High-precision time synchronization method for star sensor under moving base and inertial navigation
By using the star sensor update frequency as a reference in a moving base environment, combined with a second-order extrapolation algorithm and asynchronous Kalman filtering, the clock source of the inertial navigation system is optimized. High-precision time synchronization between the star sensor and the inertial navigation data is achieved through FPGA interrupt latching, which solves the problem of time asynchrony under moving base and improves the accuracy and stability of the navigation system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING AEROSPACE INST FOR METROLOGY & MEASUREMENT TECH
- Filing Date
- 2026-03-03
- Publication Date
- 2026-07-10
AI Technical Summary
In a moving base environment, the star sensor and the inertial measurement unit have significant time asynchrony issues, which leads to inaccurate data matching and affects the navigation accuracy and stability of the integrated navigation system.
Using the star sensor update frequency as the time synchronization reference, and combining second-order extrapolation algorithm, asynchronous Kalman filtering, multi-stage coordination and high-precision crystal oscillator optimization, high-precision time synchronization between the star sensor and inertial navigation data is achieved. Hardware-level time alignment is completed through FPGA interrupt latching function.
It achieves high-precision time synchronization between star sensor and inertial navigation data, significantly improving synchronization accuracy, adapting to the dynamic characteristics under the moving base, and ensuring the navigation accuracy and stability of the integrated navigation system.
Smart Images
Figure CN121761882B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of integrated navigation technology, and in particular to a high-precision time synchronization method between a star sensor and an inertial navigation system on a moving base, applicable to data fusion scenarios of inertial / astronomical integrated navigation systems for moving carriers such as aircraft, ships, and vehicles. Background Technology
[0002] In dynamic base environments, integrated navigation systems often employ a combination of a star sensor (CNS) and an inertial measurement unit (IMU) to achieve high-precision navigation through multi-source data fusion. The star sensor provides high-precision attitude information without accumulated errors, while the inertial measurement unit outputs continuous motion parameters. The complementary advantages of the two are key to improving navigation reliability.
[0003] However, in practical applications, star sensors and IMUs exhibit significant time asynchrony issues: First, their output frequencies differ greatly; star sensors typically update at 1-10Hz, while IMUs can update at frequencies exceeding 50Hz. Second, star sensors suffer from inherent data delays related to optical imaging and star map recognition, and this delay varies with the carrier's angular velocity. Third, different devices may power on at inconsistent times, further exacerbating time discrepancies. Fourth, the inertial navigation system's own clock has output errors, easily introducing additional synchronization biases. These problems prevent accurate matching of data from the two types of sensors at the same time scale, directly affecting data fusion accuracy and even causing fusion failure, severely restricting the navigation performance of integrated navigation systems in moving-base scenarios.
[0004] Existing time synchronization methods are mostly designed for static bases or single error sources. For example, simple interpolation and extrapolation methods are difficult to adapt to the dynamic error characteristics under moving bases. Pure software synchronization schemes are easily affected by clock drift and lack the ability to comprehensively suppress multi-source time errors, thus failing to meet the high-precision time synchronization requirements of integrated navigation systems. Summary of the Invention
[0005] This disclosure provides a high-precision time synchronization method between a star sensor and an inertial navigation system on a moving base, in order to solve the problem of time asynchrony between the star sensor and inertial navigation data during the online calibration stage of flight, achieve high-precision time synchronization between the star sensor and inertial navigation data, and ensure the navigation accuracy and stability of the integrated navigation system.
[0006] The method includes the following steps:
[0007] Establish a time scale based on the star sensor update frequency to store motion parameter data output by the IMU in real time;
[0008] A second-order extrapolation algorithm is adopted to calculate the equivalent IMU data corresponding to the star sensor data update time based on the stored IMU historical data, so as to achieve the adaptation of output frequency differences.
[0009] An asynchronous Kalman filter algorithm is introduced, which combines IMU data with star sensor measured data to complete time updates and measurement updates, thereby suppressing dynamic errors.
[0010] The inertial navigation system clock source is optimized by using a high-precision crystal oscillator to reduce the inherent error of the clock output.
[0011] Through multi-stage collaboration, time delay errors generated during star sensor optical imaging and star map recognition are compensated.
[0012] By utilizing the interrupt latch function of FPGA, the time scale of the star sensor and the inertial navigation system is unified, thus completing the time alignment at the hardware level.
[0013] Furthermore, the establishment of motion parameter data output by the real-time IMU, using the star sensor update frequency as the time synchronization reference, specifically includes:
[0014] Using the star sensor data update cycle as the sole reference time stamp for time synchronization, the core time reference system for IMU data matching and equivalent calculation is constructed by capturing the output time of each frame of valid data in real time and marking it as the synchronization reference point.
[0015] The data acquisition stage acquires key motion parameters output by the IMU in real time, specifically including the three-axis angular velocity signals of the carrier relative to the inertial coordinate system measured by the gyroscope, and the three-axis acceleration signals of the carrier relative to the inertial coordinate system containing the gravity component measured by the accelerometer.
[0016] The data storage adopts a circular buffer architecture with a cache depth of no less than 3 frames to ensure the second-order extrapolation algorithm's need to call continuous historical data. Each frame of cached data is accompanied by a local inertial navigation clock timestamp that has been synchronously calibrated with a high-precision crystal oscillator to ensure that the time stamp accuracy is compatible with the crystal oscillator frequency.
[0017] By calculating the difference between the star sensor reference time stamp and the IMU data timestamp, the time difference between the star sensor data update time and the most recent IMU data update time is accurately calculated, providing core time parameter support for the subsequent extrapolation calculation of equivalent IMU data and laying the foundation for time synchronization.
[0018] Furthermore, the second-order extrapolation algorithm is employed to calculate the equivalent IMU data corresponding to the star sensor data update time based on the stored historical IMU data, thereby achieving adaptation to output frequency differences. Specifically, this includes:
[0019] Based on the raw IMU data stored in the circular buffer, three consecutive frames of valid data prior to the current update time of the star sensor are extracted and denoted as the IMU data at the current time. , , Output data at time , , ,in For IMU, a fixed update cycle is set. It is a positive integer;
[0020] The time difference between the current update time corresponding to the star sensor reference time stamp is accurately calculated by comparing it with the timestamp of the most recent frame of IMU data. , The range of values is limited to ;
[0021] Based on three selected frames of IMU historical data, the coefficients of the second-order extrapolation formula are solved through interpolation, where the constant term... First-order coefficients Second-order coefficients ;
[0022] time difference And the coefficients obtained from the solution are substituted into the second-order extrapolation formula. The equivalent IMU data corresponding to the current update time of the star sensor is calculated. This data is precisely matched with the star sensor data in the time dimension, realizing the adaptation of the output frequency difference between the IMU and the star sensor.
[0023] Finally, the calculated equivalent IMU data is validated for reasonableness. If the data exceeds the measurement range of the IMU sensor or the rate of change from historical data exceeds a preset threshold, the result is discarded and the equivalent data from the previous frame is used as a substitute. At the same time, the buffer data refresh mechanism is triggered to ensure the validity of the data.
[0024] Furthermore, the introduction of an asynchronous Kalman filter algorithm, combined with IMU data and star sensor measured data, completes time updates and measurement updates, suppressing dynamic errors. Specifically, this includes:
[0025] The asynchronous Kalman filter model is constructed with the time synchronization error between the star sensor and the IMU, the IMU gyroscope drift and the accelerometer zero bias as the core state variables. The system state equation covering the dynamic error propagation characteristics is established. At the same time, the difference between the measured attitude information of the star sensor and the attitude information calculated from the equivalent data of the IMU is used as the observation to construct the measurement equation.
[0026] Within the interval between two consecutive data updates of the star sensor, the system state time update is completed through the state transition matrix based solely on the real-time output data of the IMU and the filtered estimation result of the previous moment, thereby predicting the dynamic error change trend in real time.
[0027] When the star sensor outputs new measured data, the measurement update process is triggered, and the equivalent IMU data obtained by the second-order extrapolation algorithm is spatiotemporally matched with the measured data of the star sensor to construct the measurement vector.
[0028] By calculating the Kalman filter gain, correcting the state estimation, and updating the covariance matrix, the predicted system state is corrected by fusing measurement information, with a focus on suppressing dynamic synchronization errors caused by carrier motion and angular velocity fluctuations; and the time synchronization error, gyroscope drift, and accelerometer bias obtained by filtering estimation are fed back to the IMU data processing link.
[0029] Furthermore, the optimization of the inertial navigation system clock source using a high-precision crystal oscillator to reduce inherent clock output errors specifically includes:
[0030] A high-precision temperature-controlled crystal oscillator is selected as the core clock source of the inertial navigation system. Through hardware circuit design, the precise clock signal connection between the crystal oscillator and the main control chip of the inertial navigation system is realized to avoid signal transmission distortion.
[0031] A clock calibration link is built based on the star sensor reference time mark. The time information of the star sensor synchronization reference point is extracted periodically and compared with the time of the inertial navigation local clock to obtain the clock deviation value.
[0032] The inertial navigation clock output is corrected in real time using a digital calibration algorithm to compensate for clock errors caused by crystal oscillator temperature drift, aging, and power supply fluctuations.
[0033] At the same time, the internal clock signal distribution link of the system is optimized, and differential transmission is adopted to reduce electromagnetic interference, ensuring that all modules such as IMU data acquisition, storage and algorithm operation use a unified optimized clock signal, thereby suppressing synchronization errors caused by clock asynchrony from the source.
[0034] Furthermore, the compensation for time delay errors generated during the optical imaging and star map recognition processes of the star sensor through multi-stage collaboration specifically includes:
[0035] First, based on the time consumption characteristics of star sensor optical imaging exposure, star map preprocessing and star recognition algorithm, combined with the carrier angular velocity change law, a mapping relationship between time delay and carrier motion state is established, and the dynamic change range of delay error is clarified.
[0036] Secondly, extract the output time of two adjacent frames of valid data from the star sensor and the corresponding IMU data timestamp to define the time interval for delay error calculation. Simultaneously collect continuous data of the three-axis angular velocity and three-axis acceleration output by the IMU within this interval. Within this time interval, select no less than 4 interpolation nodes at equal time intervals and record the original IMU data and star sensor delay estimation value corresponding to each node to ensure complete coverage of the dynamic change process of delay error.
[0037] Using time as the independent variable and IMU data as the dependent variable, a piecewise cubic spline interpolation function is constructed to ensure that the interpolation curves between adjacent nodes satisfy the continuity conditions of the first and second derivatives, thus guaranteeing data smoothness.
[0038] Based on the actual delay time of the current frame data of the star sensor, the equivalent IMU data corresponding to the real imaging time is solved by the constructed cubic spline interpolation function to achieve adaptive compensation of delay error.
[0039] Finally, the consistency between the compensated IMU data and the measured attitude information of the star sensor is verified. If the data deviation exceeds the preset threshold, the interpolation node density is adjusted or the delay characteristic model is optimized. The compensation accuracy is continuously improved through iteration.
[0040] Furthermore, the use of FPGA interrupt latching functionality to unify the time scale of the star sensor and the inertial navigation system, achieving hardware-level time alignment, includes:
[0041] Achieving high-precision timescale unification between the star sensor and the inertial navigation system through timing control at the FPGA hardware level:
[0042] First, configure the data output interrupt triggering mechanism of the star sensor and IMU in the FPGA, and set the output edge of each frame of valid data of the star sensor as the highest priority external interrupt triggering source to ensure priority acquisition of the reference time mark.
[0043] When the star sensor outputs data and triggers an interrupt, the FPGA latches the output moment in real time through hardware logic, synchronously captures the local clock count value of the inertial navigation system, and establishes a direct mapping relationship between the two.
[0044] A timescale calibration mapping table is constructed based on the latched reference timescale and the inertial navigation clock count value. The local clock timestamps of all IMU output data are uniformly converted to the star sensor reference timescale system, so as to realize that the timescales of the two types of sensor data are from the same source.
[0045] The FPGA has a built-in time synchronization verification module that compares the deviation between the converted IMU data time scale and the star sensor reference time scale in real time. If the deviation exceeds the preset threshold, a hardware clock calibration signal is triggered to correct the inertial navigation clock counting deviation.
[0046] Ultimately, the star sensor data and IMU data, after being processed with unified time stamps, are both accompanied by unified reference time stamp labels and synchronously output to the data fusion module through the FPGA high-speed interface, providing a data foundation for subsequent algorithms to complete time alignment at the hardware level.
[0047] Compared with the prior art, the beneficial effects of this disclosure are: (1) It solves the problem of time asynchrony between star sensor and inertial navigation data during the online calibration stage of flight, and realizes high-precision time synchronization between star sensor and inertial navigation data; (2) Multi-dimensional error collaborative suppression, and significantly improves synchronization accuracy: The inherent error of inertial navigation clock is reduced by high-precision crystal oscillator, the data transmission delay is compensated by cubic spline interpolation, the output frequency difference is adapted by second-order extrapolation algorithm, and the time-scale hard synchronization is realized by FPGA interrupt latch. Multiple means are used to cover the main time error sources under the moving base, and the synchronization accuracy is better than the traditional single method; (3) Adapt to the dynamic characteristics of the moving base: The time update and measurement update are completed by combining asynchronous Kalman filtering, which can dynamically adapt to the change of carrier motion state, effectively resist the influence of carrier angular velocity fluctuation, motion and other factors on the synchronization effect, and solve the problem of the decrease in synchronization accuracy of traditional methods in dynamic scenarios; (4) Ensure the navigation accuracy and stability of the integrated navigation system. Attached Figure Description
[0048] The above and other objects, features and advantages of this disclosure will become more apparent from the more detailed description of exemplary embodiments of this disclosure taken in conjunction with the accompanying drawings, in which the same reference numerals generally represent the same components.
[0049] Figure 1 This is a system diagram of a high-precision time synchronization method between a satellite sensor and an inertial navigation system based on a moving base, according to the present disclosure. Detailed Implementation
[0050] Preferred embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure will be thorough and complete, and will fully convey the scope of the present disclosure to those skilled in the art.
[0051] This disclosure provides a method for high-precision time synchronization between a star sensor and an inertial navigation system on a moving base. In one exemplary embodiment, the system processing block based on this method is as follows: Figure 1 As shown, it specifically includes:
[0052] S1, establish a time scale based on the star sensor update frequency as the time synchronization reference, and store the motion parameter data output by the IMU in real time;
[0053] S2 employs a second-order extrapolation algorithm to calculate the equivalent IMU data corresponding to the star sensor data update time based on the stored historical IMU data, thereby achieving adaptation to output frequency differences.
[0054] S3 introduces an asynchronous Kalman filter algorithm, which combines IMU data with star sensor measured data to complete time updates and measurement updates, suppressing dynamic errors;
[0055] S4 uses a high-precision crystal oscillator to optimize the inertial navigation system clock source and reduce the inherent error of the clock output.
[0056] S5 compensates for time delay errors generated during the optical imaging and star map recognition processes of the star sensor through multi-stage collaboration.
[0057] S6 utilizes the FPGA interrupt latch function to unify the time scale of the star sensor and the inertial navigation system, thus completing the time alignment at the hardware level.
[0058] Specifically, such as Figure 1 As shown, in this embodiment, the above-mentioned S1 is implemented as follows:
[0059] S11, Reference Time Stamp Definition. The data update cycle of the star sensor is set as the unique reference time stamp for time synchronization. The output time of each frame of valid data from the star sensor is captured in real time and marked as the synchronization reference point. This reference point serves as the time reference for IMU data matching and equivalent calculation.
[0060] S12, IMU data acquisition type. Real-time acquisition of the core motion parameters of the carrier output by the IMU, including the three-axis angular velocity signals of the carrier relative to the inertial coordinate system measured by the gyroscope in the IMU, and the three-axis acceleration signals of the carrier relative to the inertial coordinate system, including the gravity component, measured by the accelerometer in the IMU;
[0061] S13, Data storage mechanism. A ring buffer architecture is used to cache the raw IMU output data in real time, with a cache depth of no less than 3 frames, to ensure that the second-order extrapolation algorithm can call up continuous historical data; each frame of cached data is accompanied by a local clock timestamp of the inertial navigation system synchronously calibrated by a high-precision crystal oscillator, and the timestamp accuracy matches the crystal oscillator frequency;
[0062] S14, Time Difference Calculation. By calculating the difference between the time corresponding to the star sensor's reference time stamp and the IMU data timestamp, the time difference between the star sensor data update time and the most recent IMU data update time is accurately determined. .
[0063] Specifically, such as Figure 1 As shown, in this embodiment, the above-mentioned S2 is implemented as follows:
[0064] S21, Historical Data Selection. Based on the raw IMU data stored in the circular buffer, extract the three consecutive valid frames of data prior to the current update time of the star sensor, denoted as the IMU data at the current update time. , , Output data at time , , ,in For IMU, a fixed update cycle is set. It is a positive integer;
[0065] S22, Time Difference Determination. The time difference is precisely calculated by comparing the current update time corresponding to the star sensor's reference time stamp with the timestamp of the most recent frame of IMU data. , The range of values is ;
[0066] S23, Solving for extrapolation coefficients. Based on three selected frames of IMU historical data, the coefficients of the second-order extrapolation formula are solved through interpolation:
[0067] Constant term: ;
[0068] First-order coefficients: ;
[0069] Second-order coefficients: ;
[0070] Equivalent data calculation: time difference And the coefficients obtained from the solution are substituted into the second-order extrapolation formula:
[0071]
[0072] The equivalent IMU data corresponding to the current update time of the star sensor is calculated. This data is precisely matched with the star sensor data in the time dimension, realizing the adaptation of the output frequency difference between the IMU and the star sensor.
[0073] S24, Data Validation. The calculated equivalent IMU data is validated for reasonableness. If the data exceeds the measurement range of the IMU sensor or the rate of change from historical data exceeds a preset threshold, the result is discarded and the equivalent data from the previous frame is used as a substitute. At the same time, the buffer data refresh mechanism is triggered.
[0074] Specifically, such as Figure 1 As shown, in this embodiment, the above-mentioned S3 is implemented as follows:
[0075] S31, Filtering Model Construction. Using the time synchronization error between the star sensor and the IMU, IMU gyroscope drift, and accelerometer bias as core state variables, a system state equation incorporating dynamic error propagation characteristics is established. The measurement equation uses the difference between the measured attitude information from the star sensor and the attitude information calculated from the equivalent data of the IMU as the observable.
[0076] S32, Time Update Execution. Within the interval between two consecutive data updates from the star sensor, the system state is updated in time using the state transition matrix based solely on the real-time output data of the IMU and the filtered estimation result from the previous moment, thus predicting the dynamic error change trend in real time.
[0077] S33, Measurement Update Triggered. When the star sensor outputs new measured data, the measurement update process is triggered, and the equivalent IMU data obtained by the second-order extrapolation algorithm is spatiotemporally matched with the measured data of the star sensor to construct a measurement vector;
[0078] S34, Dynamic Error Suppression. By calculating the Kalman filter gain, correcting the state estimation, and updating the covariance matrix, the predicted system state is corrected by fusing measurement information, with a focus on suppressing dynamic synchronization errors caused by carrier motion and angular velocity fluctuations;
[0079] S35, Error Feedback Correction. The time synchronization error, gyroscope drift, and accelerometer bias obtained from the filtering estimation are fed back to the IMU data processing link to correct the subsequent calculation results of the raw IMU data and equivalent data in real time, forming a closed-loop error suppression mechanism.
[0080] Specifically, such as Figure 1 As shown, in this embodiment, the above-mentioned S4 is implemented as follows:
[0081] S41, Crystal Oscillator Selection and Configuration. A high-precision, temperature-controlled crystal oscillator with stable frequency is selected as the core clock source for the inertial navigation system. Its operating temperature range covers the service environment temperature of the carrier. Through hardware circuit design, precise clock signal interface between the crystal oscillator and the main control chip of the inertial navigation system is achieved, ensuring no additional distortion during clock signal transmission.
[0082] S42, Clock calibration mechanism. A clock calibration link is established based on the star sensor reference time scale. The time information corresponding to the star sensor synchronization reference point is extracted at preset intervals and compared with the local clock time of the inertial navigation system to calculate the clock deviation value.
[0083] S43, Dynamic Error Compensation. Based on the clock deviation value obtained from the comparison, the clock output of the inertial navigation system is corrected in real time through a digital calibration algorithm to compensate for clock errors caused by crystal oscillator temperature drift, aging, and power supply fluctuations, and to maintain the consistency between the local clock of the inertial navigation system and the reference time scale of the star sensor;
[0084] S44, Clock signal distribution. Optimize the clock signal distribution link within the inertial navigation system, adopt differential transmission to reduce the impact of electromagnetic interference on the clock signal, and ensure that all modules such as IMU data acquisition, storage, and algorithm operation use a unified clock signal optimized by the crystal oscillator, thus suppressing synchronization errors caused by clock asynchrony from the source.
[0085] Specifically, such as Figure 1As shown, in this embodiment, the above-mentioned S5 is implemented as follows:
[0086] S51, Delay Characteristic Modeling. Based on the exposure time of star sensor optical imaging, star image preprocessing, and the computation time of star recognition algorithm, combined with the carrier angular velocity variation law, a mapping relationship between time delay and carrier motion state is established, and the dynamic variation range of delay error is clarified;
[0087] S52, sampling at critical moments. Extract the output time of two adjacent frames of valid data from the star sensor and the corresponding IMU data timestamp to determine the time interval for delay error calculation, and synchronously collect continuous data of three-axis angular velocity and three-axis acceleration from the IMU within this interval;
[0088] S53, Interpolation Node Selection. Within the aforementioned time interval, select no fewer than four interpolation nodes at equal time intervals, and record the original IMU data and star sensor delay estimates corresponding to each node to ensure the dynamic change process of node coverage delay error;
[0089] S54, Cubic Spline Function Construction. Using time as the independent variable and IMU data as the dependent variable, a piecewise cubic spline interpolation function is constructed to ensure that the interpolation curves between adjacent nodes satisfy the continuity conditions of the first and second derivatives, thus guaranteeing data smoothness;
[0090] S55, Delay Error Compensation. Based on the actual delay time of the current frame data of the star sensor, the equivalent IMU data corresponding to the true imaging time of the star sensor is solved through a constructed cubic spline interpolation function, thereby achieving adaptive compensation for delay error;
[0091] S56, Compensation Effect Verification. The consistency between the compensated IMU data and the measured attitude information of the star sensor is verified. If the data deviation exceeds the preset threshold, the interpolation node density is adjusted or the delay characteristic model is optimized to iteratively improve the compensation accuracy.
[0092] Specifically, such as Figure 1 As shown, in this embodiment, the above-mentioned S6 is implemented as follows:
[0093] S61, FPGA interrupt trigger configuration. Configure the data output interrupt trigger mechanism for the star sensor and IMU at the FPGA hardware level, setting the output edge of each frame of valid data from the star sensor as the external interrupt trigger source, and setting the interrupt response priority to the highest level, ensuring that the star sensor's reference time marker is captured first.
[0094] S62, critical moment latch. When the star sensor output data triggers an FPGA interrupt, the FPGA latches the output time of the star sensor data in real time through hardware logic, and simultaneously captures the count value of the local clock of the inertial navigation system at this time, establishing a direct mapping relationship between the star sensor reference time scale and the inertial navigation clock;
[0095] S63, Timescale Calibration Mapping. Based on the star sensor reference timescale and inertial navigation clock count value latched by the FPGA, a timescale calibration mapping table is constructed to uniformly convert the local clock timestamps of all IMU output data to the star sensor reference timescale system, realizing that the timescales of the two types of sensor data are from the same source;
[0096] S64, hardware synchronization verification. The FPGA has a built-in time synchronization verification module that compares the deviation between the converted IMU data time scale and the star sensor reference time scale in real time. If the deviation exceeds a preset threshold, a hardware-level clock calibration signal is triggered to correct the inertial navigation clock counting deviation.
[0097] S65, synchronous data output. After being uniformly processed by the FPGA time stamp, both star sensor data and IMU data are accompanied by a unified reference time stamp tag, and are synchronously output to the data fusion module through the FPGA high-speed interface, ensuring that subsequent algorithms are executed based on data that has been time-aligned at the hardware level.
[0098] The above technical solutions are merely exemplary embodiments of the present invention. For those skilled in the art, based on the application methods and principles disclosed in the present invention, it is easy to make various types of improvements or modifications, and not limited to the methods described in the specific embodiments of the present invention. Therefore, the methods described above are merely preferred and not restrictive.
Claims
1. A method for high-precision time synchronization between a star sensor and an inertial navigation system on a moving base, characterized in that, Includes the following steps: S1, acquires and stores motion parameter data output by the IMU in real time; S2 employs a second-order extrapolation algorithm to calculate the equivalent IMU data corresponding to the star sensor data update time based on the stored historical IMU data, thereby achieving adaptation to output frequency differences. S3 introduces an asynchronous Kalman filter algorithm, which combines IMU data with star sensor measured data to complete time updates and measurement updates, suppressing dynamic errors; S4 uses a high-precision crystal oscillator to optimize the inertial navigation system clock source and reduce the inherent error of the clock output. S5 compensates for time delay errors generated during the optical imaging and star map recognition processes of the star sensor through multi-stage collaboration. S6 utilizes the FPGA interrupt latch function to unify the time scale of the star sensor and the inertial navigation system, thus completing the time alignment at the hardware level. Step S2 specifically includes: Based on the cached raw IMU data, extract the three consecutive frames of valid raw IMU data prior to the current update time of the star sensor, denoted as IMU in [timeframe]. , , Output data at time , , ,in For IMU, a fixed update cycle is set. It is a positive integer; Calculate the time difference between the current update time corresponding to the star sensor reference time stamp and the timestamp of the most recent frame of IMU data. , The range of values is limited to ; Based on three selected frames of IMU historical data, the coefficients of the second-order extrapolation formula are solved through interpolation, where the constant term... First-order coefficients Second-order coefficients ; time difference And the coefficients obtained from the solution are substituted into the second-order extrapolation formula. The equivalent IMU data corresponding to the current update time of the star sensor is calculated. This data is precisely matched with the star sensor data in the time dimension, realizing the adaptation of the output frequency difference between the IMU and the star sensor. The calculated equivalent IMU data is validated for reasonableness. If the data exceeds the measurement range of the IMU sensor or the rate of change from historical data exceeds a preset threshold, the equivalent IMU data at the current update time is discarded and the equivalent data from the previous frame is used as a substitute. At the same time, the buffer data refresh mechanism is triggered to ensure the validity of the data. Step S3 specifically includes: An asynchronous Kalman filter model is constructed, with the time synchronization error between the star sensor and the IMU, the IMU gyroscope drift, and the accelerometer zero bias as the core state variables. A system state equation covering the dynamic error propagation characteristics is established. At the same time, the difference between the measured attitude information of the star sensor and the attitude information calculated from the equivalent data of the IMU is used as the observation to construct the measurement equation. Within the interval between two consecutive data updates of the star sensor, the system state time update is completed through the state transition matrix based solely on the real-time output data of the IMU and the filtered estimation result of the previous moment, thereby predicting the dynamic error change trend in real time. When the star sensor outputs new measured data, the measurement update process is triggered, and the equivalent IMU data obtained by the second-order extrapolation algorithm is spatiotemporally matched with the measured data of the star sensor to construct the measurement vector.
2. The method according to claim 1, characterized in that, Step S1 specifically includes: Key motion parameters output by the IMU are acquired in real time, and each frame of data is accompanied by a local clock timestamp of the inertial navigation system. The acquired key motion parameters of the IMU are stored in a ring with a buffer depth of no less than 3 frames. The time difference between the star sensor data update time and the most recent IMU data update time is obtained by calculating the difference between the star sensor reference time stamp and the IMU data timestamp.
3. The method according to claim 1, characterized in that, Step S4 specifically includes: A high-precision temperature-controlled crystal oscillator is selected as the core clock source of the inertial navigation system. Through hardware circuit design, the precise clock signal connection between the crystal oscillator and the main control chip of the inertial navigation system is realized to avoid signal transmission distortion. A clock calibration link is built based on the star sensor reference time mark. The time information of the star sensor synchronization reference point is extracted periodically and compared with the time of the inertial navigation local clock to obtain the clock deviation value. Based on the clock deviation value obtained from the comparison, the clock output of the inertial navigation system is corrected in real time through a digital calibration algorithm to compensate for clock errors caused by crystal oscillator temperature drift, aging and power fluctuations, and to maintain the consistency between the local clock of the inertial navigation system and the reference time scale of the star sensor. Optimize the clock signal distribution link within the inertial navigation system and adopt differential transmission to reduce the impact of electromagnetic interference on the clock signal. Ensure that all modules, including IMU data acquisition, storage, and algorithm operation, use a unified clock signal optimized by the crystal oscillator, thus suppressing synchronization errors caused by clock asynchrony from the source.
4. The method according to claim 1, characterized in that, In step S5, precise compensation for the time delay error of the star sensor is achieved through multi-stage collaboration, specifically including: First, based on the time consumption characteristics of star sensor optical imaging exposure, star map preprocessing and star recognition algorithm, combined with the carrier angular velocity change law, a mapping relationship between time delay and carrier motion state is established, and the dynamic change range of delay error is clarified. Secondly, extract the output time of two adjacent frames of valid data from the star sensor and the corresponding IMU data timestamp to define the time interval for delay error calculation. Simultaneously collect continuous data of the three-axis angular velocity and three-axis acceleration output by the IMU within this interval. Within this time interval, select no less than 4 interpolation nodes at equal time intervals and record the original IMU data and star sensor delay estimation value corresponding to each node to ensure complete coverage of the dynamic change process of delay error. Using time as the independent variable and IMU data as the dependent variable, a piecewise cubic spline interpolation function is constructed to ensure that the interpolation curves between adjacent nodes satisfy the continuity conditions of the first and second derivatives, thus guaranteeing data smoothness. Based on the actual delay time of the current frame data of the star sensor, the equivalent IMU data corresponding to the real imaging time is solved by the constructed cubic spline interpolation function to achieve adaptive compensation of delay error. Finally, the consistency between the compensated IMU data and the measured attitude information of the star sensor is verified. If the data deviation exceeds the preset threshold, the interpolation node density is adjusted or the delay characteristic model is optimized. The compensation accuracy is continuously improved through iteration.
5. The method according to any one of claims 1-4, characterized in that, Step S6 specifically includes: First, configure the data output interrupt triggering mechanism of the star sensor and IMU in the FPGA, and set the output edge of each frame of valid data of the star sensor as the highest priority external interrupt triggering source to ensure priority acquisition of the reference time mark. When the star sensor outputs data and triggers an interrupt, the FPGA latches the moment when the output data triggers the interrupt in real time through hardware logic, synchronously captures the local clock count value of the inertial navigation system, and establishes a direct mapping relationship between the two. A timescale calibration mapping table is constructed based on the latched reference timescale and the inertial navigation clock count value. The local clock timestamps of all IMU output data are uniformly converted to the star sensor reference timescale system, so as to realize that the timescales of the two types of sensor data are from the same source. The FPGA has a built-in time synchronization verification module that compares the deviation between the converted IMU data time scale and the star sensor reference time scale in real time. If the deviation exceeds the preset threshold, a hardware clock calibration signal is triggered to correct the inertial navigation clock counting deviation. Ultimately, the star sensor data and IMU data, after being processed with unified time stamps, are both accompanied by unified reference time stamp labels and synchronously output to the data fusion module through the FPGA high-speed interface, providing a data foundation for subsequent algorithms to complete time alignment at the hardware level.