A positioning performance evaluation method

Abstract

The application discloses a positioning performance evaluation method, comprising the following steps: firstly, calculating the carrier-to-noise ratio of a single positioning antenna, the carrier-to-noise ratio after diversity and the diversity gain; then, calculating the satellite number density according to the current positioning requirement; subsequently, comprehensively evaluating the antenna positioning performance in combination with the satellite number density and the diversity gain, and judging whether the positioning requirement is met; if not, starting the diversity combination strategy to adjust the antenna configuration, combining and processing the signals of multiple antennas by using the maximum ratio combining diversity strategy, and effectively improving the signal quality; finally, re-executing the evaluation process and dynamically adjusting the antenna combination strategy according to the evaluation result; by comprehensively considering multiple key parameters, the method can ensure that the positioning system can meet the high-precision and high-reliability positioning requirement in different application scenarios.

Description

Technical Field

[0001] This invention relates to the field of antenna technology, and in particular to a method for evaluating positioning performance. Background Technology

[0002] In the field of satellite positioning technology, with the increasing complexity of positioning scenarios, such as urban canyons and mountainous areas, satellite signals received by a single positioning antenna are easily affected by interference such as obstruction and multipath effects, leading to a decrease in positioning accuracy and reliability. To solve this problem, diversity technology has been introduced. Diversity technology can effectively utilize the resources of multiple antennas, improve signal quality, and enhance positioning performance.

[0003] Patent application number 202311477649.8 discloses a positioning performance evaluation method, device, and electronic equipment, relating to the field of antennas. It comprehensively evaluates the positioning performance of a positioning antenna by combining the number of positioning satellites that the antenna can track and capture above a certain elevation angle, and the carrier-to-noise ratio (CNR) of the positioning satellite signals received by the antenna, resulting in a more comprehensive and accurate evaluation. The method includes: obtaining the gain of the positioning antenna in each directional range above a preset elevation angle, and the corresponding satellite number density in each directional range; determining the CNR corresponding to each directional range based on the gain of the positioning antenna in each directional range; and using the satellite number density corresponding to each CNR as an evaluation parameter to evaluate the positioning performance of the positioning antenna.

[0004] In existing satellite positioning systems, the performance of the positioning antenna directly affects positioning accuracy and reliability. While previous evaluation parameters included the number of positioning satellites that could be tracked and captured above a certain elevation angle and the carrier-to-noise ratio of the received signal, these were relatively singular in scope. Real-world positioning scenarios are complex and varied, with different satellite density requirements, and diversity techniques can improve signal quality. Summary of the Invention

[0005] This application provides a positioning performance evaluation method that comprehensively considers the current satellite density and diversity benefits to determine whether the positioning requirements are met. If not, the antenna is adjusted using a diversity merging strategy, and continuous iterative optimization is performed.

[0006] This application provides a positioning performance evaluation method, including:

[0007] S1, calculate the carrier-to-noise ratio of a single positioning antenna, the carrier-to-noise ratio after diversity, and the diversity benefit;

[0008] S2, calculate the satellite number density under the current positioning requirements;

[0009] S3. Based on the current satellite density and diversity benefits, evaluate the antenna's positioning performance to determine whether it meets the current positioning requirements; collect historical signal data and corresponding environmental parameters of the positioning antenna in different scenarios, calculate the non-steady-state signal attenuation coefficient and lock-in rate, and construct a prediction model; using the prediction model, input the scenario parameters of the current positioning requirements, predict the non-steady-state signal attenuation rate and lock-in rate, and correct the evaluation of the antenna's positioning performance; analyze the characteristics of the diversity merging strategies required for different scenarios based on the corrected evaluation method, preset multiple diversity merging strategies, and calculate their signal distortion values ​​in the current scenario;

[0010] S4. If the current positioning requirements cannot be met, the diffusing strategy will be activated to adjust the antenna.

[0011] S5, re-execute S1 to determine whether it is necessary to continue adjusting the set merging strategy.

[0012] Preferably, the step of correcting the positioning performance of the evaluation antenna specifically includes: inputting the scene parameters of the current positioning requirement into the trained prediction model, and the model outputting the predicted non-steady-state signal attenuation rate and lock-in rate; adjusting the original positioning performance evaluation formula based on the predicted non-steady-state signal attenuation rate and lock-in rate; the corrected positioning performance evaluation formula for the evaluation antenna is as follows: Where δ(t) is the attenuation coefficient of the non-steady-state signal, τ(t) is the lock-in rate of the non-steady-state signal, and P is the original positioning performance.

[0013] Preferably, the original positioning performance includes: obtaining the satellite number density by statistically analyzing the number of observable satellites within the positioning area and calculating the number of satellites per unit area or volume; substituting the satellite number density and diversity benefit weighting coefficient into the positioning performance evaluation formula to calculate the positioning performance index; comparing the calculated positioning performance index with a preset positioning demand threshold to determine whether the current positioning demand is met; the original positioning performance formula is: Where N is the satellite number density and DG is the diversity benefit.

[0014] Preferably, the diversity benefit specifically includes: for a single positioning antenna, receiving signals from different satellites, measuring signal power and noise power, and calculating the carrier-to-noise ratio (CN) of the single antenna. i Using the maximum ratio combining diversity strategy, the signals from individual antennas are combined to obtain a multi-antenna diversity benefit signal, and the carrier-to-noise ratio (CN) of the multi-antenna diversity is calculated. d and the benefits of segmentation Where i is the number of individual signal-to-noise ratios, and n is the number of individual antennas participating in diversity processing.

[0015] Preferably, the predicted non-steady-state signal attenuation rate and lock-in rate specifically include: the non-steady-state signal attenuation coefficient is the signal strength attenuation under different scenarios and time points, and the calculation formula is: The preset maximum attenuation benchmark can be defined based on the attenuation level of historical signals; the lock-on rate is the proportion of satellite signals successfully locked by the antenna in different scenarios, and the calculation formula is:

[0016] Preferably, the preset multiple grouping merging strategy includes: grouping the preset grouping merging strategies under different scenarios according to scenario characteristics, assigning a unique identifier to each group, and establishing a grouping information database; comparing and matching the antennas used by different groups to find overlapping antennas, and determining the overlapping relationship between different groups based on the overlapping antennas; formulating a multi-group collaborative handover strategy package according to the overlapping relationship between groups, and clarifying the triggering conditions between groups; and designing a transition grouping scheme for scenario handover and adjusting the grouping transition strategy according to the overlapping relationship between groups and the handover strategy package.

[0017] Preferably, the establishment of the grouping information database includes: analyzing the correlation between the features of different scenarios and the preset grouping and merging strategies; based on the feature-strategy matching results, initially dividing scenarios with similar features and suitable for the same grouping and merging strategy into the same group; assigning a unique identifier to each group, establishing a grouping information database, and recording in detail the scenario type, feature parameters and corresponding grouping and merging strategies included in the group.

[0018] Preferably, determining the overlap relationship between different groups specifically involves: obtaining the antenna number information used by each group from the group information database; classifying and organizing the obtained antenna information according to the group to form an antenna list for each group; using a double loop method to traverse and compare the antenna lists of different groups to determine whether two antennas are the same antenna; recording the successfully matched overlapping antennas to form an overlapping antenna set between the two groups, and determining that there is an overlap relationship between the two groups.

[0019] Preferably, the overlap relationship includes: identifying scenarios requiring group switching based on the overlap relationship; setting a signal strength threshold for each group, triggering a switching condition when the signal strength of the current group is lower than a certain threshold and the signal strength of the adjacent group is higher than another threshold; classifying the switching strategies according to the triggering conditions, determining the triggering conditions and the name of the target group for each switching strategy, as a switching strategy package; and verifying the switching strategy package using simulation testing to simulate different user movement scenarios and positioning requirement scenarios.

[0020] Preferably, the transition grouping scheme for scenario switching specifically includes: analyzing the overlapping area between groups, statistically analyzing the distribution density and signal coverage strength of antennas in different groups within the overlapping area; analyzing the applicability and effectiveness of each switching strategy in different scenarios after simulation; defining transition groups based on group overlap relationships and switching strategies; determining the coverage range of transition groups and formulating antenna resource allocation schemes for transition groups.

[0021] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0022] By systematically collecting and calculating the carrier-to-noise ratio of a single positioning antenna and using a maximum ratio combining diversity strategy for multi-antenna signal combining, signal quality and positioning accuracy are effectively improved. By combining diversity benefit assessment and satellite number density calculation, the performance of positioning antennas is comprehensively evaluated, and the antenna combination strategy is dynamically adjusted according to the evaluation results to ensure that the positioning system can meet the positioning requirements of high accuracy and high reliability in different application scenarios, significantly enhancing the adaptability and robustness of the positioning system.

[0023] By optimizing antenna positioning performance through a systematic process, historical signal data and environmental parameters under multiple scenarios are first collected to construct a prediction model for the non-steady-state signal attenuation coefficient and lock-in rate. Then, the positioning performance evaluation method is corrected. Subsequently, the pre-set aggregation strategy for different scenario requirements is analyzed, and the signal distortion value of each strategy under the current scenario is calculated to evaluate its feasibility and impact on the signal. The entire process comprehensively considers multiple factors such as signal strength, stability, processing time, and signal distortion, effectively improving the antenna's positioning accuracy and adaptability in different complex environments, and has significant technical effects and application value.

[0024] By pre-setting subgrouping strategies for different scenarios and grouping them according to scenario characteristics to establish an information database, the association between strategies and scenarios is managed efficiently; the overlapping relationship is determined by comparing and matching grouped antennas, laying a solid foundation for coordinated handover; handover strategy packages are formulated based on the overlapping relationship and verified by simulation to ensure accurate handover triggering; then, a transition grouping scheme is designed and the transition strategy is adjusted according to the overlapping relationship and handover strategy to ensure signal stability during the positioning handover process, effectively improving the accuracy of scenario positioning handover and signal coverage quality.

[0025] By employing a scene-switching-based transition grouping scheme, the process is divided into three stages: pre-transition, during transition, and post-transition, with ideal signal distortion values ​​set. A signal generator dynamically injects distortion values ​​at different stages to simulate strategy adjustments. Signal monitoring equipment then collects data, processes the distortion values, calculates the smoothness, and compares it with the ideal value to dynamically adjust the transition speed. This series of measures precisely controls the signal switching process, effectively reducing the impact of signal distortion on communication quality, ensuring a smooth signal transition during scene switching, and improving the stability and reliability of the positioning antenna. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating a positioning performance evaluation method according to an embodiment of the present invention. Detailed Implementation

[0027] To facilitate understanding of the present invention, a more complete description of this application will be given below with reference to the accompanying drawings, which illustrate preferred embodiments of the invention. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to enable a more thorough and complete understanding of the disclosure of the present invention.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention; the term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0029] Example 1: Figure 1 This is a flowchart illustrating a positioning performance evaluation method according to an embodiment of the present invention.

[0030] like Figure 1 As shown, a positioning performance evaluation method includes the following steps:

[0031] S1 collects and calculates the carrier-to-noise ratio of a single positioning antenna, performs diversity processing on the signals from multiple antennas according to the diversity strategy, and calculates the carrier-to-noise ratio and diversity benefits after diversity processing.

[0032] Among them, carrier-to-noise ratio is the ratio of signal power to noise power; diversity processing is the process of combining a single antenna signal into signals from multiple antennas; diversity benefit refers to the difference between the carrier-to-noise ratio of the multiple antennas after diversity processing and the average carrier-to-noise ratio of the single antenna.

[0033] Specifically, for a single positioning antenna, signals from different satellites are received, signal power and noise power are measured, and the carrier-to-noise ratio (CN) of the single antenna is calculated. i Using the maximum ratio combining diversity strategy, the signals from individual antennas are combined to obtain a multi-antenna diversity benefit signal. The carrier-to-noise ratio (CN) of the multi-antenna diversity is then calculated. d and the benefits of segmentation Where i is the number of individual signal-to-noise ratios, and n is the number of individual antennas participating in diversity processing.

[0034] S2 calculates the number density of satellites tracked and captured under the current positioning requirements based on the diversity multi-antenna carrier-to-noise ratio.

[0035] The satellite number density is based on the diversity multi-antenna carrier-to-noise ratio (CN). d By combining satellite orbit data and antenna position information, the density of satellites that can be tracked and captured under the current positioning requirements is calculated.

[0036] S3, based on the current satellite density and diversity benefits, evaluate the positioning performance of the positioning antenna to determine whether it meets the current positioning requirements.

[0037] Specifically, the satellite number density is obtained by statistically analyzing the number of observable satellites within the positioning area and calculating the number of satellites per unit area or volume. The satellite number density and diversity benefit weighting coefficient are then substituted into the positioning performance evaluation formula to calculate the positioning performance index. This calculated positioning performance index is compared with a preset positioning demand threshold (the specific positioning demand threshold depends on the specific application scenario; this application does not specify a specific threshold) to determine whether the current positioning demand is met. The positioning performance (P) formula is:

[0038]

[0039] Where N is the satellite number density.

[0040] S4. If the positioning performance of the optimal antenna after the current diversity benefit assessment cannot meet the current positioning requirements, then the diversity merging strategy is initiated to adjust the antenna.

[0041] The divergence merging strategy involves reselecting antennas based on current positioning requirements (such as accuracy and speed), choosing suitable single or multiple antennas for merging. Antenna selection criteria can be based on performance, location, or dynamic adjustment of multiple antenna combinations. Antenna merging can be achieved through physical merging (multi-antenna arrays) or virtual merging (such as signal processing in software-defined radio).

[0042] S5: After adjusting the set merging strategy, re-execute S1 to determine whether the set merging strategy needs to be adjusted until the current positioning requirements are met.

[0043] For example, suppose we have three individual positioning antennas (antenna A, antenna B, and antenna C), each receiving signals from different satellites. We measure the signal power and noise power of each antenna and calculate the carrier-to-noise ratio (CN) of each antenna. i (Unit: dB-Hz): Antenna A: CN A =45dB-Hz, Antenna B:CN B =42dB-Hz, Antenna C:CN C =40dB-Hz

[0044] The maximum ratio combining diversity strategy is used to combine the signals from individual antennas. The carrier-to-noise ratio (CN) of the multi-antenna array after diversity is calculated. d Diversity benefits (DG): First calculate the average carrier-to-noise ratio: Assuming the carrier-to-noise ratio (CNR) of the multi-antenna after diversity is CN d =47dB-Hz (calculated using the maximum ratio combining diversity strategy; actual calculations may involve more complex signal processing, this is a simplified assumption), then the diversity benefit is: DG = 47 - 42.33 = 4.67dB.

[0045] Assuming a combination of satellite orbit data and antenna position information, under current positioning requirements, the carrier-to-noise ratio (CN) of diversity multi-antenna systems can be used. d =47dB-Hz, capable of tracking and capturing 10 satellites. The positioning area is 100 square kilometers. The formula for calculating the satellite density N is: but

[0046] Substituting N = 0.1 and DG = 4.67 into the positioning performance (P) formula: Among them, log 10 (1.467)≈0.166. Assuming that the preset location requirement threshold is not specifically limited, but for the sake of illustration, we assume that the threshold is 0.02. Since P=0.0166<0.02, it is initially determined that the current location requirement is not met.

[0047] Let's reselect the antenna. Suppose we add an antenna D with a carrier-to-noise ratio CN. D =43dB-Hz, recalculated multi-antenna carrier-to-noise ratio and diversity benefits after diversity: new average carrier-to-noise ratio: Assuming the new diversity multi-antenna carrier-to-noise ratio CN' d =48dB-Hz, new diversity benefit: DG' = 48 - 42.5 = 5.5dB. With the satellite density unchanged, P' = 0.019 still does not meet the requirements, so the antenna combination is adjusted further.

[0048] The antenna combination was readjusted, antenna B was removed, and antennas A, C, and D were retained. The new average carrier-to-noise ratio was then recalculated. Assuming the new diversity multi-antenna carrier-to-noise ratio CN' d = 49dB-Hz, new diversity benefits: DG ‘ = 49 - 42.67 = 6.33 dB, re-evaluate positioning performance, positioning performance P ‘ =0.021>0.02, which meets the current positioning requirements.

[0049] The technical solutions described in the embodiments of this application have at least the following technical effects or advantages:

[0050] By systematically collecting and calculating the carrier-to-noise ratio of a single positioning antenna and using a maximum ratio combining diversity strategy for multi-antenna signal combining, signal quality and positioning accuracy are effectively improved. By combining diversity benefit assessment and satellite number density calculation, the performance of positioning antennas is comprehensively evaluated, and the antenna combination strategy is dynamically adjusted according to the evaluation results to ensure that the positioning system can meet the positioning requirements of high accuracy and high reliability in different application scenarios, significantly enhancing the adaptability and robustness of the positioning system.

[0051] Example 2: In Example 1, the evaluation of positioning antenna performance and the selection of diversity merging strategies were mainly based on single model training and judgment using relatively fixed environmental parameters and signal characteristics. However, in real-world, complex, and ever-changing positioning scenarios, signal attenuation characteristics, noise interference levels, and satellite signal locking difficulty vary significantly across different scenarios. If only a unified model is used for predicting non-steady-state signal attenuation coefficients, calculating lock-in rates, and selecting diversity merging strategies, it will be difficult to comprehensively and accurately adapt to the needs of various scenarios. This will inevitably lead to inconsistent prediction accuracy and limited generalization ability of the model in different scenarios. Due to the historical diversity in signal characteristics and environmental parameter combinations across different scenarios, the requirements for positioning performance evaluation indicators and diversity merging strategy selection will inevitably differ. To further refine and differentiate positioning performance evaluation and diversity merging strategy selection to obtain more accurate and reliable positioning results in different scenarios, it is necessary to comprehensively consider multiple dimensions such as scenario type, signal characteristics, and environmental parameters for further optimization and improvement. Therefore, Example 2 is proposed.

[0052] In some embodiments, when evaluating the positioning performance of the positioning antenna, step S3 further includes:

[0053] S31 collects historical signal data and corresponding environmental parameters of the positioning antenna in different scenarios.

[0054] Historical signal data includes signal strength, noise level, number of satellites, and positioning accuracy.

[0055] Specifically, for scenarios where data collection is indeed necessary, such as urban canyons, open areas, forests, and indoor spaces, positioning antennas and corresponding data recording equipment should be used. Appropriate data collection time periods should be set according to the characteristics of the scenario to ensure coverage of various weather conditions and time periods (day / night). Historical signal data should be recorded, including signal strength (usually in dBm), noise level (usually in dB), number of satellites, and positioning accuracy (e.g., meter-level, centimeter-level). Corresponding environmental parameters should also be recorded, including weather conditions (sunny, cloudy, rainy, snowy, etc.), time (specific moment), movement speed (if applicable), and building density (for urban canyon scenarios).

[0056] S32 calculates the attenuation coefficient and lock-in rate of the unsteady signal based on historical data and constructs a prediction model.

[0057] Among them, the non-steady-state signal attenuation coefficient represents the signal strength attenuation under different scenarios and time points. The preset maximum attenuation benchmark can be defined based on the attenuation level of historical signals; the lock-on rate is the proportion of satellite signals that the antenna successfully locks onto in different scenarios.

[0058] Specifically, the collected data is integrated into a unified dataset, ensuring that each record includes signal strength, noise level, number of satellites, positioning accuracy, and corresponding environmental parameters. Meaningful features are extracted from the raw data, such as the rate of change of signal strength, the stability of noise level, and the distribution of satellite numbers. Correlation analysis and feature importance assessment are used to select the features that have the greatest impact on the attenuation coefficient and lock-in rate of the non-steady-state signal, reducing model complexity and improving prediction performance. For each record, an attenuation interval is defined based on timestamps or location information, such as a continuous period of time or a path. Within each attenuation interval, the difference between the initial and final values ​​of the signal strength is calculated to obtain the attenuation amount. The attenuation amount is normalized using a preset maximum attenuation benchmark to obtain the non-steady-state signal attenuation coefficient.

[0059] S33 uses a trained prediction model, inputs the scene parameters of the current positioning requirement, predicts the non-steady-state signal attenuation rate and lock-in rate, and corrects the antenna positioning performance evaluation method.

[0060]

[0061] Where δ(t) is the attenuation coefficient of the nonsteady-state signal, and τ(t) is the lock-in rate of the nonsteady-state signal.

[0062] Specifically, the scenario parameters of the current positioning requirement (such as weather, time, and movement speed) are input into the trained prediction model, and the model outputs the predicted non-steady-state signal attenuation rate and lock-in rate. Based on the predicted non-steady-state signal attenuation rate and lock-in rate, the original positioning performance evaluation formula is adjusted.

[0063] S34, based on the revised evaluation method, analyze the characteristics of the set merging strategy required for different scenarios, and pre-set multiple set merging strategies.

[0064] The diversity merging strategies required for different scenarios are characterized by the methods used for merging all antennas involved in the current scenario. For example, when a high-density obstruction area appears in an open area, the diversity merging strategy for adjusting the antennas is defined as follows: the transition from the open area to the high-density obstruction area is one diversity merging strategy; the transition from the high-density obstruction area to its end point is another; and the transition from the high-density obstruction area to the open area is yet another. Different diversity merging strategies encompass different antenna transition or selection methods.

[0065] Specifically, based on the revised evaluation method, the positioning performance requirements of different scenarios are analyzed. For example, for scenarios requiring high accuracy, a merging strategy that enhances signal strength and stability might be selected. For scenarios requiring fast positioning, a merging strategy that improves tracking rate and reduces processing time might be selected. Based on the characteristics of the strategies, multiple combinations of merging strategies are preset, each combination targeting a specific scenario.

[0066] S35, for each preset set union strategy, calculate its signal distortion value in the current scene.

[0067] Specifically, for each preset pass-through strategy, the feasibility of the pass-through strategy is simulated in advance to determine if it can meet the simulated positioning requirements. If the strategy is highly feasible, the differences in the waveform, spectrum, and other characteristics of the merged signal are calculated to obtain the signal distortion value. The formula for calculating the signal distortion value is:

[0068]

[0069] Where, x i It is the original signal value. It is the merged signal value, and N is the number of signal samples.

[0070] For example, if the data collection scenario is determined to be an urban canyon, a positioning antenna and accompanying data recording equipment are used. The data collection period is set to 24 consecutive hours each during the day and night to ensure coverage of different weather conditions (sunny, cloudy, light rain). Historical signal strength (in dBm) is recorded. For instance, on a clear day, the signal strength recorded by antenna A in 100 measurements is [-85, -84, -86, ..., -83] dBm (specific values ​​can be simulated based on actual conditions); on a cloudy night, the signal strength recorded by antenna A is [-90, -89, -91, ..., -88] dBm, etc. Noise level (in dB) is also recorded. For example, the noise level of antenna A on a clear day is [50, 51, 49, ..., 52] dB; on a cloudy night, it is [55, 54, 56, ..., 53] dB. The number of satellites recorded on a clear day fluctuates between [8, 10]; on a cloudy night, it fluctuates between [6, 8]. Positioning accuracy (e.g., meter-level): On a clear day, the accuracy is [2,3] meters; on a cloudy night, it's [4,5] meters. Corresponding environmental parameters are recorded as follows: Weather: Sunny, Cloudy, Light Rain. Time: Specific time, such as [08:00:00, 08:00:01, ..., 20:00:00] during the day; [20:00:00, 20:00:01, ..., 08:00:00] at night. Movement speed: Assuming the object is in an urban canyon, its movement speed is randomly selected between [0,5] m / s. Building density: In the urban canyon scenario, the building density is set to 0.8 (hypothetical value, representing the proportion of the area occupied by buildings).

[0071] Assume the preset maximum attenuation benchmark is 10dB (this can be set based on historical experience or actual needs). Taking the signal strength of antenna A during a clear day and a light rainy night as examples, calculate the signal strength attenuation. For example, during a clear day, the initial signal strength is -80dBm, and the ending value is -85dBm, with an attenuation of 5dB; during a light rainy night, the initial signal strength is -85dBm, and the ending value is -95dBm, with an attenuation of 10dB. For all recorded data, define attenuation intervals based on timestamps (e.g., one minute per interval), and calculate the difference between the initial and ending values ​​of the signal strength within each interval to obtain the attenuation. Normalize the attenuation using the preset maximum attenuation benchmark to obtain the non-steady-state signal attenuation coefficient. For example, if the attenuation in a certain interval is 5dB, the normalized non-steady-state signal attenuation coefficient is 5 / 10 = 0.5. The lock-in rate is the proportion of satellite signals that the antenna successfully locks onto in different scenarios. For example, on a clear day, if antenna A successfully locks on 90 out of 100 attempts, the lock rate is 90 / 100 = 0.9; on a rainy night, if antenna A successfully locks on 40 out of 80 attempts, the lock rate is 40 / 80 = 0.5. The collected data is integrated into a unified dataset, ensuring that each record includes signal strength, noise level, number of satellites, positioning accuracy, and corresponding environmental parameters. Meaningful features are extracted from the raw data, such as the rate of change of signal strength (calculated as the ratio of the difference in signal strength between adjacent time points to the time interval), the stability of noise level (calculated as the variance of noise level), and the distribution of the number of satellites. Correlation analysis and feature importance assessment are used to select the features that have the greatest impact on the attenuation coefficient and lock rate of the non-steady-state signal, and a predictive model is constructed. For example, using a linear regression model, assuming that after feature selection, the rate of change of signal strength and weather conditions are chosen as input features, the non-steady-state signal attenuation coefficient and lock rate are predicted.

[0072] Assume the scenario parameters for the current positioning requirement are: cloudy weather, daytime, and movement speed of 3 m / s. These parameters are input into a trained prediction model, which outputs a predicted non-steady-state signal attenuation rate δ(t) = 0.3 and a lock-in rate τ(t) = 0.7. Assuming that in the previous evaluation, P = 0.021 (the positioning performance index that meets the requirements in Example 1), then the corrected positioning performance P' = 0.021·(1-0.3 / 0.7) = 0.021·(1-0.4286) ≈ 0.012. If the preset positioning requirement threshold is still 0.02, then P' < 0.02, and the positioning requirement is not met.

[0073] For urban canyon scenarios, the positioning performance requirements under different weather and time conditions are analyzed. For example, on cloudy nights, due to severe signal obstruction, a merging strategy to enhance signal strength is needed; on clear days, a merging strategy that improves tracking rate and reduces processing time may be required. Strategy 1: For cloudy nights, when transitioning from an open area to a high-density building area, a diversity merging strategy that increases the number of antennas (e.g., from 3 to 4 antennas) is adopted. Strategy 2: For clear days, when the movement speed is relatively fast (greater than 3 m / s), a diversity merging strategy that selects the two antennas with the strongest signal strength for merging is adopted. Multiple combinations of diversity merging strategies are preset, each combination targeting a specific scenario.

[0074] The simulation uses a signal combining strategy, taking Strategy 1 as an example, to simulate the signal combining process after increasing the number of antennas in a cloudy daytime scenario. Assume the original signal value is x_i, and the combined signal value is {x_i}. For example, the original signal value is [-85, -84, -86, ..., -83] dBm, and the combined signal value after adding antennas is [-84, -83, -85, ..., -82] dBm (simulated data). The signal distortion value is calculated: the number of signal samples N = 100 (assumed). According to the formula MSE = (1 / N)·∑_{i=1}^{N}(x_i-{x_i}), the distortion is calculated as follows: 2 Calculate the signal distortion value. For example, the calculated MSE is (1 / 100)·[(-85-(-84))]. 2 +(-84-(-83)) 2 +…+(-83-(-82)) 2 ]≈1.

[0075] The technical solutions described in the embodiments of this application above have at least the following technical effects or advantages:

[0076] By optimizing antenna positioning performance through a systematic process, historical signal data and environmental parameters under multiple scenarios are first collected to construct a prediction model for the non-steady-state signal attenuation coefficient and lock-in rate. Then, the positioning performance evaluation method is corrected. Subsequently, the pre-set aggregation strategy for different scenario requirements is analyzed, and the signal distortion value of each strategy under the current scenario is calculated to evaluate its feasibility and impact on the signal. The entire process comprehensively considers multiple factors such as signal strength, stability, processing time, and signal distortion, effectively improving the antenna's positioning accuracy and adaptability in different complex environments, and has significant technical effects and application value.

[0077] Example 3: In Example 2, when facing location handover requirements in different scenarios, a unified set-based merging and handover strategy was used to handle all scenarios. However, different scenarios have significantly different characteristics. For example, urban canyon scenarios suffer from severe signal obstruction and significant multipath effects, while open area scenarios have relatively better signal propagation conditions. Signal strength variation patterns and antenna coverage effects vary across different scenarios. Using a unified strategy inevitably fails to fully adapt to the characteristics of each scenario, leading to inaccurate handover triggering timing and unstable signal coverage in some scenarios, making it difficult to guarantee the accuracy and reliability of location handover. Since there have been significant differences in signal characteristics and handover effects across different scenarios historically, the processing requirements for location handover will inevitably differ. To further refine and differentiate the location handover strategy and obtain more accurate and stable location handover results, it is necessary to comprehensively consider multiple factors such as scenario characteristics, set-based merging strategies, and handover strategies for further optimization and improvement. Therefore, Example 3 is introduced.

[0078] In some embodiments, step S35 further includes, for each preset set merging strategy:

[0079] S351, group the preset subgroups and merging strategies under different scenarios according to scenario characteristics, assign a unique identifier to each group, and establish a grouping information database.

[0080] Specifically, the correlation between the characteristics of different scenarios and the preset merging strategy is analyzed. For example, in urban canyon scenarios, due to severe signal obstruction and significant multipath effects, the merging strategy may be more suitable because it only needs to select the path with the strongest signal, resulting in lower computational complexity.

[0081] Based on the feature-policy matching results, scenes with similar features and suitable for the same set merging strategy are initially grouped into the same group. For example, urban canyon scenes and indoor dense building scenes are grouped together because they both have the characteristics of severe signal obstruction and obvious multipath effects, making them suitable for merging strategies.

[0082] Each group is assigned a unique identifier, and a group information database is established to record in detail the scene types, feature parameters, and corresponding set merging strategies contained in the group. For example, the database records that group 1 contains an urban canyon scene, with feature parameters such as building density and height, and the set merging strategy is to select antennas with stronger signal strength for merging.

[0083] The database records the scene types, scene feature parameters, and corresponding set merging strategies included in the grouping.

[0084] S352 compares and matches the antennas used in different formations to find overlapping antennas, and determines the overlapping relationship between different formations based on the overlapping antennas.

[0085] Overlapping antennas are antennas shared by two or more formations. For example, in urban canyon formations and open area formations, some antennas located in the boundary area between the two may serve both formations simultaneously; these antennas are overlapping antennas.

[0086] Specifically, the antenna numbering information for each group is retrieved from the grouping information database. This antenna information is then categorized and organized according to the group, forming an antenna list for each group. For example, for group A, its antenna list L is generated. A ={a1, a2, ..., a m}, where a i Let L represent the i-th antenna in group A; for group B, compile its antenna list L. B ={b1, b2, ..., b m}, b j This represents the j-th antenna in group B.

[0087] A double loop is used to iterate through and compare the antenna lists of different groups to determine whether two antennas are the same antenna. For example, for group A and group B, the antenna list L of group A is traversed first. A Each antenna a in i Then iterate through the antenna list L of group B. B Each antenna b in j Compare a i and b j Are they the same antenna? If a i and b j If their unique identifiers are the same, they are considered to be the same antenna, meaning they overlap.

[0088] Record the successfully matched overlapping antennas to form an overlapping antenna set C between the two groups, and determine that there is an overlapping relationship between the two groups.

[0089] S353, based on the overlapping relationship between groups, formulate a multi-group collaborative switching strategy package and clarify the triggering conditions between groups.

[0090] Specifically, based on the overlap relationship, scenarios requiring group switching are identified. For example, when moving from group A to the overlapping area of ​​group A and group B, it may be necessary to consider switching to group B.

[0091] A signal strength threshold is set for each group. A handover condition is triggered when the signal strength of the current group falls below a certain threshold, and the signal strength of an adjacent group rises above another threshold. To avoid frequent handovers due to short-term signal fluctuations, a time window and statistical method for signal strength measurement can be set. For example, the average signal strength of group A and group B is calculated over a continuous period of T time units. Handover is only triggered when the average signal strength of group A falls below a certain threshold. A-LOW Furthermore, the average signal strength of group B is higher than that of group S. B-HIGH The switch is only triggered at that time.

[0092] The switching strategies are categorized according to their triggering conditions. For each switching strategy, the triggering conditions and the switching target group name are determined, forming a switching strategy package.

[0093] The handover strategy package was verified using simulation software. Different user movement scenarios and positioning needs were simulated to check whether the handover strategy could be triggered correctly, and whether the signal quality and positioning performance after handover met the requirements. The handover strategy was adjusted as appropriate.

[0094] S354. Based on the overlap between groups and the switching strategy package, design a transition grouping scheme for scene switching and adjust the grouping transition strategy.

[0095] In some embodiments, the transition grouping scheme for scene switching is designed to specifically include:

[0096] Analyze the overlapping areas between antenna groups and statistically analyze the distribution density and signal coverage strength of different antenna groups within the overlapping areas.

[0097] Specifically, for overlapping areas, it is necessary to determine the boundaries, size, topography, and distribution of surrounding buildings of the overlapping areas.

[0098] Analyze the applicability and effectiveness of each switching strategy in different scenarios after simulation. For example, evaluate the performance of the signal strength-based switching strategy in areas with large signal fluctuations, and check whether frequent switching or untimely switching will occur.

[0099] Transitional groups are defined based on group overlap relationships and handover strategies. A transitional group refers to a temporary group or a specially configured group used for a smooth transition during group handover. For example, in the overlapping area of ​​group A and group B, the overlapping antenna set C is set as a transitional group C, which integrates the overlapping antenna resources of group A and group B to provide more stable signal coverage.

[0100] Determine the coverage area of ​​the transition group and formulate an antenna resource allocation plan for the transition group. Based on the signal strength and coverage area of ​​different antenna groups within the overlapping area, select an appropriate number of antennas to join the transition group. For example, select antennas with high signal strength and complementary coverage areas to improve the overall signal quality of the transition group.

[0101] It should be noted that when switching and transitioning from the current group to the transition group, and then from the transition group to the target group, it is necessary to ensure that the signals within the switching group meet the current positioning requirements.

[0102] The technical solutions described in the embodiments of this application have at least the following technical effects or advantages:

[0103] By pre-setting subgrouping strategies for different scenarios and grouping them according to scenario characteristics to establish an information database, the association between strategies and scenarios is managed efficiently; the overlapping relationship is determined by comparing and matching grouped antennas, laying a solid foundation for coordinated handover; handover strategy packages are formulated based on the overlapping relationship and verified by simulation to ensure accurate handover triggering; then, a transition grouping scheme is designed and the transition strategy is adjusted according to the overlapping relationship and handover strategy to ensure signal stability during the positioning handover process, effectively improving the accuracy of scenario positioning handover and signal coverage quality.

[0104] Example 4: In Example 3, although the scene-switching-based transition grouping scheme can achieve signal switching, its control over signal distortion during the transition process is rather coarse. Only a general transition strategy is set, and the impact of dynamic changes in signal distortion at different stages on antenna quality is not fully considered. During different scene transitions, the degree and trend of signal distortion vary significantly. If a uniform transition strategy is adopted, it is difficult to accurately control signal distortion in complex and ever-changing scenarios, easily leading to signal instability and decreased positioning quality. To more accurately control distortion during signal switching, the scene-switching transition grouping scheme is further restricted.

[0105] In some embodiments, step S354, designing a transition grouping scheme for scene switching, further includes:

[0106] Based on the scene switching transition grouping scheme, the transition grouping process is divided into three stages: before transition, during transition, and after transition, and an ideal signal distortion value is set for each stage. Before transition, a signal generator is used to inject the signal distortion value under the merging strategy combination into the transition grouping area. During transition, the injected value is gradually increased to simulate the dynamic adjustment of the strategy. After transition, the injected value is gradually decreased. Signal monitoring equipment is used to collect signal data in real time for these three stages, process the distortion value, calculate the smoothness of each stage, and compare it with the ideal value. If the distortion value exceeds the ideal value, the transition speed is slowed down; if the distortion value is lower than the ideal value, the transition speed is accelerated.

[0107] Specifically, the transition grouping scheme based on scene switching divides the transition grouping process into three stages: pre-transition, during transition, and post-transition. An ideal signal distortion value is set for each stage. For example, in the pre-transition stage, the signal distortion value should be controlled at a low level to ensure the stability of the current grouped signal; the ideal signal distortion value is set to not exceed X1 (where X1 is a specific value, which can be set according to actual conditions). In the during transition stage, due to signal switching and merging strategy adjustments, a certain degree of signal distortion is allowed, but it must be within a certain range; the ideal signal distortion value is set between X1 and X2 (where X2 is a specific value, and X2 > X1; X2 can be set according to actual conditions). In the post-transition stage, the signal should recover to a stable state as quickly as possible; the ideal signal distortion value is set to not exceed X3 (where X3 is a specific value, and X3 > X1). <X1)。

[0108] Before the transition, a signal generator actively injects signal distortion values ​​under the merging strategy combination into the transition grouping area. During this stage, the signal distortion value is relatively small, mainly simulating the slight fluctuations of the current grouped signal as it approaches the transition area. For example, by adjusting the phase or amplitude of the signal generator, a certain degree of distortion can be generated in the signal, while maintaining the overall signal quality.

[0109] During the transition, the signal distortion value injected by the signal generator is gradually increased to simulate the dynamic adjustment of the merging strategy combination during the switching process; after the transition, the signal distortion value injected by the signal generator is gradually reduced to restore the signal to a stable state.

[0110] Signal data from the three stages of the transition grouping area are collected in real time using signal monitoring equipment. The collected data includes parameters such as signal amplitude, frequency, and phase, as well as signal distortion values ​​calculated based on these parameters. The collected signal distortion values ​​are processed to calculate the signal distortion smoothness for each stage. The calculated signal distortion smoothness for each stage is compared and analyzed with the set ideal signal distortion value. If the signal distortion value exceeds the ideal signal distortion value, the transition speed is slowed down; if the signal distortion value is lower than the ideal signal distortion value, the transition speed is accelerated.

[0111] For example, if the signal distortion smoothness exceeds the ideal range in the pre-transition stage, it indicates that the current grouping signal fluctuates significantly when approaching the transition region. The execution speed of the transition strategy should be appropriately slowed down to allow the signal more time to adapt to the changes. If the signal distortion smoothness is within the ideal range but close to the upper limit in the mid-transition stage, the current transition strategy speed can be maintained. If the signal distortion smoothness exceeds the ideal range in the mid-transition stage, the transition strategy speed should be slowed down immediately, or even the handover should be paused until the signal stabilizes before continuing. If the signal distortion smoothness exceeds the ideal range in the post-transition stage, it indicates that the target grouping signal recovery is unstable. The adjustment time in the post-transition stage should be appropriately extended, and the transition strategy speed should be reduced.

[0112] The acquisition of signal data in the three stages of the transition grouping area includes parameters such as signal amplitude, frequency, and phase, as well as the calculated signal distortion value.

[0113] The technical solutions described in the embodiments of this application have at least the following technical effects or advantages:

[0114] By employing a scene-switching-based transition grouping scheme, the process is divided into three stages: pre-transition, during transition, and post-transition, with ideal signal distortion values ​​set. A signal generator dynamically injects distortion values ​​at different stages to simulate strategy adjustments. Signal monitoring equipment then collects data, processes the distortion values, calculates the smoothness, and compares it with the ideal value to dynamically adjust the transition speed. This series of measures precisely controls the signal switching process, effectively reducing the impact of signal distortion on communication quality, ensuring a smooth signal transition during scene switching, and improving the stability and reliability of the positioning antenna.

[0115] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A positioning performance evaluation method, characterized in that, include: S1, calculate the carrier-to-noise ratio of a single positioning antenna, the carrier-to-noise ratio after diversity, and the diversity benefit; S2, calculate the satellite number density under the current positioning requirements; S3. Based on the current satellite density and diversity benefits, evaluate the antenna's positioning performance to determine whether it meets the current positioning requirements; collect historical signal data and corresponding environmental parameters of the positioning antenna in different scenarios, calculate the non-steady-state signal attenuation coefficient and lock-in rate, and construct a prediction model; using the prediction model, input the scenario parameters of the current positioning requirements, predict the non-steady-state signal attenuation rate and lock-in rate, and correct the evaluation of the antenna's positioning performance; analyze the characteristics of the diversity merging strategies required for different scenarios based on the corrected evaluation method, preset multiple diversity merging strategies, and calculate their signal distortion values ​​in the current scenario; S4. If the current positioning requirements cannot be met, the diffusing strategy will be activated to adjust the antenna. S5, re-execute S1 to determine whether it is necessary to continue adjusting the set merging strategy.

2. The positioning performance evaluation method as described in claim 1, characterized in that, The process of revising and evaluating the antenna's positioning performance specifically includes: inputting the scene parameters of the current positioning requirement into a trained prediction model, which outputs the predicted non-steady-state signal attenuation rate and lock-in rate; adjusting the original positioning performance evaluation formula based on the predicted non-steady-state signal attenuation rate and lock-in rate; and revising the antenna's positioning performance formula as follows: Where δ(t) is the attenuation coefficient of the non-steady-state signal, τ(t) is the lock-in rate of the non-steady-state signal, and P is the original positioning performance.

3. The positioning performance evaluation method as described in claim 2, characterized in that, The original positioning performance includes: obtaining the satellite number density by statistically analyzing the number of observable satellites within the positioning area and calculating the number of satellites per unit area or volume; substituting the satellite number density and diversity benefit weighting coefficient into the positioning performance evaluation formula to calculate the positioning performance index; comparing the calculated positioning performance index with a preset positioning demand threshold to determine whether the current positioning demand is met; the original positioning performance calculation formula is: Where N is the satellite number density and DG is the diversity benefit.

4. The positioning performance evaluation method as described in claim 3, characterized in that, The diversity benefits specifically include: for a single positioning antenna, receiving signals from different satellites, measuring signal power and noise power, and calculating the carrier-to-noise ratio (CN) of the single antenna. i Using the maximum ratio combining diversity strategy, the signals from individual antennas are combined to obtain a multi-antenna diversity benefit signal, and the carrier-to-noise ratio (CN) of the multi-antenna diversity is calculated. d and the benefits of segmentation Where i is the number of individual signal-to-noise ratios, and n is the number of individual antennas participating in diversity processing.

5. The positioning performance evaluation method as described in claim 2, characterized in that, The predicted non-steady-state signal attenuation rate and lock-in rate specifically include: the non-steady-state signal attenuation coefficient, which represents the signal strength attenuation under different scenarios and time points, calculated using the following formula: The preset maximum attenuation benchmark can be defined based on the attenuation level of historical signals; the lock-on rate is the proportion of satellite signals successfully locked by the antenna in different scenarios, and the calculation formula is:

6. The positioning performance evaluation method as described in claim 1, characterized in that, The preset multiple grouping strategies include: grouping preset grouping strategies under different scenarios according to scenario characteristics, assigning a unique identifier to each group, and establishing a grouping information database; comparing and matching the antennas used by different groups to find overlapping antennas, and determining the overlap relationship between different groups based on the overlapping antennas; formulating a multi-group collaborative handover strategy package based on the overlap relationship between groups, and clarifying the triggering conditions between groups; and designing a transition grouping scheme for scenario handover based on the overlap relationship between groups and the handover strategy package, and adjusting the grouping transition strategy.

7. The positioning performance evaluation method as described in claim 6, characterized in that, The establishment of the grouping information database includes: analyzing the correlation between the features of different scenarios and the preset grouping and merging strategies; based on the feature-strategy matching results, initially dividing scenarios with similar features and suitable for the same grouping and merging strategy into the same group; assigning a unique identifier to each group, establishing a grouping information database, and recording in detail the scenario types, feature parameters and corresponding grouping and merging strategies included in the group.

8. The positioning performance evaluation method as described in claim 6, characterized in that, The determination of the overlap relationship between different groups specifically involves: obtaining the antenna number information used by each group from the group information database, classifying and organizing the obtained antenna information according to the group, and forming an antenna list for each group; A double loop is used to traverse and compare the antenna lists of different groups to determine whether two antennas are the same antenna; the successfully matched overlapping antennas are recorded to form a set of overlapping antennas between the two groups, and the overlapping relationship between the two groups is confirmed.

9. The positioning performance evaluation method as described in claim 8, characterized in that, The overlapping relationship includes: identifying scenarios requiring group switching based on the overlapping relationship; setting a signal strength threshold for each group, triggering a switching condition when the signal strength of the current group is lower than a certain threshold and the signal strength of the adjacent group is higher than another threshold; classifying the switching strategies according to the triggering conditions, determining the triggering conditions and the name of the target group for each switching strategy, as a switching strategy package; and verifying the switching strategy package using simulation testing to simulate different user movement scenarios and positioning requirement scenarios.

10. The positioning performance evaluation method as described in claim 6, characterized in that, The transition grouping scheme for scenario switching specifically includes: analyzing the overlapping area between groups, statistically analyzing the distribution density and signal coverage strength of antennas in different groups within the overlapping area; analyzing the applicability and effectiveness of each switching strategy in different scenarios after simulation; defining transition groups based on group overlap relationships and switching strategies; determining the coverage range of transition groups and formulating antenna resource allocation schemes for transition groups.

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