A dual-antenna hierarchical coarse-fine combined angle measurement method, device, equipment and storage medium

Abstract

The application provides a double-antenna hierarchical coarse-precision combined angle measurement method, device, equipment and storage medium, TDOA measurement data of a double-antenna array is acquired first, and grid search angle estimation is performed on the TDOA measurement data to generate a preliminary angle estimation value; then, whether to enable precise refinement estimation is judged based on a signal quality driven intelligent switching strategy, when a preset condition is met, fine grid search and interpolation optimization are performed with the preliminary angle estimation value as the center to generate a high-precision angle estimation value; finally, the preliminary angle estimation value and the high-precision angle estimation value are fused according to a reliability weighting algorithm, and a final angle measurement value is output. The problem that in double-antenna TDOA angle measurement, signal quality and real-time requirement fluctuation lead to difficulty in balancing between measurement accuracy and processing efficiency is solved.

Description

Technical Field

[0001] This invention relates to the field of dual-antenna angle measurement, and particularly to a method, apparatus, equipment, and storage medium for dual-antenna layered coarse and fine combined angle measurement. Background Technology

[0002] Dual-antenna array angle measurement systems have significant application value in fields such as maritime navigation, wireless communication, and radar positioning. However, existing dual-antenna angle measurement methods typically offer only two options: rapid coarse estimation or high-precision fine estimation. On the one hand, coarse algorithms, while computationally simple, have limited accuracy; on the other hand, precise algorithms, while meeting high-precision requirements, are computationally intensive and have poor real-time performance, making it difficult to achieve both simultaneously. Furthermore, existing technologies lack adaptive switching and fusion mechanisms when input signal quality or real-time requirements change. Additionally, the inherent 180° directional ambiguity of dual antennas often requires additional information to effectively eliminate, further increasing system complexity and computational burden.

[0003] In view of the above, this application is hereby submitted. Summary of the Invention

[0004] This invention discloses a method, apparatus, device, and storage medium for dual-antenna layered coarse and fine combined angle measurement, aiming to solve the problem that fluctuations in signal quality and real-time requirements in dual-antenna TDOA angle measurement make it difficult to achieve a balance between measurement accuracy and processing efficiency.

[0005] The first embodiment of the present invention provides a method for measuring angles by combining two antennas in a layered coarse and fine configuration, comprising: Acquire TDOA measurement data of the dual-antenna array, and perform grid search angle estimation on the TDOA measurement data to generate preliminary angle estimates; The intelligent switching strategy driven by signal quality determines whether to enable precise refinement estimation. When the preset conditions are met, fine grid search and interpolation optimization are performed with the preliminary angle estimate as the center to generate a high-precision angle estimate. The preliminary angle estimate and the high-precision angle estimate are fused using a confidence-weighted algorithm to output the final angle measurement.

[0006] Preferably, the step of performing grid search angle estimation on the TDOA measurement data to generate preliminary angle estimates specifically involves: Obtain the measured time difference of the signal received by the dual antennas, τ_measured, and the corresponding measurement uncertainty σ_tdoa. Based on the antenna baseline length d and the speed of light c, generate a candidate incident angle sequence {θ} within this range, θ∈[–90°, 90°], with a step size Δθ_coarse=1°. i}; For each candidate incident angle θ in the sequence i Theoretical time difference is calculated based on a geometric model: τ_theory(θ i )=d×sin(θ i ) / c; And calculate the measurement error: error(θ i )=|τ_measured τ_theory(θ i )|; Based on the error and measurement uncertainty, calculate the weight value for each candidate angle: weight(θ i )=exp(-error 2 (θ i ) / (2σ 2 _tdoa)); Based on the weight values, the candidate angle with the largest weight is selected as the preliminary angle estimate: θ_coarse=argmax{weight(θ i )}.

[0007] Preferably, the intelligent switching strategy based on signal quality driving specifically includes: Multiple signal quality metrics are acquired, including TDOA measurement accuracy, input signal confidence, real-time budget, and historical performance statistics. The strategy adaptively generates accuracy and time thresholds based on historical performance statistics. When the confidence level of the input signal is greater than 0.8 and the TDOA measurement accuracy is less than the accuracy threshold, the strategy directly selects the accurate algorithm. When the confidence level of the input signal is less than 0.3 or the real-time budget is less than the time threshold, the strategy selects only the coarse algorithm.

[0008] Preferably, the step of performing fine-grid search and interpolation optimization centered on the preliminary angle estimate to generate a high-precision angle estimate specifically involves: Centered on the initial angle estimate, a fine search window is defined as θ_search_range=[θ_coarse-Δθ_window,θ_coarse+Δθ_window], where Δθ_window is the preset window width and θ_coarse is the initial angle estimate. Within a fine search window, a fine-grid search is performed with a step size Δθfine; for each candidate incident angle θ i Calculate the error from all θ iThe three adjacent candidate angles with the smallest errors are selected, and their corresponding error values ​​are used to fit a quadratic parabola on the coordinate plane. The fitting model is as follows: error(θ) = a(θ) θ0) 2 +b(θ θ0)+c, where a, b, and c are fitting coefficients, θ0 is the candidate angle with the smallest error among the three points, and θ is the incident angle of the signal; Calculate the high-precision angle estimate based on the fitting coefficients a and b: θ_fine = θ0 b / 2a.

[0009] Preferably, the step of fusing the preliminary angle estimate and the high-precision angle estimate using a confidence-weighted algorithm to output the final angle measurement value specifically involves: The confidence scores of the preliminary angle estimate and the high-precision angle estimate are normalized to generate initial weights for the preliminary angle estimate and the high-precision angle estimate. A consistency index is calculated based on the deviation between the preliminary angle estimate and the high-precision angle estimate. The initial weights of the preliminary angle estimate and the high-precision angle estimate are then corrected based on the consistency index. Finally, the corrected weights are renormalized to generate the final weights of the preliminary angle estimate and the high-precision angle estimate. The final angle measurement value is generated based on the initial angle estimate and the final weight of the high-precision angle estimate.

[0010] Preferably, it further includes: Based on the inherent characteristic of the TDOA geometric relationship sin(θ)=sin(180°-θ) of the dual-antenna system, the 180° directional ambiguity is detected for the final angle measurement value θ_final, and corresponding candidate angle pairs are generated: θ1=θ_final and θ2=180°-θ_final; Calculate the matching scores of the candidate angle pairs θ1 and θ2 under the constraints of historical continuity, multi-frequency phase difference analysis, and signal strength pattern matching strategy, respectively. The matching scores are weighted and fused to calculate the comprehensive confidence of each candidate angle, and the candidate angle with higher confidence is selected as the direction angle after disambiguation. When the confidence level of the disambiguated direction angle is greater than 0.7, the angle is output and the ambiguity is marked as resolved; otherwise, the ambiguity is maintained and two candidate angles are output simultaneously.

[0011] A second embodiment of the present invention provides a dual-antenna layered coarse and fine combined angle measurement device, comprising: A preliminary angle estimation value generation unit is used to acquire TDOA measurement data of the dual-antenna array and perform grid search angle estimation on the TDOA measurement data to generate a preliminary angle estimation value. A high-precision angle estimation value generation unit is used to determine whether to enable precise refinement estimation based on a signal quality-driven intelligent switching strategy. When the preset conditions are met, fine grid search and interpolation optimization are performed with the preliminary angle estimation value as the center to generate a high-precision angle estimation value. The final angle measurement output unit is used to fuse the preliminary angle estimate and the high-precision angle estimate according to the confidence weighting algorithm and output the final angle measurement value.

[0012] The third embodiment of the present invention provides a dual-antenna layered coarse and fine combined angle measurement device, including a memory and a processor. The memory stores a computer program, which can be executed by the processor to implement the dual-antenna layered coarse and fine combined angle measurement method as described in any of the above embodiments.

[0013] The fourth embodiment of the present invention provides a computer-readable storage medium storing a computer program, which can be executed by a processor of the device in which the computer-readable storage medium is located, to implement a dual-antenna layered coarse and fine combined angle measurement method as described in any of the above claims.

[0014] Based on the dual-antenna layered coarse-fine combined angle measurement method, apparatus, device, and storage medium provided by this invention, the method first acquires TDOA measurement data of a dual-antenna array and performs grid search angle estimation on the TDOA measurement data to generate a preliminary angle estimate. Next, based on a signal quality-driven intelligent switching strategy, it determines whether to enable precise refinement estimation. When preset conditions are met, a fine grid search and interpolation optimization are performed centered on the preliminary angle estimate to generate a high-precision angle estimate. Finally, the preliminary angle estimate and the high-precision angle estimate are fused using a confidence-weighted algorithm to output the final angle measurement value. This method solves the problem in dual-antenna TDOA angle measurement where fluctuations in signal quality and real-time requirements make it difficult to achieve a balance between measurement accuracy and processing efficiency. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a dual-antenna layered coarse and fine combined angle measurement method provided in the first embodiment of the present invention; Figure 2 This is a schematic diagram of a dual-antenna layered coarse and fine combined angle measuring device provided in the second embodiment of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0017] To better understand the technical solution of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0019] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0020] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0021] The terms "first" and "second" used in the embodiments are merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first" and "second" can be interchanged in a specific order or sequence where permitted. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein.

[0022] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] This invention discloses a method, apparatus, device, and storage medium for dual-antenna layered coarse and fine combined angle measurement, aiming to solve the problem that fluctuations in signal quality and real-time requirements in dual-antenna TDOA angle measurement make it difficult to achieve a balance between measurement accuracy and processing efficiency.

[0024] The first embodiment of the present invention provides a dual-antenna layered coarse and fine combined angle measurement method, which can be executed by a measuring device, specifically by one or more processors within the measuring device, to at least implement the following steps: S101, acquire TDOA measurement data of the dual-antenna array, and perform grid search angle estimation on the TDOA measurement data to generate preliminary angle estimation values; In this embodiment, the measuring device can be a workstation, desktop computer, laptop computer, server, or other terminal with data processing capabilities. The auxiliary measuring device can be equipped with a corresponding operating system and application software, and the functions required in this embodiment can be realized through the combination of the operating system and application software.

[0025] Specifically, in this embodiment, the system receives measurement data from the TDOA input interface, including the time difference measurement value τ_measured of the dual-antenna received signals and the corresponding measurement uncertainty σ_tdoa. The system first determines the measurement based on a preset antenna baseline length d (typically 10 meters) and the speed of light constant c = 3 × 10⁻⁶. 8 m / s, within the angle search space θ∈[-90°,90°], generate a candidate incident angle sequence {θ} with a fixed step size Δθ_coarse=1°. i The sequence contains 181 equally spaced candidate angle points, covering all incident directions that a dual-antenna system could theoretically detect.

[0026] For each angle value θ in the candidate angle sequence i The system calculates the theoretical time difference based on a geometric model of a dual-antenna array. Considering the two antennas are located at the origin P1=[0,0] and position P2=[d,0] respectively, when the plane wave travels at an angle θ... i At incidence, the time difference between the arrival of the wavefront at the two antennas follows the geometric relationship τ_theory(θ). i )=d×sin(θ i The formula ) / c represents the direct mathematical relationship between the incident angle and the time difference measurement. By substituting each candidate angle into this formula, the system can predict the theoretical time difference that should be observed at that angle.

[0027] To evaluate the matching degree of each candidate angle, the absolute error (error(θ)) between the theoretical prediction and the actual measurement is calculated. i )=|τ_measured τ_theory(θ i This error value directly reflects the degree of deviation between the candidate angle and the true incident direction. Based on the error magnitude and measurement uncertainty, the system uses a Gaussian weighting function to calculate the confidence weight (weight(θ)) for each candidate angle.i )=exp(-error 2 (θ i ) / (2σ 2 It should be understood that: when the error is close to zero, the weight approaches 1, indicating that the candidate angle is highly credible; when the error is much greater than the measurement uncertainty, the weight approaches 0, indicating that the candidate angle is unlikely to be the true direction.

[0028] After the weights are calculated, the system determines the optimal candidate angle through a global optimization search, and selects the candidate angle with the largest weight value using the argmax operation: θ_coarse = argmax{weight(θ i The initial angle estimate is used as the initial angle estimate. It should be understood that this selection process essentially implements maximum likelihood estimation, finding the incident angle most likely to produce the observed result given the measurement data and error model. To improve computational efficiency, the system uses a pre-computed lookup table to store the angle-time difference mapping relationship, avoiding repetitive trigonometric function calculations, and supports parallel processing to accelerate the grid search process. When the weight value exceeds a preset threshold during the search, the system can also enable an early termination mechanism to further reduce computation time while ensuring accuracy.

[0029] S102, based on the signal quality-driven intelligent switching strategy, determine whether to enable precise refinement estimation. When the preset conditions are met, perform fine grid search and interpolation optimization centered on the preliminary angle estimation value to generate a high-precision angle estimation value. In this embodiment, the specific judgment process of the intelligent switching strategy is as follows: First, key signal quality indicators are collected from multiple data sources, including the measurement accuracy parameter σ_tdoa obtained from the TDOA extraction module, which is used to quantify the uncertainty level of time difference measurement; the input signal confidence obtained from the signal preprocessing unit, which is used to reflect the signal-to-noise ratio and stability of the original signal; the real-time budget time_budget obtained from the system resource manager, which represents the maximum computation time that can be allocated within the current processing cycle; and the historical performance statistics history_performance maintained by the performance monitoring module, which is used to record the accuracy performance and time consumption of different algorithm combinations in past processing cycles.

[0030] Based on historical performance statistical data, an adaptive threshold generation mechanism is adopted to dynamically adjust the critical parameters for switching judgment. The calculation of the precision threshold is based on the historical measurement precision distribution. By analyzing the statistical characteristics of σ_tdoa within the past N processing cycles, the system calculates its mean μ_precision and standard deviation σ_precision, and then generates the current precision threshold according to the formula threshold_precision = μ_precision + k1×σ_precision, where k1 is an adjustment factor, usually taking a value of 1.5 to ensure the stability of the threshold. Similarly, the time threshold is determined by analyzing the historical processing time distribution, and the calculation formula is threshold_time = μ_time - k2×σ_time, where μ_time and σ_time are the mean and standard deviation of the historical processing time respectively, and k2 is a time margin coefficient, usually taking a value of 0.5 to reserve sufficient processing time margin.

[0031] The decision logic of the switching strategy is implemented based on a multi-condition judgment tree: When it is detected that the input signal confidence input_confidence > 0.8 and at the same time the TDOA measurement precision σ_tdoa < threshold_precision, the system determines that the current signal quality is excellent and the measurement precision meets the prerequisite conditions of the high-precision algorithm. At this time, the switching strategy directly selects precise refinement processing, skipping the rough estimation stage to save computing resources and obtain the best precision performance.

[0032] On the contrary, when the system detects that the input signal confidence input_confidence < 0.3 or the real-time budget time_budget < threshold_time, it indicates that the current processing conditions are not conducive to the execution of the high-precision algorithm. Either the signal quality is too poor to ensure the convergence of the precise algorithm, or the time budget is insufficient to complete complex iterative calculations. At this time, the switching strategy selects to only use the fast rough estimation module, sacrificing some precision to ensure the real-time response ability of the system; For medium signal quality conditions between the above two extreme cases, the system adopts a hierarchical combination algorithm strategy, that is, both the rough estimation and precise estimation modules are enabled, and two results are obtained in parallel or serially, and then the adaptive fusion module intelligently fuses them according to their respective confidences. To optimize the long-term performance of the system, the switching strategy also implements a dynamic learning mechanism. By continuously monitoring the actual effects of various strategy combinations, using the sliding window statistical method to update the historical performance data, and adjusting the values of threshold_precision and threshold_time accordingly, the self-optimization and environmental adaptation of the switching strategy are achieved.

[0033] Furthermore, in this embodiment, the initial angle estimate θ_coarse from the fast coarse estimation module is received as the starting point for optimization. A multi-scale refinement search strategy is used to significantly improve the angle measurement accuracy. First, a fine search window is constructed centered on the coarse estimation result. The search boundary θ_search_range=[θ_coarse-Δθ_window,θ_coarse+Δθ_window] is defined according to the preset window width parameter Δθ_window=5°. This window design is based on the analysis of the typical error range of the coarse estimation, ensuring that the true angle value falls within the search window with a high probability, while avoiding an excessively large search range that would lead to a sharp increase in computational complexity.

[0034] Within a defined fine-grained search window, the system employs a significantly reduced step size Δθ_fine = 0.05° for fine-grid searching. Compared to the coarse estimation step size of 1°, the angular resolution of the fine-grained search is improved by 20 times, enabling the system to detect finer angular variations and capture local optima. For each candidate angle θ in the fine-grained grid... i The system repeatedly executes the theoretical time difference calculation τ_theory(θ) i )=d×sin(θ i ) / c and error evaluation error(θ) i )=|τ_measured-τ_theory(θ i A high-resolution error distribution curve is constructed by traversing all candidate points within the window.

[0035] After the fine-grid search is completed, the system identifies the point with the smallest error and its adjacent region from all candidate angles, and selects the three consecutive candidate angles with the smallest error values ​​as the basic data points for interpolation optimization. Let these three angle points be θ. -1 ,θ0,θ +1 Where θ0 corresponds to the minimum error value, and the corresponding error values ​​are respectively error -1 error0, error +1 Based on the distribution of these three data points on the error-angle coordinate plane, the system uses a quadratic parabolic model for curve fitting, with the fitting equation being error(θ) = a(θ - θ0). 2 +b(θ-θ0)+c, where θ0 is the origin of the fitted coordinate system to simplify the computational complexity.

[0036] The fitting coefficients are obtained by establishing a system of linear equations. Substituting the three known data points into the fitting equations yields: error -1 =a(θ -1 -θ0) 2 +b(θ -1-θ0)+c, error0=c, error +1 =a(θ +1 -θ0) 2 +b(θ +1 -θ0)+c. Since θ0 is the center point, c=error0 can be obtained immediately, and then the values ​​of parameters a and b can be determined by solving the system of two linear equations. After the fitting is completed, the system uses the extreme value characteristics of the quadratic function to calculate the high-precision angle estimate. According to the parabola vertex formula θ_fine=θ0-b / (2a), the minimum point of the error function is determined, and the angle corresponding to this point is the optimal angle estimate after refinement.

[0037] To ensure the numerical stability of the interpolation calculation, the system performs a validity check on the fitting coefficient 'a'. When |a| < ε_threshold, it indicates that the error curve is close to linear. In this case, the system reverts to linear interpolation or directly uses θ0 as the accurate estimate. Simultaneously, the system implements a boundary check mechanism to ensure that the calculated θ_fine is within the original search window. When it exceeds the boundary, appropriate boundary truncation is performed. The entire refinement process achieves adaptive optimization through iterative convergence judgment. The optimization process terminates when the angle change between adjacent iterations |θ_fine^(k+1) - θ_fine^(k)| < ε_convergence = 0.001° or the maximum number of iterations is reached, outputting the final high-precision angle estimate.

[0038] S103, The preliminary angle estimate and the high-precision angle estimate are fused according to the confidence weighting algorithm to output the final angle measurement value.

[0039] In this embodiment, the confidence scores of the two estimation results are first normalized, and the total confidence score is calculated as total_confidence = confidence_coarse + confidence_fine. Then, the initial weights for the coarse and precise estimates are calculated separately: w_coarse_init=confidence_coarse / total_confidence The normalization process, w_fine_init=confidence_fine / total_confidence, ensures that the mathematical constraint w_coarse_init+w_fine_init=1 for the weight coefficients is satisfied.

[0040] To assess the degree of consistency between the two estimates, the system calculates the angular deviation consistency_error = |θ_coarse - θ_fine|. A consistency metric, consistency = exp(-consistency_error / consistency_threshold), is constructed based on a preset consistency threshold. This metric is designed using an exponential decay function; consistency approaches 1 when two estimates are highly consistent, and approaches 0 when significant deviations exist. The consistency threshold is typically set according to the application's accuracy requirements, and for high-precision applications, it is usually around 1-2° to ensure the system can effectively identify abnormal estimation biases.

[0041] Based on the calculated consistency index, the system intelligently adjusts the initial weights to reflect the reliability of the estimation results. The adjustment strategy follows the consistency enhancement principle: when consistency > 0.7, it indicates that the two estimation results mutually validate each other and have high credibility, at which point positive adjustment is applied. w_coarse_temp=w_coarse_init×(1+α×consistency) w_fine_temp=w_fine_init×(1+α×consistency); Where α = 0.2 is the correction gain coefficient; when consistency ≤ 0.7, it indicates that there is inconsistency in the estimation results, which may be due to signal anomalies or algorithm convergence problems. In this case, a negative correction is applied. w_coarse_temp=w_coarse_init×(1-β×(1-consistency)) w_fine_temp=w_fine_init×(1-β×(1-consistency)); Where β=0.3 is the penalty coefficient, which reduces the impact of unreliable estimates on the final result by lowering the weight.

[0042] After the corrected weights are calculated, the system performs a renormalization operation to restore the weight constraints. The corrected weight sum w_sum = w_coarse_temp + w_fine_temp is calculated, and then the final weights are generated. w_coarse_final=w_coarse_temp / w_sum w_fine_final=w_fine_temp / w_sum; Ensure that the final weight coefficients satisfy the normalization condition w_coarse_final+w_fine_final=1.

[0043] Based on the calculated final weight coefficients, the system uses the weighted average formula: The final angle measurement is generated by calculating θ_final = w_coarse_final × θ_coarse + w_fine_final × θ_fine. To further improve the fusion quality, a historical trend correction mechanism can be introduced. When historical angle sequences are available, the predicted angle is calculated. θ_predicted = predict_angle(history_angles), and then evaluate the consistency between the fusion result and the predicted trend: trend_consistency = exp(-|θ_final - θ_predicted| / trend_threshold); When trend_consistency < 0.6, the trend correction θ_final_corrected = γ × θ_final + (1-γ) × θ_predicted is applied, where γ = 0.7 is the trend weight factor.

[0044] The final output includes the angle estimate θ_final, the fusion confidence score confidence_final = w_coarse_final × confidence_coarse + w_fine_final × confidence_fine, and the processing method identifier.

[0045] In one possible embodiment of the present invention, it further includes: This paper addresses 180° directional ambiguity detection and elimination based on the inherent geometric constraints of a dual-antenna system. Since the TDOA measurement of a dual-antenna array follows the geometric relationship τ=d×sin(θ) / c, and the sine function has the mathematical symmetry property sin(θ)=sin(180°-θ), the incident angle θ and its symmetric angle 180°-θ will produce identical time difference measurements, resulting in inherent 180° directional ambiguity. When the adaptive fusion module outputs the final angle measurement value θ_final, the directional ambiguity processing module automatically initiates the ambiguity detection program. Based on the sign characteristics of θ_final, it generates corresponding candidate angle pairs: the primary candidate angle θ1=θ_final retains the original estimation result, while the mirror candidate angle θ2 is calculated piecewise according to the angle range. When θ_final≥0°, θ2=180°-θ_final; when θ_final<0°, θ2=-180°-θ_final, ensuring that the generated candidate angle pairs physically correspond to valid incident directions.

[0046] For the generated candidate angle pairs, the system employs three parallel strategies to calculate matching scores to evaluate their respective credibility. The historical continuity constraint strategy first checks the availability of historical angle data. When an angle history sequence {θ_history} exists and its length exceeds a preset threshold, the angle change trend θ_trend = estimate_trend({θ_history}) is estimated using linear regression or a weighted average method. Then, the deviation distances between the two candidate angles and the historical trend are calculated: distance1 = |θ1 - θ_trend| and distance2 = |θ2 - θ_trend|. Based on these deviation distances, a Gaussian decay function is used to calculate the historical consistency score: score_history1 = exp(-distance1). 2 / (2σ 2 _trend)) and score_history2=exp(-distance2 2 / (2σ 2 _trend)), where σ_trend is the standard deviation of historical angle variation, usually taken as 2°-5° to accommodate the normal range of angle variation.

[0047] The multi-frequency phase difference analysis strategy utilizes the phase characteristic differences of signals at different frequencies during propagation to determine direction. When multi-frequency TDOA data is available, the system calculates the theoretical phase slopes for each candidate angle: slope_theory1 = 2π × f0 × d × cos(θ1) / c and slope_theory2 = 2π × f0 × d × cos(θ2) / c, where f0 is the carrier frequency, d is the antenna baseline length, and c is the speed of light. Simultaneously, the actual phase slope is extracted from the multi-frequency observation data. `slope_observed = extract_phase_slope(multi_freq_data)` calculates the phase matching score by comparing the degree of matching between theoretical and observed values. `score_phase1 = exp(-|slope_theory1 - slope_observed|` 2 / (2σ 2 _phase)) and score_phase2=exp(-|slope_theory2-slope_observed| 2 / (2σ 2 _phase), where σ_phase is the uncertainty of the phase measurement.

[0048] The signal strength pattern matching strategy is based on discriminant analysis of the asymmetric characteristics of the antenna array pattern. The system calculates the expected received signal strength patterns at two candidate angles based on antenna design parameters and installation geometry. pattern_expected1= calculate_antenna_pattern(θ1,antenna_config) pattern_expected2= calculate_antenna_pattern(θ2,antenna_config); Then, the observed intensity patterns are extracted from the actual received signals: `pattern_observed=extract_amplitude_pattern(received_signals)` calculates the intensity pattern matching score using the Pearson correlation coefficient. score_amplitude1=pearson_correlation(pattern_expected1,pattern_observed) score_amplitude2=pearson_correlation(pattern_expected2,pattern_observed).

[0049] Weight coefficients are dynamically allocated based on the availability and quality of data for each strategy. A weight vector w = [w_history, w_phase, w_amplitude] is defined. When the input data for a strategy becomes unavailable, the corresponding weight is automatically set to 0, and the remaining weights are redistributed proportionally to ensure a total weight of 1. A comprehensive confidence score is calculated for each candidate perspective. confidence1=w_history*score_history1+w_phase*score_phase1+w_amplitude×score_amplitude1 confidence2=w_history*score_history2+w_phase*score_phase2+w_amplitude×score_amplitude2.

[0050] The preferred direction is determined by comparing the magnitudes of the two combined confidence scores. θ_preferred=argmax{θ1,θ2} corresponds to max{confidence1,confidence2}, and the corresponding maximum confidence score is denoted as direction_confidence=max{confidence1,confidence2}.

[0051] The final ambiguity elimination decision is based on a confidence threshold. When direction_confidence > 0.7, it indicates that the ambiguity elimination strategy has sufficient confidence. The system outputs a single disambiguated angle θ_output = θ_preferred and sets the ambiguity resolution flag ambiguity_resolved = true, providing a clear direction determination result to the upper-layer application. When direction_confidence ≤ 0.7, it indicates that the existing information is insufficient to reliably eliminate directional ambiguity. The system adopts a conservative strategy to maintain the ambiguity state, while outputting two candidate angles θ_output = [θ1, θ2] and their corresponding confidence scores [confidence1, confidence2], and sets ambiguity_resolved = false. This leaves the final direction determination decision to the upper-layer application system or operator, ensuring that potentially erroneous single direction information is not provided under uncertain conditions.

[0052] A second embodiment of the present invention provides a dual-antenna layered coarse and fine combined angle measurement device, comprising: The preliminary angle estimation value generation unit 201 is used to acquire the TDOA measurement data of the dual antenna array and perform grid search angle estimation on the TDOA measurement data to generate a preliminary angle estimation value. The high-precision angle estimation value generation unit 202 is used to determine whether to enable precise refinement estimation based on the intelligent switching strategy driven by signal quality. When the preset conditions are met, fine grid search and interpolation optimization are performed with the preliminary angle estimation value as the center to generate a high-precision angle estimation value. The final angle measurement output unit 203 is used to fuse the preliminary angle estimate and the high-precision angle estimate according to the confidence weighting algorithm and output the final angle measurement value.

[0053] The third embodiment of the present invention provides a dual-antenna layered coarse and fine combined angle measurement device, including a memory and a processor. The memory stores a computer program, which can be executed by the processor to implement the dual-antenna layered coarse and fine combined angle measurement method as described in any of the above embodiments.

[0054] The fourth embodiment of the present invention provides a computer-readable storage medium storing a computer program, which can be executed by a processor of the device in which the computer-readable storage medium is located, to implement a dual-antenna layered coarse and fine combined angle measurement method as described in any of the above claims.

[0055] Based on the dual-antenna layered coarse-fine combined angle measurement method, apparatus, device, and storage medium provided by this invention, the method first acquires TDOA measurement data of a dual-antenna array and performs grid search angle estimation on the TDOA measurement data to generate a preliminary angle estimate. Next, based on a signal quality-driven intelligent switching strategy, it determines whether to enable precise refinement estimation. When preset conditions are met, a fine grid search and interpolation optimization are performed centered on the preliminary angle estimate to generate a high-precision angle estimate. Finally, the preliminary angle estimate and the high-precision angle estimate are fused using a confidence-weighted algorithm to output the final angle measurement value. This method solves the problem in dual-antenna TDOA angle measurement where fluctuations in signal quality and real-time requirements make it difficult to achieve a balance between measurement accuracy and processing efficiency.

[0056] Exemplary examples show that the computer program described in the third and fourth embodiments of the present invention can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in implementing a dual-antenna layered coarse-fine combined angle measurement device. For example, the apparatus described in the second embodiment of the present invention.

[0057] The processor referred to can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the dual-antenna layered coarse-fine combined angle measurement method, connecting various parts of the method through various interfaces and lines.

[0058] The memory can be used to store the computer program and / or modules. The processor, by running or executing the computer program and / or modules stored in the memory, and by calling the data stored in the memory, implements various functions of a dual-antenna layered coarse and fine combined angle measurement method. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, text conversion function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, text message data, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital card (SD card), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0059] If the implemented module is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0060] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0061] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for measuring angles using a dual-antenna layered coarse and fine combination, characterized in that, include: Acquire TDOA measurement data of the dual-antenna array, and perform grid search angle estimation on the TDOA measurement data to generate preliminary angle estimates; A signal quality-driven intelligent switching strategy determines whether to enable precise fine-grained estimation. When preset conditions are met, a fine-grid search and interpolation optimization are performed centered on the preliminary angle estimate to generate a high-precision angle estimate. Specifically, a fine-grid search window θ_search_range=[θ_coarse-Δθ_window,θ_coarse+Δθ_window] is defined centered on the preliminary angle estimate, where Δθ_window is the preset window width and θ_coarse is the preliminary angle estimate. A fine-grid search is performed within the fine-grid search window with a step size Δθfine. For each candidate incident angle θ... i Calculate the error from all θ i The three adjacent candidate angles with the smallest errors are selected, and their corresponding error values ​​are used to fit a quadratic parabola on the coordinate plane. The fitting model is: error(θ) = a(θ) θ0) 2 +b(θ Let θ0)+c, where a, b, and c are fitting coefficients, θ0 is the candidate angle with the smallest error among the three points, and θ is the incident angle of the signal; calculate the high-precision angle estimate based on the fitting coefficients a and b: θ_fine=θ0 b / 2a; The preliminary angle estimate and the high-precision angle estimate are fused using a confidence-weighted algorithm to output the final angle measurement.

2. The method for measuring angles using a dual-antenna layered coarse and fine combination according to claim 1, characterized in that, The specific steps of performing grid search angle estimation on the TDOA measurement data to generate preliminary angle estimates are as follows: Obtain the measured time difference of the signal received by the dual antennas, τ_measured, and the corresponding measurement uncertainty σ_tdoa. Based on the antenna baseline length d and the speed of light c, generate a candidate incident angle sequence {θ_coarse = 1° in the angle search space θ ∈ [–90°, 90°] with a step size Δθ_coarse = 1°. i }; For each candidate incident angle θ in the sequence i Theoretical time difference is calculated based on a geometric model: τ_theory(θ i )=d×sin(θ i ) / c; And calculate the measurement error: error(θ i )=|τ_measured τ_theory(θ i )|; Based on the error and measurement uncertainty, calculate the weight value for each candidate angle: weight(θ i )=exp(-error 2 (i i ) / (2σ 2 _tdoa)); Based on the weight values, the candidate angle with the largest weight is selected as the preliminary angle estimate: θ_coarse=argmax{weight(θ i )}。 3. The method for measuring angles using a dual-antenna layered coarse and fine combination according to claim 1, characterized in that, The intelligent handover strategy based on signal quality is specifically as follows: Multiple signal quality metrics are acquired, including TDOA measurement accuracy, input signal confidence, real-time budget, and historical performance statistics. The strategy adaptively generates accuracy and time thresholds based on historical performance statistics. When the confidence level of the input signal is greater than 0.8 and the TDOA measurement accuracy is less than the accuracy threshold, the strategy directly selects the accurate algorithm. When the confidence level of the input signal is less than 0.3 or the real-time budget is less than the time threshold, the strategy selects only the coarse algorithm.

4. The method for measuring angles using a dual-antenna layered coarse and fine combination according to claim 1, characterized in that, The process of fusing the preliminary angle estimate and the high-precision angle estimate using a confidence-weighted algorithm to output the final angle measurement value is as follows: The confidence scores of the preliminary angle estimate and the high-precision angle estimate are normalized to generate initial weights for the preliminary angle estimate and the high-precision angle estimate. A consistency index is calculated based on the deviation between the preliminary angle estimate and the high-precision angle estimate. The initial weights of the preliminary angle estimate and the high-precision angle estimate are then corrected based on the consistency index. Finally, the corrected weights are renormalized to generate the final weights of the preliminary angle estimate and the high-precision angle estimate. The final angle measurement value is generated based on the initial angle estimate and the final weight of the high-precision angle estimate.

5. The method for measuring angles using a dual-antenna layered coarse and fine combination according to claim 1, characterized in that, Also includes: Based on the inherent characteristic of the TDOA geometric relationship sin(θ)=sin(180°-θ) of the dual-antenna system, the 180° directional ambiguity is detected for the final angle measurement value θ_final, and corresponding candidate angle pairs are generated: θ1=θ_final and θ2=180°-θ_final; Calculate the matching scores of the candidate angle pairs θ1 and θ2 under the constraints of historical continuity, multi-frequency phase difference analysis, and signal strength pattern matching strategy, respectively. The matching scores are weighted and fused to calculate the comprehensive confidence of each candidate angle, and the candidate angle with higher confidence is selected as the direction angle after disambiguation. When the confidence level of the disambiguated direction angle is greater than 0.7, the disambiguated direction angle is output and the ambiguity is marked as resolved; otherwise, the ambiguity is maintained and two candidate angles are output simultaneously.

6. A dual-antenna layered coarse and fine combined angle measuring device, characterized in that, include: A preliminary angle estimation value generation unit is used to acquire TDOA measurement data of the dual-antenna array and perform grid search angle estimation on the TDOA measurement data to generate a preliminary angle estimation value. A high-precision angle estimation unit is used to determine whether to enable precise fine estimation based on a signal quality-driven intelligent switching strategy. When preset conditions are met, it performs fine-grid search and interpolation optimization centered on the preliminary angle estimation value to generate a high-precision angle estimation value. Specifically, it defines a fine search window θ_search_range=[θ_coarse-Δθ_window,θ_coarse+Δθ_window] centered on the preliminary angle estimation value, where Δθ_window is the preset window width and θ_coarse is the preliminary angle estimation value; it performs fine-grid search within the fine search window with a step size Δθfine; and it performs a fine-grid search for each candidate incident angle θ. i Calculate the error from all θ i The three adjacent candidate angles with the smallest errors are selected, and their corresponding error values ​​are used to fit a quadratic parabola on the coordinate plane. The fitting model is: error(θ) = a(θ) θ0) 2 +b(θ Let θ0)+c, where a, b, and c are fitting coefficients, θ0 is the candidate angle with the smallest error among the three points, and θ is the incident angle of the signal; calculate the high-precision angle estimate based on the fitting coefficients a and b: θ_fine=θ0 b / 2a; The final angle measurement output unit is used to fuse the preliminary angle estimate and the high-precision angle estimate according to the confidence weighting algorithm and output the final angle measurement value.

7. A dual-antenna layered coarse and fine combined angle measuring device, characterized in that, The method includes a memory and a processor, wherein the memory stores a computer program that can be executed by the processor to implement a dual-antenna layered coarse and fine combined angle measurement method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The device contains a computer program that can be executed by a processor of the device on which the computer-readable storage medium is located, to implement the dual-antenna layered coarse and fine combined angle measurement method as described in any one of claims 1 to 5.

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