A target positioning method and device, electronic equipment, storage medium and program product

Abstract

Embodiments of the present application disclose a target positioning method and device, electronic equipment, storage medium and program product. The method comprises: for a target to be positioned, obtaining a Beidou signal received by the target and a common sense signal of a mobile communication network; positioning based on the Beidou signal to obtain a Beidou positioning result of the target, and positioning based on the common sense signal to obtain a common sense positioning result of the target; determining a Beidou weight corresponding to the Beidou signal and a common sense weight corresponding to the common sense signal; and based on the Beidou weight and the common sense weight, weighting and fusing the Beidou positioning result and the common sense positioning result to obtain a target positioning result of the target. The technical solution of the embodiments of the present application can realize accurate positioning of the target.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of positioning technology, and in particular to a target positioning method, device, electronic device, storage medium and program product. Background Technology

[0002] In applications such as intelligent transportation, drone navigation, and emergency rescue, accurate positioning of specific targets (such as vehicles, drones, and trapped personnel) is key to improving system efficiency, operational safety, and rescue success rates.

[0003] In the process of realizing this invention, the inventors discovered the following technical problem in the prior art: the target positioning accuracy is not high. Summary of the Invention

[0004] This invention provides a target positioning method, device, electronic device, storage medium, and program product, which solves the problem of low target positioning accuracy.

[0005] According to one aspect of the present invention, a target localization method is provided, which may include:

[0006] For the target to be located, acquire the BeiDou signals received through the target and the sensing signals from the mobile communication network;

[0007] Positioning is performed based on BeiDou signals to obtain the BeiDou positioning result of the target, and positioning is performed based on synesthetic signals to obtain the synesthetic positioning result of the target.

[0008] Determine the BeiDou weights corresponding to the BeiDou signals and the synsensory weights corresponding to the synsensory signals;

[0009] Based on BeiDou weights and sensory weights, the BeiDou positioning results and sensory positioning results are weighted and fused to obtain the target positioning result.

[0010] According to another aspect of the present invention, a target positioning device is provided, which may include:

[0011] The signal acquisition module is used to acquire the BeiDou signal and the sensing signal of the mobile communication network received by the target to be located.

[0012] The first positioning module is used to perform positioning based on BeiDou signals to obtain the BeiDou positioning result of the target, and to perform positioning based on sensory signals to obtain the sensory positioning result of the target.

[0013] The weight determination module is used to determine the BeiDou weight corresponding to the BeiDou signal and the synsensory weight corresponding to the synsensory signal;

[0014] The second positioning module is used to perform weighted fusion of BeiDou positioning results and sensory positioning results based on BeiDou weight and sensory weight to obtain the target positioning result.

[0015] According to another aspect of the present invention, an electronic device is provided, which may include:

[0016] At least one processor; and

[0017] A memory that is communicatively connected to at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by at least one processor, such that when the at least one processor executes the program, it implements the target localization method provided in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided having computer instructions stored thereon for causing a processor to execute and implement the target localization method provided in any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer program product is provided, on which a computer program is stored, which, when executed by a processor, implements the target localization method provided in any embodiment of the present invention.

[0021] The technical solution of this invention involves acquiring, for a target to be located, BeiDou signals and sensing signals from a mobile communication network received by the target. These sensing signals have a high sampling rate and strong dynamic response capability, making them suitable for short-term, high-precision positioning. The BeiDou signals possess global coverage and high stability, making them suitable for long-term, continuous positioning. Positioning is performed based on the BeiDou signals to obtain the target's BeiDou positioning result, and positioning is performed based on the sensing signals to obtain the target's sensing positioning result. The BeiDou weight corresponding to the BeiDou signals and the sensing weight corresponding to the sensing signals are determined. Therefore, based on the BeiDou weights and sensing weights, the BeiDou positioning result and the sensing positioning result can be weighted and fused to obtain the target's positioning result. Compared to other solutions that rely solely on sensory signals or BeiDou signals for positioning, or that switch between BeiDou and sensory positioning based on signal thresholds, the above-mentioned technical solution obtains the final target positioning result by weightedly fusing the BeiDou positioning results and the sensory positioning results. This not only fully utilizes the positioning advantages of each BeiDou positioning and sensory positioning, but also avoids positioning jumps and ensures continuous positioning trajectory. Thus, it solves the problem of low target positioning accuracy and achieves the effect of precise target positioning.

[0022] It should be understood that the description in this section is not intended to identify key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only 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 a flowchart of a target localization method provided according to an embodiment of the present invention;

[0025] Figure 2 This is a flowchart of another target localization method provided according to an embodiment of the present invention;

[0026] Figure 3 This is a flowchart of another target localization method provided according to an embodiment of the present invention;

[0027] Figure 4 This is a flowchart of another target localization method provided according to an embodiment of the present invention;

[0028] Figure 5 This is a flowchart of another target localization method provided according to an embodiment of the present invention;

[0029] Figure 6 This is a structural block diagram of a target positioning device according to an embodiment of the present invention;

[0030] Figure 7 This is a schematic diagram of the structure of an electronic device that implements the target positioning method of this invention. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. The same applies to "target," "original," etc., and will not be repeated here. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0033] It should be noted that the collection, gathering, updating, analysis, processing, use, transmission, and storage of user personal information involved in the technical solution of this invention all comply with relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. Necessary measures are taken to prevent unauthorized access to user personal information data and to maintain user personal information security and network security.

[0034] Figure 1 This is a flowchart of a target localization method provided by an embodiment of the present invention. This embodiment is applicable to target localization, especially to low-altitude target localization. The method can be executed by the target localization device provided by this embodiment of the present invention. This device can be implemented by software and / or hardware, and can be integrated into an electronic device, which can be a terminal device or a server. The terminal device can be the target to be localized.

[0035] See Figure 1 The method of this invention specifically includes the following steps:

[0036] S110. For the target to be located, acquire the BeiDou signal received through the target and the sensing signal of the mobile communication network.

[0037] For the target to be located, two types of positioning signals can be obtained: BeiDou signals and sensing signals from the mobile communication network. This mobile communication network can be a 5G network, a 5G-A network, or a future mobile communication network (such as a 6G network), depending on the specific circumstances, and is not specifically limited here. It should be noted that in the following examples, "5G-A network" may be used to represent the mobile communication network, but this is merely an example and not a specific limitation.

[0038] Specifically, the target receives BeiDou signals from the BeiDou Satellite Navigation System via an onboard BeiDou receiver. Optionally, in this embodiment, the BeiDou receiver may employ a dual-frequency design, possessing Real-time Kinematic (RTK) functionality and supporting sub-meter positioning accuracy. Further, after acquiring the BeiDou signal, it may be preprocessed, such as by performing at least one of down-conversion, amplification, and filtering, to improve subsequent positioning accuracy.

[0039] The target communicates with the sensing base station of the mobile communication network via its onboard communication module and receives sensing signals. In this embodiment of the invention, optionally, the sensing base station can be a 5G-A sensing base station, which uses the millimeter-wave frequency band, employs a target antenna array, and supports beamforming and multiple-input multiple-output (MIMO) technology for multiple users, thereby improving signal coverage and positioning accuracy.

[0040] Building upon this, an optional heterogeneous data adaptation layer can be implemented to address the issue of heterogeneous sampling rates and formats between synesthesia and BeiDou. For example, sampling rate synchronization can be performed, such as using the 200Hz high sampling rate of 5G-A synesthesia as a benchmark to dynamically complete the BeiDou signal. During completion, real-time motion synesthesia signals can be referenced to ensure that the sampling rate of the fused input signal is uniformly 200Hz, thus preventing low sampling rates from dragging down the update rate. As another example, signal format unification can be implemented, converting distance, angle, and velocity data from synesthesia signals into unified positioning feature vectors along with position, clock bias, and satellite almanac data from BeiDou signals, eliminating format heterogeneity barriers. The two working together can achieve dynamic, robust, and high-update-rate signal fusion.

[0041] Alternatively, for high-precision time synchronization, a dual-guarantee mechanism is constructed, with BeiDou time synchronization as the primary method and Network Time Protocol (NTP) as a backup. The prerequisite for timestamp alignment is a consistent initial time base. It is necessary to ensure that the timestamps of the 5G-A sensing base station and the BeiDou receiver originate from the same time base, thereby avoiding interpolation failure due to initial time deviations. Key technical details are as follows:

[0042] 1) Timing signal access: The sensing base station receives B1I band signals through the built-in Beidou timing module, and the Beidou receiver directly extracts timing information from the positioning signal. Both are based on the atomic clock time of the Beidou satellite.

[0043] 2) Time synchronization accuracy: By comparing the carrier phase of the timing signal, the time deviation between the sensing base station and the Beidou receiver is controlled within ±1 microsecond, which is much lower than the sensing sampling period, ensuring that the initial timestamp has no significant deviation.

[0044] 3) Anti-blocking backup: If the BeiDou timing signal is blocked for a short period of time, both the sensing base station and the BeiDou receiver will use the local constant temperature crystal oscillator to maintain time accuracy. The time drift within 1 minute is ≤50 nanoseconds to avoid synchronization failure.

[0045] For low-altitude scenarios with stable network coverage, NTP synchronization is used as a backup, specifically implemented as follows:

[0046] 1) Both the 5G-A sensing base station and the Beidou receiver are connected to the same high-precision NTP server. The server calibrates its local time through the Beidou time synchronization module to ensure that its own time is synchronized with Coordinated Universal Time (UTC).

[0047] 2) The sensing base station and the Beidou receiver send a time synchronization request to the NTP server every 10 seconds, with the synchronization accuracy controlled within ±10 microseconds, to meet the needs of medium and low precision low-altitude scenarios;

[0048] 3) Synchronization priority setting: BeiDou time synchronization is used first. If the BeiDou signal is interrupted for 30 consecutive seconds, it will automatically switch to NTP synchronization. There is no time base jump during the switching process, ensuring data continuity.

[0049] S120. Positioning is performed based on BeiDou signals to obtain the BeiDou positioning result of the target, and positioning is performed based on synoptic signals to obtain the synoptic positioning result of the target.

[0050] The process involves positioning based on BeiDou signals to obtain the target's BeiDou positioning result. For example, the BeiDou signal acquired by S110 undergoes error correction. This correction can be achieved by using parameters from the navigation message to correct satellite clock errors, or by employing modeling or differential methods to mitigate ionospheric and tropospheric delay errors. Then, a positioning algorithm (such as least squares or Kalman filtering) is used to calculate the position, yielding the BeiDou positioning result. This result may include the target's geographic coordinates (latitude, longitude, and altitude), and may also include information reflecting the current positioning quality, such as the positioning accuracy factor and covariance matrix.

[0051] Positioning is performed based on sensory signals to obtain the sensory positioning result of the target. For example, if based on the time difference of arrival, the positioning method of hyperbolic intersection of observed values ​​can be used; if based on the angle of arrival, triangulation can be used; if based on the round-trip time, the distance can be calculated from the measured values, and then combined with the measured values ​​from multiple base stations for trilateration; of course, several of the above methods can be combined for positioning, which can be set according to actual needs, and no specific limitation is made here.

[0052] S130. Determine the BeiDou weight corresponding to the BeiDou signal and the synsensory weight corresponding to the synsensory signal.

[0053] Practical experience has shown that sensory signals, with their high sampling rate and strong dynamic response, are suitable for short-term, high-precision positioning; while BeiDou signals offer global coverage and high stability, making them suitable for long-term, continuous positioning. Further practical experience reveals that if positioning is performed separately based on sensory and BeiDou signals, and then switching between BeiDou and sensory positioning based on actual conditions (e.g., whether the BeiDou signal strength exceeds a preset strength threshold) results in either a significant positioning jump during the switch. Clearly, neither approach relying solely on sensory or BeiDou signals for positioning, nor switching between BeiDou and sensory positioning based on signal thresholds, can guarantee the accuracy of target positioning.

[0054] In order to fully utilize the respective positioning advantages of BeiDou positioning and sensory positioning, and to avoid positioning jumps, this invention proposes a technical solution to obtain the final target positioning result by weighted fusion of BeiDou positioning results and sensory positioning results. The premise of weighted fusion is to determine the weights of each.

[0055] Based on this, an exemplary weight determination scheme is given here: determine the BeiDou reliability corresponding to the BeiDou signal and the synesthetic reliability corresponding to the synesthetic signal; then, based on the BeiDou reliability and the BeiDou basic error variance corresponding to the BeiDou signal, obtain the BeiDou positioning error variance, and based on the synesthetic reliability and the synesthetic basic error variance corresponding to the synesthetic signal, obtain the synesthetic positioning error variance; thus, based on the BeiDou positioning error variance and the synesthetic positioning error variance, determine the BeiDou weight corresponding to the BeiDou signal and the synesthetic weight corresponding to the synesthetic signal.

[0056] Determining the BeiDou reliability corresponding to a BeiDou signal can specifically involve collecting scene parameters such as the number of visible BeiDou satellites and the intensity of electromagnetic interference, and then deriving the BeiDou reliability based on these parameters. For example, a BeiDou reliability function can be constructed based on normalized scene parameters to quantify its reliability in the current scene. The reliability function reflects the coupling relationship between scene parameters, and its specific definition is as follows:

[0057] Beidou Reliability C b : Normalized value N of the number of visible satellites in BeiDou sat,norm Normalized value of electromagnetic interference intensity I e,norm The joint decision, reflecting the observation integrity and anti-interference capability of the BeiDou system, is expressed by the following formula:

[0058] C b =a1N sat,norm + (1- a1) I e,norm ;

[0059] Where a1 is the weight coefficient, determined through offline training, for example, by fitting based on 100,000 sets of real-world test data from different scenarios, so that C b The correlation coefficient R with the variance of BeiDou positioning error 2 ≥0.92 ensures a strong correlation between BeiDou reliability and actual positioning performance. When C b When the value is less than 0.3, the reliability of the BeiDou signal is determined to be low, which can trigger the abnormal suppression logic.

[0060] Determining the synesthetic credibility of a synesthetic signal involves collecting scene parameters such as the synesthetic signal-to-noise ratio (SNR) and the target's synesthetic velocity (also known as target velocity), and then deriving the synesthetic credibility based on the SNR and velocity. For example, a synesthetic credibility function can be constructed based on normalized scene parameters to quantify its reliability in the current scene. The credibility function reflects the coupling relationship between scene parameters and is defined as follows:

[0061] Synesthesia credibility C s : Normalized value of inductive signal-to-noise ratio (SNR) s,norm Normalized value of target velocity v t,norm Together, they determine the noise suppression capability and high dynamic adaptability of synesthesia, as shown in the following formula:

[0062] C s = a2SNR s,norm + (1- a2) v t,norm ;

[0063] Where a2 is the weight coefficient, which can be determined through offline training, for example, by fitting data based on 80,000 sets of high-speed mobile scene data, so that C s The correlation coefficient R with the variance of the synesthetic positioning error 2 ≥0.89. When C s When the value is less than 0.25, the reliability of the synesthetic signal is determined to be low, triggering the anomaly suppression logic.

[0064] Furthermore, the positioning error variance is obtained based on the confidence level and the basic error variance, and then the weights are determined based on the positioning error variance. For example, the linear minimum variance fusion criterion is used to map the confidence level to the positioning error variance, and then the weights are calculated. The core logic is that the smaller the positioning error variance of the data source, the higher its weight ratio, ensuring that the error of the final positioning result is minimized.

[0065] Specifically, error variance mapping: Assume the basic error variance of BeiDou is k. b The fundamental error variance of synesthesia is k. s Then the variance of the positioning error between the two types of data sources is:

[0066] R b 2 = kb \C b (The variance of BeiDou's basic error is inversely proportional to its reliability.)

[0067] R s 2 = k s \C s (The variance of the fundamental error of synesthesia is inversely proportional to the reliability of synesthesia);

[0068] Based on this, and according to the linear minimum variance criterion, the synesthesia weight w s With BeiDou weight w b Satisfy w s +w b = 1, and its calculation formula is:

[0069] w s = R b 2 \( R b 2 + R s 2 );

[0070] w b = R s 2 \( R s 2 + R b 2 ).

[0071] Building upon this, optionally, to ensure the scenario adaptability of the weight calculation model, dynamic parameter optimization can be achieved through offline training combined with online calibration. Specifically, during the offline training phase, measured data from four typical low-altitude scenarios are collected. With the goal of minimizing the root mean square of the positioning error, gradient descent is used to optimize a1, a2, and k. b and k s These parameters ultimately reduce the root mean square error (RMSE) of the model output weights by more than 40% compared to traditional list-based rules. During the online calibration phase, the system automatically extracts 1000 sets of positioning data for the current scene every hour of operation, calculates the deviation between the actual error and the model prediction error, and if the deviation is greater than 5%, k is fine-tuned using the least squares method. b and k s This avoids model inaccuracies caused by hardware aging during long-term operation.

[0072] The above example can monitor the scene and data status in real time, dynamically allocate the weights of sensing and Beidou, and on this basis, it can also identify and suppress abnormal data, further ensuring positioning accuracy.

[0073] S140. Based on the BeiDou weight and the sensory weight, the BeiDou positioning results and the sensory positioning results are weighted and fused to obtain the target positioning result.

[0074] The positioning results obtained from the preceding steps and their corresponding weights are then weighted and fused to obtain the final optimized target positioning result. Continuing with the example above, the final optimized target positioning result can be obtained based on the weight w. s and w b The positioning results from synesthesia and BeiDou are weighted and fused, and the fusion formula is P. fusion =w s P s + w b P b , where P s For the results of synesthetic localization, P b The result is the BeiDou positioning information.

[0075] Based on this, optionally, to avoid the influence of a single-dimensional anomaly on the overall result, local credibility corrections can be added to the weights of each dimension. Specifically, assume the d-th dimension of synesthesia (d=x, y, z, v) x , v y , v z The local confidence level of ) is c. s,d The local confidence level of BeiDou in the corresponding dimension is c. b,d The corrected dimension weights are:

[0076] w s,d = (w s c s,d )\ (w s c s,d + w b c b,d );

[0077] The revised formula for fusion across all dimensions is now updated to: d fusion =w s,d d s + w b,d d b This ensures that a single-dimensional anomaly only affects itself and does not spread to other dimensions. Wherein, d s For the d-th dimension of synesthesia localization result, d b This is the positioning result for the dth dimension of the BeiDou system.

[0078] Alternatively, an exponentially weighted moving average algorithm can be used to dynamically adjust the smoothing coefficient based on the target's motion state, eliminating deviations between the fusion result and historical trajectories and avoiding positioning jumps. The exponentially weighted moving average smoothing formula is as follows:

[0079] P smooth (k)=a1(k)Pfusion (k)+[1-a1(k)] P smooth (k-1);

[0080] Where a1(k) is the scene adaptive smoothing coefficient of the k-th frame, P fusion (k) represents the fusion position of the kth frame.

[0081] Through the above examples, the target localization result, localization confidence level, and scene label can be output, with an update rate maintained at 200Hz, meeting the real-time tracking requirements of highly dynamic targets.

[0082] The technical solution of this invention involves acquiring, for a target to be located, BeiDou signals and sensing signals from a mobile communication network received by the target. These sensing signals have a high sampling rate and strong dynamic response capability, making them suitable for short-term, high-precision positioning. The BeiDou signals possess global coverage and high stability, making them suitable for long-term, continuous positioning. Positioning is performed based on the BeiDou signals to obtain the target's BeiDou positioning result, and positioning is performed based on the sensing signals to obtain the target's sensing positioning result. The BeiDou weight corresponding to the BeiDou signals and the sensing weight corresponding to the sensing signals are determined. Therefore, based on the BeiDou weights and sensing weights, the BeiDou positioning result and the sensing positioning result can be weighted and fused to obtain the target's positioning result. Compared to other solutions that rely solely on sensory signals or BeiDou signals for positioning, or that switch between BeiDou and sensory positioning based on signal thresholds, the above-mentioned technical solution obtains the final target positioning result by weightedly fusing the BeiDou positioning results and the sensory positioning results. This not only fully utilizes the positioning advantages of each BeiDou positioning and sensory positioning, but also avoids positioning jumps and ensures continuous positioning trajectory. Thus, it solves the problem of low target positioning accuracy and achieves the effect of precise target positioning.

[0083] Figure 2 This is a flowchart of another target positioning method provided by an embodiment of the present invention. This embodiment is based on and optimized from the above-described technical solutions. In this embodiment, optionally, determining the BeiDou weight corresponding to the BeiDou signal and the synesthetic weight corresponding to the synesthetic signal includes: determining the scene in which the target is located; responding to the scene, determining the scene compensation weight, and adjusting the BeiDou basic weight corresponding to the BeiDou signal according to the scene compensation weight to obtain the BeiDou weight corresponding to the BeiDou signal; and adjusting the synesthetic basic weight corresponding to the synesthetic signal according to the scene compensation weight to obtain the synesthetic weight corresponding to the synesthetic signal. The explanations of terms that are the same as or corresponding to those in the above embodiments are not repeated here.

[0084] See Figure 2 The method in this embodiment may specifically include the following steps:

[0085] S210. For the target to be located, acquire the BeiDou signal and the sensing signal of the mobile communication network received through the target.

[0086] S220. Positioning is performed based on BeiDou signals to obtain the BeiDou positioning result of the target, and positioning is performed based on synoptic signals to obtain the synoptic positioning result of the target.

[0087] S230. Determine the scene in which the target is located.

[0088] For example, the target's sensing velocity can be used to determine whether the scene in which the target is located is a high-speed moving scene. For instance, a sensing velocity amplitude > 5 m / s indicates a high-speed moving scene, such as a drone's high-speed cruise or a low-altitude logistics vehicle transfer scenario, while a velocity amplitude between 3 and 5 m / s indicates a medium-speed transitional scene. The sensing velocity can be calculated using the 5G-A sensing Doppler frequency shift.

[0089] For example, the location of a target can be determined as an obstructed area based on the number of visible BeiDou satellites or the BeiDou signal strength. For instance, an obstructed area is considered to be one where the number of visible BeiDou satellites is less than 4 or the BeiDou signal strength is less than -120 dBm, such as urban areas with buildings, canyons, or forest cover. The number of visible BeiDou satellites can be the number of valid satellites counted in real-time by the BeiDou receiver.

[0090] For example, the location of a target can be determined as an open airspace scenario based on the number of visible BeiDou satellites and the confidence level of the sensing system. For instance, an open airspace scenario is defined as one where the number of visible BeiDou satellites is ≥6 and the confidence level of the sensing system is ≥80%, such as a remote open area or an unobstructed high-altitude scenario. The confidence level of the sensing system can be considered an indicator of the reliability of sensing positioning.

[0091] For example, the location of a target can be determined as an electromagnetic interference scenario based on the BeiDou Position Dilution of Precision (DOP) and the Signal-to-Noise Ratio (SNR). For instance, an electromagnetic interference scenario might be identified when the BeiDou DOP > 3 (significantly diluted positioning accuracy) and the SNR ≥ 15 dB (effective anti-interference capability), such as the area around an urban substation or a region with dense electromagnetic interference.

[0092] Of course, in addition to the scenarios mentioned above, the target may be in other scenarios, which depends on the actual situation and is not specifically limited here.

[0093] S240. In response to the scenario, determine the scenario compensation weight, and adjust the BeiDou basic weight corresponding to the BeiDou signal according to the scenario compensation weight to obtain the BeiDou weight corresponding to the BeiDou signal, and adjust the synesthetic basic weight corresponding to the synesthetic signal according to the scenario compensation weight to obtain the synesthetic weight corresponding to the synesthetic signal.

[0094] The process involves determining the scene compensation weights corresponding to the scene, and then adjusting the base weights using these scene compensation weights to obtain the final applied weights. In this embodiment, optionally, if the scene is a high-speed moving scene, the scene compensation weights can be determined based on the target's sensory speed; alternatively, if the scene is an obstructed area scene, the scene compensation weights can be determined based on the number of visible BeiDou satellites; further optionally, if the scene is an open airspace scene, the scene compensation weights can be determined based on the BeiDou positioning covariance; and still alternatively, if the scene is an electromagnetic interference scene, the scene compensation weights can be determined based on the BeiDou positioning accuracy factor.

[0095] Based on the above examples, several specific implementation processes for this step are given here as examples.

[0096] A linear weighted logic of basic weight + scenario compensation weight is adopted, with the total weight limited to 1. At the same time, upper and lower limits of the weight are set to avoid excessive reliance on a single data source. The calculation steps for each scenario are as follows:

[0097] In high-speed mobile scenarios, the basic weights are set as follows: based on the dynamic tracking advantage of the 200Hz high sampling rate of the sensor, the basic weights are 0.6 for the sensor and 0.4 for the Beidou; the scenario compensation weights are calculated as follows: the weights are linearly adjusted according to the magnitude of the speed exceeding the threshold, and the sensor weight increases by 2% for every 1m / s increase in speed above 5m / s; the final weights are determined as follows: sensor weight = 0.6 + 0.06 = 0.66, Beidou weight = 1 - 0.66 = 0.34.

[0098] Special constraints: If the speed is greater than 20m / s, the upper limit of the sensing weight is 0.8, and Beidou retains 0.2 to ensure the redundancy of the absolute positioning reference.

[0099] In the obstructed area scenario, the basic weights are set as follows: based on the advantage of the perceptual millimeter-wave diffraction blind spot compensation, the basic weights are perceptual 0.7 and BeiDou 0.3; the scenario compensation weights are calculated: adjusted linearly according to the gap in the number of visible BeiDou satellites, with the perceptual weight increasing by 10% for every 1 satellite less than 4. For example, if there are 2 visible BeiDou satellites, the compensation weight = 2 × 10% = 20%; the final weights are determined: perceptual weight = 0.7 + 0.2 = 0.9, triggering the upper limit constraint of 0.8, and the actual weight is 0.8, while the BeiDou weight = 0.2; based on this, a special constraint is that if the number of visible BeiDou satellites = 0, the perceptual weight is still retained at 0.8.

[0100] In open airspace scenarios, a basic weight is set to balance the high precision of BeiDou with the dynamic performance of sensing, with a basic weight of 0.5 for sensing and 0.5 for BeiDou; the scenario compensation weight is calculated based on the accuracy advantage of BeiDou, and if the BeiDou positioning covariance... The BeiDou weight is increased by 10%; the final weights are determined as follows: BeiDou weight = 0.5 + 0.1 = 0.6, and the sensing weight = 0.4, prioritizing positioning accuracy while considering dynamic response. The BeiDou positioning covariance can be considered as an indicator of BeiDou positioning uncertainty.

[0101] In electromagnetic interference scenarios, the following basic weights are set: based on the advantages of inductive electromagnetic interference resistance, the basic weights are inductive 0.6 and Beidou 0.4; the scenario compensation weights are calculated: adjusted according to the Beidou DOP exceeding the standard, for every 1 more than 3 DOP, the inductive weight increases by 10%, such as Beidou DOP=4.5, the compensation weight=1.5×10%=15%; the final weights are determined: inductive weight=0.6+0.15=0.75, Beidou weight=0.25.

[0102] Based on the examples above, optionally, after applying the weights, the system verifies the fusion positioning effect at intervals of 0.005 seconds using a synesthetic sampling period. If the target is not met, dynamic fine-tuning is performed. The specific verification and optimization logic is as follows:

[0103] 1) Performance verification indicators: High-speed mobile scene verification "positioning update rate ≥ 10Hz + trajectory adjacent frame deviation ≤ 0.1m"; occluded area scene verification "positioning uninterrupted duration > 10s + planar error ≤ 0.5m"; open airspace scene verification "positioning accuracy ≤ 2cm"; electromagnetic interference scene verification "error fluctuation ≤ 5%";

[0104] 2) Weighting fine-tuning rules: If the indicator does not meet the standard, the weight will be adjusted by 5% until the standard is met;

[0105] 3) Scene switching smoothness guarantee: When switching scenes, the weight is adjusted every 10ms, with a single adjustment range of ≤5%, and the complete switch is completed within 50ms to avoid changes in the positioning trajectory.

[0106] S250. Based on BeiDou weights and sensory weights, the BeiDou positioning results and sensory positioning results are weighted and fused to obtain the target positioning result.

[0107] The technical solution of this invention dynamically adjusts the cooperation strategy between sensory and BeiDou systems by real-time monitoring of the target's motion state and scene characteristics, so that sensory positioning and BeiDou positioning can complement each other's advantages. For example, in high-speed moving scenes, the sensory weight is increased, and a high update rate of 10Hz is used to ensure dynamic response; in open airspace scenes, the BeiDou weight is increased, and centimeter-level accuracy is used to ensure static stability; and in electromagnetic interference scenes and obstructed area scenes, the BeiDou ground-based augmentation dual virtual reference station constraint is introduced, while simultaneously increasing the sensory weight to resist dynamic interference and signal loss, thereby further improving the accuracy of target positioning.

[0108] Figure 3 This is a flowchart of another target positioning method provided by an embodiment of the present invention. This embodiment is based on and optimized from the above-described technical solutions. In this embodiment, optionally, positioning based on BeiDou signals to obtain the BeiDou positioning result of the target may include: obtaining prior constraints applied in the BeiDou calculation process based on sensing signals; and performing positioning based on the prior constraints and BeiDou signals to obtain the BeiDou positioning result of the target. The explanations of terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0109] See Figure 3 The method in this embodiment may specifically include the following steps:

[0110] S310. For the target to be located, acquire the BeiDou signal and the sensing signal of the mobile communication network received through the target.

[0111] S320. Obtain the prior constraints applied in the BeiDou solution process based on the sensing signals, and perform positioning based on the prior constraints and BeiDou signals to obtain the BeiDou positioning result of the target.

[0112] The core of the sensing-assisted BeiDou solution is to transform the high dynamic and real-time sensing signals obtained by 5G-A sensing into prior constraints in the BeiDou solution process. By limiting the reasonable range of BeiDou solution in advance and providing reference benchmarks, the dependence of BeiDou on satellite signals is reduced. Especially in weak signal, obstructed areas or high-speed moving scenarios, the BeiDou convergence time can be significantly shortened and the solution error reduced.

[0113] In this embodiment of the invention, optionally, 5G-A sensing is based on millimeter-wave radar and a massive MIMO antenna array, which can capture the dynamic parameters of the target and environmental perception information in real time. This information serves as prior information to assist BeiDou in its calculations. For example, prior information can be the target velocity, which can be calculated using Doppler frequency shift. Specifically, after the sensing base station transmits a signal, it receives the frequency change of the target's reflected signal and infers the target's radial velocity. Then, multiple base stations collaboratively calculate the target velocity. Its key characteristic is a sampling rate of 200Hz, with an error ≤0.1m / s in high-speed moving scenarios. Alternatively, prior information can be a position interval, which can be determined by angle and distance measurement using multiple base stations. Its key characteristic is sub-meter accuracy in position interval measurement, which can narrow the search range for BeiDou calculations. Furthermore, prior information can be the direction of motion and acceleration. Its key characteristic is strong dynamics, which can eliminate abnormal solutions with sudden changes in motion state in BeiDou calculations. For example, prior information can be perceptual confidence, which can be calculated based on the signal-to-noise ratio of the sensing signal and the consistency of data from multiple base stations; its key characteristic can be the quantification of the reliability of prior information, guiding the weight allocation during BeiDou calculation. Of course, other types of prior information may also exist, without specific limitations here.

[0114] Based on this, the aforementioned prior information can be obtained from the sensing signals, and this prior information can be transformed into prior constraints applied in the BeiDou solution process. Therefore, prior constraints can be obtained from the sensing signals, and these prior constraints can serve as boundary conditions or reference benchmarks for BeiDou solution, thus avoiding unconstrained divergence during BeiDou solution.

[0115] For example, the prior information can be velocity prior information, which can be converted into velocity constraints. These velocity constraints can be determined as follows: obtain the synesthetic perception confidence and the target's synesthetic velocity based on the synesthetic perception confidence, determine the velocity deviation range based on the synesthetic perception confidence, and use the synesthetic velocity as the initial velocity and the BeiDou velocity within the velocity deviation range as the velocity constraint.

[0116] Constraint rules: When BeiDou calculates BeiDou velocity, the sensory velocity obtained by sensing is used as the reference value, i.e., the initial velocity. The allowable velocity deviation range is determined by the sensory perception confidence level. The higher the sensory perception confidence level, the smaller the velocity deviation range.

[0117] For example, if the sensory velocity is 10 m / s eastward horizontally and 0.5 m / s vertically upward, with a sensory perception confidence level of 95%, then the BeiDou velocity calculated by BeiDou needs to be 9.8~10.2 m / s eastward and 0.4~0.6 m / s vertically upward. If the sensory perception confidence level drops to 70%, the velocity deviation range can be widened to 9.5~10.5 m / s eastward and 0.3~0.7 m / s vertically upward.

[0118] Constraint effect: Traditional BeiDou calculations require iterations starting from 0 m / s. With the introduction of this speed constraint, the sensor speed can be used directly as the initial reference, reducing the number of iterations. Especially in high-speed mobile scenarios, the convergence time of BeiDou speed calculations can be shortened by more than 40%.

[0119] For example, the prior information can be location interval prior information, which can be converted into location boundary constraints. These location boundary constraints can be determined as follows: the location interval where the synesthetic positioning result is located is obtained based on the synesthetic signal, and the location interval is used as the location boundary constraint.

[0120] Constraint rule: When BeiDou calculates its position using pseudorange, the calculation result must be limited to the position range defined by the sensor. If the calculation result exceeds the position range, it is judged as an abnormal solution and discarded. The system then iterates again based on the possible values ​​within the position range.

[0121] For example, through the collaboration of three base stations, the sensing location is determined to be within the range of x=100.2~100.7m, y=50.3~50.8m, and z=20.1~20.4m. If the BeiDou solution initially obtains x=101.5m, it will discard the result and recalculate the position corresponding to the pseudorange within the location range.

[0122] Constraint effect: In obstructed scenarios, BeiDou is prone to position jumps due to pseudorange errors. This constraint can directly eliminate abnormal solutions with jumps, ensuring the rationality of BeiDou's solution results. Positioning errors can be reduced by 60% in obstructed scenarios.

[0123] Prior information can be prior information about the direction of motion, which can be converted into state continuity constraints. These state continuity constraints can be determined as follows: the direction of motion and the synesthetic velocity of the target are obtained based on the synesthetic signal, and the direction of motion and the synesthetic velocity are used as state continuity constraints.

[0124] Constraints: When BeiDou calculates the position of two adjacent frames, it is necessary to ensure that the direction of position change is consistent with the direction of motion of the synesthetic fitting, and the velocity corresponding to the position change does not exceed 120% of the synesthetic velocity obtained by synesthetic sensing.

[0125] For example, if the motion direction of the target in the synesthetic fitting is 45° northwest and the synesthetic velocity is 8 m / s, the BeiDou solution position in the k-th frame is (100, 50). If the solution position in the (k+1)-th frame is (102, 53), it is considered abnormal and needs to be recalculated based on the northwest direction. If the solution position is (98, 48) and the BeiDou velocity corresponding to the position difference between the two frames is 9.2 m / s, it is considered a valid solution.

[0126] Constraint Function: This constraint addresses the issue of motion state misjudgment during BeiDou reconvergence after signal interruption. Traditional BeiDou reconvergence may assume uniform linear motion of the target; however, if the actual target changes direction, the calculation deviation will be large. Introducing this constraint allows for accurate matching of the target's actual motion state based on prior knowledge of the motion direction through synesthesia, reducing the planar error after reconvergence from 2cm to less than 0.8cm.

[0127] Prior information can be prior information on the confidence level of synesthetic perception, which can be converted into weight constraints. These weight constraints can be determined as follows: obtain the confidence level of synesthetic perception based on the synesthetic signal, and determine the weight constraints of the representation used in the BeiDou calculation process to assign weights to the pseudorange data of different satellites based on the confidence level of synesthetic perception.

[0128] Constraint rules: When performing BeiDou calculations, weights are assigned to pseudorange data from different satellites. At the same time, the influence of prior information can be adjusted by combining the confidence level of the sensing perception. When the confidence level of the sensing perception is ≥80%, the influence of prior constraints on BeiDou calculations increases to 60%; when the confidence level of the sensing perception is <60%, the influence decreases to 30%.

[0129] For example, if the confidence level of the syn-sensory perception is 90%, when BeiDou is calculating, if the pseudorange error of a certain satellite is large, the calculation result corresponding to the pseudorange will be corrected first by the syn-sensory velocity / position prior. If the confidence level of the syn-sensory perception is 50%, BeiDou will only use the syn-sensory prior as an auxiliary reference and mainly rely on the fusion of pseudorange data from multiple satellites.

[0130] Constraint function: Avoids the amplification of errors caused by strong reliance on priors when the sensing signal is poor. When the accuracy of the sensing itself is low, it can automatically reduce its impact on BeiDou, balance the relationship between prior assistance and satellite signal dominance, and reduce the BeiDou clock error calculation error from 0.1ns to 0.03ns in electromagnetic interference scenarios.

[0131] Based on this, taking the scenario of drone flight in an urban canyon as an example, we will explain the process of sensory prior assistance for BeiDou solution.

[0132] 1) Prior information collection: The 5G-A sensing base station collects the three-dimensional velocity (8.5m / s eastward, 0m / s vertically), position range (x=200.3~200.8m, y=150.2~150.7m, z=30.0~30.3m) and direction of motion (eastward, no turning) of the drone in real time, and calculates the sensing confidence level of 92%;

[0133] 2) Prior constraint generation: Based on the above prior information, four types of prior constraints are generated: velocity constraints, weight constraints, position boundary constraints, and state continuity constraints.

[0134] 3) BeiDou initialization: The BeiDou receiver receives pseudorange data from 5 satellites. When calculating the initial position, it directly uses the location range of the sensed position as the search range, instead of the traditional 10m×10m large-scale search. The initial calculation time is shortened from 0.5s to 0.1s.

[0135] 4) Application of prior constraints during the solution process:

[0136] When iterating the velocity, if the result of a certain iteration is 9.0 m / s eastward, which exceeds the upper limit of the synesthesia velocity constraint of 8.7 m / s, it will be automatically corrected to 8.7 m / s, and the pseudorange deviation will be recalculated based on this velocity.

[0137] When calculating the position of the third frame, the satellite signal was blocked by buildings, and the pseudorange error of a certain satellite reached 1.2m. At this time, based on the 92% confidence level of the synesthetic perception, the position solution corresponding to the satellite was corrected by using the synesthetic position interval x=200.3~200.8m first to avoid position jumps.

[0138] 5) Verification and feedback of solution results: After BeiDou outputs the solution results, it compares them with the prior constraints of the sensing. If they are met, the final result is output; if they are not met, a re-solution is triggered, and the influence of the prior constraints is reduced simultaneously to ensure the reliability of the results.

[0139] After introducing the prior constraint of sensing, the performance improvement of BeiDou solution is reflected in three aspects: convergence speed, anti-interference capability, and dynamic adaptability. The specific differences are as follows:

[0140] 1) Regarding the solution convergence time, from 10-30 minutes for star-based augmentation and 1-5 epochs for reconvergence, it has been improved to reduce the convergence time of star-based augmentation to within 5 minutes and reconvergence to ≤1 epoch;

[0141] 2) Regarding positioning errors in occlusion scenarios, the previous situation where the planar error exceeded 2cm when obstructed by buildings and positioning was impossible after 10 seconds of signal interruption has been improved to a situation where the planar error is ≤0.8cm when obstructed and positioning can still be achieved based on prior knowledge within 30 seconds of signal interruption.

[0142] 3) Regarding high-speed dynamic adaptability, the trajectory tracking lag has improved from more than 0.5m when the target speed is >10m / s to ≤0.1m when the target speed is ≤20m / s;

[0143] 4) Regarding stability under electromagnetic interference, the clock error calculation error caused by electromagnetic interference exceeded 0.1ns, reducing the positioning accuracy to the decimeter level. This was improved to a clock error calculation error ≤0.03ns, maintaining the positioning accuracy at the centimeter level.

[0144] S330. Based on the synesthetic signal, the target's synesthetic positioning result is obtained.

[0145] S340. Determine the BeiDou weight corresponding to the BeiDou signal and the synsensory weight corresponding to the synsensory signal.

[0146] S350. Based on BeiDou weights and sensory weights, the BeiDou positioning results and sensory positioning results are weighted and fused to obtain the target positioning result.

[0147] The technical solution of this invention transforms the high dynamic and real-time sensing signals acquired by 5G-A sensing into prior constraints in the BeiDou solution process. By pre-defining the reasonable range of BeiDou solution and providing a reference benchmark, it reduces BeiDou's dependence on satellite signals. Especially in weak signal, obstructed areas, or high-speed moving scenarios, it can significantly shorten the BeiDou convergence time and reduce the solution error, thereby shortening the target positioning time and further improving the target positioning accuracy.

[0148] Moreover, the above technical solution activates the integrated sensing capability of 5G-A, increasing the utilization rate of sensing resources from <30% to ≥80%, solving the problem of wasted time and frequency resources, and achieving the effect of efficient utilization of sensing resources and improving positioning accuracy; in addition, by sharing sensing-BeiDou signals, redundant calculations are reduced, and the network and terminal computing load during multi-target tracking is reduced by more than 30%.

[0149] Figure 4 This is a flowchart of another target positioning method provided by an embodiment of the present invention. This embodiment is based on and optimized from the above-described technical solutions. In this embodiment, optionally, after obtaining the BeiDou positioning result of the target based on BeiDou signals and the sensory positioning result of the target based on sensory signals, the above-described target positioning method further includes: obtaining the positional deviation between the BeiDou positioning result and the sensory positioning result; determining the BeiDou positioning covariance and the sensory perception confidence level; and correcting the sensory positioning result according to the positional deviation when the BeiDou positioning covariance and the sensory perception confidence level meet preset quantization conditions. The explanations of terms that are the same as or corresponding to those in the above embodiments are not repeated here.

[0150] See Figure 4 The method in this embodiment may specifically include the following steps:

[0151] S410. For the target to be located, acquire the BeiDou signal and the sensing signal of the mobile communication network received through the target.

[0152] S420. Positioning is performed based on BeiDou signals to obtain the BeiDou positioning result of the target, and positioning is performed based on synoptic signals to obtain the synoptic positioning result of the target.

[0153] S430. Obtain the positional deviation between the BeiDou positioning result and the sensory positioning result.

[0154] S440. Determine the BeiDou positioning covariance and the confidence level of the sensing perception. If the BeiDou positioning covariance and the confidence level of the sensing perception meet the preset quantization conditions, correct the sensing positioning results based on the position deviation.

[0155] For example, the location deviation scenarios described in this step mainly occur in urban canyons and densely built-up areas. After the sensory signals are reflected by the walls, the actual location of the target is easily misjudged, resulting in a deviation between the sensory positioning result and the true location.

[0156] Based on this, the quantification logic is as follows:

[0157] 1) The BeiDou positioning results obtained synchronously at the same time are denoted as P1, such as x P1 =300.21m, y P1 =180.53m, z P1 =25.12m, and the result of the synesthetic positioning is recorded as P2, such as x P2 =301.52m, y P2 =181.28m, z P2 =25.08m;

[0158] 2) Calculate the positional deviation ΔP in three-dimensional space: that is, the straight-line distance between two points. At the same time, calculate the one-way deviation in the three dimensions of x, y, and z respectively. For example, the deviation in the x direction is Δx = 301.52 - 300.21 = 1.31m, the deviation in the y direction is Δy = 181.28 - 180.53 = 0.75m, and the deviation in the z direction is Δz = 25.08 - 25.12 = -0.04m.

[0159] 3) Credibility-weighted correction: If the BeiDou positioning covariance is <10 -4 m 2 If the confidence level of the sensing is less than 80% (i.e., the preset quantization conditions are met), then the above ΔP is directly used as the final quantization value; if the BeiDou signal is slightly interfered with, the average value of the position deviation of three consecutive frames can be taken as the quantization result to avoid single-frame anomalies.

[0160] Quantification result range: In urban canyon scenarios, the positional deviation is typically 0.8~1.5m; in open airspace scenarios, the positional deviation can be reduced to 0.2~0.5m.

[0161] Based on this, alternatively, in addition to the location deviation scenarios described above, there may also be cumulative error scenarios. These cumulative error scenarios mainly occur in long-endurance scenarios, such as forest fire prevention patrols, where continuous operation exceeds 1 hour. The clock drift and signal link loss of the sensing base station accumulate over time, causing the sensing positioning deviation to gradually increase. In this case, the following steps can be performed:

[0162] In response to the cumulative error correction event, the cumulative position deviation of the synesthetic positioning and the cumulative position deviation of the BeiDou system are determined. The cumulative position deviation of the synesthetic positioning is the deviation between the synesthetic positioning result and the synesthetic position at the time of the first correction, and the cumulative position deviation of the BeiDou system is the deviation between the BeiDou positioning result and the BeiDou position at the time of the first correction.

[0163] The cumulative error between the cumulative positional deviation of the synesthesia and the cumulative positional deviation of the BeiDou system is obtained.

[0164] In response to the linear growth of the cumulative error over a preset number of consecutive cycles, the cumulative error of the next cycle is predicted by linear fitting to trigger the cumulative error correction process.

[0165] For example, the quantization logic is as follows:

[0166] 1) Set the BeiDou calibration cycle: Based on the characteristics of the cumulative error of the sensing, the cumulative error quantization is triggered once every 30 seconds by default, and the cycle can be adjusted according to the scenario;

[0167] 2) Calculate the cumulative deviation increment: using the synesthetic position at the time of the first correction as the initial reference P. sense,0 During the nth correction, the cumulative positional deviation P of synesthesia is calculated. sense,n -P sense,0 With "BeiDou cumulative position offset P" bd,n -P bd,0 The difference between the two values ​​is the cumulative error (or the quantized value of the cumulative error), denoted as ΔP. acc ;

[0168] 3) Trend fitting optimization: If ΔP for three consecutive periods acc If the error increases linearly, the cumulative error of the next cycle can be predicted by linear fitting, triggering correction in advance to avoid the deviation from exceeding the threshold.

[0169] Quantization result range: After continuous operation for 1 hour, the cumulative error quantization value is usually 0.5~1.2m; after Beidou periodic correction, the cumulative error quantization value can be controlled within 0.1m.

[0170] In this example, the absolute positioning data of BeiDou ground-based / satellite-based augmentation is used as the reference, and the relative positioning deviation of 5G-A sensing is synchronously corrected every 1Hz, which can avoid the accumulation of errors during long-term operation of sensing.

[0171] Based on any of the above technical solutions, alternatively, in addition to the two scenarios mentioned above, there may also be a speed deviation scenario. This speed deviation scenario mainly occurs in high-speed movement scenarios. The Doppler frequency shift of the sensing signal is affected by environmental noise interference, which easily overestimates or underestimates the target speed, resulting in a deviation between the sensing speed measurement result and the actual speed. In this case, the following steps can be performed:

[0172] The BeiDou velocity of the target is obtained based on the BeiDou signal, and the synsensory velocity of the target is obtained based on the synsensory signal. The velocity deviation between the BeiDou velocity and the synsensory velocity is also obtained.

[0173] When the required correction is determined based on the speed deviation, the synesthesia speed is corrected based on the speed deviation.

[0174] For example, the quantization logic can be as follows:

[0175] 1) Using the absolute velocity calculated by BeiDou (i.e., BeiDou velocity, denoted as V1) as a benchmark, compare it with the velocity measurement results of synesthesia (i.e., synesthesia velocity, denoted as V2).

[0176] 2) Calculate the velocity deviation (denoted as ΔV): including the unidirectional deviation of the three-dimensional velocity and the velocity amplitude deviation;

[0177] 3) Dynamic threshold screening: If the velocity amplitude deviation is >0.2m / s, it can be determined as a valid deviation and quantified; if the velocity amplitude deviation is ≤0.2m / s, it is determined as normal fluctuation and is not included in the correction range.

[0178] Quantification result range: In high-speed moving scenarios, the speed deviation is typically 0.15~0.4m / s; in low-speed moving scenarios, the speed deviation can be reduced to 0.05~0.1m / s.

[0179] S450. Determine the BeiDou weight corresponding to the BeiDou signal and the synsensory weight corresponding to the synsensory signal.

[0180] S460. Based on BeiDou weights and sensory weights, the BeiDou positioning results and sensory positioning results are weighted and fused to obtain the target positioning result.

[0181] The technical solution of this invention uses the absolute positioning benchmark of the BeiDou system as a reference to quantify the deviations generated by 5G-A sensing during the positioning process, such as multipath error, non-line-of-sight error, and long-term cumulative error. The deviation correction command is transmitted to the sensing solution link through a closed-loop feedback mechanism to dynamically adjust the state estimation parameters of the sensing, thereby reducing the uncertainty of sensing positioning. In other words, by correcting the sensing deviation through BeiDou, the target positioning accuracy is further improved.

[0182] Based on this, optionally, in scenarios with positional deviations, the above target localization method also includes:

[0183] A position deviation correction command is generated based on the position deviation, and then passed to the local sensor filter. The local sensor filter updates the observation noise covariance matrix according to the position deviation correction command, and adjusts the initial value of the position estimate according to the updated observation noise covariance matrix for positioning.

[0184] Alternatively, in scenarios involving velocity deviation, the aforementioned target localization method may further include:

[0185] A speed deviation correction command is generated based on the speed deviation and then passed to the inductive local filter. The inductive local filter updates the process noise covariance matrix according to the speed deviation correction command and adjusts the speed calculation coefficients of the Doppler frequency shift according to the updated process noise covariance matrix for speed measurement.

[0186] The sources of the aforementioned two technical solutions are strongly correlated with the scene. The types of deviations can be classified and defined first, and then accurate quantification can be achieved based on BeiDou absolute positioning data. The core of quantification is the difference analysis between BeiDou positioning results and sensory positioning results. At the same time, the credibility of both BeiDou positioning covariance and sensory perception confidence is combined to eliminate abnormal interference and ensure the reliability of quantification results.

[0187] For example, the closed-loop feedback model is the core of ensuring full-process automation from deviation quantification to correction execution to effect verification. It needs to be deeply coupled with the local inductive filter to achieve continuous optimization of inductive deviation through a cycle of deviation decision-dynamic correction-effect verification. The closed-loop feedback model consists of two parts: the closed-loop feedback process and the key control mechanism, as detailed below:

[0188] (1) Closed-loop feedback process

[0189] 1) Deviation detection trigger: Deviation detection can be triggered if any of the following conditions are met: Sensing confidence level < 80%; BeiDou positioning covariance < 10. -4 m 2 (BeiDou reference is reliable); the preset cumulative error correction period has been reached;

[0190] 2) Deviation Quantification Calculation: Following the example above, quantify the position deviation ΔP and velocity deviation ΔV, and label the deviation level. For example, <0.3m is a low deviation level; 0.3~0.8m is a medium deviation level; >0.8m is a high deviation level.

[0191] 3) Correction Decision Judgment: Based on the deviation level and scenario type, determine whether correction is necessary and the correction intensity:

[0192] Low deviation level: Only the deviation is recorded, and no correction is performed to avoid over-adjustment that could cause fluctuations in synesthetic calculation;

[0193] Medium deviation level: Perform local correction, only adjusting the observation noise parameters of the synsensory solution;

[0194] High deviation level: Perform global correction while adjusting the state estimate of synesthesia and observation noise;

[0195] 4) Dynamic correction execution:

[0196] The correction command is transmitted to the sensing local filter through the sensing-BeiDou signal interaction interface. The sensing local filter loads the new parameters when solving the next frame. The specific execution steps are as follows:

[0197] R-matrix (i.e., observation noise covariance matrix) adjustment (position deviation correction):

[0198] The observation noise covariance matrix R of the inductive local filter is a 3×3 diagonal matrix, and the position deviation correction command R is used. pos (k), update the diagonal elements of matrix R:

[0199] R(k) = diag[R pos (k), R pos (k), R pos (k)];

[0200] Mechanism of action: The smaller the value of the R matrix, the higher the confidence in the synesthetic position observations during filtering; by dynamically reducing the R value through the position deviation ΔP, the contribution of the position observations to the filtering results can be enhanced, thus offsetting the position deviation;

[0201] Q-matrix (i.e., process noise covariance matrix) adjustment (velocity deviation correction):

[0202] The process noise covariance matrix Q is a 6×6 diagonal matrix, and the speed deviation correction command Q is used. vel (k), update the diagonal elements of the velocity dimension of the Q matrix:

[0203] Q(k) = diag[Q pos0 Q pos0 Q pos0 Q vel (k), Q vel (k), Q vel (k)];

[0204] Q pos0 The initial value of the position process noise is fixed at 0.05 m².

[0205] Mechanism of action: The smaller the Q matrix value, the more confident the filter is in the assumption that the velocity remains stable; by dynamically reducing the Q value through the velocity deviation ΔV, random fluctuations in velocity observations can be suppressed, thus offsetting the velocity deviation.

[0206] The correction command is transmitted to the inductive local filter, and the specific correction actions are as follows:

[0207] Position deviation correction: Adjust the initial value of the synesthesia position estimate, for example, set the initial value of the synesthesia position estimate of the next frame to the current synesthesia position ΔP, directly offsetting the quantized position deviation;

[0208] Velocity deviation correction: Correct the Doppler frequency shift solution coefficient of the syn-sensory system. For example, if ΔVx = 0.3 m / s, the velocity solution coefficient corresponding to the Doppler frequency shift will be reduced by 5% to make the subsequent velocity measurement results closer to the BeiDou reference.

[0209] 5) Verification of calibration effect: Within 10ms after calibration, re-detect the synesthesia deviation: if the deviation decreases by ≥50%, the calibration is deemed effective and the current calibration parameters are maintained; if the deviation decreases by <50%, the deviation quantification and calibration decision are re-executed.

[0210] (2) Key control mechanisms

[0211] 1) Deviation threshold protection: Set the maximum correction limit, such as single position deviation correction ≤ 0.8m, single velocity deviation correction ≤ 0.3m, to avoid over-correction caused by BeiDou single frame anomalies;

[0212] 2) Scene adaptive adjustment: In urban canyon scenarios, real-time feedback is prioritized; in long-endurance scenarios, periodic feedback is prioritized, and the period is dynamically adjusted according to the target's movement speed.

[0213] 3) Dual-reference redundancy judgment: If the BeiDou positioning covariance > 10 -3 m 2 Then, the consistency of multi-base station sensing signals can be introduced as an auxiliary reference to avoid invalid correction when the BeiDou reference is unreliable.

[0214] The above example demonstrates a closed-loop feedback mechanism for correcting 5G-A sensing cumulative errors using BeiDou-enhanced absolute positioning data (i.e., BeiDou positioning data), including error quantization calculation and feedback correction processes, which further improves target positioning accuracy.

[0215] Figure 5 This is a flowchart of another target positioning method provided by an embodiment of the present invention. This embodiment is based on and optimized from the above-described technical solutions. In this embodiment, optionally, after acquiring the BeiDou signal received through the target and the sensing signal from the mobile communication network, the above-described target positioning method further includes: preprocessing the BeiDou signal and the sensing signal respectively, updating the BeiDou signal according to the preprocessing result of the BeiDou signal, and updating the sensing signal according to the preprocessing result of the sensing signal. The explanations of terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0216] See Figure 5 The method in this embodiment may specifically include the following steps:

[0217] S510. For the target to be located, acquire the BeiDou signal and the sensing signal of the mobile communication network received through the target.

[0218] S520. Perform preprocessing on the BeiDou signal and the syn-sensing signal respectively, update the BeiDou signal based on the preprocessing results of the BeiDou signal, and update the syn-sensing signal based on the preprocessing results of the syn-sensing signal.

[0219] For example, the motion state of a target can be identified based on the sensing signal, the calibration frequency can be determined based on the motion state, and the BeiDou signal can be interpolated and completed based on the calibration frequency.

[0220] Here is an example of dynamic timing calibration. Its core logic is to adaptively adjust the calibration frequency and method based on the target motion state and data sampling characteristics, thereby avoiding the deviation caused by fixed calibration. The specific steps are as follows:

[0221] Motion state recognition: Real-time acquisition of the target's sensor speed and acceleration via 5G-A sensing determines whether the target is in a constant speed, accelerating, or turning state. For example, acceleration > 0.5 m / s² is determined as acceleration, and turning angle is determined as turning angle. It was determined to be a turn;

[0222] Calibration frequency adaptation: The calibration interval is dynamically adjusted according to the motion state. For example, calibration is performed every 10ms in acceleration / turning scenarios and every 50ms in constant speed scenarios to ensure time synchronization accuracy in high dynamic scenarios.

[0223] Cross-source timing matching: Based on the 5G-A sensing high sampling rate, the BeiDou signal is dynamically interpolated and completed. The interpolation takes into account the target motion trend rather than traditional linear interpolation to avoid time deviation.

[0224] The above example demonstrates how dynamic timing calibration of the target's motion state is achieved through motion state recognition, calibration frequency adaptation, and cross-source timing matching. This is a crucial step in ensuring accurate target positioning.

[0225] For example, the tilt angle of the base station corresponding to the sensing signal is obtained, the origin position of the sensing relative coordinate system is corrected according to the tilt angle, and the mapping offset from the sensing relative coordinate system to the BeiDou absolute coordinate system is corrected according to the number of BeiDou visible satellites and the reflection characteristics of the sensing signal. Based on the corrected origin position and mapping offset, the sensing signal is transformed from the sensing relative coordinate system to the BeiDou absolute coordinate system.

[0226] Here is an example of environmental perception coordinate mapping. Its core logic can be to dynamically correct the mapping relationship between the relative sensing coordinate system and the BeiDou absolute coordinate system by combining real-time data such as base station attitude and environmental occlusion. The specific steps are as follows:

[0227] Base station attitude compensation: A miniature gyroscope is built into the 5G-A sensing base station to collect the tilt angle of the base station in real time, and the origin position of the sensing relative coordinate system is corrected according to the tilt angle to eliminate installation deviation.

[0228] Occlusion bias compensation: By analyzing the reflection characteristics of the synsensory signal and combining the number of visible BeiDou satellites, the mapping offset from the synsensory relative coordinate system to the BeiDou absolute coordinate system is dynamically adjusted. For example, the offset in the occluded area is 0.05-0.1m, and the offset in the unoccluded area is 0.

[0229] Real-time mapping update: The base station attitude and occlusion status data are synchronized every 20ms to update the coordinate mapping relationship, thereby ensuring that the spatial deviation is ≤0.1m when the relative coordinate system of the sensory system is converted to the absolute coordinate system of Beidou.

[0230] The above example, by integrating the coordinate mapping mechanism of base station attitude and environmental occlusion and the real-time mapping update logic, eliminates spatial reference deviation and achieves accurate mapping from sensory signals to BeiDou signals, which is a key step in ensuring accurate target positioning.

[0231] For example, the scene in which the target is located is determined, and features in the BeiDou signal and / or features in the sensing signal are filtered based on key features of scene matching.

[0232] Here is an example of attention feature filtering. Its core logic can be based on the positioning accuracy requirement, automatically identifying and retaining key features, and eliminating redundant data to avoid the loss of key information in traditional dimensionality reduction. The specific steps are as follows:

[0233] Feature importance learning: A feature-precision correlation model is trained using historical positioning data to identify key features in different scenarios. For example, in high-speed scenarios, the key features are synesthetic Doppler frequency shift and BeiDou clock error, while in open scenarios, the key features are BeiDou carrier phase and synesthetic perception confidence.

[0234] Dynamic feature filtering: Real-time judgment of the current scene, filtering data according to the rule of 100% retention rate of key features and ≥60% removal rate of redundant features. For example, in the electromagnetic interference scene, Beidou pseudorange abnormal data is removed and the sensing angle data is retained.

[0235] Lightweight data output: The volume of filtered data is reduced by more than 50%, and key features are not lost. It can be directly input into the subsequent fusion module, avoiding repeated feature extraction, and the data processing latency is reduced to less than 15ms.

[0236] S530. Positioning is performed based on BeiDou signals to obtain the BeiDou positioning result of the target, and positioning is performed based on synoptic signals to obtain the synoptic positioning result of the target.

[0237] S540. Determine the BeiDou weight corresponding to the BeiDou signal and the synsensory weight corresponding to the synsensory signal.

[0238] S550. Based on BeiDou weights and sensory weights, the BeiDou positioning results and sensory positioning results are weighted and fused to obtain the target positioning result.

[0239] The technical solution of this invention improves the preprocessing accuracy from meter level to centimeter level through a dynamic scene adaptive high-precision preprocessing mechanism, such as dynamic temporal calibration, environmental perception coordinate mapping and attention feature selection, and can be adapted to all low-altitude scenes, which helps to improve the target positioning accuracy in low-altitude scenes.

[0240] To put it another way, the aforementioned technical solutions can be integrated into a low-altitude, highly robust, and precise positioning system based on 5G-A dynamic-assisted BeiDou calculations. This aims to achieve efficient collaboration and error complementarity optimization between the two systems, addressing the shortcomings of traditional single-system approaches in terms of positioning accuracy, dynamic response, and environmental adaptability. This system utilizes a parallel input fusion algorithm to deeply fuse 5G-A sensing signals and BeiDou signals at the signal layer, thereby improving overall positioning performance.

[0241] The system may include a data acquisition module, a fusion processing module, and an application output module. The data acquisition module is responsible for synchronously acquiring 5G-A sensing signals and BeiDou signals, ensuring their alignment in both time and space. The application output module is responsible for outputting high-precision and high-reliability target positioning results. The fusion processing module is the core component, employing a parallel input fusion algorithm to preprocess the 5G-A sensing signals and BeiDou signals separately, and analyzing their deviation characteristics through an error model to achieve complementary error optimization. Specifically, the 5G-A sensing signal has a high sampling rate and strong dynamic response capability, suitable for short-term high-precision positioning; while the BeiDou signal has global coverage and high stability, suitable for long-term continuous positioning. Through the fusion algorithm, the system can dynamically adjust the weight distribution of the two signals, allowing them to complement each other's advantages, thereby improving positioning accuracy and robustness.

[0242] Furthermore, the system introduces a dynamic coordination mechanism, adjusting the collaboration strategy between 5G-A sensing signals and BeiDou signals in real time based on environmental changes and target motion status, ensuring stable operation even in complex scenarios. Simultaneously, the sensing signals can assist the BeiDou calculation process, improving its positioning capabilities under obstructed or weak signal conditions; while the BeiDou signals can correct deviations in 5G-A sensing, reducing the impact of interference such as multipath effects.

[0243] The system's workflow is as follows: First, it collects and preprocesses 5G-A sensing signals and BeiDou signals. Then, it performs signal-level fusion and error complementarity optimization through a parallel fusion algorithm. Finally, it adjusts system parameters using a dynamic collaborative mechanism and outputs high-precision and high-reliability target positioning results. It can be widely used in demanding scenarios such as intelligent transportation, drone navigation, and emergency rescue.

[0244] Figure 6 This is a structural block diagram of a target positioning device provided in an embodiment of the present invention. This device is used to execute the target positioning method provided in any of the above embodiments. This device and the target positioning methods of the above embodiments belong to the same inventive concept. Details not described in detail in the embodiments of the target positioning device can be found in the embodiments of the target positioning methods described above. See also... Figure 6 The device may specifically include: a signal acquisition module 610, a first positioning module 620, a weight determination module 630, and a second positioning module 640.

[0245] Among them, the signal acquisition module 610 is used to acquire the Beidou signal and the sensing signal of the mobile communication network received by the target to be located.

[0246] The first positioning module 620 is used for positioning based on BeiDou signals to obtain the BeiDou positioning result of the target, and for positioning based on sensing signals to obtain the sensing positioning result of the target.

[0247] The weight determination module 630 is used to determine the BeiDou weight corresponding to the BeiDou signal and the synsensory weight corresponding to the synsensory signal.

[0248] The second positioning module 640 is used to perform weighted fusion of BeiDou positioning results and sensory positioning results based on BeiDou weight and sensory weight to obtain the target positioning result.

[0249] Optionally, the weight determination module 630 may include:

[0250] Scene determination unit, used to determine the scene in which the target is located;

[0251] The first weight determination unit is used to respond to the scene, determine the scene compensation weight, adjust the BeiDou basic weight corresponding to the BeiDou signal according to the scene compensation weight to obtain the BeiDou weight corresponding to the BeiDou signal, and adjust the synesthetic basic weight corresponding to the synesthetic signal according to the scene compensation weight to obtain the synesthetic weight corresponding to the synesthetic signal.

[0252] Based on this, optionally, the scenario is a high-speed moving scenario, and the scenario compensation weight is determined according to the target's synesthetic speed; and / or,

[0253] The scene is an occluded area scene, and the scene compensation weight is determined based on the number of visible BeiDou satellites; and / or,

[0254] The scenario is an open airspace scenario, and the scenario compensation weight is determined based on the BeiDou positioning covariance; and / or,

[0255] The scenario is an electromagnetic interference scenario, and the scenario compensation weight is determined based on the BeiDou positioning accuracy factor.

[0256] Another optional scene determination unit is specifically used for at least one of the following:

[0257] Based on the target's synesthetic speed, determine whether the scene in which the target is located is a high-speed moving scene;

[0258] Based on the number of visible BeiDou satellites or the strength of the BeiDou signal, determine whether the scene in which the target is located is an obstructed area.

[0259] Based on the number of visible BeiDou satellites and the confidence level of the sensing, determine whether the scene in which the target is located is an open airspace scene;

[0260] Based on the BeiDou positioning accuracy factor and the signal-to-noise ratio, determine whether the scene in which the target is located is an electromagnetic interference scene.

[0261] Optionally, the weight determination module 630 may include:

[0262] The credibility determination unit is used to determine the credibility of the BeiDou signal and the credibility of the sensing signal.

[0263] The positioning error variance acquisition unit can be used to obtain the BeiDou positioning error variance based on the BeiDou reliability and the BeiDou basic error variance corresponding to the BeiDou signal, and to obtain the sensing positioning error variance based on the sensing reliability and the sensing basic error variance corresponding to the sensing signal.

[0264] The second weight determination unit is used to determine the BeiDou weight corresponding to the BeiDou signal and the synsensory weight corresponding to the synsensory signal based on the BeiDou positioning error variance and the synsensory positioning error variance.

[0265] Based on this, the optional credibility determination unit is specifically used for:

[0266] Acquire the number of visible BeiDou satellites, the signal-to-noise ratio of the sensing system, the intensity of electromagnetic interference, and the sensing speed of the target;

[0267] The BeiDou reliability corresponding to the BeiDou signal is obtained based on the number of visible BeiDou satellites and the intensity of electromagnetic interference. The syn-sensory reliability corresponding to the syn-sensory signal is obtained based on the syn-sensory signal-to-noise ratio and syn-sensory speed.

[0268] Optionally, the first positioning module 620 may include:

[0269] The prior constraint acquisition unit is used to obtain the prior constraints applied in the BeiDou solution process based on the sensing signal.

[0270] The BeiDou positioning result acquisition unit is used to perform positioning based on prior constraints and BeiDou signals to obtain the BeiDou positioning result of the target.

[0271] Based on this, optionally, the prior constraints include at least one of velocity constraints, weight constraints, position boundary constraints, and state continuity constraints. The prior constraints result in a cell, specifically used for at least one of the following:

[0272] The synesthetic perception confidence and the target's synesthetic velocity are obtained based on the synesthetic perception confidence. The velocity deviation range is determined based on the synesthetic perception confidence. The synesthetic velocity is taken as the initial velocity and the BeiDou velocity is within the velocity deviation range as the velocity constraint applied in the BeiDou solution process.

[0273] The location range of the synesthetic positioning result is obtained based on the synesthetic signal, and the location range is used as the location boundary constraint applied in the BeiDou solution process;

[0274] The target's motion direction and perceived velocity are obtained based on the synesthetic signals, and these are used as state continuity constraints in the BeiDou calculation process; and...

[0275] Based on the synsensory perception confidence level, the weight constraints for assigning weights to pseudorange data of different satellites by the representation used in the BeiDou solution process are determined.

[0276] Optionally, the above-mentioned target positioning device may further include:

[0277] The position deviation acquisition module is used to obtain the BeiDou positioning result of the target after positioning based on BeiDou signals and the syn-sensory positioning result of the target after positioning based on syn-sensory signals. The module then obtains the position deviation between the BeiDou positioning result and the syn-sensory positioning result.

[0278] The sensory positioning result correction module is used to determine the BeiDou positioning covariance and sensory perception confidence level, and correct the sensory positioning result based on the position deviation when the BeiDou positioning covariance and sensory perception confidence level meet the preset quantization conditions.

[0279] In addition, optionally, the above-mentioned target positioning device may further include:

[0280] The third positioning module is used to generate a position deviation correction command based on the position deviation, and pass the position deviation correction command to the sensor local filter so that the sensor local filter updates the observation noise covariance matrix according to the position deviation correction command, and adjusts the initial value of the position estimate according to the updated observation noise covariance matrix for positioning.

[0281] Alternatively, the aforementioned target positioning device may further include:

[0282] The cumulative position deviation determination module is used to determine the synesthetic cumulative position deviation and the BeiDou cumulative position deviation in response to the cumulative error correction event. The synesthetic cumulative position deviation is the deviation between the synesthetic positioning result and the synesthetic position at the time of the first correction, and the BeiDou cumulative position deviation is the deviation between the BeiDou positioning result and the BeiDou position at the time of the first correction.

[0283] The cumulative error acquisition module is used to obtain the cumulative error between the cumulative position deviation of the synaptic system and the cumulative position deviation of the BeiDou system.

[0284] The cumulative error correction module is used to respond to the linear increase of the cumulative error over a preset number of consecutive cycles, and to predict the cumulative error of the next cycle through linear fitting to trigger the cumulative error correction process.

[0285] Optionally, the above-mentioned target positioning device may further include:

[0286] The velocity deviation acquisition module is used to obtain the BeiDou velocity of the target based on the BeiDou signal and the synsensory velocity of the target based on the synsensory signal, and to obtain the velocity deviation between the BeiDou velocity and the synsensory velocity.

[0287] The synesthetic velocity correction module is used to correct the synesthetic velocity based on the velocity deviation when it is determined that correction is needed based on the velocity deviation.

[0288] Optionally, the aforementioned target positioning device may further include:

[0289] The speed measurement module is used to generate a speed deviation correction command based on the speed deviation, and then pass the speed deviation correction command to the inductive local filter so that the inductive local filter updates the process noise covariance matrix according to the speed deviation correction command, and adjusts the speed calculation coefficient of Doppler frequency shift according to the updated process noise covariance matrix to perform speed measurement.

[0290] Optionally, the above-mentioned target positioning device may further include:

[0291] The sensing signal update module is used to acquire the BeiDou signal and the sensing signal of the mobile communication network received through the target, preprocess the BeiDou signal and the sensing signal respectively, update the BeiDou signal according to the preprocessing result of the BeiDou signal, and update the sensing signal according to the preprocessing result of the sensing signal.

[0292] Based on this, optionally, the sensing signal update module includes at least one of the following units:

[0293] The interpolation and completion unit is used to identify the motion state of the target based on the sensing signal, determine the calibration frequency based on the motion state, and interpolate and complete the BeiDou signal according to the calibration frequency.

[0294] The sensing signal conversion unit is used to obtain the tilt angle of the base station corresponding to the sensing signal, correct the origin position of the sensing relative coordinate system based on the tilt angle, and correct the mapping offset from the sensing relative coordinate system to the BeiDou absolute coordinate system based on the number of visible BeiDou satellites and the reflection characteristics of the sensing signal. Then, based on the corrected origin position and mapping offset, the sensing signal is converted from the sensing relative coordinate system to the BeiDou absolute coordinate system.

[0295] The feature filtering unit is used to determine the scene in which the target is located, and to filter features in the BeiDou signal and / or features in the sensing signal based on the key features of scene matching.

[0296] The target positioning device provided in this embodiment of the invention solves the problem of low target positioning accuracy by having various modules work together, thus achieving the effect of precise target positioning.

[0297] The target positioning device provided in the embodiments of the present invention can execute the target positioning method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0298] It is worth noting that in the above-described embodiments of the target positioning device, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0299] Figure 7 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0300] like Figure 7As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0301] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0302] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as target localization methods.

[0303] In some embodiments, the target localization method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the target localization method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the target localization method by any other suitable means (e.g., by means of firmware).

[0304] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-chips or system-on-a-chips (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0305] Computer programs used to implement the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0306] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0307] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0308] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0309] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0310] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication unit 19, or installed from storage unit 18, or installed from ROM 12. When the computer program is executed by processor 11, it performs the functions defined in the methods of the embodiments of the present invention.

[0311] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0312] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A target localization method, characterized in that, include: For the target to be located, acquire the BeiDou signal and the sensing signal of the mobile communication network received through the target; Positioning is performed based on the BeiDou signal to obtain the BeiDou positioning result of the target, and positioning is performed based on the sensory signal to obtain the sensory positioning result of the target; Determine the BeiDou weight corresponding to the BeiDou signal and the synsensory weight corresponding to the synsensory signal; Based on the BeiDou weight and the synergistic weight, the BeiDou positioning result and the synergistic positioning result are weighted and fused to obtain the target positioning result of the target. The step of obtaining the BeiDou positioning result of the target based on the BeiDou signal includes: Based on the aforementioned sensing signal, the prior constraints applied in the BeiDou solution process are obtained; The target's BeiDou positioning result is obtained by performing positioning based on the prior constraints and the BeiDou signal; The prior constraints include at least one of velocity constraints and state continuity constraints. Obtaining the prior constraints applied in the BeiDou calculation process based on the sensing signal includes at least one of the following steps: Based on the synesthetic signal, the synesthetic perception confidence level and the synesthetic velocity of the target are obtained. The velocity deviation range is determined based on the synesthetic perception confidence level. The synesthetic velocity, as the initial velocity, and the BeiDou velocity falling within the velocity deviation range are used as the velocity constraint applied in the BeiDou calculation process. The motion direction and sensing velocity of the target are obtained based on the sensing signal, and the motion direction and sensing velocity are used as the state continuity constraints applied in the BeiDou calculation process.

2. The method according to claim 1, characterized in that, The determination of the BeiDou weight corresponding to the BeiDou signal and the sensing weight corresponding to the sensing signal includes: Determine the scene in which the target is located; In response to the scenario, a scenario compensation weight is determined, and the BeiDou basic weight corresponding to the BeiDou signal is adjusted according to the scenario compensation weight to obtain the BeiDou weight corresponding to the BeiDou signal. In addition, the synesthetic basic weight corresponding to the synesthetic signal is adjusted according to the scenario compensation weight to obtain the synesthetic weight corresponding to the synesthetic signal.

3. The method according to claim 2, characterized in that, The scenario is a high-speed moving scenario, and the scenario compensation weight is determined based on the target's synesthetic speed; and / or, The scenario described is an obstructed area scenario, and the scenario compensation weight is determined based on the number of visible BeiDou satellites; and / or, The scenario described is an open airspace scenario, and the scenario compensation weights are determined based on the BeiDou positioning covariance; and / or, The scenario described is an electromagnetic interference scenario, and the scenario compensation weight is determined based on the BeiDou positioning accuracy factor.

4. The method according to claim 2, characterized in that, Determining the scene in which the target is located includes at least one of the following steps: Based on the target's sensory velocity, determine whether the scene in which the target is located is a high-speed moving scene; Based on the number of visible BeiDou satellites or the strength of the BeiDou signal, determine whether the scene in which the target is located is an obstructed area. Based on the number of visible BeiDou satellites and the confidence level of the sensing, it is determined whether the scene in which the target is located is an open airspace scene; Based on the BeiDou positioning accuracy factor and the signal-to-noise ratio, it is determined whether the scene in which the target is located is an electromagnetic interference scene.

5. The method according to claim 1, characterized in that, The determination of the BeiDou weight corresponding to the BeiDou signal and the sensing weight corresponding to the sensing signal includes: Determine the BeiDou reliability corresponding to the BeiDou signal and the syn-sensing reliability corresponding to the syn-sensing signal; Based on the BeiDou reliability and the BeiDou basic error variance corresponding to the BeiDou signal, the BeiDou positioning error variance is obtained; and based on the sensing reliability and the sensing basic error variance corresponding to the sensing signal, the sensing positioning error variance is obtained. Based on the variance of the BeiDou positioning error and the variance of the sensing positioning error, the BeiDou weight corresponding to the BeiDou signal and the sensing weight corresponding to the sensing signal are determined.

6. The method according to claim 5, characterized in that, Determining the BeiDou reliability corresponding to the BeiDou signal and the synoptic reliability corresponding to the synoptic signal includes: The number of visible BeiDou satellites, the signal-to-noise ratio, the intensity of electromagnetic interference, and the sensing speed of the target are obtained. The BeiDou reliability corresponding to the BeiDou signal is obtained based on the number of visible BeiDou satellites and the electromagnetic interference intensity, and the syn-sensory reliability corresponding to the syn-sensory signal is obtained based on the syn-sensory signal-to-noise ratio and the syn-sensory velocity.

7. The method according to claim 1, characterized in that, The prior constraints also include location boundary constraints, and the process of obtaining the prior constraints applied in the BeiDou calculation process based on the sensing signal further includes: The location interval of the sensing positioning result is obtained based on the sensing signal, and the location interval is used as the location boundary constraint applied in the BeiDou calculation process.

8. The method according to claim 1, characterized in that, After obtaining the BeiDou positioning result of the target based on the BeiDou signal and the synoptic positioning result of the target based on the synoptic signal, the method further includes: The positional deviation between the BeiDou positioning result and the syn-sensory positioning result is obtained; Determine the BeiDou positioning covariance and the sensing confidence level. If the BeiDou positioning covariance and the sensing confidence level meet the preset quantization conditions, correct the sensing positioning result based on the position deviation.

9. The method according to claim 8, characterized in that, Also includes: A position deviation correction command is generated based on the position deviation, and the position deviation correction command is passed to the sensory local filter so that the sensory local filter updates the observation noise covariance matrix according to the position deviation correction command, and adjusts the initial value of the position estimate according to the updated observation noise covariance matrix for positioning.

10. The method according to claim 8, characterized in that, Also includes: In response to a cumulative error correction event, the cumulative position deviation of the synesthetic system and the cumulative position deviation of the BeiDou system are determined, wherein the cumulative position deviation of the synesthetic system is the deviation between the synesthetic positioning result and the synesthetic position at the time of the first correction, and the cumulative position deviation of the BeiDou system is the deviation between the BeiDou positioning result and the BeiDou position at the time of the first correction; The cumulative error between the inductive cumulative position deviation and the BeiDou cumulative position deviation is obtained; In response to the linear growth of the cumulative error over a preset number of consecutive cycles, the cumulative error of the next cycle is predicted by linear fitting to trigger the cumulative error correction process.

11. The method according to claim 1, characterized in that, Also includes: The BeiDou velocity of the target is obtained based on the BeiDou signal, and the syn-sensory velocity of the target is obtained based on the syn-sensory signal, and the velocity deviation between the BeiDou velocity and the syn-sensory velocity is obtained. If it is determined that correction is needed based on the speed deviation, the synesthetic speed is corrected based on the speed deviation.

12. The method according to claim 11, characterized in that, Also includes: A speed deviation correction command is generated based on the speed deviation, and the speed deviation correction command is passed to the inductive local filter so that the inductive local filter updates the process noise covariance matrix according to the speed deviation correction command, and adjusts the speed calculation coefficients of the Doppler frequency shift according to the updated process noise covariance matrix to perform speed measurement.

13. The method according to claim 1, characterized in that, After acquiring the BeiDou signal and the sensing signal of the mobile communication network received through the target, the method further includes: The BeiDou signal and the sensing signal are preprocessed respectively, and the BeiDou signal is updated according to the preprocessing result of the BeiDou signal, and the sensing signal is updated according to the preprocessing result of the sensing signal.

14. The method according to claim 13, characterized in that, The preprocessing of the BeiDou signal and the sensing signal includes at least one of the following steps: The motion state of the target is identified based on the sensing signal, the calibration frequency is determined based on the motion state, and the BeiDou signal is interpolated and completed based on the calibration frequency. The tilt angle of the base station corresponding to the sensing signal is obtained; the origin position of the sensing relative coordinate system is corrected according to the tilt angle; the mapping offset from the sensing relative coordinate system to the BeiDou absolute coordinate system is corrected according to the number of visible BeiDou satellites and the reflection characteristics of the sensing signal; and the sensing signal is transformed from the sensing relative coordinate system to the BeiDou absolute coordinate system according to the corrected origin position and the mapping offset; and, The scene in which the target is located is determined, and features in the BeiDou signal and / or features in the sensing signal are filtered based on key features of the scene matching.

15. A target positioning device, characterized in that, include: The signal acquisition module is used to acquire the BeiDou signal and the sensing signal of the mobile communication network received by the target to be located. The first positioning module is used to perform positioning based on the BeiDou signal to obtain the BeiDou positioning result of the target, and to perform positioning based on the sensing signal to obtain the sensing positioning result of the target. The weight determination module is used to determine the BeiDou weight corresponding to the BeiDou signal and the synsensory weight corresponding to the synsensory signal. The second positioning module is used to perform weighted fusion of the BeiDou positioning result and the sensory positioning result based on the BeiDou weight and the sensory weight to obtain the target positioning result of the target. The first positioning module includes: The prior constraint obtaining unit is used to obtain the prior constraints applied in the BeiDou calculation process based on the sensing signal. The BeiDou positioning result obtaining unit is used to perform positioning based on the prior constraints and the BeiDou signal to obtain the BeiDou positioning result of the target; The prior constraints include at least one of velocity constraints and state continuity constraints, and the BeiDou positioning result acquisition unit is specifically used for at least one of the following: Based on the synesthetic signal, the synesthetic perception confidence level and the synesthetic velocity of the target are obtained. The velocity deviation range is determined based on the synesthetic perception confidence level. The synesthetic velocity, as the initial velocity, and the BeiDou velocity falling within the velocity deviation range are used as the velocity constraint applied in the BeiDou calculation process. The motion direction and sensing velocity of the target are obtained based on the sensing signal, and the motion direction and sensing velocity are used as the state continuity constraints applied in the BeiDou calculation process.

16. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to cause the at least one processor to perform the target localization method as described in any one of claims 1-14.

17. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute and implement the target localization method as described in any one of claims 1-14.

18. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the target localization method as described in any one of claims 1-14.

Images