510 . The method 500 then proceeds to block 512 .

[0067] According to an exemplary embodiment, the data decoding / parsing process 500 for multi-source aircraft track data fusion may include determining whether the data length is abnormal at block 512. If so, the method 500 proceeds to block 522. If not, the method 500 proceeds to block 514.

[0068] According to an exemplary embodiment, the data decoding / parsing process 500 for multi-source aircraft track data fusion may include extracting data types from the read data at block 514 . The method 500 then proceeds to block 516 .

[0069] According to an exemplary embodiment, the data decoding / parsing process 500 for multi-source aircraft track data fusion may include extracting data items from the read data at block 516 . The method 500 then proceeds to block 518 .

[0070] According to an exemplary embodiment, the data decoding / parsing processing method 500 for multi-source aircraft track data fusion may include determining whether the extracted data is normal at block 518. If not, the method 500 proceeds to block 522. If yes, the method 500 proceeds to block 520.

[0071] According to an exemplary embodiment, the data decoding / parsing processing method 500 for multi-source aircraft track data fusion may include determining whether the data frame decoding / parsing is complete at block 520. If so, the method 500 returns to block 504. If not, the method 500 returns to block 516 to continue extracting data items.

[0072] According to an exemplary embodiment, the data decoding / parsing processing method 500 for multi-source aircraft track data fusion may include sending a decoding failure message at block 522. The method 500 then returns to block 504. According to an exemplary embodiment, the monitoring of data quality is considered, and when data anomalies occur, the abnormal data is filtered out, and various abnormal messages are periodically counted and recorded in the software log.

[0073] According to an exemplary embodiment, the data decoding / parsing process 500 for multi-source aircraft track data fusion may include releasing system resources at block 524 .

[0074] Figure 6 FIG. 6 is a flowchart of a multi-source aircraft track data fusion method 600 according to an aspect of the present disclosure. According to an exemplary embodiment, the multi-source aircraft track data fusion method 600 may be performed by combining the above Figure 1 The data processing unit 104 described herein or in combination with Figure 2 The track fusion unit 210 described above is used to perform the above operation.

[0075] According to an exemplary embodiment, the multi-source aircraft track data fusion method 600 may include obtaining (e.g., from the above combination) at block 602. Figure 2 The method 600 then proceeds to block 604 .

[0076] According to an exemplary embodiment, the multi-source aircraft track data fusion method 600 may include dividing the flight area of ​​the target aircraft into a plurality of mosaic windows at block 604. For example, dividing the mosaic windows may be based on a priori empirical rules. Subsequently, the method 600 proceeds to block 606.

[0077] According to an exemplary embodiment, the multi-source aircraft track data fusion method 600 may include determining a static weight coefficient of the track by a mosaic area method in box 606. According to an exemplary embodiment, the mosaic area method may include the accuracy of target monitoring by track monitoring equipment of multiple sources (such as but not limited to various sensors, etc.), and assigning corresponding weight parameters (i.e., static weights) to each source in each mosaic window for statically updating the unified integrated track. Accuracy refers to the actual accuracy of the positioning capabilities of different systems (e.g., Beidou, ADS-B, radar) in different regions in practice.

[0078] According to an exemplary but non-limiting embodiment, by separately counting the errors of the track data of different sources in all regions in advance, the signal errors of different sources in the mosaic area can be calculated. Assuming that the error of the track contained in the signal of a certain source in the region is found to be the smallest through calculation, then in this region, the signal of the source will be assigned a higher mosaic window weight coefficient. The mosaic window weight coefficient belongs to the setting of a priori empirical rules. The mosaic area method can improve the processing speed of track fusion, and the processing efficiency of the fusion algorithm can be adjusted by setting the mosaic area to meet the processing requirements of engineering applications.

[0079] According to an exemplary embodiment, block 606 further includes determining a covariance weight coefficient for the track based on the covariance of the track data subset. According to an exemplary embodiment, determining the covariance weight coefficient for the track based on the covariance of the track data subset may include determining the measured covariance of the state traversals r and θ for each source within the mosaic window area. and Where r and θ are the slant distance and azimuth in polar coordinates, and the measurement covariance of r and θ is and The smaller the covariance is, the greater the weight is, which can effectively improve the accuracy of the unified integrated track.

[0080] According to an exemplary embodiment, in the horizontal XY coordinate system, the equivalent covariance calculated by the error ellipse contour curve in the X and Y directions is and For example:

[0081]

[0082] According to an exemplary embodiment, the equivalent covariance calculated by the error ellipse contour curve in the X and Y directions is and It can be used as the covariance weight coefficient of the track of each source in the current mosaic window.

[0083] According to an exemplary embodiment, the static weighting coefficient obtained in block 606 may include a mosaic window weight coefficient or a covariance weight coefficient or any combination thereof. According to a preferred embodiment, the static weight may be based on normalization of the mosaic window weight and the covariance weight. By incorporating the normalization attribute into the calculation process, the obtained result may have a normalized effect.

[0084] According to an exemplary embodiment, the multi-source aircraft track data fusion method 600 may include determining a dynamic weight based on dynamic extrapolation of the track data subset at block 608. According to an exemplary embodiment, the dynamic extrapolation at block 608 is a dynamic extrapolation based on a time interval. According to an exemplary embodiment, after reliable multi-source track data is determined by a static weighting coefficient, a dynamic position offset is obtained by dynamically extrapolating the track data based on a difference between a timestamp of the track and a current time using a kinematic equation to make it closer to the true position of the aircraft.

[0085] According to an exemplary embodiment, the multi-source aircraft track data fusion method 600 may include, at block 610, combining the static weight coefficient with the dynamic weight coefficient to obtain a fusion coefficient of the multi-source fused track, thereby determining a fused unified integrated track based on the fusion coefficients of the redundant fused tracks. In the case where reliable multi-source track data is selected by the obtained static weight coefficient, a dynamic position shift may be performed on the track based on the difference between the timestamp of the track and the current time using a kinematic equation to make it closer to the actual position of the aircraft.

[0086] According to an exemplary embodiment, before combining the static weight coefficient with the dynamic weight coefficient, the obtained static weight and dynamic weight may be normalized.

[0087] The above description is only an exemplary embodiment of the present invention. However, the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present invention should be included in the protection scope of the present invention.

[0088] The various illustrative logical blocks, modules, and circuits described in conjunction with the present disclosure may be implemented or executed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device (PLD), discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any commercially available processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0089] The steps of the method or algorithm described in conjunction with the present disclosure may be implemented directly in hardware, in a software module executed by a processor, or in a combination of the two. The software module may reside in any form of storage medium known in the art. Some examples of usable storage media include random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, and the like. The software module may include a single instruction, or many instructions, and may be distributed over several different code segments, distributed between different programs, and distributed across multiple storage media. A storage medium may be coupled to a processor so that the processor can read and write information from / to the storage medium. Alternatively, a storage medium may be integrated into a processor.

[0090] The methods disclosed herein include one or more steps or actions for achieving the described methods. These method steps and / or actions may be interchangeable with each other without departing from the scope of the claims. In other words, unless a specific order of steps or actions is specified, the order and / or use of specific steps and / or actions may be changed without departing from the scope of the claims.

[0091] The processor can execute software stored on a machine-readable medium. The processor can be implemented with one or more general and / or special processors. Examples include microprocessors, microcontrollers, DSP processors, and other circuit systems that can execute software. Software should be broadly interpreted as meaning instructions, data, or any combination thereof, whether it is called software, firmware, middleware, microcode, hardware description language, or other. As an example, a machine-readable medium may include RAM (random access memory), flash memory, ROM (read-only memory), PROM (programmable read-only memory), EPROM (erasable programmable read-only memory), EEPROM (electrically erasable programmable read-only memory), registers, disks, optical disks, hard drives, or any other suitable storage media, or any combination thereof. The machine-readable medium may be implemented in a computer program product. The computer program product may include packaging materials.

[0092] In a hardware implementation, the machine-readable medium may be a part of a processing system that is separate from the processor. However, as will be readily appreciated by those skilled in the art, the machine-readable medium or any part thereof may be external to the processing system. As an example, the machine-readable medium may include a transmission line, a carrier modulated by data, and / or a computer product separate from the wireless node, all of which may be accessed by the processor via a bus interface. Alternatively or in addition, the machine-readable medium or any part thereof may be integrated into the processor, such as a cache and / or a general register file may be the case.

[0093] The processing system can be configured as a general processing system having one or more microprocessors providing processor functionality and external memory providing at least a portion of the machine-readable medium, all linked together with other supporting circuitry via an external bus architecture. Alternatively, the processing system can be implemented with an ASIC (Application Specific Integrated Circuit) with a processor, bus interface, user interface (in the case of an access terminal), supporting circuitry, and at least a portion of the machine-readable medium integrated in a single chip, or with one or more FPGAs (field programmable gate arrays), PLDs (programmable logic devices), controllers, state machines, gating logic, discrete hardware components, or any other suitable circuitry, or any combination of circuits capable of performing the various functionalities described throughout this disclosure. Those skilled in the art will recognize how to best implement the functionality described with respect to the processing system, depending on the specific application and the overall design constraints imposed on the overall system.

[0094] The machine-readable medium may include several software modules. These software modules include instructions that cause a processing system to perform various functions when executed by a device (such as a processor). These software modules may include a transmission module and a reception module. Each software module may reside in a single storage device or be distributed across multiple storage devices. As an example, when a triggering event occurs, a software module may be loaded into a RAM from a hard drive. During the execution of the software module, the processor may load some instructions into a cache to increase access speed. One or more cache lines may then be loaded into a general register file for execution by the processor. When the functionality of a software module is described below, it will be understood that such functionality is implemented by the processor when the processor executes instructions from the software module.

[0095] If implemented in software, each function can be stored on or transmitted by one or more instructions or codes on a computer-readable medium. Computer-readable media include both computer storage media and communication media, and these media include any media that facilitate the transfer of a computer program from one place to another. Storage media can be any available media that can be accessed by a computer. As an example and not limitation, such computer-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage devices, or any other media that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer. Any connection is also properly referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technology (such as infrared (IR), radio, and microwave), the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technology (such as infrared, radio, and microwave) is included in the definition of the medium. As used herein, disk and disc include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Disks, where disks often reproduce data magnetically, and discs reproduce data optically with lasers. Thus, in some aspects, computer-readable media may include non-transitory computer-readable media (e.g., tangible media). Additionally, for other aspects, computer-readable media may include transient computer-readable media (e.g., signals). Combinations of the above should also be included within the scope of computer-readable media.

[0096] Thus, some aspects may include a computer program product for performing the operations presented herein. For example, such a computer program product may include a computer-readable medium having stored (and / or encoded) thereon instructions that can be executed by one or more processors to perform the operations described herein. In some aspects, a computer program product may include packaging materials.

[0097] It will be understood that the claims are not limited to the precise configuration and components illustrated above. Various changes, substitutions and variations may be made in the arrangement, operation and details of the methods and apparatus described above without departing from the scope of the claims.

Claims

1. A multi-source track data fusion method, comprising: receiving a plurality of track data about a target from a plurality of sources; Parsing the plurality of track data based on respective data formats of the plurality of track data and repackaging the plurality of track data into the same data format; unifying the data reference of the plurality of track data; performing correlation processing on the plurality of track data to determine a track data subset with a high correlation; as well as The track data subsets are subjected to weighted average fusion to determine a fused track, wherein weights used for the weighted average fusion include static weights based on covariances of the track data subsets and dynamic weights based on dynamic extrapolation of the track data subsets.

2. The multi-source track data fusion method according to claim 1, wherein: Unifying the data references of the plurality of track data includes unifying one or more of a coordinate system, a time, and an altitude reference of the plurality of track data.

3. The multi-source track data fusion method according to claim 1, wherein: Performing correlation processing on the plurality of track data to determine a track data subset with high correlation includes determining the credibility of each track data in the plurality of track data based on a multi-source fusion track credibility criterion, and determining the track data subset with high correlation based on the credibility.

4. The multi-source track data fusion method as described in claim 3 further includes determining the credibility of the track data by comparing each track data in the multiple track data with one or more of the position, speed, flight number, and altitude information of the unified comprehensive track.

5. The multi-source track data fusion method according to claim 1, wherein: The multiple sources include a Beidou navigation satellite system, and a data format of the track data of the Beidou navigation satellite system includes a short message.

6. The multi-source track data fusion method according to claim 5, wherein: The plurality of sources also includes one or more of radar and ADS-B.

7. The multi-source track data fusion method according to claim 1, further comprising: dividing the flight area of ​​the target into a plurality of mosaic windows; as well as determining, in each mosaic window, a measurement covariance for each source of the plurality of sources; The static weight is determined based on the measured covariance, wherein determining the static weight includes calculating the equivalent covariance of each source as the covariance weight of the track data of the source in the mosaic window according to the error ellipse contour curve, wherein the static weight includes the covariance weight.

8. The multi-source track data fusion method according to claim 7, further comprising: Based on the accuracy of the monitoring of the target by the multiple sources, the multiple sources are respectively assigned mosaic window weights corresponding to their track data in each mosaic window, wherein the static weights also include the mosaic window weights.

9. The multi-source track data fusion method as claimed in claim 8, wherein the static weight is further based on the normalization of the mosaic window weight and the covariance weight.

10. The multi-source track data fusion method according to claim 1, further comprising: Based on the difference between the timestamp of the track and the current time, the dynamic position shift of the track is performed using the kinematic equation to obtain the dynamic weight.

11. A multi-source track data fusion device, comprising: A receiving unit, used for receiving a plurality of track data about a target from a plurality of sources; Data processing unit for: Parsing the plurality of track data based on respective data formats of the plurality of track data and repackaging the plurality of track data into the same data format; unifying the data reference of the plurality of track data; performing correlation processing on the plurality of track data to determine a track data subset with a high correlation; as well as Performing weighted average fusion on the track data subsets to determine a fused track, wherein the weights used for the weighted average fusion include static weights based on covariance of the track data subsets and dynamic weights based on dynamic extrapolation of the track data subsets; as well as The output unit outputs the fused track data to the back-end device for display or call.

12. The multi-source track data fusion device according to claim 11, wherein: The multiple sources include one or more of a Beidou navigation satellite system, a radar, and an ADS-B, and a data format of the track data of the Beidou navigation satellite system includes a short message.

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