In-situ online self-calibration method and device for MEMS tilt sensor
By applying an excitation voltage signal to the MEMS tilt sensor during manufacturing and use, calculating the virtual scaling factor and zero point, and estimating the drift, online self-calibration is achieved, solving the time drift problem of MEMS tilt sensors and reducing cost and time.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NORTH ELECTRON RES INST ANHUI CO LTD
- Filing Date
- 2023-09-15
- Publication Date
- 2026-08-04
AI Technical Summary
MEMS tilt sensors suffer from time drift, which existing technologies struggle to effectively compensate for and calibrate, leading to a decrease in accuracy over long-term operation. Furthermore, traditional solutions are either costly or inefficient.
By applying an excitation voltage signal to the MEMS tilt sensor during manufacturing and use, recording the output signal, calculating the virtual scaling factor and virtual zero point, and estimating the scaling factor and zero point drift, online self-calibration is achieved.
It achieves self-calibration of MEMS tilt sensors, reduces time drift error, saves economic and time costs, and eliminates the need for disassembly and return to the factory for calibration.
Smart Images
Figure CN117213528B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to an in-situ online self-calibration method and device for MEMS tilt sensors, belonging to the field of sensing equipment technology. Background Technology
[0002] MEMS (Micro Electro Mechanical System) devices offer advantages such as small size, light weight, low power consumption, and low cost. MEMS sensors are gradually replacing traditional mechanical sensors, becoming the mainstream miniature sensors. Common products include MEMS pressure sensors, MEMS accelerometers, MEMS gyroscopes, and MEMS microphones. MEMS tilt sensors can be widely used in safety and health monitoring of key facilities such as bridges, dams, and dangerous mountains; industrial automation attitude control; and anti-tipping early warning for large machinery, vehicles, and ships, demonstrating broad application prospects and socio-economic value.
[0003] All sensors suffer from time drift, including MEMS tilt sensors. Time drift is a random drift process without a strict pattern, making it difficult to compensate for through pre-testing and modeling. However, many applications of tilt sensors require long-term operation. The traditional approach is to select a higher-precision tilt sensor so that its accuracy still meets requirements after drift errors occur during its lifespan. The biggest drawback of this method is that using a higher-precision sensor significantly increases the cost, typically by one to two orders of magnitude. Another solution is to disassemble the tilt sensor, return it to the factory for recalibration and reinstallation, which also leads to a significant increase in cost and time. Summary of the Invention
[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide an in-situ online self-calibration method and device for MEMS tilt sensors, thereby reducing the time drift error of MEMS tilt sensors.
[0005] To achieve the above objectives, the present invention is implemented using the following technical solution:
[0006] In a first aspect, the present invention provides an in-situ online self-calibration method for a MEMS tilt sensor, comprising:
[0007] When the target MEMS tilt sensor is manufactured and during use, an excitation voltage signal is applied to its self-detection port and the output signal is recorded.
[0008] The virtual scaling factor and virtual zero point of the target MEMS tilt sensor are calculated based on the excitation voltage signal and the output signal at the time of manufacture and during use.
[0009] The scaling factor drift and zero-point drift are calculated based on the virtual scaling factor and the virtual zero point when used in the target MEMS tilt sensor.
[0010] The actual output signal of the target MEMS tilt sensor is obtained during use, and the tilt angle is calculated based on the scaling factor drift and the zero-point drift.
[0011] Optionally, the calculation of the virtual scaling factor and virtual zero point of the target MEMS tilt sensor at the time of manufacture and during use based on the excitation voltage signal and the output signal includes:
[0012] Based on the excitation voltage signal and output signal of the target MEMS tilt sensor at the time of manufacture, a first-order linear relationship at the time of manufacture is constructed:
[0013] (Y1,Y2,…,Y n )=A(V1,V2,…,V n )+B
[0014] In the formula, n≥2, which represents the number of excitation voltage signals at the time of factory shipment; V n Y n These are the nth excitation voltage signal and output signal at the factory; A and B are the virtual scale factor and virtual zero point at the factory.
[0015] Solve the first-order linear relationship at the time of manufacture to obtain the virtual scale factor A and virtual zero B at the time of manufacture;
[0016] Based on the excitation voltage signal and output signal of the target MEMS tilt sensor during use, a first-order linear relationship is constructed for its use:
[0017] (Y′1,Y′2,…,Y′ m )=A′(V′1,V′2,…,V′ m )+B′
[0018] In the formula, m≥2 represents the number of excitation voltage signals during use; V′ m 、Y′ m Here are the m-th excitation voltage signal and output signal used in the application; A′ and B′ are the virtual scale factor and virtual zero point used in the application.
[0019] Solve the first-order linear relation used in the application to obtain the virtual scale factor A′ and virtual zero B′.
[0020] Optionally, the first-order linear relationship at the time of manufacture and during use is solved by fitting using the least squares method.
[0021] Optionally, the scaling factor drift and zero drift during use are:
[0022]
[0023] ΔBias=k Bias ·(B ′ -B)
[0024] In the formula, ΔSF and ΔBias are the scale factor drift and zero point drift during use, respectively, and A and B are the virtual scale factor and virtual zero point at the factory. ′ B ′ Here, SF represents the virtual scaling factor and virtual null point used in practice, k represents the scaling factor of the target MEMS tilt sensor, and k represents the virtual scaling factor and virtual null point used in practice. SF k Bias This is a preset scaling factor.
[0025] Optionally, the tilt angle is:
[0026]
[0027] In the formula, Y0 ′ The actual output signal during use, θ is the self-calibrated tilt angle, and Bias is the zero position of the target MEMS tilt sensor.
[0028] Secondly, the present invention provides an in-situ online self-calibration device for a MEMS tilt sensor, comprising:
[0029] The test recording module is used to apply an excitation voltage signal to the self-detection port of the target MEMS tilt sensor at the time of manufacture and during use, and record the output signal respectively.
[0030] The virtual calculation module is used to calculate the virtual scaling factor and virtual zero point of the target MEMS tilt sensor at the time of manufacture and during use, based on the excitation voltage signal and the output signal.
[0031] The offset calculation module is used to calculate the scale factor drift and zero point drift when the target MEMS tilt sensor is used, based on the virtual scale factor and the virtual zero point.
[0032] The self-calibration module is used to acquire the actual output signal of the target MEMS tilt sensor when it is in use, and to calculate the tilt angle based on the scaling factor drift and the zero-point drift.
[0033] Thirdly, the present invention provides an electronic device, including a processor and a storage medium;
[0034] The storage medium is used to store instructions;
[0035] The processor is configured to operate according to the instructions to perform the steps according to the method described above.
[0036] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.
[0037] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:
[0038] This invention provides an in-situ online self-calibration method and apparatus for a MEMS tilt sensor. Since the excitation signal input to the self-detection port of the MEMS tilt sensor directly acts on the MEMS structure and is converted into an electrical signal output through an ASIC detection circuit, the measurement results of this method cover the drift of both the MEMS structure and the ASIC detection circuit. Therefore, it can effectively calibrate the drift of the MEMS tilt sensor output signal. Furthermore, this invention eliminates the need to disassemble and return the tilt sensor to the factory for recalibration and reinstallation, saving both economic and time costs. Attached Figure Description
[0039] Figure 1 This is a schematic diagram of the installation state of the MEMS tilt sensor provided in the embodiment of the present invention during actual use;
[0040] Figure 2 This is a flowchart of an in-situ online self-calibration method for a MEMS tilt sensor provided in an embodiment of the present invention;
[0041] Figure 3 This is a schematic diagram of the fitting curve between the excitation voltage applied at the factory and the output signal provided in an embodiment of the present invention;
[0042] Figure 4 This is a schematic diagram of the fitting curves of the excitation voltage applied at the factory and during use, provided by an embodiment of the present invention, and the output signal. Detailed Implementation
[0043] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.
[0044] Example 1:
[0045] When MEMS tilt sensors are actually used, they are not in a horizontal position in most environments, but rather at a certain tilt angle, denoted as θ; for example Figure 1 As shown, at this time, the MEMS tilt sensor will measure the tilt angle and output a signal, denoted as Y0:
[0046] Y0=SF·sinθ+Bias
[0047] In the formula, SF and Bias are the calibration factor and zero point, respectively, calibrated once before leaving the factory. This is typically achieved by correcting the values in the registers of the sensor's internal ASIC circuit to bring the sensor's scaling factor and zero point to the set values. Therefore, the measured tilt angle θ can be calculated using the following formula:
[0048]
[0049] The time drift of tilt sensor output can be mainly categorized into scale factor drift and zero-point drift. Scale factor drift manifests as the output signal shifting over time for the same input tilt angle, while zero-point drift manifests as the output signal shifting over time when the input tilt angle is zero. For tilt sensors, the factors causing scale factor and zero-point drift can be divided into two parts: MEMS structure drift and ASIC circuit drift.
[0050] To address the drift error of MEMS tilt sensors, such as Figure 2 As shown, this embodiment provides an in-situ online self-calibration method for a MEMS tilt sensor, including the following steps:
[0051] Step S1: Apply an excitation voltage signal to the self-detection port of the target MEMS tilt sensor at the time of manufacture and during use, and record the output signal.
[0052] Step S2: Calculate the virtual scaling factor and virtual zero point of the target MEMS tilt sensor at the time of manufacture and during use based on the excitation voltage signal and the output signal.
[0053] (1) Condition at the time of manufacture:
[0054] Based on the excitation voltage signal and output signal of the target MEMS tilt sensor at the time of manufacture, a first-order linear relationship at the time of manufacture is constructed:
[0055] (Y1,Y2,…,Y n )=A(V1,V2,…,V n )+B
[0056] In the formula, n≥2, which represents the number of excitation voltage signals at the time of factory shipment; V n Y n These are the nth excitation voltage signal and output signal at the factory; A and B are the virtual scale factor and virtual zero point at the factory.
[0057] Solve the first-order linear relationship at the time of manufacture to obtain the virtual scaling factor A and virtual zero B at the time of manufacture.
[0058] like Figure 3As shown, the fitting curves of the first-order linear relationship at the time of manufacture include Y1, Y2, Y3, V1, V2, and V3.
[0059] (2) Usage:
[0060] Based on the excitation voltage signal and output signal of the target MEMS tilt sensor during use, a first-order linear relationship is constructed for its use:
[0061] (Y′1,Y′2,…,Y′ m )=A′(V′1,V′2,…,V′ m )+B′
[0062] In the formula, m≥2 represents the number of excitation voltage signals during use; V′ m 、Y′ m Here are the m-th excitation voltage signal and output signal used in the application; A′ and B′ are the virtual scale factor and virtual zero point used in the application.
[0063] Solve the first-order linear relation used in the application to obtain the virtual scale factor A′ and virtual zero B′.
[0064] like Figure 4 As shown, the fitting curves of the first-order linear relationship at the time of manufacture include Y′1, Y′2, Y′3, V′1, V′2, V′3.
[0065] The applied input excitation voltage signal needs to have an appropriate amplitude to ensure that the output signal of the MEMS tilt sensor does not exceed the sensor's output range. Since the application of the excitation voltage generates an electrostatic force along the sensitive axis on the MEMS sensing structure of the sensor, and this electrostatic force coincides with the component of gravitational acceleration caused by tilting along the sensitive axis, the gravitational acceleration component generated by the unknown angle can be treated as a constant. By fitting the excitation voltage at multiple points with the output signal, the scale factor drift and zero-point drift of the sensor can be separated.
[0066] In one alternative implementation, the first-order linear relationship at the time of manufacture and at the time of use is solved by fitting using the least squares method.
[0067] Step S3: Calculate the scale factor drift and zero point drift when the target MEMS tilt sensor is used based on the virtual scale factor and the virtual zero point.
[0068] Since the excitation signal input to the self-detection port of the MEMS tilt sensor acts directly on the MEMS structure and is converted into an electrical signal output by the ASIC detection circuit, the measurement results of this method cover the drift of both the MEMS structure and the ASIC detection circuit. The excitation signal input to the self-detection port of the MEMS tilt sensor and its output signal share almost the same signal path as the gravitational acceleration component excitation and output signal generated by the actual tilt angle sensed by the sensor on the sensitive structure. Therefore, it can be assumed that the actual drift of the sensor is the same as the drift of the self-detection port input excitation signal and its output signal. That is, the scale factor drift and zero-point drift of the sensor can be calculated from the relationship between the self-detection port input excitation signal and the output signal detected online in situ by the sensor. This calculation is affected by the sensor's sensitive structure design and circuit design. Once the sensor is determined, this calculation relationship is also determined.
[0069] The scaling factor drift and zero drift during use are as follows:
[0070]
[0071] ΔBias=k Bias ·(B ′ -B)
[0072] In the formula, ΔSF and ΔBias are the scale factor drift and zero point drift during use, respectively, and A and B are the virtual scale factor and virtual zero point at the factory. ′ B ′ Here, SF represents the virtual scaling factor and virtual null point used in practice, k represents the scaling factor of the target MEMS tilt sensor, and k represents the virtual scaling factor and virtual null point used in practice. SF k Bias This is a preset scaling factor.
[0073] Step S4: Obtain the actual output signal of the target MEMS tilt sensor when it is in use, and calculate the tilt angle based on the scale factor drift and the zero-point drift.
[0074] The tilt angle is:
[0075]
[0076] In the formula, Y0 ′ The actual output signal during use, where v is the self-calibrated tilt angle and Bias is the zero position of the target MEMS tilt sensor.
[0077] Example 2:
[0078] This invention provides an in-situ online self-calibration device for a MEMS tilt sensor, comprising:
[0079] The test recording module is used to apply an excitation voltage signal to the self-detection port of the target MEMS tilt sensor at the time of manufacture and during use, and record the output signal respectively.
[0080] The virtual calculation module is used to calculate the virtual scaling factor and virtual zero point of the target MEMS tilt sensor at the time of manufacture and during use, based on the excitation voltage signal and the output signal.
[0081] The offset calculation module is used to calculate the scale factor drift and zero point drift when the target MEMS tilt sensor is used, based on the virtual scale factor and the virtual zero point.
[0082] The self-calibration module is used to acquire the actual output signal of the target MEMS tilt sensor when it is in use, and to calculate the tilt angle based on the scaling factor drift and the zero-point drift.
[0083] Example 3:
[0084] Based on Embodiment 1, the present invention provides an electronic device, including a processor and a storage medium;
[0085] The storage medium is used to store instructions;
[0086] The processor is configured to operate according to the instructions to perform the steps according to the method described above.
[0087] Example 4:
[0088] Based on Embodiment 1, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.
[0089] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0090] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0091] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0092] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0093] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.
Claims
1. An in-situ online self-calibration method for a MEMS tilt sensor, characterized in that, include: When the target MEMS tilt sensor is manufactured and during use, an excitation voltage signal is applied to its self-detection port and the output signal is recorded. The virtual scaling factor and virtual zero point of the target MEMS tilt sensor at the time of manufacture and during use are calculated based on the excitation voltage signal and the output signal, including: Based on the excitation voltage signal and output signal of the target MEMS tilt sensor at the time of manufacture, a first-order linear relationship at the time of manufacture is constructed: ； In the formula, , which is the number of excitation voltage signals at the time of factory delivery; The first one at the time of manufacture One excitation voltage signal and one output signal; These are the virtual scale factor and virtual zero point set at the factory; Solve the first-order linear relationship at the time of manufacture to obtain the virtual scaling factor at the time of manufacture. and virtual zero ; Based on the excitation voltage signal and output signal of the target MEMS tilt sensor during use, a first-order linear relationship is constructed for its use: ； In the formula, , where is the number of excitation voltage signals during use; For the first time when using One excitation voltage signal and one output signal; These are the virtual scale factors and virtual zeros used during application; Solve the first-order linear relation to obtain the virtual scaling factor for use. and virtual zero ; The scaling factor drift is calculated based on the virtual scaling factor and the virtual null point when used in the target MEMS tilt sensor. and zero drift : ； ； In the formula, The scaling factor for the target MEMS tilt sensor. The preset scaling factor; The actual output signal of the target MEMS tilt sensor is obtained during use, and the tilt angle is calculated based on the scaling factor drift and the zero-point drift.
2. The in-situ online self-calibration method for MEMS tilt sensors according to claim 1, characterized in that, The first-order linear relationships at the time of manufacture and during use are both solved by fitting using the least squares method.
3. The in-situ online self-calibration method for MEMS tilt sensors according to claim 1, characterized in that, The tilt angle is: ； In the formula, The actual output signal during use This is the tilt angle after self-calibration. This is the zero position of the target MEMS tilt sensor.
4. An in-situ online self-calibration device for a MEMS tilt sensor, characterized in that, include: The test recording module is used to apply an excitation voltage signal to the self-detection port of the target MEMS tilt sensor at the time of manufacture and during use, and record the output signal respectively. The virtual calculation module is used to calculate the virtual scaling factor and virtual zero point of the target MEMS tilt sensor at the time of manufacture and during use, based on the excitation voltage signal and the output signal, including: Based on the excitation voltage signal and output signal of the target MEMS tilt sensor at the time of manufacture, a first-order linear relationship at the time of manufacture is constructed: ； In the formula, , which is the number of excitation voltage signals at the time of factory delivery; The first one at the time of manufacture One excitation voltage signal and one output signal; These are the virtual scale factor and virtual zero point set at the factory; Solve the first-order linear relationship at the time of manufacture to obtain the virtual scaling factor at the time of manufacture. and virtual zero ; Based on the excitation voltage signal and output signal of the target MEMS tilt sensor during use, a first-order linear relationship is constructed for its use: ； In the formula, , where is the number of excitation voltage signals during use; For the first time when using One excitation voltage signal and one output signal; These are the virtual scale factors and virtual zeros used during application; Solve the first-order linear relation to obtain the virtual scaling factor for use. and virtual zero ; The offset calculation module is used to calculate the scale factor drift when the target MEMS tilt sensor is used, based on the virtual scaling factor and the virtual null position. and zero drift : ； ； In the formula, The scaling factor for the target MEMS tilt sensor. The preset scaling factor; The self-calibration module is used to acquire the actual output signal of the target MEMS tilt sensor when it is in use, and to calculate the tilt angle based on the scaling factor drift and the zero-point drift.
5. An electronic device, characterized in that, Including processor and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-3.
6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-3.