Method for optimizing control of a MEMS navigation system and MEMS navigation system
By employing a multi-chip array redundancy architecture and weighted calculation techniques in the MEMS navigation system, the signal interference problem of MEMS inertial sensors in complex vibration environments was solved, achieving high-precision and stable navigation performance.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- MT MICROSYST
- Filing Date
- 2025-10-20
- Publication Date
- 2026-07-07
AI Technical Summary
In existing technologies, MEMS inertial sensors have difficulty effectively suppressing vibration interference in complex and variable airborne vibration environments, which limits the stability and robustness of high-precision navigation systems.
A redundant architecture of multiple MEMS inertial measurement chip arrays is adopted. The central chip is used as a reference source for noise compensation and weight allocation to achieve weighted summation, reduce system error and improve measurement accuracy.
In complex vibration environments, MEMS navigation systems maintain high precision and stability, expanding application scenarios and enhancing the system's fault tolerance and anti-interference performance.
Smart Images
Figure CN121207142B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of inertial navigation technology, and in particular to an optimized control method for a MEMS navigation system and a MEMS navigation system. Background Technology
[0002] In airborne platforms such as drones and aircraft, MEMS inertial sensors are often used to measure parameters such as attitude, velocity, and position in real time to achieve high-precision navigation. However, the airborne vibration environment is extremely complex and diverse, covering various scenarios such as takeoff and landing, cruise, maneuvering, and sudden impacts. During takeoff and landing, low-frequency vibrations and pressure fluctuations caused by aerodynamic disturbances, airflow turbulence and wake interference at high speeds, as well as engine oscillations and mechanical resonances result in vibrations ranging from tens to hundreds of Hz. Sudden mechanical impacts, turbulence, and flight maneuvers such as high-speed turns, pitches, accelerations, and decelerations can also trigger instantaneous high-amplitude vibrations, severely interfering with the output signals of inertial sensors. These vibration scenarios include both continuous disturbances and instantaneous sudden interferences, with dramatic dynamic changes, greatly increasing the risk of signal distortion in inertial sensors and limiting the stability and robustness of high-precision navigation systems.
[0003] In existing technologies, mechanical vibration reduction combined with filtering methods is usually used to suppress vibration interference. However, in severe vibration scenarios, it is difficult to ensure continuous and high-precision measurement output of the measurement system. The vibration suppression effect is not good enough and cannot meet the complex and variable vibration characteristics of the airborne environment, which restricts the development of high-precision navigation systems. Summary of the Invention
[0004] This invention provides an optimized control method and a MEMS navigation system to address the problem that existing vibration interference suppression methods are not effective enough and cannot cope with severe vibration scenarios, thus hindering the development of high-precision navigation systems.
[0005] In a first aspect, embodiments of the present invention provide an optimized control method for a MEMS navigation system, applied to a MEMS navigation system including multiple MEMS inertial measurement chips; the method includes:
[0006] Acquire measurement data from the target MEMS inertial measurement chip; wherein the target MEMS inertial measurement chip is one of multiple MEMS inertial measurement chips;
[0007] Noise compensation parameters are determined based on the measurement data of the target MEMS inertial measurement chip, and vibration compensation is performed on each MEMS inertial measurement chip based on the noise compensation parameters.
[0008] Acquire measurement data from each MEMS inertial measurement chip, and determine the weight of each MEMS inertial measurement chip based on the measurement data.
[0009] Based on the weights of each MEMS inertial measurement chip, the measurement data of each MEMS inertial measurement chip are weighted and summed to obtain the target measurement data.
[0010] Secondly, embodiments of the present invention provide a MEMS navigation system, including: a plurality of MEMS inertial measurement chips and a main control chip;
[0011] The main control chip includes a processor and a memory. The memory is used to store computer programs, and the processor is used to call and run the computer programs stored in the memory to execute the steps of the MEMS navigation system optimization control method provided in the first aspect of the above embodiments.
[0012] This invention provides an optimized control method and a MEMS navigation system. The optimized control method is applied to a MEMS navigation system, which includes multiple MEMS inertial measurement chips. The method includes: acquiring measurement data of a target MEMS inertial measurement chip; wherein the target MEMS inertial measurement chip is one of multiple MEMS inertial measurement chips; determining noise compensation parameters based on the measurement data of the target MEMS inertial measurement chip, and performing vibration compensation on each MEMS inertial measurement chip based on the noise compensation parameters; acquiring measurement data of each MEMS inertial measurement chip, and determining the weight of each MEMS inertial measurement chip based on the measurement data; and performing a weighted summation of the measurement data of each MEMS inertial measurement chip based on the weights of each MEMS inertial measurement chip to obtain the target measurement data. In this embodiment of the invention, the target MEMS inertial measurement chip is used as a benchmark to uniformly correct the common vibration errors of each MEMS inertial measurement chip, thereby reducing system errors. At the same time, multiple MEMS inertial measurement chips are redundantly set and weighted fusion is used to reduce random errors. Furthermore, dynamic weight allocation can automatically weaken the influence of abnormal chips, further improving the measurement accuracy of navigation parameters, enhancing system performance, overcoming the inherent defects of MEMS devices such as low accuracy and susceptibility to environmental influences, enabling the system to maintain stable performance in complex vibration environments, and expanding application scenarios. Attached Figure Description
[0013] Figure 1 This is a flowchart illustrating the implementation of an optimized control method for a MEMS navigation system provided in an embodiment of the present invention.
[0014] Figure 2 This is a schematic diagram of the distribution of a MEMS inertial measurement chip provided in an embodiment of the present invention;
[0015] Figure 3 This is a schematic diagram of the structure of the MEMS navigation system optimization control device provided in an embodiment of the present invention;
[0016] Figure 4 This is a schematic diagram of the structure of a main control chip provided in an embodiment of the present invention. Detailed Implementation
[0017] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
[0018] See Figure 1 The document illustrates a flowchart of an optimized control method for a MEMS navigation system provided by an embodiment of the present invention, which is described in detail below:
[0019] The above-mentioned MEMS navigation system optimization control method is applied to a MEMS navigation system, which includes multiple MEMS inertial measurement chips 2;
[0020] For details, please refer to Figure 2 Multiple MEMS inertial measurement chips 2 (hereinafter referred to as chips) are arranged on the base plate 1 to form an array-type redundant architecture. Each chip is independently compensated and calibrated. If one chip is interfered with or fails, the other chips can still output normally. Each MEMS inertial measurement chip 2 consists of a three-axis gyroscope and a three-axis accelerometer.
[0021] For example, in one possible implementation, the number of MEMS inertial measurement chips 2 can be 5;
[0022] One of the MEMS inertial measurement chips 2 is located at the center of the base plate 1 of the MEMS navigation system; the other MEMS inertial measurement chips 2 are symmetrically distributed around the MEMS inertial measurement chip 2 at the center, forming an overall layout around the center.
[0023] refer to Figure 2The base plate 1 is rectangular, with one MEMS inertial measurement chip 2 at the center and the other four located at the four corners of the rectangular base plate 1, equidistant from the central chip, forming a cross-shaped symmetrical structure. Alternatively, the base plate 1 can be circular, with one MEMS inertial measurement chip 2 at the center and the other four evenly distributed around the center chip on the same circumference (adjacent chips form a 90° angle, creating a "quadrilateral vertex"). This 4+1 arrangement ensures diverse spatial coverage and effectively reduces random errors. Furthermore, chips in different positions can complement each other when measuring the same motion, thus reducing the potential errors of a single chip. This multi-chip array redundancy architecture not only increases the resistance of the MEMS inertial measurement chip 2 to external interference and noise but also significantly improves its performance in harsh environments.
[0024] Based on the above, refer to Figure 1 The above-mentioned MEMS navigation system optimization control methods include:
[0025] S101: Acquire measurement data of the target MEMS inertial measurement chip; wherein, the target MEMS inertial measurement chip is one of multiple MEMS inertial measurement chips 2;
[0026] The target MEMS inertial measurement chip is one of multiple MEMS inertial measurement chips 2, serving as a "reference source" to reflect "relatively real motion / vibration information" and infer the vibration interference patterns of other chips.
[0027] Therefore, the target MEMS inertial measurement chip needs to meet the conditions of more stable noise characteristics, higher measurement accuracy, and less susceptibility to vibration interference.
[0028] In one possible implementation, the MEMS inertial measurement chip 2 at the center position is used as the target MEMS inertial measurement chip.
[0029] This application can use the MEMS inertial measurement chip 2 at the center position as the target MEMS inertial measurement chip. The central chip is located at the geometric center of the base plate 1, which is closer to the center of motion and rotation of the carrier (such as a drone or vehicle). Its measurement value is minimally affected by "deformation of the base plate 1" and "local vibration". Its positional stability makes its measurement value closer to the actual motion state of the carrier.
[0030] Furthermore, the center position is usually far away from the interface circuits and external environmental interference sources at the edge of the base plate 1, resulting in lower electromagnetic interference sensitivity, more stable noise characteristics, better temperature stability, and higher measurement accuracy.
[0031] In one possible implementation, the various MEMS inertial measurement chips 2 are of the same model but have different bandwidths;
[0032] The MEMS inertial measurement chip 2 at the center position is taken as the target MEMS inertial measurement chip, and the target MEMS inertial measurement chip has the largest bandwidth.
[0033] refer to Figure 2 In this application, all MEMS inertial measurement chips 2 are of the same model to ensure "consistency of core parameters" (such as zero bias stability and scaling factor error), and to avoid data conflicts caused by inherent chip deviations, thereby reducing measurement accuracy.
[0034] The "bandwidth" of the MEMS inertial measurement chip 2 determines the "range of motion frequencies" that it can accurately measure. The larger the bandwidth, the stronger the chip's response to high-frequency motion (such as rapid vibration and instantaneous acceleration), but it is also more likely to introduce high-frequency noise. The smaller the bandwidth, the smoother the measurement data (lower noise), but it will lose high-frequency motion details.
[0035] The central chip is located at the geometric center of the base plate 1, which is closer to the center of motion of the carrier (such as a drone or robot). Its measurement value is minimally affected by local vibrations of the base plate 1 (the edge chips may generate additional high-frequency disturbances due to deformation of the base plate 1). Therefore, when high-frequency motion can be captured with the maximum bandwidth, the signal-to-noise ratio (effective signal / noise) is higher.
[0036] Meanwhile, the central chip, as the reference source, needs to prioritize the complete capture of motion across the entire frequency domain—especially the rapid dynamic response of the carrier (such as sudden turns and emergency braking)—ensuring that the system does not lose critical motion details. Edge chips, on the other hand, can serve as an auxiliary source, providing smooth data with lower bandwidth for redundancy verification. If all chips have the same bandwidth, their measurement data's "frequency domain coverage" completely overlaps, resulting in more "redundancy verification of repetitive information" during fusion. However, differentiated bandwidth allows the data from edge chips to form a "supplementary reference" in the low-frequency band, creating "frequency domain complementarity" with the high-frequency data from the central chip.
[0037] S102: Determine the noise compensation parameters based on the measurement data of the target MEMS inertial measurement chip, and perform vibration compensation on each MEMS inertial measurement chip 2 based on the noise compensation parameters;
[0038] If the noise characteristics of each chip are significantly different, the fusion algorithm may have difficulty distinguishing between "real motion differences" and "noise differences", which may lead to distortion of the fusion results. Therefore, this application uses the central chip as a reference to generate compensation parameters to perform vibration compensation on each MEMS inertial measurement chip 2, thereby achieving "noise alignment" of the measurement data of each chip and laying the foundation for subsequent multi-chip fusion.
[0039] In one possible implementation, S102 may include:
[0040] S1021: Extract vibration features based on the measurement data from the target MEMS inertial measurement chip;
[0041] Since the target MEMS inertial measurement chip (central chip) is least affected by local vibrations, the vibration components in its data are more likely to reflect "system-level common vibrations". This application extracts vibration characteristics and identifies key features of vibration interference based on the measurement data of the target MEMS inertial measurement chip.
[0042] For example, vibration characteristics may include: standard deviation, dominant frequency amplitude, and energy spectrum integral.
[0043] S1022: Determine the noise compensation parameters based on the vibration characteristics;
[0044] In one possible implementation, the vibration characteristics may include: standard deviation, dominant frequency amplitude, and energy spectrum integral; S1022 may include:
[0045] 1. Construct a quantitative index for vibration intensity based on standard deviation, dominant frequency amplitude, and energy spectrum integral;
[0046] Standard deviation, dominant frequency amplitude, and energy spectrum integral describe vibration characteristics from different dimensions.
[0047] The standard deviation reflects the overall dispersion of the vibration signal; the larger the value, the more severe the random fluctuations of the vibration.
[0048] The amplitude of the dominant frequency characterizes the amplitude of the most energetic dominant vibration frequency and is directly related to the intensity of periodic vibration.
[0049] Energy spectrum integration reflects the total energy of vibration by integrating the power spectral density of the vibration signal over the effective frequency band.
[0050] This application constructs a vibration intensity quantification index based on the above three vibration characteristics, transforming multidimensional characteristics into directly comparable single-value indexes, which facilitates subsequent matrix modeling.
[0051] In one possible implementation, a quantitative index of vibration intensity is constructed based on the standard deviation, dominant frequency amplitude, and energy spectrum integral, which may include:
[0052] (1) Normalize the standard deviation, the dominant frequency amplitude and the energy spectrum integral respectively to obtain the normalized standard deviation, the normalized dominant frequency amplitude and the normalized energy spectrum integral;
[0053] (2) Based on the normalized standard deviation, the normalized main frequency amplitude and the normalized energy spectrum integral, the vibration intensity quantitative index is constructed in combination with the first formula;
[0054] The first formula may include:
[0055]
[0056]
[0057] in, As a quantitative index of vibration intensity, , , These are the weighting coefficients. The normalized standard deviation The normalized amplitude of the main frequency. This is the normalized energy spectrum integral.
[0058] This application integrates the normalized standard deviation, normalized dominant frequency amplitude, and normalized energy spectrum integral based on weighting coefficients to obtain a quantitative index of vibration intensity, which more comprehensively and accurately measures the strength of vibration and provides a quantitative basis for subsequent noise compensation based on vibration intensity.
[0059] The weighting coefficients reflect the proportion of influence of the three features in assessing vibration intensity and can be adjusted according to actual application needs.
[0060] Since the vibration mainly manifests as random fluctuations without a clear dominant frequency, it can increase... The value of the vibration intensity; some vibrations, such as those inside precision instruments caused by minute frictions of various components and air disturbances, are caused by a combination of small, irregular factors. They lack a dominant frequency with concentrated energy, and the energy spectrum is relatively dispersed. In such cases, the amplitude of the dominant frequency and the integral of the energy spectrum cannot adequately characterize the core characteristics of the vibration. However, the standard deviation can effectively reflect the dispersion of such random vibrations. The standard deviation alone is sufficient to characterize the vibration intensity. Therefore, the standard deviation can also be used as the quantitative indicator of vibration intensity. and All are set to 0.
[0061] 2. Establish an initial noise correction matrix based on the vibration intensity quantification index;
[0062] The noise correction matrix is essentially a mapping table of "vibration intensity → compensation parameters". Its dimensions correspond to the measurement dimensions of the MEMS chip (e.g., 3-axis acceleration + 3-axis angular velocity, corresponding to a 6×6 matrix). It transforms the abstract vibration intensity into quantified correction coefficients that can be directly applied to each measurement axis, thereby achieving "targeted compensation".
[0063] For example, the values of each element are determined based on a pre-set model using a vibration intensity quantification index, while off-diagonal elements can be determined based on vibration modal characteristics. The pre-set model can be a machine learning model. Specifically, the model can be tested under various typical vibration conditions in a laboratory setting to obtain training samples (vibration intensity quantification index - correction coefficient), and the machine learning model can be trained to achieve an accurate mapping from vibration intensity to the correction coefficient.
[0064] The modal coupling degree of a vibration mode directly determines the magnitude of the off-diagonal elements. A higher modal coupling degree corresponds to a larger off-diagonal element value, indicating stronger inter-axis crosstalk and requiring greater correction. Conversely, a lower modal coupling degree results in a smaller off-diagonal element value. Based on this, a linear relationship between modal coupling degree and off-diagonal elements can be established through simulation experiments, thereby determining the off-diagonal elements based on the modal coupling degree. The formula for modal coupling degree is a conventional technique and will not be elaborated upon here.
[0065] 3. Correct the initial noise correction matrix according to the upper and lower bound constraints of the noise correction matrix to obtain the noise correction matrix.
[0066] The initial noise correction matrix may cause the compensation parameters to exceed the reasonable range due to extreme vibration characteristics or model errors. It is necessary to ensure its physical validity through constraint correction, eliminate the deviation between the theoretical model and the actual scene, and ensure the safety and validity of the compensation parameters.
[0067] Based on the chip's measurement range and noise characteristics, upper and lower limits are set for each element, and element values that exceed the range are clamped to the upper and lower limits.
[0068] S1023: Perform vibration compensation on each MEMS inertial measurement chip 2 according to the noise compensation parameters.
[0069] In one possible implementation, the noise compensation parameter is a noise correction matrix; S1023 may include:
[0070] For any MEMS inertial measurement chip 2:
[0071] 1. Obtain the initial observation noise covariance matrix of the MEMS inertial measurement chip 2;
[0072] 2. Add the noise correction matrix to the initial observation noise covariance matrix of the MEMS inertial measurement chip 2 to obtain the corrected observation noise covariance matrix;
[0073] 3. Based on the corrected observation noise covariance matrix, an adaptive Kalman filter algorithm is used to perform vibration compensation on the raw measurement data of the MEMS inertial measurement chip 2.
[0074] The initial observation noise covariance matrix reflects the inherent noise characteristics of the chip itself, preserving the intrinsic noise differences between chips. The noise correction matrix reflects the additional common interference from the current vibration environment on the chip's measurements. Adding the two together yields the comprehensive noise characteristics, which is the corrected observation noise covariance matrix. This fusion of "individuality + commonality" allows the compensation to adapt to the characteristics of different chips while uniformly addressing system-level vibration interference.
[0075] The noise covariance matrix is the standard input parameter for Kalman filtering. Each chip is independently compensated and calibrated, and no changes are required to the program of each chip. Vibration compensation can be achieved simply by dynamically updating its noise covariance matrix, which is easy to port to existing MEMS navigation systems.
[0076] S103: Acquire the measurement data of each MEMS inertial measurement chip 2, and determine the weight of each MEMS inertial measurement chip 2 based on the measurement data of each MEMS inertial measurement chip 2.
[0077] This application evaluates the "quality" of measurement data from each MEMS inertial measurement chip 2 and assigns reasonable weights for subsequent fusion; chips with high data quality have higher weights, while chips with poor data quality have lower weights.
[0078] In one possible implementation, S103 may include:
[0079] S1031: For any MEMS inertial measurement chip 2:
[0080] 1. Determine the variance of the measurement data of the MEMS inertial measurement chip 2;
[0081] 2. Determine the weight of the MEMS inertial measurement chip 2 based on the variance of the measurement data of the MEMS inertial measurement chip 2; wherein the variance is inversely proportional to the weight of the MEMS inertial measurement chip 2.
[0082] In one possible implementation, S1033 may include:
[0083] 1. Based on the variance of the measurement data of the MEMS inertial measurement chip 2, and in conjunction with the second formula, determine the weight of the MEMS inertial measurement chip 2;
[0084] The second formula may include:
[0085]
[0086] in, For the first The weights of the two MEMS inertial measurement chips. For the first The variance of the measurement data from MEMS inertial measurement chip 2 , , The number of MEMS inertial measurement chips 2.
[0087] The variance of the MEMS inertial measurement chip 2 directly reflects the stability of its measurement data. Small variance means low data dispersion, more stable measurement (low noise), and should therefore be given higher weight; large variance means drastic data fluctuations, severe interference (such as vibration, electromagnetic noise), and should therefore be given lower weight.
[0088] This application automatically adjusts itself when the environment or variance changes, and can adapt to complex scenarios.
[0089] S104: Based on the weights of each MEMS inertial measurement chip 2, the measurement data of each MEMS inertial measurement chip 2 are weighted and summed to obtain the target measurement data.
[0090] Finally, the data is weighted and summed to achieve optimal fusion of multi-chip data. This fully utilizes the information from high-quality data while suppressing interference from low-quality data, resulting in target measurement data that combines high precision and high reliability.
[0091] In one possible implementation, prior to S1031, S103 may further include:
[0092] S1032: Determine the variance and mean of the measurement data of each MEMS inertial measurement chip 2;
[0093] S1033: Based on the variance and mean of the measurement data of each MEMS inertial measurement chip 2, identify the abnormal MEMS inertial measurement chip 2 and remove it.
[0094] For example, the central chip can be used as a benchmark to calculate the deviation between each MEMS inertial measurement chip 2 and the central chip. If the deviation exceeds a threshold, it is identified as an abnormal chip and removed. Abnormal values can also be identified based on the symmetry of vibration interference at symmetrical positions. If the variance / mean deviation of a chip on one side is significantly higher than that of the symmetrical side and exceeds the normal fluctuation range, the chip may have abnormal vibration response due to problems such as loose local structure or poor solder joint contact, and should be marked accordingly.
[0095] This application, through outlier identification, can prevent abnormal data from affecting the overall results and enhance the system's fault tolerance.
[0096] In summary, this application uses the target MEMS inertial measurement chip as a benchmark to determine compensation parameters, effectively suppressing the influence of vibration interference on multiple chips and improving data consistency. Furthermore, by combining weight allocation, high-quality data dominates the fusion result, reducing interference from inferior data. Finally, by weighted summation, high-precision and high-reliability target measurement data is output, giving full play to the redundancy and synergy advantages of the multi-chip layout, overcoming the inherent defects of low precision and susceptibility to environmental influences of MEMS devices, enabling the system to maintain stable performance in complex vibration environments, and expanding application scenarios.
[0097] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
[0098] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.
[0099] Figure 3 A schematic diagram of the structure of the MEMS navigation system optimization control device provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below:
[0100] like Figure 3 As shown, a MEMS navigation system optimization control device is applied to a MEMS navigation system, which includes multiple MEMS inertial measurement chips 2; the device includes:
[0101] The first parameter acquisition module 21 is used to acquire measurement data of the target MEMS inertial measurement chip; wherein the target MEMS inertial measurement chip is one of a plurality of MEMS inertial measurement chips 2;
[0102] The vibration compensation module 22 is used to determine the noise compensation parameters based on the measurement data of the target MEMS inertial measurement chip, and to perform vibration compensation on each MEMS inertial measurement chip 2 according to the noise compensation parameters.
[0103] The second parameter acquisition module 23 is used to acquire the measurement data of each MEMS inertial measurement chip 2 and determine the weight of each MEMS inertial measurement chip 2 based on the measurement data of each MEMS inertial measurement chip 2.
[0104] The fusion module 24 is used to perform weighted summation of the measurement data of each MEMS inertial measurement chip 2 based on the weight of each MEMS inertial measurement chip 2 to obtain the target measurement data.
[0105] In one possible implementation, the vibration compensation module 22 may include:
[0106] The feature extraction unit is used to extract vibration features based on the measurement data of the target MEMS inertial measurement chip;
[0107] The compensation parameter determination unit is used to determine the noise compensation parameters based on the vibration characteristics.
[0108] The compensation unit is used to perform vibration compensation on each MEMS inertial measurement chip 2 according to the noise compensation parameters.
[0109] In one possible implementation, the noise compensation parameter is a noise correction matrix; the compensation unit can be specifically used for any MEMS inertial measurement chip 2:
[0110] 1. Obtain the initial observation noise covariance matrix of the MEMS inertial measurement chip 2;
[0111] 2. Add the noise correction matrix to the initial observation noise covariance matrix of the MEMS inertial measurement chip 2 to obtain the corrected observation noise covariance matrix;
[0112] 3. Based on the corrected observation noise covariance matrix, an adaptive Kalman filter algorithm is used to perform vibration compensation on the raw measurement data of the MEMS inertial measurement chip 2.
[0113] In one possible implementation, the vibration characteristics include: standard deviation, dominant frequency amplitude, and energy spectrum integral; the compensation parameter determination unit may include:
[0114] The index construction sub-unit is used to construct a quantitative index of vibration intensity based on the standard deviation, dominant frequency amplitude, and energy spectrum integral.
[0115] The initial matrix establishes sub-units, which are used to establish the initial noise correction matrix based on the vibration intensity quantification index;
[0116] The matrix correction sub-unit is used to correct the initial noise correction matrix according to the upper and lower bound constraints of the noise correction matrix, so as to obtain the noise correction matrix.
[0117] In one possible implementation, the index construction subunit can be specifically used for:
[0118] 1. Normalize the standard deviation, the dominant frequency amplitude, and the energy spectrum integral respectively to obtain the normalized standard deviation, the normalized dominant frequency amplitude, and the normalized energy spectrum integral.
[0119] 2. Based on the normalized standard deviation, the normalized dominant frequency amplitude, and the normalized energy spectrum integral, a vibration intensity quantification index is constructed using the first formula.
[0120] The first formula may include:
[0121]
[0122]
[0123] in, As a quantitative index of vibration intensity, , , These are the weighting coefficients. The normalized standard deviation The normalized amplitude of the main frequency. This is the normalized energy spectrum integral.
[0124] In one possible implementation, the second parameter acquisition module 23 can be specifically used for:
[0125] For any MEMS inertial measurement chip 2:
[0126] 1. Determine the variance of the measurement data of the MEMS inertial measurement chip 2;
[0127] 2. Determine the weight of the MEMS inertial measurement chip 2 based on the variance of the measurement data of the MEMS inertial measurement chip 2; wherein the variance is inversely proportional to the weight of the MEMS inertial measurement chip 2.
[0128] In one possible implementation, determining the weight of the MEMS inertial measurement chip 2 based on the variance of its measurement data may include:
[0129] (1) Based on the variance of the measurement data of the MEMS inertial measurement chip 2, the weight of the MEMS inertial measurement chip 2 is determined in conjunction with the second formula;
[0130] The second formula may include:
[0131]
[0132] in, For the first The weights of the two MEMS inertial measurement chips. For the first The variance of the measurement data from MEMS inertial measurement chip 2 , , The number of MEMS inertial measurement chips 2.
[0133] In one possible implementation, the number of MEMS inertial measurement chips 2 is 5;
[0134] One of the MEMS inertial measurement chips 2 is located at the center of the base plate 1 of the MEMS navigation system; the other MEMS inertial measurement chips 2 are symmetrically distributed around the MEMS inertial measurement chip 2 at the center, forming an overall layout around the center.
[0135] In one possible implementation, the various MEMS inertial measurement chips 2 are of the same model but have different bandwidths;
[0136] The MEMS inertial measurement chip 2 at the center position is taken as the target MEMS inertial measurement chip, and the target MEMS inertial measurement chip has the largest bandwidth.
[0137] Figure 4 This is a schematic diagram of the main control chip 3 provided in an embodiment of the present invention. Figure 4 As shown, the main control chip 3 in this embodiment includes a processor 30 and a memory 31. The memory 31 stores a computer program 32. When the processor 30 executes the computer program 32, it implements the steps in the above-described method embodiments. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each module / unit in the above-described device embodiments.
[0138] For example, the computer program 32 can be divided into one or more modules / units, which are stored in the memory 31 and executed by the processor 30 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 32 in the main control chip 3.
[0139] The main control chip 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will understand that... Figure 4 This is merely an example of the main control chip 3 and does not constitute a limitation on the main control chip 3. It may include more or fewer components than shown, or combine certain components, or use different components.
[0140] The processor 30 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.
[0141] The memory 31 can be an internal storage unit of the main control chip 3. The memory 31 can also be an external storage device of the main control chip 3, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the main control chip 3. Furthermore, the memory 31 can include both internal storage units of the main control chip 3 and external storage devices. The memory 31 is used to store the computer program 32 and other programs and data required by the main control chip 3. The memory 31 can also be used to temporarily store data that has been output or will be output.
[0142] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.
[0143] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.
[0144] This invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.
[0145] Computer programs include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.
[0146] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.
[0147] Corresponding to the above embodiments, this embodiment of the invention also provides a MEMS navigation system, including: a plurality of MEMS inertial measurement chips 2 and a main control chip 3;
[0148] The main control chip 3 includes a processor and a memory. The memory is used to store computer programs, and the processor is used to call and run the computer programs stored in the memory to execute the steps of the MEMS navigation system optimization control method provided in the above embodiments.
[0149] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.
Claims
1. An optimized control method for a MEMS navigation system, characterized in that, The method is applied to a MEMS navigation system, which includes multiple MEMS inertial measurement chips; the method includes: Acquire measurement data of the target MEMS inertial measurement chip; wherein the target MEMS inertial measurement chip is one of the plurality of MEMS inertial measurement chips; Noise compensation parameters are determined based on the measurement data of the target MEMS inertial measurement chip, and vibration compensation is performed on each MEMS inertial measurement chip based on the noise compensation parameters. Acquire measurement data from each MEMS inertial measurement chip, and determine the weight of each MEMS inertial measurement chip based on the measurement data. Based on the weights of each MEMS inertial measurement chip, the measurement data of each MEMS inertial measurement chip are weighted and summed to obtain the target measurement data. The step of determining noise compensation parameters based on the measurement data of the target MEMS inertial measurement chip, and performing vibration compensation on each MEMS inertial measurement chip based on the noise compensation parameters, includes: Vibration features are extracted based on the measurement data from the target MEMS inertial measurement chip. Based on the vibration characteristics, the noise compensation parameters are determined; the noise compensation parameters are noise correction matrices. For any MEMS inertial measurement chip: Obtain the initial observation noise covariance matrix of the MEMS inertial measurement chip; The noise correction matrix is added to the initial observation noise covariance matrix of the MEMS inertial measurement chip to obtain the corrected observation noise covariance matrix. Based on the corrected observation noise covariance matrix, an adaptive Kalman filter algorithm is used to perform vibration compensation on the raw measurement data of the MEMS inertial measurement chip.
2. The MEMS navigation system optimization control method according to claim 1, characterized in that, The vibration characteristics include: standard deviation, dominant frequency amplitude, and energy spectrum integral; determining the noise compensation parameters based on the vibration characteristics includes: A quantitative index of vibration intensity is constructed based on the standard deviation, the dominant frequency amplitude, and the energy spectrum integral. Based on the aforementioned vibration intensity quantification index, an initial noise correction matrix is established; The initial noise correction matrix is corrected according to the upper and lower bound constraints of the noise correction matrix to obtain the noise correction matrix.
3. The MEMS navigation system optimization control method according to claim 2, characterized in that, The step of constructing a vibration intensity quantification index based on the standard deviation, the dominant frequency amplitude, and the energy spectrum integral includes: The standard deviation, the dominant frequency amplitude, and the energy spectrum integral are normalized respectively to obtain the normalized standard deviation, the normalized dominant frequency amplitude, and the normalized energy spectrum integral. The vibration intensity quantification index is constructed based on the normalized standard deviation, the normalized dominant frequency amplitude, and the normalized energy spectrum integral, combined with the first formula. The first formula includes: in, The vibration intensity is quantified as follows: , , These are the weighting coefficients. The normalized standard deviation is... The normalized main frequency amplitude value, The normalized energy spectrum integral is given.
4. The MEMS navigation system optimization control method according to any one of claims 1 to 3, characterized in that, The step of determining the weight of each MEMS inertial measurement chip based on the measurement data of each MEMS inertial measurement chip includes: For any MEMS inertial measurement chip: Determine the variance of the measurement data of this MEMS inertial measurement chip; The weight of the MEMS inertial measurement chip is determined based on the variance of its measurement data; wherein the variance is inversely proportional to the weight of the MEMS inertial measurement chip.
5. The MEMS navigation system optimization control method according to claim 4, characterized in that, The step of determining the weight of the MEMS inertial measurement chip based on the variance of the measurement data of the MEMS inertial measurement chip includes: The weights of the MEMS inertial measurement chip are determined based on the variance of the measurement data of the MEMS inertial measurement chip and the second formula. The second formula includes: in, For the first The weights of each MEMS inertial measurement chip. For the first The variance of measurement data from a MEMS inertial measurement chip. , , This refers to the number of MEMS inertial measurement chips.
6. The MEMS navigation system optimization control method according to any one of claims 1 to 3, characterized in that, The number of MEMS inertial measurement chips is 5; One MEMS inertial measurement chip is located at the center of the base plate of the MEMS navigation system; the other MEMS inertial measurement chips are symmetrically distributed around the MEMS inertial measurement chip at the center, forming an overall layout around the center.
7. The MEMS navigation system optimization control method according to claim 6, characterized in that, The various MEMS inertial measurement chips have the same model number but different bandwidths; The MEMS inertial measurement chip at the center position is taken as the target MEMS inertial measurement chip, and the target MEMS inertial measurement chip has the largest bandwidth.
8. A MEMS navigation system, characterized in that, include: Multiple MEMS inertial measurement chips and main control chips; The main control chip includes a processor and a memory. The memory is used to store computer programs, and the processor is used to call and run the computer programs stored in the memory to execute the steps of the MEMS navigation system optimization control method as described in any one of claims 1 to 7.