Unmanned aerial vehicle cluster dual-mode positioning fusion method, device, equipment and storage medium

Abstract

The application discloses a unmanned aerial vehicle cluster dual-mode positioning fusion method and device, equipment and storage medium, the method comprises the steps that global navigation satellite system GNSS positioning data and the first quality factor of terminal equipment are acquired;UWB positioning data and the second quality factor of terminal equipment are acquired;The precision bottom proportion coefficient of the variance ratio of UWB and GNSS positioning is acquired;According to the first quality factor, the second quality factor and the precision bottom proportion coefficient, the fusion weight is calculated;GNSS positioning data and UWB positioning data are adaptively weighted and fused based on the fusion weight, and the fusion positioning data is obtained;When the positioning dominant mode switches, the preset raised cosine window function is used for smooth transition processing to the switching process, and the target fusion positioning data is output.The precision bottom proportion coefficient is used for optimizing the weight distribution by calibrating the basic difference, and the raised cosine window function is used for eliminating the control impact caused by mode switching, so that the motion trajectory stability of terminal equipment is improved.

Description

Technical Field

[0001] This invention relates to the field of radio navigation and cooperative positioning technology, and more specifically, to a method, apparatus, device, and storage medium for dual-mode positioning fusion of unmanned aerial vehicle (UAV) swarms. Background Technology

[0002] In scenarios involving coordinated movement of multiple terminal devices, these devices need to acquire high-precision three-dimensional positions in real time to maintain formation. Currently, real-time dynamic differential positioning technology of Global Navigation Satellite Systems (GNSS) is the primary positioning method. However, GNSS positioning accuracy deteriorates when faced with multipath effects, ionospheric disturbances, or radio frequency interference.

[0003] To address the aforementioned issues, existing technologies typically employ ultra-wideband as a secondary positioning source for dual-mode fusion. However, existing dual-mode fusion schemes suffer from the following objective technical drawbacks: First, existing solutions mostly adopt static area division or fixed threshold switching mechanisms, assuming that the positioning variance of GNSS and UWB is on the same order of magnitude. They do not handle the inherent difference in accuracy between the two source systems in the underlying logic, which leads to the weight allocation deviating from the optimal solution.

[0004] Second, existing technologies use instantaneous step logic when switching positioning sources. The jump in output position is dynamically equivalent to an instantaneous velocity impact, causing the motion control system of the terminal device to oscillate.

[0005] Third, for the non-line-of-sight propagation problem in UWB systems, existing solutions usually rely on simple hard rejection using absolute signal levels, which is susceptible to common-mode translation interference caused by distance or attitude changes. Furthermore, direct rejection leads to the loss of residual geometric constraint information, reducing the availability of the system in occluded environments.

[0006] Fourth, for scenarios involving concurrent data interaction among a massive number of terminal devices, existing telemetry backhaul protocols do not perform statistical compression on the frequently reported location data, resulting in excessive air interface resource consumption and making it highly susceptible to channel congestion. Summary of the Invention

[0007] This invention provides a method, apparatus, device, and storage medium for dual-mode positioning fusion of unmanned aerial vehicle (UAV) swarms, in order to improve technical problems such as unreasonable weight allocation, mode switching jumps, and low accuracy of non-line-of-sight interference identification in the process of multi-source positioning data fusion.

[0008] To address the aforementioned technical problems, the first aspect of this invention provides a dual-mode positioning fusion method for unmanned aerial vehicle (UAV) swarms, applied to a terminal device, the method comprising: Acquire the Global Navigation Satellite System (GNSS) positioning data of the terminal device and determine the first quality factor of the GNSS positioning data; Acquire ultra-wideband (UWB) positioning data from the terminal device and determine a second quality factor for the UWB positioning data; Obtain a preset accuracy base ratio coefficient, wherein the accuracy base ratio coefficient represents the ratio of the positioning variance of the UWB positioning system to the positioning variance of the GNSS positioning system. The fusion weight is calculated based on the first quality factor, the second quality factor, and the accuracy base ratio coefficient. Based on the fusion weight, the GNSS positioning data and the UWB positioning data are adaptively weighted and fused to obtain fused positioning data. When the positioning-dominant mode switches, the switching process is smoothly processed according to the preset raised cosine window function, and the target fusion positioning data is output.

[0009] Preferably, the step of calculating the fusion weight based on the first quality factor, the second quality factor, and the accuracy base ratio coefficient includes: The fusion weights are calculated using the following formula:

[0010] in, Indicates the fusion weights, This represents the accuracy base scaling factor. This represents the first quality factor. This represents the second quality factor.

[0011] Preferably, the step of calculating the fusion weight based on the first quality factor, the second quality factor, and the accuracy base ratio coefficient further includes: Calculate the three-dimensional Euclidean distance between the GNSS positioning data and the UWB positioning data; If the three-dimensional Euclidean distance is greater than a preset deviation threshold and the duration reaches a preset number of cycles, it is determined that the GNSS positioning system is being interfered with. The value of the fusion weight is forcibly set to zero, and the UWB dominant mode is triggered.

[0012] Preferably, after adaptively weighting and fusing the GNSS positioning data and the UWB positioning data based on the fusion weights to obtain the fused positioning data, the method further includes: The target positioning dominant mode is determined according to the preset range to which the value of the fusion weight belongs; wherein, the target positioning dominant mode includes GNSS dominant mode, dual-mode fusion GNSS dominant mode, dual-mode fusion UWB dominant mode and UWB dominant mode. The step of smoothly transitioning the switching process according to a preset raised cosine window function and outputting target fused positioning data when the positioning dominant mode switches includes: If the target positioning dominant mode is switched, a preset switching transition period is obtained; During the switching transition period, the continuously changing transition weights are calculated using the raised cosine window function; The target fusion positioning data is output based on the continuously changing transition weights and the weighted calculation is performed, with continuous position and continuous velocity.

[0013] Preferably, the step of acquiring the ultra-wideband (UWB) positioning data of the terminal device includes: Receive ultra-wideband signals from multiple beacon base stations and obtain the first measurement constraint equation based on the time difference of arrival; Acquire the altitude data collected by the barometric altimeter in the terminal device, and construct a barometric altitude constraint condition in the vertical dimension based on the altitude data; Substitute the air pressure altitude constraint into the first measurement constraint equation for dimensionality reduction to obtain the target measurement constraint equation with the vertical dimension unknown eliminated. The target measurement constraint equation is solved by weighted least squares method to obtain the UWB positioning data.

[0014] Preferably, before solving the target measurement constraint equation using the weighted least squares method to obtain the UWB positioning data, the method further includes: Obtain the time variance of the received signal strength indication of the ultra-wideband signal within a preset time window, and the location calculation residual; The time variance and the positioning solution residual are normalized and jointly calculated to obtain the non-line-of-sight interference decision index; If the non-line-of-sight interference decision index is greater than the first preset threshold and less than or equal to the second preset threshold, the weight of the corresponding beacon base station in the target measurement constraint equation is reduced. If the non-line-of-sight interference decision index is greater than the second preset threshold, the corresponding beacon base station will be removed from the target measurement constraint equation.

[0015] Preferably, after outputting the target fusion positioning data, the method further includes: Calculate the position difference component between the target fused positioning data of adjacent periods; Based on the preset mapping relationship between maneuver amplitude levels and Huffman coding, the position difference component is compressed and encoded to obtain compressed telemetry data. The compressed telemetry data is transmitted to the data processing node via a wireless communication network.

[0016] A second aspect of the present invention provides a dual-mode positioning fusion device for unmanned aerial vehicle (UAV) swarms, comprising: The first acquisition module is used to acquire GNSS positioning data of the terminal device and determine the first quality factor of the GNSS positioning data; The second acquisition module is used to acquire the ultra-wideband (UWB) positioning data of the terminal device and determine the second quality factor of the UWB positioning data. The third acquisition module is used to acquire a preset accuracy base ratio coefficient, wherein the accuracy base ratio coefficient represents the ratio of the positioning variance of the UWB positioning system to the positioning variance of the GNSS positioning system. The weight calculation module is used to calculate the fusion weight based on the first quality factor, the second quality factor, and the precision base ratio coefficient. The fusion processing module is used to adaptively weight and fuse the GNSS positioning data and the UWB positioning data based on the fusion weight to obtain fused positioning data; The smooth output module is used to smoothly transition the switching process according to a preset raised cosine window function when the positioning dominant mode is switched, and output the target fused positioning data.

[0017] A third aspect of the present invention provides an electronic device, comprising: At least one processor; and a memory communicatively connected to at least one of the processors; The memory stores instructions that can be executed by at least one of the processors, which are executed by at least one of the processors to enable the at least one of the processors to perform the UAV swarm dual-mode positioning fusion method as described in any one of the above descriptions.

[0018] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the dual-mode positioning fusion method for unmanned aerial vehicle (UAV) swarms described above.

[0019] Compared with existing technologies, the beneficial effect of this invention lies in improving the robustness of positioning fusion accuracy: by obtaining the accuracy basis proportionality coefficient characterizing the ratio of GNSS and UWB positioning variances, the fundamental differences between the two are calibrated from the underlying physical facts. This adaptive weighting mechanism, while conforming to the Bayesian optimal fusion criterion, can still provide smoother and more accurate positioning output even when GNSS quality deteriorates but remains within a reasonable range.

[0020] The dynamic shocks of the control system are eliminated: a raised cosine window function is introduced during the switching of the dominant mode, so that the output position during the transition period is in a first-order continuous state. This smooth transition process transforms the originally step-like position jump into a bounded acceleration control input, improving the trajectory tracking stability of the terminal equipment.

[0021] Improved efficiency and accuracy of high and low elevation geometric dimensions: To address the problem of excessively high vertical geometric attenuation exponent caused by the coplanar deployment of UWB base stations, algebraic dimensionality reduction was performed by introducing barometric altitude constraints, which effectively eliminated ill-conditioned equations in the vertical direction, allowing the weighted least squares solution to focus entirely on the horizontal dimension and improving the convergence of the solution results.

[0022] It improves the accuracy of non-line-of-sight interference detection and the number of available base stations: It uses the variance of signal strength instead of the absolute mean to determine interference, thus immunizing against signal attenuation interference caused by distance changes; and it adopts a two-level processing strategy that combines soft weighting and hard rejection, which maximizes the retention of ranging geometry information of lightly obstructed base stations while filtering out severely contaminated ones, thereby improving the positioning availability in complex environments. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is the main flowchart of the dual-mode positioning fusion method for UAV swarms of the present invention; Figure 2 This is the first sub-flowchart of the UAV swarm dual-mode positioning fusion method of the present invention; Figure 3 This is the second sub-flowchart of the UAV swarm dual-mode positioning fusion method of the present invention; Figure 4 This is the third sub-flowchart of the UAV swarm dual-mode positioning fusion method of the present invention; Figure 5 This is the fourth sub-flowchart of the UAV swarm dual-mode positioning fusion method of the present invention; Figure 6 This is a block diagram of the dual-mode positioning fusion device for drone swarms according to the present invention. Detailed Implementation

[0025] 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 specific embodiments. Those skilled in the art will understand that the following embodiments are only used to explain the technical logic and execution mechanism of this invention and should not be considered as limiting the scope of protection of this invention.

[0026] refer to Figure 1 , Figure 1 This is a main flowchart of a dual-mode positioning fusion method for UAV swarms provided in an embodiment of the present invention. The UAV swarm dual-mode positioning fusion method is applied to a terminal device. In a specific implementation scenario, the terminal device is a UAV participating in swarm flight, internally equipped with underlying hardware such as a microcontroller unit, a GNSS receiver module, a UWB receiver module, a barometer, and a wireless communication network transceiver module. The UAV swarm dual-mode positioning fusion method includes the following steps: Step S110: Obtain the Global Navigation Satellite System (GNSS) positioning data of the terminal device and determine the first quality factor of the GNSS positioning data; In this embodiment, the terminal device receives satellite signals in a preset frequency band and differential correction data from a data processing node (such as a main base station) through its internally integrated GNSS receiver (e.g., a receiver module supporting real-time dynamic differential RTK technology). By performing double-difference calculation on carrier phase observations, the terminal device obtains its Global Navigation Satellite System (GNSS) positioning data. This GNSS positioning data characterizes the terminal device's three-dimensional absolute coordinates in the world geodetic coordinate system.

[0027] Due to the dynamic changes in the space environment and ionospheric state, the confidence level of the above calculation results fluctuates. Therefore, this step further determines the first quality factor (Qg) of the GNSS positioning data according to preset multi-dimensional quality assessment rules. Specifically, the microcontroller unit extracts multiple parameters for the current observation period by parsing the data message format output by the GNSS receiver (e.g., NMEA standard message), including: the number of effective satellites participating in the positioning calculation (fsat), the position accuracy attenuation factor PDOP (fpdop), the carrier-to-noise ratio C / N0 (fcn0), the RTK fixed state flag (frtk), and the differential correction data age (fage).

[0028] Subsequently, the microcontroller performs interval normalization on the aforementioned parameters. For example, the effective satellite count is linearly mapped to a value of 0 to 1 within the interval [4, 12], the PDOP is inversely mapped within the interval [1, 6], and the carrier-to-noise ratio is mapped within the interval [25, 45] dB-Hz. After normalization, a weighted summation is performed according to the preset sensitivity weights of each parameter. The calculation formula is as follows:

[0029] The resulting first quality factor is a continuous value within the range of [0,1], which objectively reflects the uncertainty of the current period's GNSS positioning data in the information theory sense.

[0030] Step S120: Obtain the ultra-wideband (UWB) positioning data of the terminal device and determine the second quality factor of the UWB positioning data; While acquiring GNSS positioning data, or in an asynchronous concurrent state, the UWB receiving antenna of the terminal device receives ultra-wideband electromagnetic wave signals emitted by multiple beacon base stations distributed around the target airspace. Due to the nanosecond-level extremely short pulse characteristics of ultra-wideband signals, the terminal device can accurately capture the time of arrival (TOA) of each beacon base station signal and calculate the ultra-wideband UWB positioning data of the terminal device.

[0031] To measure the reliability of the location source data, the microcontroller unit of the terminal device calculates the second quality factor (Qu) of the ultra-wideband (UWB) positioning data. The second quality factor mainly consists of four parameters reflecting the radio propagation characteristics and geometric topology: the number of visible base stations (fbs), the geometrical precision attenuation factor GDOP (fgdop), the mean of the received signal strength indication (frssi), and the positioning solution residual (fres).

[0032] The normalized summation formula for the above parameters is:

[0033] Because UWB signals and GNSS signals are heterogeneous in physical frequency bands and propagation paths, the determination process of the second quality factor is independent of the calculation of the first quality factor, thereby establishing two independent data quality assessment links within the terminal equipment.

[0034] Step S130: Obtain a preset accuracy base ratio coefficient, wherein the accuracy base ratio coefficient represents the ratio of the positioning variance of the UWB positioning system to the positioning variance of the GNSS positioning system. Because Global Navigation Satellite Systems (GNSS) offer native carrier phase measurement accuracy superior to centimeter-level, while Ultra Wideband (UWB) systems are limited by measurement bandwidth, inter-station clock synchronization residuals, and residual multipath propagation, their inherent measurement noise bases (i.e., "accuracy foundations") are not on the same order of magnitude. If this physical fact is ignored at the underlying data processing level and data is directly weighted and merged, the positioning results will deviate from the true expected value.

[0035] To address this issue, the terminal device's memory pre-programs a precision baseline scaling factor, either statically or dynamically generated under calibration conditions. Under an ideal, unobstructed calibration environment, the positioning variance calibration value of the GNSS positioning system was statistically obtained. ) and the positioning variance calibration value of the UWB positioning system ( In this embodiment, the typical standard deviation envelope of the GNSS positioning system is 0.02 meters, while the typical standard deviation of the measurement error (including thermal noise, multipath error, clock synchronization residual, etc.) of the UWB positioning system after being synthesized by the geometric amplification effect (HDOP) is 0.10 meters.

[0036] Based on statistical mapping relationships, this precision base proportionality coefficient Defined as:

[0037] in, and These are the baseline quality factors under the calibration environment. By substituting actual physical statistical parameters, this accuracy basis proportionality coefficient is calculated and set to 16.2 (which can be rounded to 16 in embedded fixed-point operations). This parameter explicitly declares to the fusion algorithm the physical premise that "under the same nominal quality factor, the absolute accuracy of GNSS is much higher than that of UWB".

[0038] Step S140: Calculate the fusion weight based on the first quality factor, the second quality factor, and the accuracy base ratio coefficient; The terminal device triggers a fusion interrupt task in each calculation cycle. This task, within the arithmetic logic unit of the microcontroller, combines the multi-sensor optimal fusion criterion (Bayesian precision-weighted fusion rule), the first quality factor, the second quality factor, and the precision base ratio coefficient to calculate the fusion weight for the current cycle. ).

[0039] The step of calculating the fusion weight based on the first quality factor, the second quality factor, and the accuracy base ratio coefficient further includes calculating the fusion weight according to the following formula:

[0040] In this computational structure, the precision base scaling factor is multiplied by a multiplier. With the first quality factor Binding multiplication essentially amplifies the weighting basis of GNSS positioning data in the inverse variance domain. This is particularly relevant when GNSS signals experience slight degradation (e.g., ...). When it drops from 0.85 to 0.35, due to The scaling effect ensures that the calculated fusion weights remain above 0.9, thus preventing the algorithm from prematurely discarding high-precision GNSS data due to minor perturbations; conversely, only when the Global Navigation Satellite System experiences severe obstruction (GNAS) will the fusion weights be reduced. When the weight approaches 0, the fusion weight will smoothly and quickly slide to the UWB side, ensuring the objectivity and rationality of the decision-making logic.

[0041] Step S150: Based on the fusion weight, adaptive weighted fusion is performed on the GNSS positioning data and the UWB positioning data to obtain fused positioning data; After obtaining the fusion weights, the microcontroller extracts the current period's Global Navigation Satellite System (GNSS) positioning data (denoted as GNSS) stored in the random access memory. ) and ultra-wideband (UWB) positioning data (denoted as The microcontroller performs vector linear combination operations, and the calculation formula is as follows:

[0042] This adaptive weighted fusion process maps two heterogeneous 3D coordinates to the same fused coordinate space, outputting fused positioning data for a single cycle. This data comprehensively incorporates the global stability of GNSS and the local anti-interference characteristics of UWB.

[0043] Step S160: When the positioning dominant mode is switched, the switching process is smoothly processed according to the preset raised cosine window function, and the target fusion positioning data is output.

[0044] In the control of high-speed motion of terminal devices (such as drone formation flight), if the underlying positioning system causes a sharp reversal of the fusion weights due to occlusion or interference, it will lead to discrete step jumps in the output three-dimensional coordinate sequence. At the derivative level of the control dynamics system, this position step is equivalent to generating a huge instantaneous discrete velocity impact, which can induce servo control oscillations or even instability in the terminal device's flight control system.

[0045] To eliminate this physical destructive force, the terminal device incorporates a switching smoothing buffer queue. The microcontroller continuously monitors the current positioning dominance mode (i.e., weighting). (Regarding the interval state), when a cross-quadrant switch occurs in the positioning dominant mode, the output of directly weighted raw fused positioning data is stopped. Instead, a smooth transition mechanism based on a preset raised cosine window function is activated. In this transition mechanism, the weight values ​​no longer rely on abrupt changes in the single-cycle quality factor, but instead undergo a time-domain distributed gradual change along the envelope of the raised cosine curve. After this mathematical modification, the output target fused positioning data ensures the continuity of the first derivative (velocity) and the boundedness of the second derivative (acceleration) in the time series, thereby minimizing the disturbance of coordinate source switching to the control system.

[0046] refer to Figure 2 , Figure 2 This is the first sub-flowchart of the dual-mode positioning fusion method for UAV swarms of the present invention, which mainly describes the logic for reliable detection and forced hard switching of external malicious interference or deceptive signals.

[0047] Step S210: Calculate the three-dimensional Euclidean distance between the GNSS positioning data and the UWB positioning data; In addition to the continuous weighted fusion based on quality factors described above, this embodiment also provides a hard isolation channel to prevent GNSS spoofing. Within each processing cycle, the microcontroller calculates the three-dimensional Euclidean distance (i.e., the magnitude vector of the coordinate difference) between the currently acquired GNSS positioning data (3D coordinate point A) and the UWB positioning data (3D coordinate point B) in the world coordinate system, denoted as... This distance physically characterizes the degree of divergence between the positioning results of two independent positioning systems for the same physical entity.

[0048] Step S220: If the three-dimensional spatial Euclidean distance is greater than a preset deviation threshold and the duration reaches a preset number of cycles, it is determined that the GNSS positioning system is being interfered with. The terminal device establishes a sliding counter in memory. Based on the variances of the two sources, this embodiment calculates the standard deviation of the dual-source independent positioning error. Based on the principle of normal distribution confidence intervals, select... (For example, a calculated value of 0.306 meters) is used as a preset deviation threshold. When the calculated three-dimensional Euclidean distance is greater than this preset deviation threshold, the sliding counter increments. If the state of this distance exceeding the threshold persists for a preset number of cycles on the time axis (for example, three consecutive processing cycles to avoid false alarms caused by random noise in a single frame), then the logic control unit definitively determines that the current Global Navigation Satellite System (GNSS) positioning system has been subjected to artificial suppression interference or false coordinate spoofing. At this time, although the first quality factor (Qg) may show a fake high score, the data has actually been contaminated.

[0049] Step S230: Force the value of the fusion weight to zero and trigger entry into UWB dominant mode.

[0050] After generating the interference confirmation flag, the terminal device immediately masks the adaptive weights generated in step S140 and forces the value register of the fusion weights to be overwritten to zero via a system interrupt. Therefore, GNSS positioning data from the Global Navigation Satellite System is used in the formula. The contribution of the terminal device to the positioning calculation data link is completely reset to zero, and the terminal device's positioning data link is unconditionally switched and triggered to enter the UWB-dominated mode, which relies entirely on UWB data for positioning. This mechanism provides a safety redundancy for the terminal device, ensuring that it can maintain stable trajectory control based on the backup link even in strong electromagnetic countermeasures environments.

[0051] refer to Figure 3 , Figure 3 This is the second sub-flowchart of the dual-mode positioning fusion method for UAV swarms of the present invention, which further refines the execution details of the raised cosine window function and the dominant mode determination logic.

[0052] Step S310: Determine the target positioning dominant mode according to the preset range to which the value of the fusion weight belongs; wherein, the target positioning dominant mode includes GNSS dominant mode, dual-mode fusion GNSS dominant mode, dual-mode fusion UWB dominant mode and UWB dominant mode. During the normal adaptive fusion process in steps S140 and S150, the state machine inside the microcontroller continuously monitors the fusion weights. The instantaneous value is calculated and mapped to four discrete preset intervals to determine the current target localization dominant mode of the system. Specifically: when At this time, the terminal device determines that it is in GNSS-dominated mode. In this case, the positioning output is actually entirely determined by the Global Navigation Satellite System, typically corresponding to ideal conditions with open skies and a fixed RTK solution. At this point, it is determined that the system is in dual-mode fusion GNSS-dominant mode. At this time, some satellite signals are blocked, and the ranging information from the ultra-wideband system begins to provide supplementary constraints, but the overall position reference still leans towards GNSS. At this time, it is determined that the system is in dual-mode fusion UWB as the primary mode. In this mode, the GNSS signal quality has been significantly attenuated, and the system positioning results heavily rely on the ranging constraints provided by surrounding beacon base stations. At this time, it is determined that the system is in UWB-dominated mode. This mode means that GNSS has lost its positioning capability, and UWB positioning data has completely taken over the system coordinate output.

[0053] Step S320: If the target positioning dominant mode is switched, obtain the preset switching transition period; The state machine compares the current cycle Compared with the previous cycle The target location is set to the dominant mode flag to detect whether a mode transition has occurred (especially in severe fault scenarios where the mode changes directly from the first quadrant to the fourth quadrant). Once a mode switching event is triggered, the microcontroller immediately reads the preset switching transition period from its internal read-only memory. The period is a time window parameter that is precisely defined in physical time (e.g., 0.75 seconds, corresponding to 15 discrete sampling frames under a 20Hz system clock cycle), and its design purpose is to ensure that the flight controller has sufficient time to absorb position deviations.

[0054] Step S330: During the switching transition period, the continuously changing transition weight is calculated using the raised cosine window function; After entering the handover transition period, the terminal device freezes the channel that calculates weights in real time based on signal quality and switches to a time-based function generator mode. Taking the recovery process from UWB-dominated mode to GNSS-dominated mode as an example, the microcontroller calculates continuously changing transition weights by calling trigonometric function instructions from the mathematical operation library. During reverse switching, the calculation is performed. The function changes over time. The shift generates a continuous nonlinear smooth sequence between 0 and 1, and in Starting point and The first derivative at the endpoint is zero.

[0055] Step S340: Perform weighted calculation based on the continuously changing transition weights, and output the target fusion positioning data with continuous position and continuous velocity.

[0056] By using the continuously varying transition weights generated by the window function to replace the step weights in the formula, the linear weighted calculation of GNSS and UWB positioning data is re-executed. Because the non-linear continuous change in weights suppresses steep error input caused by inconsistent data source locations, after the last frame of the switching transition cycle, the target fused positioning data output by the terminal device to the external control bus achieves first-order continuity of position coordinates in a physically meaningful sense (without instantaneous velocity spikes). This smoothly transforms error jumps into small, bounded acceleration fluctuations, ensuring smooth flight path tracking of the swarm formation.

[0057] refer to Figure 4 , Figure 4 This is the third sub-flowchart of the UAV swarm dual-mode positioning fusion method of the present invention, involving the core steps of UWB bottom-level dimensionality reduction calculation and anti-multipath non-line-of-sight interference.

[0058] Step S410: Receive ultra-wideband signals sent by multiple beacon base stations and obtain the first measurement constraint equation based on the time difference of arrival; To achieve accurate acquisition of ultra-wideband (UWB) positioning data, the terminal device's UWB receiving antenna receives signals distributed across the site on a specific physical channel frequency band. The microcontroller unit (MCU) transmits ultra-wideband pulsed electromagnetic signals from several beacon base stations. Utilizing the Time Difference of Arrival (TDOA) positioning principle, it selects one base station as a reference station. By extracting the arrival timestamps of the preambles of each received pulse and subtracting the transmission time difference from the beacon base stations, it generates a set of hyperbolic measurement constraint surfaces representing the distance difference. This data is then processed to form a three-dimensional coordinate position of the terminal device. and common distance difference parameters The first measurement constraint equation. Since there are four unknowns in the three-dimensional space plus the time deviation in the conventional full-dimensional solution, this equation requires the construction of a huge nonlinear matrix system.

[0059] Step S420: Obtain the altitude data collected by the barometric altimeter in the terminal device, and construct the barometric altitude constraint conditions in the vertical dimension based on the altitude data; Because UWB beacon base stations in cluster performance venues are typically coplanar (with minimal height difference between antennas), the vertical geometric attenuation factor (VDOP) deteriorates drastically. If the elevation coordinates are solved solely using the UWB ultra-wideband system, the resulting matrix condition number is extremely large, exhibiting ill-conditioned equations, which leads to… The shaft calculation error can reach hundreds of meters. To solve this problem, the terminal device acquires the absolute air pressure value in real time from the airborne barometric altimeter (such as the high-precision MS5611 sensor) via I2C or SPI bus, and converts it into high-precision altitude data (with error controlled within...). (Meter-level). This independent and stable altitude data was mapped to a unified spatial coordinate system, constructing a rigid vertical-dimensional barometric altitude constraint (i.e., ).

[0060] Step S430: Substitute the air pressure altitude constraint into the first measurement constraint equation for dimensionality reduction to obtain the target measurement constraint equation with the vertical dimension unknown eliminated. After obtaining the barometric altitude constraints, the microcontroller performs an algebraic dimensionality reduction replacement operation in its memory computing space. Specifically, it replaces the vertical unknowns in the first measurement constraint equation matrix. The variable column uses constants provided by the barometric altimeter. Perform a deterministic substitution. This algebraic process of "transposition and deletion" will transform the original variable involving 4 degrees of freedom ( The ill-conditioned equations for three-dimensional spatial positioning are forcibly suppressed and reduced in dimension to contain only 3 degrees of freedom unknowns (horizontal coordinates). and distance offset The target measurement constraint equation (belonging to the 2.5D localization model) is used. This dimensionality reduction operation directly eliminates the singular columns that cause the system covariance matrix to diverge, allowing the solution task to focus entirely on the horizontal geometric dimension, which has extremely strong advantages for the UWB system.

[0061] Step S440: Obtain the time variance of the received signal strength indication of the ultra-wideband signal within a preset time window, and the location calculation residual; Before solving the target measurement constraint equations, a link-level "quality control" step is required to prevent non-line-of-sight multipath signal contamination of the solution matrix due to building obstruction or changes in aircraft attitude. For each beacon base station's receiving link, the terminal device buffers and statistically analyzes the received signal strength indication dataset within a preset time window (e.g., the past second, containing 20 consecutive RF sampling frames), and then calculates the time variance reflecting the severity of signal fluctuations. At the same time, the microcontroller calculates the positioning solution residual of the base station link in the TDOA positioning model in the previous processing cycle. The reason for choosing the time variance of the signal strength indicator instead of the mean is that the variance can keenly capture the multipath interference ripple caused by the lack of direct waves after obstruction (i.e., periodic fluctuations caused by flight), while also being naturally immune to common-mode translation interference caused by changes in transmission distance or deviations in transmit power.

[0062] Step S450: Normalize and jointly calculate the time variance and the positioning solution residual to obtain the non-line-of-sight interference decision index. After obtaining the fluctuation characteristics of the two dimensions mentioned above, the microcontroller divides the current link's time variance by the network-wide baseline variance (normalization). Similarly, baseline normalization is performed on the positioning solution residual. Subsequently, a specific engineering weight allocation (e.g., assigning a weight of 0.6 to the variance and a weight of 0.4 to the residual) is used to perform a linear joint mapping between the two. Through this cross-validation algorithm that combines the physical properties of the microscopic signal layer (variance) with the consistency of the macroscopic geometric layer solution (residual), a comprehensive non-line-of-sight interference decision index is calculated. This metric quantitatively assesses the degree of certainty that the base station link is contaminated by non-line-of-sight diffraction signals.

[0063] Step S460: If the non-line-of-sight interference decision index is greater than the first preset threshold and less than or equal to the second preset threshold, the weight of the corresponding beacon base station in the target measurement constraint equation is reduced. The terminal equipment implements a two-tiered handling strategy of "hardware and software dual protection". If the non-line-of-sight interference decision index of a certain beacon base station is determined ( If the multipath error exceeds a first preset threshold (e.g., 1.5, indicating suspected mild occlusion) but does not exceed a second preset threshold (e.g., 2.5) indicating extremely severe occlusion, the microcontroller does not directly discard the beacon base station. Instead, it performs a soft weight reduction on the diagonal weight coefficients corresponding to the base station in the weighted least squares matrix (e.g., reducing them to 1 / 4 of their original weights). This strategy effectively suppresses multipath error inflation while preserving the residual edge geometric constraint information in the mildly damaged signal to the maximum extent, preventing the geometric redundancy from collapsing due to the large-scale abandonment of base stations.

[0064] Step S470: If the non-line-of-sight interference decision index is greater than the second preset threshold, the corresponding beacon base station is removed from the target measurement constraint equation; Conversely, when the microcontroller detects that the non-line-of-sight interference decision index exceeds the second preset threshold (i.e., deep occlusion occurs, and multipath error has reached meter-level contamination), the negative impact of the pseudorange error introduced at this time far outweighs the geometric positioning gain it provides. The state machine then triggers a hard isolation mechanism, forcibly stripping, clearing, and hard-removing the corresponding beacon base station's observation row data from the matrix of the target measurement constraint equation. The purified measurement equation, after this filtering process, is then fed to the next mathematical solver.

[0065] Step S480: Solve the target measurement constraint equation using the weighted least squares method to obtain the UWB positioning data.

[0066] For the target measurement constraint equations after pressure-altitude dimensionality reduction (step S430) and purification and screening by a two-stage non-line-of-sight quality inspection strategy (steps S440-S470), the microcontroller unit calls the classic Chan two-step positioning algorithm to perform two iterations of weighted least squares (WLS) derivation of the overdetermined equation system. Since the elevation degree of freedom interference term that easily causes matrix oscillations has been successfully removed beforehand, the inverse matrix operation process of the weighted least squares method has extremely high numerical stability and convergence speed. After the operation is completed, ultra-wideband UWB positioning data with horizontal spatial accuracy better than decimeter level is successfully output. This data is then passed to step S120 for calculating the second quality factor, thereby completing the entire system-level data fusion lifecycle.

[0067] refer to Figure 5 , Figure 5 This is the fourth sub-flowchart of the dual-mode positioning fusion method for UAV clusters of the present invention, which focuses on dealing with the challenges of channel congestion and data compression under high-frequency positioning conditions of massive terminal clusters.

[0068] Step S510: Calculate the position difference components between the target fused positioning data of adjacent periods; To achieve cluster collaborative control and ground monitoring, terminal devices must report their frequently updated positioning results (e.g., 20Hz refresh rate) to the ground station. Directly sending complete three-dimensional floating-point numbers in absolute coordinates (typically up to 24 bytes per packet) would quickly exhaust the air interface bandwidth under concurrent interaction of tens of thousands of clusters. Based on the principle of continuity of cluster motion trajectories at the millisecond microscale, the microcontroller caches the historical fused positioning data reported in the previous frame in its storage area and performs a spatial difference operation between this data and the latest target fused positioning data acquired in the current period. This calculates the change in spatial physical displacement of the terminal device on the three-dimensional coordinate axes, i.e., the position difference component. ).

[0069] Step S520: Based on the preset mapping relationship between maneuver amplitude level and Huffman coding, the position difference component is compressed and encoded to obtain compressed telemetry data. After obtaining the position difference components of the three axes, considering that the UAV spends most of its flight cycle in hovering fine-tuning or low-speed level flight, and that the probability of large maneuver jumps is extremely low, this embodiment adopts a lossless compression mechanism based on statistical probability. The microcontroller unit is pre-configured with a set of maneuver amplitude level trees (e.g., hovering level <0.5cm, slow level 0.5-2cm, level flight level, rapid maneuver level, etc.) established based on prior maneuver amplitude statistics. According to information entropy theory, the higher the probability of occurrence of a level, the shorter the feature prefix code string (Huffman tree node) will be assigned. The microcontroller unit projects the parsed position difference components to the corresponding level and translates them into high-density bit streams of varying lengths according to the mapping table between statically or dynamically configured maneuver amplitude levels and Huffman codes. This bit-level assembly effectively eliminates redundant data segments in the binary structure, generating extremely short and concise compressed telemetry data packets (compressing the single-frame payload from 24 bytes to less than 10 bytes), making the information entropy of the data approach the lower bound of Shannon theory.

[0070] Step S530: The compressed telemetry data is sent to the data processing node via a wireless communication network.

[0071] After the aforementioned differential and statistical compression and reassembly, the data frames are pushed into the physical layer transmission queue of the terminal device. Using onboard wireless communication network interface modules (such as Wi-Fi transceivers or cellular uplink interfaces), and applying A-MPDU (Aggregated MAC Protocol Data Unit) frame aggregation and encapsulation technology to further reduce preamble transmission overhead, these compressed telemetry data are transmitted at high frequency and concurrently to the main base station, AP router, or other dedicated data processing nodes in the airspace. Because this mechanism significantly reduces the original bit rate of the payload on the source side (compression ratio reaching 2.8:1), it effectively suppresses the potential degradation of the wireless channel signal-to-noise ratio and collision congestion problems caused by the concurrent influx of tens of thousands of high-frequency heartbeat packets, thus breaking down the final communication barrier between the cluster positioning fusion method and the control center closed loop.

[0072] refer to Figure 6 , Figure 6 This is a block diagram of a dual-mode positioning fusion device for UAV swarms provided in an embodiment of the present invention. The device completely corresponds to the aforementioned dual-mode positioning fusion method for UAV swarms, embodying a device-level architecture that abstracts algorithms into logical functional components. Specifically, the device includes: The first acquisition module is used to acquire GNSS positioning data from the terminal device and determine the first quality factor of the GNSS positioning data; this module is connected to the GNSS baseband processing hardware and data parsing interface.

[0073] The second acquisition module is used to acquire the ultra-wideband (UWB) positioning data of the terminal device and determine the second quality factor of the UWB positioning data; this module independently parses the ultra-wideband message and forms a dual data bus with the first acquisition module.

[0074] The third acquisition module is used to acquire a preset accuracy base ratio coefficient, wherein the accuracy base ratio coefficient represents the ratio of the positioning variance of the UWB positioning system to the positioning variance of the GNSS positioning system; it is responsible for pulling the calibrated global variance reference system from the read-only storage area or the system configuration bus.

[0075] The weight calculation module is used to calculate the fusion weight based on the first quality factor, the second quality factor, and the precision base ratio coefficient; this module has a built-in digital multiplication and division unit, which is the core algorithm engine for realizing Bayesian adaptive weighting.

[0076] The fusion processing module is used to adaptively weight and fuse the GNSS positioning data and the UWB positioning data based on the fusion weight to obtain fused positioning data; this module performs matrix multiplication and addition merging of position parameters in a unified three-dimensional vector coordinate space.

[0077] The smoothing output module is used to smoothly transition the target fused positioning data when the positioning dominant mode switches, based on a preset raised cosine window function. Located at the end of the system data stream, this module acts as a signal filter, smoothly transmitting continuous physical quantities to the external attitude actuator.

[0078] Since the underlying working principle of each functional module of the device is completely consistent with the logical reasoning process of the method embodiment section above and is mapped one by one, the execution details of each module will not be repeated here.

[0079] Furthermore, this invention also discloses an electronic device. This electronic device can be physically configured as a standalone flight control computer, an edge computing box, or a highly integrated microcontroller motherboard. Its internal physical architecture includes: at least one processor, and a memory electrically connected to at least one of the processors. The memory (which can be a non-volatile flash memory, random access memory, or other physical medium) stores a sequence of computer operation instructions that can be directly retrieved and executed by at least one of the processors. When the terminal device is powered on and enters the operating system mode, the instruction sequence is loaded into the execution pipeline by at least one of the processors and executed in a clockwise manner, enabling the at least one processor to coordinate with peripheral sensors and communication network cards to accurately implement the aforementioned... Figures 1 to 5 The logical actions of the dual-mode positioning fusion method for UAV swarms, which are recorded in any one or more combinations.

[0080] Furthermore, the substantial technical contribution of this invention can also be embodied in a computer-readable storage medium, such as a Universal Serial Bus memory, a solid-state drive, or a remotely deployed cloud-distributed software package. This medium physically stores a compiled computer program (or microcode firmware). These specific sequences of digitally encoded instructions, when loaded into the processor of the target terminal or base station and decoded and executed, can force the processing entity to establish a complete hardware and software scheduling environment, thereby realizing and implementing the overall processing flow of the UAV swarm dual-mode positioning fusion method as detailed and disclosed in any of the foregoing embodiments.

[0081] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A dual-mode positioning fusion method for unmanned aerial vehicle (UAV) swarms, characterized in that, Applied to a terminal device, the method includes: Acquire the Global Navigation Satellite System (GNSS) positioning data of the terminal device and determine the first quality factor of the GNSS positioning data; Acquire ultra-wideband (UWB) positioning data from the terminal device and determine a second quality factor for the UWB positioning data; Obtain a preset accuracy base ratio coefficient, wherein the accuracy base ratio coefficient represents the ratio of the positioning variance of the UWB positioning system to the positioning variance of the GNSS positioning system. The fusion weight is calculated based on the first quality factor, the second quality factor, and the accuracy base ratio coefficient. Based on the fusion weight, the GNSS positioning data and the UWB positioning data are adaptively weighted and fused to obtain fused positioning data. When the positioning-dominant mode switches, the switching process is smoothly processed according to the preset raised cosine window function, and the target fusion positioning data is output.

2. The UAV swarm dual-mode positioning fusion method according to claim 1, characterized in that, The step of calculating the fusion weight based on the first quality factor, the second quality factor, and the precision base ratio coefficient includes: The fusion weights are calculated using the following formula: in, Indicates the fusion weights, This represents the accuracy base scaling factor. This represents the first quality factor. This represents the second quality factor.

3. The UAV swarm dual-mode positioning fusion method according to claim 1, characterized in that, The step of calculating the fusion weight based on the first quality factor, the second quality factor, and the accuracy base scaling factor further includes: Calculate the three-dimensional Euclidean distance between the GNSS positioning data and the UWB positioning data; If the three-dimensional Euclidean distance is greater than a preset deviation threshold and the duration reaches a preset number of cycles, it is determined that the GNSS positioning system is being interfered with. The value of the fusion weight is forcibly set to zero, and the UWB dominant mode is triggered.

4. The UAV swarm dual-mode positioning fusion method according to claim 1, characterized in that, After adaptively weighting and fusing the GNSS positioning data and the UWB positioning data based on the fusion weights to obtain the fused positioning data, the process further includes: The target positioning dominant mode is determined according to the preset range to which the value of the fusion weight belongs; wherein, the target positioning dominant mode includes GNSS dominant mode, dual-mode fusion GNSS dominant mode, dual-mode fusion UWB dominant mode and UWB dominant mode. The step of smoothly transitioning the switching process according to a preset raised cosine window function and outputting target fused positioning data when the positioning dominant mode switches includes: If the target positioning dominant mode is switched, a preset switching transition period is obtained; During the switching transition period, the continuously changing transition weights are calculated using the raised cosine window function; The target fusion positioning data is output based on the continuously changing transition weights and the weighted calculation is performed, with continuous position and continuous velocity.

5. The UAV swarm dual-mode positioning fusion method according to claim 1, characterized in that, The step of acquiring the ultra-wideband (UWB) positioning data of the terminal device includes: Receive ultra-wideband signals from multiple beacon base stations and obtain the first measurement constraint equation based on the time difference of arrival; Acquire the altitude data collected by the barometric altimeter in the terminal device, and construct a barometric altitude constraint condition in the vertical dimension based on the altitude data; Substitute the air pressure altitude constraint into the first measurement constraint equation for dimensionality reduction to obtain the target measurement constraint equation with the vertical dimension unknown eliminated. The target measurement constraint equation is solved by weighted least squares method to obtain the UWB positioning data.

6. The UAV swarm dual-mode positioning fusion method according to claim 5, characterized in that, Before obtaining the UWB positioning data by solving the target measurement constraint equation using the weighted least squares method, the method further includes: Obtain the time variance of the received signal strength indication of the ultra-wideband signal within a preset time window, and the location calculation residual; The time variance and the positioning solution residual are normalized and jointly calculated to obtain the non-line-of-sight interference decision index; If the non-line-of-sight interference decision index is greater than the first preset threshold and less than or equal to the second preset threshold, the weight of the corresponding beacon base station in the target measurement constraint equation is reduced. If the non-line-of-sight interference decision index is greater than the second preset threshold, the corresponding beacon base station will be removed from the target measurement constraint equation.

7. The UAV swarm dual-mode positioning fusion method according to claim 1, characterized in that, After the output target fusion positioning data is obtained, it also includes: Calculate the position difference component between the target fused positioning data of adjacent periods; Based on the preset mapping relationship between maneuver amplitude levels and Huffman coding, the position difference component is compressed and encoded to obtain compressed telemetry data. The compressed telemetry data is transmitted to the data processing node via a wireless communication network.

8. A dual-mode positioning fusion device for unmanned aerial vehicle (UAV) swarms, characterized in that, include: The first acquisition module is used to acquire GNSS positioning data of the terminal device and determine the first quality factor of the GNSS positioning data; The second acquisition module is used to acquire the ultra-wideband (UWB) positioning data of the terminal device and determine the second quality factor of the UWB positioning data. The third acquisition module is used to acquire a preset accuracy base ratio coefficient, wherein the accuracy base ratio coefficient represents the ratio of the positioning variance of the UWB positioning system to the positioning variance of the GNSS positioning system. The weight calculation module is used to calculate the fusion weight based on the first quality factor, the second quality factor, and the precision base ratio coefficient. The fusion processing module is used to adaptively weight and fuse the GNSS positioning data and the UWB positioning data based on the fusion weight to obtain fused positioning data; The smooth output module is used to smoothly transition the switching process according to a preset raised cosine window function when the positioning dominant mode is switched, and output the target fused positioning data.

9. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to at least one of the processors; wherein, The memory stores instructions that can be executed by at least one of the processors, which enable at least one of the processors to perform the UAV swarm dual-mode positioning fusion method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the dual-mode positioning fusion method for unmanned aerial vehicle (UAV) swarms as described in any one of claims 1 to 7.

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