Gamepad posture solving and self-calibration method based on multi-sensor fusion

Abstract

The application discloses a game handle posture solution and self-calibration method based on multi-sensor fusion. The system is composed of a data fusion layer, a self-calibration state detection layer and a state interpretation and feedback layer. The three layers complete posture estimation, state calibration and result interpretation in sequence, and realize adaptive optimization through closed-loop adjustment between result sets. The method introduces a double-channel adaptive confidence stability modulation algorithm, establishes two core mechanisms of confidence double-channel modulation mechanism and stability time double-channel modulation mechanism, which are used for cooperative evaluation of the reliability and stability of the posture result, and simultaneously drives the system parameter adaptive adjustment and the posture result output through a double-layer feedback mechanism, so that a dynamic balance between robustness and responsiveness is realized, and the accuracy and interpretability of the posture solution are improved.

Description

Technical Field

[0001] This invention relates to the field of multi-sensor fusion attitude calculation technology, and more specifically, to a method for calculating and self-calibrating the attitude of a game controller based on multi-sensor fusion. Background Technology

[0002] Multi-sensor fusion attitude calculation technology is mainly used to determine the orientation and attitude of a device in three-dimensional space under multi-source signal conditions, and is widely used in game control, motion capture, and human-computer interaction. For game controllers, the attitude calculation results directly affect the operation accuracy and interactive experience, so it is necessary to maintain the stability and reliability of the attitude output while ensuring real-time performance. Existing technologies typically perform fusion calculations using sensor signals from gyroscopes, accelerometers, and magnetometers, use filtering algorithms to estimate the attitude direction, and perform calibration under specific conditions to reduce drift errors.

[0003] The existing technology has the following shortcomings:

[0004] Existing attitude estimation methods largely rely on fixed fusion parameters and static calibration strategies. Under conditions of magnetic interference, temperature drift, and high-dynamic operation, attitude estimation results are prone to drift and instability, leading to decreased control accuracy. While some systems correct errors through offline calibration or periodic resets, they lack online self-calibration capabilities during operation and cannot adaptively adjust the calculation parameters according to environmental changes and convergence trends. Furthermore, traditional attitude estimation processes only output attitude angle or direction information, without quantitatively evaluating the reliability and stability of the estimation results, making it difficult to provide consistent and interpretable attitude quality feedback. To address these issues, this invention proposes a solution. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a game controller posture calculation and self-calibration method based on multi-sensor fusion to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for calculating and self-calibrating the posture of a game controller based on multi-sensor fusion includes:

[0008] The data fusion layer collects and synchronizes multiple sensor signals, uses extended Kalman filtering to fuse them, completes attitude estimation, and outputs a fusion result set.

[0009] The self-calibration state detection layer identifies error sources and environmental changes based on the fusion result set, performs lightweight self-calibration and outputs a calibration state set when the stability conditions are met;

[0010] The state interpretation and feedback layer interprets the fusion result set and the calibration state set. It generates an interpretation feedback set based on the confidence dual-path modulation mechanism and the stabilization time dual-path modulation mechanism, and drives the parameter adjustment of the data fusion layer and the self-calibration state detection layer and the coordinated display of external results through the dual-layer feedback mechanism.

[0011] In a preferred embodiment, the data fusion layer employs an extended Kalman filter. During state prediction and observation update, the filter weights and state update step size are adjustable based on the consistency and dynamic level of the sensor signals. This completes attitude estimation and outputs a fusion result set, achieving an adaptive balance between robustness and response speed.

[0012] In a preferred embodiment, the fusion result set includes attitude orientation, uncertainty, motion intensity, and environmental state information; attitude orientation is used to characterize the direction and rotation state of the handle in three-dimensional space; uncertainty is used to characterize the reliability range of the estimation results and reflect the consistency of observations; motion intensity is used to characterize the current motion amplitude and dynamic level; and environmental state information is used to characterize the level of external disturbances.

[0013] In a preferred embodiment, the self-calibration state detection layer initiates lightweight self-calibration when it determines that the environment is stable and the attitude change is below the set conditions. Lightweight self-calibration updates key parameters in a small-amplitude, short-duration online correction manner without interrupting attitude calculation. The trigger threshold and iteration frequency are set as adjustable parameters to limit the start conditions and update rhythm, respectively. After the self-calibration is completed, a calibration state set is output.

[0014] In a preferred embodiment, the calibration state set includes calibration stage, calibration confidence, convergence rate, calibration completion flag, and environmental stability; the calibration stage is used to indicate initial detection, parameter estimation, and convergence confirmation; the calibration confidence is used to characterize the reliability of the calibration result; the convergence rate is used to characterize the rate of error reduction and stabilization; the calibration completion flag is used to determine whether the termination condition has been met; and the environmental stability is used to characterize the impact of short-term external changes on the calibration.

[0015] In a preferred embodiment, the state interpretation and feedback layer includes the following process:

[0016] A joint analysis of the fusion result set and the calibration status set is used to form a judgment basis;

[0017] Based on the judgment criteria, a dual-path adaptive confidence-stabilized modulation algorithm is constructed, and a confidence-based dual-path modulation mechanism and a stabilization-time dual-path modulation mechanism are established. The comprehensive confidence and the expected stabilization time are calculated respectively.

[0018] Attitude health is generated by combining the overall confidence level and the expected settling time. An interpretability consistency constraint is then applied to the overall confidence level and the expected settling time to obtain the interpretability consistency constraint result. Attitude health serves as the overall quality evaluation index of the attitude results, and the interpretability consistency constraint result is used to reconcile the inconsistent trends shown in the display of the overall confidence level and the expected settling time.

[0019] An interpretation feedback set is generated and written into the attitude health, overall confidence, expected stabilization time and interpretation consistency constraint results;

[0020] A two-layer feedback mechanism is established, in which the explanatory feedback set is used for both the adaptive adjustment of internal parameters and the coordinated display of external results.

[0021] In a preferred embodiment, the judgment criteria include a structural consistency path, a historical similarity path, a convergence speed path, and a working condition benchmark path; the structural consistency path assesses the degree of matching between observation and estimation; the historical similarity path assesses the similarity between the current state and representative working condition segments in the working condition category library and its applicable boundaries; the convergence speed path characterizes the error reduction rate and stage progress; and the working condition benchmark path provides the reference recovery characteristics and stability range of this type of working condition.

[0022] In a preferred embodiment, the dual-path adaptive confidence stabilization modulation algorithm includes: normalizing and monotonically mapping the scores obtained from the structural consistency path and the historical similarity path respectively, then fusing them using a geometric mean to generate a comprehensive confidence score, and tightening the comprehensive confidence score when any score is too low; applying the error decay rate calculated by the convergence speed path to the reference time given by the operating condition reference path through an exponential mapping to generate the expected stabilization time, and determining the interval description of the expected stabilization time based on the environmental stability.

[0023] In a preferred embodiment, the dual-layer feedback mechanism internally generates four types of strategy indicators—fusion robustness strategy, fusion response strategy, calibration timing strategy, and calibration rhythm strategy—based on the overall confidence level and the expected stabilization time. Externally, it coordinates and displays the attitude health, overall confidence level, and expected stabilization time based on the interpretation consistency constraint results, so as to output consistent and interpretable attitude results.

[0024] In a preferred embodiment, the filtering weights and state update step size of the data fusion layer are adjusted by combining the robustness strategy and the response strategy, and the trigger threshold and iteration frequency of the self-calibration state detection layer are adjusted according to the calibration timing strategy and the calibration rhythm strategy.

[0025] The effects and advantages of this invention's multi-sensor fusion-based game controller posture calculation and self-calibration method are as follows:

[0026] This invention provides a method and system for game controller attitude estimation and self-calibration based on multi-sensor fusion. The system comprises a data fusion layer, a self-calibration state detection layer, and a state interpretation and feedback layer. These three layers sequentially complete attitude estimation, state calibration, and result interpretation, forming a closed-loop adjustment through a fusion result set, a calibration state set, and an interpretation feedback set. Building upon multi-sensor fusion and online self-calibration, the system introduces a dual-path adaptive confidence-stabilized modulation algorithm, establishing two core mechanisms: a confidence-based dual-path modulation mechanism and a stabilization-time dual-path modulation mechanism. These mechanisms are used to collaboratively evaluate the reliability and stability of the results in the state interpretation and feedback layer. Through the dual-layer feedback mechanism, the interpretation feedback set is applied to both the data fusion layer and the self-calibration state detection layer, driving adaptive adjustment of internal parameters and interpretation consistency constraints. During overall operation, the system achieves closed-loop linkage between data fusion, state calibration, and result interpretation. It can dynamically optimize operating parameters based on environmental changes and convergence trends, maintaining an adaptive balance between robustness and responsiveness, thereby significantly improving the accuracy, stability, and interpretability of the attitude estimation results. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0028] Figure 2 This is a schematic diagram of the system structure of the present invention;

[0029] Figure 3 This is a schematic diagram of the dual-path adaptive confidence-stabilized modulation algorithm of the present invention;

[0030] Figure 4 This is a schematic diagram of the two-layer feedback mechanism of the present invention. Detailed Implementation

[0031] 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.

[0032] This invention discloses a game controller attitude estimation and self-calibration method based on multi-sensor fusion. It consists of a data fusion layer, a self-calibration state detection layer, and a state interpretation and feedback layer. These three layers sequentially complete attitude estimation, state calibration, and result interpretation, forming a closed-loop adjustment through a fusion result set, a calibration state set, and an interpretation feedback set. Building upon multi-sensor fusion and online self-calibration, this method introduces a dual-path adaptive confidence-stabilized modulation algorithm. It establishes two core mechanisms: a confidence-based dual-path modulation mechanism and a stabilization-time dual-path modulation mechanism. These mechanisms are used to collaboratively evaluate the reliability and stability of the result interpretation. Through a dual-layer feedback mechanism, the evaluation results are used for internal parameter adjustment and external result display, respectively, thus forming an adaptive closed-loop optimization during attitude estimation.

[0033] This method is used for game controller posture estimation and self-calibration under multi-sensor fusion conditions. The system consists of a data fusion layer, a self-calibration state detection layer, and a state interpretation and feedback layer. These three layers sequentially perform posture estimation, state calibration, and result interpretation, and are transferred and fed back between layers through a fusion result set, a calibration state set, and an interpretation feedback set. (Refer to...) Figure 1 , Figure 1 This is a schematic diagram of the method flow of the present invention.

[0034] The data fusion layer acquires and synchronizes multiple sensor signals, fuses them using an extended Kalman filter, completes attitude estimation, and outputs a fusion result set. The fusion result set includes attitude orientation, uncertainty, motion intensity, and environmental state information. Specifically, attitude orientation characterizes the handle's orientation and rotation in three-dimensional space; uncertainty characterizes the reliability range of the estimation results and reflects observation consistency; motion intensity characterizes the current motion amplitude and dynamic level; and environmental state information characterizes the level of external disturbance.

[0035] The self-calibration state detection layer identifies error sources and environmental changes based on the fusion result set. When stability conditions are met, it performs lightweight self-calibration and outputs a calibration state set. Lightweight self-calibration refers to online correction of key parameters with small amplitude and short duration without interrupting the solution. The calibration state set includes calibration stage, calibration confidence, convergence rate, calibration completion flag, and environmental stability. Among them, the calibration stage is used to indicate the initial detection, parameter estimation, and convergence confirmation; the calibration confidence is used to characterize the reliability of the calibration result; the convergence rate is used to characterize the rate of error reduction and stabilization; the calibration completion flag is used to determine whether the termination condition has been met; and the environmental stability is used to characterize the level of change in the external environment in a short period of time.

[0036] The state interpretation and feedback layer interprets the fusion result set and the calibration state set. It generates an interpretation feedback set based on the confidence dual-path modulation mechanism and the stabilization time dual-path modulation mechanism. Through the dual-layer feedback mechanism, it drives the adaptive adjustment of parameters of the data fusion layer and the self-calibration state detection layer and coordinates the display of external results.

[0037] The system integrates the result set and calibration status set for analysis, forming judgment criteria based on four pathways: structural consistency, historical similarity, convergence speed, and operating condition baseline. Specifically, the structural consistency pathway provides an immediate judgment based on the consistency between current observations and estimates; the historical similarity pathway provides a time-dimensional judgment based on the similarity level and applicable boundaries with representative operating condition segments in the operating condition category library; the convergence speed pathway characterizes the short-term stability trend based on the error reduction rate and stage progress; and the operating condition baseline pathway provides the recovery characteristics of this type of operating condition based on empirical information from the operating condition category library.

[0038] Building upon this foundation, the system introduces a dual-path adaptive confidence-stabilized modulation algorithm to jointly calculate the confidence-based dual-path modulation mechanism and the steady-time dual-path modulation mechanism. This algorithm establishes a synergistic adjustment relationship between reliability and stability through geometric consistency fusion and exponential convergence time mapping, achieving adaptive optimization of result interpretation.

[0039] Based on the judgment criteria of the four pathways, the system sets up two core mechanisms. The confidence-based dual-path modulation mechanism combines the structural consistency pathway and the historical similarity pathway to output a comprehensive confidence score, used to define the reliability range and applicable scenarios of the results. The stabilization-time dual-path modulation mechanism combines the convergence speed pathway and the operating condition baseline pathway to output the expected stabilization time, while also providing a confidence level description for the time interval.

[0040] The system calculates attitude health by combining the confidence level and the expected settling time from the results of two core mechanisms, which is used to visually evaluate the overall quality of the handle's attitude. To avoid contradictions in the interpretation conclusions, interpretation consistency constraints are applied to the display of the combined confidence level and the expected settling time. The interpretation consistency constraint results are obtained from the constraint's on / off state and constraint direction.

[0041] The interpretation feedback set incorporates attitude health, overall confidence, expected settling time, and interpretation consistency constraint results. A two-layer feedback mechanism is established for the interpretation feedback set, feeding back internally to drive parameter updates and externally to output attitude results. Internally, the system calls the overall confidence to generate a robust fusion strategy and a fusion response strategy. These two strategies work together at the data fusion layer, achieving a balance between robustness and responsiveness in the fusion results. It also calls the expected settling time to generate a calibration timing strategy and a calibration rhythm strategy. These two strategies work together at the self-calibration state detection layer, achieving a balance between timing and rhythm in the calibration process. Externally, the interpretation feedback set uses attitude health as the overall evaluation index and coordinates the overall confidence and expected settling time based on the interpretation consistency constraint results, providing consistent, stable, and interpretable attitude result output.

[0042] The implementation process and operational effects of the method of the present invention will be described in detail below with reference to specific embodiments. It should be understood that the embodiments are only used to illustrate the technical solution of the present invention, and not to limit it. The relevant steps, parameters, and module divisions can be appropriately adjusted without changing the essence of the invention.

[0043] For ease of understanding, the following embodiments are described under a unified system architecture, which can be modified equivalently to meet actual needs. (Refer to...) Figure 2 , Figure 2 This is a schematic diagram of the system structure of the present invention. The system includes the following functional units.

[0044] The data acquisition and synchronization unit acquires multi-source signals from gyroscopes, accelerometers, and magnetometers, and performs time synchronization and data cleaning. The attitude fusion calculation unit performs extended Kalman filtering based on the cleaned sensor data and outputs a fusion result set. The self-calibration state detection unit identifies error sources and environmental changes based on the fusion result set, performs lightweight self-calibration when stability conditions are met, and outputs a calibration state set. The confidence dual-path modulation unit merges the structural consistency path and the historical similarity path, outputting a comprehensive confidence score. The stabilization time dual-path modulation unit merges the convergence velocity path and the operating condition reference path, outputting the predicted stabilization time. The interpretation generation and feedback unit generates an interpretation feedback set and uses a two-layer feedback mechanism to feed it back internally to drive parameter updates and output attitude results externally.

[0045] In the main embodiment, the method is applied to the attitude calculation and self-calibration scenario of a game controller based on a nine-axis sensor. The controller integrates a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer. The system consists of a data fusion layer, a self-calibration state detection layer, and a state interpretation and feedback layer. The three layers sequentially complete attitude estimation, state calibration, and result interpretation, and perform inter-layer transfer and feedback through a fusion result set, a calibration state set, and an interpretation feedback set.

[0046] The data fusion layer consists of a data acquisition and synchronization unit that collects and synchronizes multiple sensor signals, including angular velocity from the gyroscope, linear acceleration from the accelerometer, and magnetic field strength from the magnetometer. In each sampling period, the system performs time synchronization and anomaly removal on the nine-axis data, forming a continuous sensor data stream. This layer employs an extended Kalman filter, which is fused by the attitude fusion calculation unit. The Kalman filtering process includes two stages: state prediction and observation update, where the filter weights... and state update step size This is an adjustable parameter.

[0047] In one optional embodiment, the implementation of the extended Kalman filter includes the following steps. First, during the state prediction phase, the system uses the angular velocity measured by the gyroscope to perform a priori estimation of the attitude direction, and describes the spatial rotation relationship of the handle in quaternion form. The prediction calculation can be expressed as: ;in, Indicates the attitude and direction at the previous moment. This represents the angular velocity at the current moment. Indicates the sampling period. This represents quaternion multiplication. The system thus obtains the predicted attitude direction and its error covariance, which is used to characterize the current level of uncertainty.

[0048] During the observation update phase, the system incorporates the linear acceleration from the accelerometer. magnetic field strength of the magnetometer Using the observational information, observational constraints are established based on the direction of gravity and the direction of geomagnetism, and the observational residuals are calculated: ;in, This represents the fused measurement vector. This represents the mapping function from attitude direction to the observation space. The system dynamically adjusts the filter weights based on the magnitude of the observation residuals. ,when Decrease when larger To enhance robustness, when Increase when smaller To improve response speed. State update step size. This is used to control the magnitude of attitude correction, and its value changes dynamically with the intensity of the action. It increases under high dynamic conditions. To accelerate response and reduce in static or low-dynamic states To suppress overcorrection, the system updates the attitude orientation accordingly. ;in, Let be the Kalman gain matrix. Through the above calculations, the system achieves an adaptive balance between robustness and responsiveness in attitude estimation under different dynamic conditions.

[0049] After completing attitude estimation, a fusion result set is output, which includes attitude orientation, uncertainty, motion intensity, and environmental state information, providing input for subsequent self-calibration and interpretation.

[0050] The self-calibration state detection layer identifies error sources and environmental changes based on the fusion result set. When stability conditions are met, the self-calibration state detection unit performs lightweight self-calibration. Lightweight self-calibration refers to online correction of key parameters with small amplitude and short duration without interrupting the solution process. The calibration process trigger threshold... With iteration frequency These are adjustable parameters. The system dynamically adjusts these two parameters based on environmental stability and convergence speed to ensure that the calibration process is both stable and efficient.

[0051] In one alternative embodiment, the lightweight self-calibration process includes the following steps.

[0052] The system first calculates stability indices based on the attitude orientation, uncertainty, and environmental state information in the fusion result set. This is used to determine whether the current operating conditions meet the requirements of low dynamics and low disturbance. The stability index can be expressed as: ;in, The standardized magnitude representing the rate of change of attitude direction. Indicates the amplitude of environmental state fluctuations. , These are the weighting coefficients.

[0053] When stability index Exceeding the trigger threshold At this time, the system triggers a lightweight self-calibration. Trigger threshold. The value is dynamically adjusted based on environmental stability: when the external magnetic field or temperature fluctuates greatly, the threshold is increased to prevent false triggering; when the environment is stable, the threshold is decreased to shorten the response time.

[0054] During the self-calibration phase, the system uses a short-time sliding window to statistically analyze recent attitude estimation data and calculates the corrections for the gyroscope bias and magnetometer scaling factor. The system calculates the convergence rate after each iteration. : ;in, Indicates the first The attitude estimation error of the next iteration. If the convergence rate is higher than a preset percentage, the system reduces the iteration frequency. To reduce computational load; if convergence speed is low, increase [the computational load]. To accelerate error convergence. Iteration frequency. Therefore, it maintains an adaptive inverse relationship with the convergence speed.

[0055] In this way, the system can complete the online correction of key parameters without interrupting attitude calculation, thus achieving a balance between stability and efficiency in the calibration process.

[0056] After calibration is completed, a calibration status set is output, which includes calibration stage, calibration confidence, convergence rate, calibration completion flag, and environmental stability, providing a basis for subsequent status interpretation and feedback.

[0057] The state interpretation and feedback layer interprets the fusion result set and the calibration state set. It generates an interpretation feedback set based on the confidence dual-path modulation mechanism and the stabilization time dual-path modulation mechanism. Through the dual-layer feedback mechanism, it drives the adaptive adjustment of parameters of the data fusion layer and the self-calibration state detection layer and coordinates the display of external results.

[0058] The system integrates the result set and calibration status set for analysis, and uses four pathways—structural consistency, historical similarity, convergence speed, and operating condition baseline—to form the judgment criteria. The structural consistency pathway outputs a structural consistency score. Historical similarity pathway outputs historical similarity score Convergence speed path outputs convergence speed score Operating condition reference path outputs operating condition reference time .

[0059] In one alternative embodiment, the four pathways form the basis for judgment as follows.

[0060] The structural consistency pathway is used to evaluate the degree of consistency between the current observations and the fusion estimate. The system calculates a structural consistency score based on the attitude orientation and sensor observations in the fusion result set. : ;in, This indicates the attitude orientation observed by the accelerometer and magnetometer. This indicates the fusion pose direction output by the data fusion layer. This is the normalization constant. The closer This indicates a higher degree of consistency between the current attitude estimation result and the observed data.

[0061] The historical similarity pathway is used to measure the similarity between the current state and typical operational scenarios. The system constructs the current state vector based on the action intensity and environmental state information in the fusion result set. and compared with representative working condition samples in the working condition category library. Perform comparison and calculate historical similarity score. : ;in, The first in the working condition category library Feature vectors of samples with similar operating conditions. The larger the value, the closer the current state is to the stable operating condition category.

[0062] The convergence rate pathway is used to characterize the short-term stability trend of the self-calibration process. The system calculates a convergence rate score based on the convergence rate information in the calibration state set. : ;in, Indicates the first The attitude estimation error of the second lightweight self-calibration. The larger the value, the faster the error decreases, indicating that the system is in the rapid convergence phase.

[0063] The operating condition reference path is used to reflect the recovery time characteristics under different action categories. Based on the environmental stability of the calibration state set, the system retrieves the operating condition sample most similar to the current state from a preset operating condition category library and extracts its reference recovery time as the operating condition reference time. And it is corrected based on the real-time environmental conditions during the explanation and update process.

[0064] Through the calculation of the above four paths, the system forms a feature set of attitude states, which provides the input basis for the subsequent confidence dual-path modulation mechanism and steady-time dual-path modulation mechanism.

[0065] The confidence-based dual-path modulation unit and the steady-time dual-path modulation unit are based on this, each with two core mechanisms: a confidence-based dual-path modulation mechanism and a steady-time dual-path modulation mechanism. The confidence-based dual-path modulation mechanism combines the outputs of the structural consistency path and the historical similarity path to generate a comprehensive confidence score. This is used to define the reliability range and applicable scenarios of the attitude estimation results. The dual-path modulation mechanism for settling time combines the outputs of the convergence speed path and the operating condition reference path to generate the predicted settling time. The confidence level for the time interval is explained.

[0066] In one optional embodiment, a dual-path adaptive confidence-stabilized modulation algorithm is introduced to establish a confidence-based dual-path modulation mechanism and a stabilization-time dual-path modulation mechanism, thereby achieving a comprehensive confidence level. Compared to the expected stabilization time Adaptive generation. (Refer to...) Figure 3 , Figure 3 This is a schematic diagram of the dual-path adaptive confidence-stabilized modulation algorithm of the present invention.

[0067] The confidence-based dual-path modulation mechanism integrates the judgment results of the structural consistency path and the historical similarity path to define the reliability range of the attitude estimation results. The system first calculates the structural consistency score based on the structural consistency path. The score is calculated based on a standardized innovation consistency index, used to characterize the degree of consistency between current observations and fusion estimates. The system calculates a normalized statistic using the innovation residuals and their covariance in the extended Kalman filter process. And obtain scores through index mapping. : ;in, The sum of squares statistic, representing the ratio of innovation residuals to covariance, is used to reflect the degree of agreement between observed and predicted results. When... A smaller value indicates that the current filtering process is stable and the estimation results are reliable.

[0068] The system then obtains historical similarity scores from the historical similarity pathway. The score is calculated by matching the fusion results with representative work condition segments from the work condition category library, based on features such as attitude direction, motion intensity, and uncertainty, to characterize the similarity between the current state and historical work conditions. To achieve robust fusion without introducing additional weights, the system uses a geometric mean to calculate the overall confidence level. ;in, The overall confidence score is used to comprehensively reflect the reliability of the attitude estimation results. The geometric averaging method automatically tightens the overall confidence score when the score of any path is low, thereby maintaining the robustness and bias prevention of the calculation results.

[0069] The steady-time dual-path modulation mechanism integrates the outputs of the convergence velocity path and the operating condition reference path to estimate the expected time required for the system to reach a steady state. The system scores the convergence velocity based on the calibration state set. Compared with the operating condition reference time Calculate the expected settling time: ;in, The estimated settling time is used to characterize the time required for the attitude result to reach a stable state from the current state; The baseline settling time for this type of operating condition in the operating condition category library; A convergence speed score is used to characterize the rate of error descent. This is a time adjustment coefficient used to control the degree to which the convergence rate affects the time prediction. When the convergence rate is high, the exponential term decreases, and the expected settling time is shortened accordingly; when the convergence rate is low, the expected settling time reverts to the baseline value, achieving a smooth transition.

[0070] To further reflect the stability of the external environment, the system uses the environmental stability of the calibration state set. Provide a description of the time interval: ;in, This is the time interval deviation, used to describe the range of fluctuations above and below the expected settling time; This is the interval scaling coefficient, used to adjust the influence of environmental stability on the interval width. The higher the environmental stability, the narrower the interval and the more concentrated the results; the lower the environmental stability, the wider the interval, to reflect adaptive tolerance to external disturbances.

[0071] Through the calculations performed using the aforementioned dual-path modulation mechanism, the system obtains a comprehensive confidence level. Compared to the expected stabilization time Two core metrics provide the input basis for posture health calculation and the generation of the interpretation feedback set.

[0072] The system is based on the overall confidence level Compared to the expected stabilization time Calculate posture health This is used to characterize the overall quality of the handle attitude calculation. To maintain consistency in the interpretation results, the system... and Applying interpretability consistency constraints yields interpretability consistency constraint results. This is used to prevent contradictory interpretations of the conclusions.

[0073] In one alternative embodiment, posture health The calculation, based on the normalized mapping relationship between comprehensive confidence level and settling time, unifies the reliability and stability indicators to the same dimension, thereby forming an intuitive attitude quality evaluation. The system can implement this calculation using a weighted geometric mean or an exponential fusion method.

[0074] In one alternative embodiment, the consistency constraint results are interpreted. The determination is based on the changing trends and relative deviations of the two indicators. When... and When a reversal trend is observed, the system initiates consistency constraints to ensure that both changes are synchronized within an allowable range. When the two changes in the same direction and the deviation is below a threshold, the system maintains the constraint closed state, thereby ensuring that the interpretation output is consistent in terms of time and reliability.

[0075] The explanation of the generation and feedback unit will affect the attitude health. Overall confidence level Expected stabilization time Interpreting the results of consistency constraints An explanatory feedback set is integrated, and a two-layer feedback mechanism is established for this set: one layer feeds back internally to drive parameter updates, and the other layer outputs attitude results externally. (Refer to...) Figure 4 , Figure 4 This is a schematic diagram of the two-layer feedback mechanism of the present invention.

[0076] Internally, the system uses the comprehensive confidence level of the interpretation feedback set to generate robust fusion strategies and fusion response strategies. These two strategies work together at the data fusion layer to achieve a balance between robustness and responsiveness in the fusion result. Similarly, it uses the estimated stabilization time to generate calibration timing and calibration rhythm strategies. These two strategies work together at the self-calibration state detection layer to achieve a balance between timing and rhythm in the calibration process. The robust fusion strategies and fusion response strategies from the interpretation generation and feedback unit are then distributed to the attitude fusion calculation unit, while the calibration timing and calibration rhythm strategies are distributed to the self-calibration state detection unit.

[0077] The robust fusion strategy is used to improve the robustness of the data fusion layer when external interference is strong or confidence levels are low. The system increases the filtering weights. and reduce the state update step size. To suppress the sudden impact of abnormal observations on attitude estimation, a fusion response strategy is used to improve the response speed of the fusion layer under high dynamic conditions or with high confidence levels. The system reduces filter weights and appropriately increases the state update step size, enabling the estimation results to follow motion changes more quickly. The calibration timing strategy is based on the expected settling time. Determine the trigger window for lightweight self-calibration when When the time is short and the environmental stability is high, the system triggers self-calibration in advance; when When the time is long or the stability is low, the system postpones calibration to avoid repeated corrections. The calibration rhythm strategy dynamically adjusts the step size and frequency of self-calibration based on the convergence speed and attitude change trend. The system adjusts the trigger threshold... With iteration frequency The update cycle is shortened during high convergence phases and the pace is slowed down during low convergence phases to maintain the balance and stability of the solution process. The four types of strategies are uniformly issued by the interpretation and feedback layer, and the data fusion layer and self-calibration state detection layer update parameters in real time according to the strategy instructions, realizing closed-loop self-calibration adjustment of the attitude estimation process.

[0078] Externally, the feedback set is interpreted as attitude health. As an overall evaluation indicator, and based on the results of the interpretation consistency constraint. Overall confidence level Compared to the expected stabilization time It provides coordinated display, offering consistent, stable, and interpretable attitude results output.

[0079] During overall operation, this system establishes a two-layer feedback path—the fusion layer and the calibration layer—based on a dual-path modulation mechanism for confidence level and a dual-path modulation mechanism for settling time. This enables coordinated optimization of attitude estimation, online self-calibration, and interpretation feedback. By dynamically adjusting the overall confidence level and the predicted settling time, the system achieves an adaptive balance between robustness and responsiveness, while maintaining the consistency and interpretability of the attitude results output.

[0080] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0081] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0082] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0083] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0084] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0085] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for calculating and self-calibrating the posture of a game controller based on multi-sensor fusion, characterized in that, include: The data fusion layer collects and synchronizes multiple sensor signals, uses extended Kalman filtering to fuse them, completes attitude estimation, and outputs a fusion result set. The self-calibration state detection layer identifies error sources and environmental changes based on the fusion result set, performs lightweight self-calibration and outputs a calibration state set when the stability conditions are met; The state interpretation and feedback layer interprets the fusion result set and the calibration state set. It generates an interpretation feedback set based on the confidence dual-path modulation mechanism and the stabilization time dual-path modulation mechanism, and drives the parameter adjustment of the data fusion layer and the self-calibration state detection layer and the coordinated display of external results through the dual-layer feedback mechanism. The state interpretation and feedback layer includes the following process: A joint analysis of the fusion result set and the calibration status set is used to form a judgment basis; Based on the judgment criteria, a dual-path adaptive confidence-stabilized modulation algorithm is constructed, and a confidence-based dual-path modulation mechanism and a stabilization-time dual-path modulation mechanism are established. The comprehensive confidence and the expected stabilization time are calculated respectively. Attitude health is generated by combining the overall confidence level and the expected stabilization time, and the interpretation consistency constraint is applied to the overall confidence level and the expected stabilization time to obtain the interpretation consistency constraint result; Attitude health, as an overall quality evaluation index of attitude results, is used to interpret consistency constraint results to reconcile when there is an inconsistent trend between the overall confidence level and the expected settling time. An interpretation feedback set is generated and written into the attitude health, overall confidence, expected stabilization time and interpretation consistency constraint results; A two-layer feedback mechanism is established, in which the explanatory feedback set is used for both the adaptive adjustment of internal parameters and the coordinated display of external results.

2. The method for game controller posture calculation and self-calibration based on multi-sensor fusion according to claim 1, characterized in that, The data fusion layer employs an extended Kalman filter. During state prediction and observation update, the filter weights and state update step size are adjustable based on the consistency and dynamic level of the sensor signals. This completes attitude estimation and outputs a fusion result set, achieving an adaptive balance between robustness and response speed.

3. The method for game controller posture calculation and self-calibration based on multi-sensor fusion according to claim 2, characterized in that, The fusion result set includes attitude orientation, uncertainty, motion intensity, and environmental state information; attitude orientation is used to characterize the orientation and rotation state of the handle in three-dimensional space; uncertainty is used to characterize the reliability range of the estimation results and reflect the consistency of observations; Motion intensity is used to characterize the current amplitude and dynamic level of motion; Environmental status information is used to characterize the level of external disturbances.

4. The method for game controller posture calculation and self-calibration based on multi-sensor fusion according to claim 1, characterized in that, The self-calibration state detection layer initiates lightweight self-calibration when it determines that the environment is stable and the attitude change is below the set conditions. Lightweight self-calibration updates key parameters in a small-amplitude, short-duration online correction manner without interrupting attitude calculation. The trigger threshold and iteration frequency are set as adjustable parameters to limit the start conditions and update rhythm, respectively. After the self-calibration is completed, a calibration state set is output.

5. The method for game controller posture calculation and self-calibration based on multi-sensor fusion according to claim 4, characterized in that, The calibration status set includes calibration stage, calibration confidence level, convergence rate, calibration completion flag, and environmental stability; the calibration stage is used to indicate initial detection, parameter estimation, and convergence confirmation; the calibration confidence level is used to characterize the reliability of the calibration results. Convergence rate is used to characterize the rate at which error decreases and stabilizes; The calibration completion indicator is used to determine whether the termination condition has been met. Environmental stability is used to characterize the impact of short-term external changes on calibration.

6. The method for game controller posture calculation and self-calibration based on multi-sensor fusion according to claim 1, characterized in that, The judgment criteria include structural consistency path, historical similarity path, convergence speed path, and operating condition benchmark path. The structural consistency pathway assesses the degree of matching between observations and estimates; the historical similarity pathway assesses the similarity between the current state and representative working condition segments in the working condition category library and its applicable boundaries; the convergence speed pathway characterizes the error reduction rate and stage progress. The operating condition reference path provides the reference recovery characteristics and stability range for this operating condition.

7. The method for game controller posture calculation and self-calibration based on multi-sensor fusion according to claim 1, characterized in that, The dual-path adaptive confidence stabilization modulation algorithm includes: normalizing and monotonically mapping the scores obtained from the structural consistency path and the historical similarity path respectively, then fusing them using a geometric mean to generate a comprehensive confidence score, and tightening the comprehensive confidence score when any score is too low; applying the error decay rate calculated by the convergence speed path to the reference time given by the operating condition reference path through an exponential mapping to generate the expected stabilization time, and determining the interval description of the expected stabilization time based on the environmental stability.

8. The method for game controller posture calculation and self-calibration based on multi-sensor fusion according to claim 1, characterized in that, The dual-layer feedback mechanism internally generates four types of strategy indicators—fusion robust strategy, fusion response strategy, calibration timing strategy, and calibration rhythm strategy—based on the overall confidence level and the expected stabilization time. Externally, it coordinates and displays the attitude health, overall confidence level, and expected stabilization time based on the interpretation consistency constraint results, so as to output consistent and interpretable attitude results.

9. The method for game controller posture calculation and self-calibration based on multi-sensor fusion according to claim 8, characterized in that, The filtering weights and state update step size of the data fusion layer are adjusted by combining robust and responsive strategies, and the trigger threshold and iteration frequency of the self-calibration state detection layer are adjusted according to calibration timing and calibration rhythm strategies.

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