An adaptive inertial navigation error correction method, device, medium and equipment

Abstract

The application provides a self-adaptive inertial navigation error correction method and device, medium and equipment. Inertial sensor data of a target object in a balanced state and a moving state is collected to obtain static inertial sensor data and dynamic inertial sensor data. Based on a static zero bias estimation model, a zero bias error prediction result corresponding to the static inertial sensor data and the dynamic inertial sensor data is calculated. Based on the dynamic zero bias error prediction result and the static zero bias error prediction result, an error factor is calculated to correct the dynamic inertial sensor data. By collecting the inertial sensor data of the target object in the balanced state and the moving state respectively, and using the static zero bias estimation model to calculate the zero bias error prediction in the balanced state and the moving state respectively, the zero bias error prediction in the two states is combined to determine the error factor, and the inertial sensor data is corrected according to the error factor, so as to alleviate the error accumulation problem of the inertial navigation system and improve the long-term accuracy of the inertial navigation system.

Description

Technical Field

[0001] This application relates to the field of inertial navigation system technology, specifically to an adaptive inertial navigation error correction method, device, medium, and equipment. Background Technology

[0002] An Inertial Navigation System (INS) is an autonomous navigation system that uses inertial sensors (such as accelerometers and gyroscopes) to measure the motion state of a vehicle (such as a car). Because inertial navigation does not rely on external signal input, it has good stealth and anti-interference capabilities, and is therefore widely used in aerospace, marine, and vehicle navigation fields. However, inertial navigation systems have a drawback: over time, navigation errors accumulate, leading to a decrease in system accuracy. This error accumulation mainly stems from the zero-bias error of the inertial sensors themselves; that is, even when stationary, the sensors output non-zero values, thus introducing long-term drift errors.

[0003] To address the error accumulation problem in Inertial Navigation Systems (INS), integrated navigation systems are typically used, where the INS is combined with other auxiliary navigation systems (such as GPS and visual navigation systems). A typical scheme combining GPS and INS can correct INS errors over long periods of operation. However, GPS signals are susceptible to environmental interference (such as blockages and multipath effects) and may fail. In complex environments, such as urban canyons, forested areas, or underwater environments, GPS signals are often unreliable. Therefore, a scheme is needed to suppress or correct errors without relying on external navigation signals. Summary of the Invention

[0004] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide an adaptive inertial navigation error correction method, apparatus, medium, and device.

[0005] According to one aspect of this application, an adaptive inertial navigation error correction method is provided, comprising: acquiring inertial sensor data of a target object in equilibrium to obtain static inertial sensor data; calculating a static zero-bias error prediction result corresponding to the static inertial sensor data based on a static zero-bias estimation model; acquiring inertial sensor data of the target object in motion to obtain dynamic inertial sensor data; extracting a zero-bias drift signal from the dynamic inertial sensor data to obtain a dynamic zero-bias drift signal; performing zero-bias error prediction on the dynamic zero-bias drift signal using the static zero-bias estimation model to obtain a dynamic zero-bias error prediction result; calculating an error factor based on the dynamic zero-bias error prediction result and the static zero-bias error prediction result; and correcting the dynamic inertial sensor data using the error factor to obtain corrected dynamic inertial sensor data.

[0006] In one embodiment, acquiring inertial sensor data of the target object in equilibrium to obtain static inertial sensor data includes: acquiring inertial sensor data of the target object in uniform linear motion to obtain the static inertial sensor data; wherein, the static inertial sensor data includes acceleration data and angular velocity data.

[0007] In one embodiment, acquiring inertial sensor data of the target object in equilibrium to obtain static inertial sensor data includes: acquiring inertial sensor data of the target object in equilibrium to obtain initial inertial data; filtering the initial inertial data using a low-pass filter and a high-pass filter respectively to obtain multi-scale low-frequency filter coefficients and multi-scale high-frequency filter coefficients; filtering the multi-scale low-frequency filter coefficients and the multi-scale high-frequency filter coefficients to obtain filtered multi-scale low-frequency filter coefficients and filtered multi-scale high-frequency filter coefficients; and reconstructing the filtered multi-scale low-frequency filter coefficients and the filtered multi-scale high-frequency filter coefficients to obtain the static inertial sensor data.

[0008] In one embodiment, calculating the static zero-bias error prediction result corresponding to the static inertial sensor data based on the static zero-bias estimation model includes: calculating the prior distribution of the static inertial sensor data; updating the prior distribution to obtain the posterior distribution; and calculating the static zero-bias error prediction result based on the prior distribution and the posterior distribution.

[0009] In one embodiment, extracting the zero-bias drift signal from the dynamic inertial sensor data to obtain the dynamic zero-bias drift signal includes: performing frequency decomposition on the dynamic inertial sensor data to obtain windowed spectrograms of the dynamic inertial sensor data at different frequency indices; dividing the windowed spectrograms into high-frequency windowed spectrograms and low-frequency windowed spectrograms; using the low-frequency windowed spectrogram as a low-frequency potential zero-bias drift signal; and extracting the zero-bias drift signal from the low-frequency potential zero-bias drift signal to obtain the dynamic zero-bias drift signal.

[0010] In one embodiment, extracting the zero-bias drift signal from the low-frequency potential zero-bias drift signal to obtain the dynamic zero-bias drift signal includes: calculating the power spectrum of the low-frequency windowed spectrum corresponding to the low-frequency potential zero-bias drift signal; calculating the power spectral density based on the power spectrum; calculating the corresponding density estimation weight based on the power spectral density; and calculating the dynamic zero-bias drift signal based on the density estimation weight and the low-frequency windowed spectrum.

[0011] In one embodiment, calculating the error factor based on the dynamic zero-bias error prediction result and the static zero-bias error prediction result includes: calculating the similarity probability of the zero-bias estimates in the dynamic zero-bias error prediction result and the static zero-bias error prediction result; calculating the distance value between the dynamic zero-bias error prediction result and the static zero-bias error prediction result based on the similarity probability of the zero-bias estimates; and calculating the error factor based on the distance value between the dynamic zero-bias error prediction result and the static zero-bias error prediction result.

[0012] According to another aspect of this application, an adaptive inertial navigation error correction device is provided, comprising: a static data acquisition module for acquiring inertial sensor data of a target object in equilibrium state to obtain static inertial sensor data; a static error prediction module for calculating a static zero-bias error prediction result corresponding to the static inertial sensor data based on a static zero-bias estimation model; a dynamic data acquisition module for acquiring inertial sensor data of the target object in motion state to obtain dynamic inertial sensor data; a dynamic drift extraction module for extracting a zero-bias drift signal from the dynamic inertial sensor data to obtain a dynamic zero-bias drift signal; a dynamic error prediction module for performing zero-bias error prediction on the dynamic zero-bias drift signal using the static zero-bias estimation model to obtain a dynamic zero-bias error prediction result; an error factor calculation module for calculating an error factor based on the dynamic zero-bias error prediction result and the static zero-bias error prediction result; and a dynamic data correction module for correcting the dynamic inertial sensor data using the error factor to obtain corrected dynamic inertial sensor data.

[0013] According to another aspect of this application, a computer-readable storage medium is provided, the storage medium storing a computer program for performing any of the methods described above.

[0014] According to another aspect of this application, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; the processor being configured to perform any of the methods described above.

[0015] This application provides an adaptive inertial navigation error correction method, apparatus, medium, and device. It obtains static inertial sensor data by collecting inertial sensor data of a target object in equilibrium; calculates the static zero-bias error prediction result corresponding to the static inertial sensor data based on a static zero-bias estimation model; collects inertial sensor data of the target object in motion to obtain dynamic inertial sensor data; extracts the zero-bias drift signal from the dynamic inertial sensor data to obtain the dynamic zero-bias drift signal; uses the static zero-bias estimation model to predict the zero-bias error of the dynamic zero-bias drift signal to obtain the dynamic zero-bias error prediction result; calculates the error factor based on the dynamic zero-bias error prediction result and the static zero-bias error prediction result; and corrects the dynamic inertial sensor data using the error factor to obtain corrected dynamic inertial sensor data. By separately collecting inertial sensor data of the target object in equilibrium and motion, and using the static zero-bias estimation model to calculate the zero-bias error prediction in equilibrium and motion respectively, the error factor is determined by combining the zero-bias error predictions in both states, and the sensor data is corrected according to the error factor, thereby alleviating the error accumulation problem of the inertial navigation system and improving the long-term accuracy of the inertial navigation system. Attached Figure Description

[0016] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0017] Figure 1 This is a flowchart illustrating an exemplary embodiment of the adaptive inertial navigation error correction method provided in this application.

[0018] Figure 2 This is a schematic diagram of the structure of an adaptive inertial navigation error correction device provided in an exemplary embodiment of this application.

[0019] Figure 3 This is a structural diagram of an electronic device provided in an exemplary embodiment of this application. Detailed Implementation

[0020] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0021] Figure 1 This is a flowchart illustrating an exemplary embodiment of the adaptive inertial navigation error correction method provided in this application. Figure 1As shown, the adaptive inertial navigation error correction method includes the following steps:

[0022] Step 110: Collect inertial sensor data of the target object in equilibrium state to obtain static inertial sensor data.

[0023] This application uses inertial sensor data collected from the target object in equilibrium (uniform linear motion), i.e., static inertial sensor data, as reference data for subsequent correction of dynamic inertial sensor data.

[0024] Step 120: Based on the static zero-bias estimation model, calculate the static zero-bias error prediction result corresponding to the static inertial sensor data.

[0025] This application utilizes a static zero-bias estimation model to calculate the static zero-bias error prediction results corresponding to static inertial sensor data, thereby determining the zero-bias error prediction results of the target object in equilibrium.

[0026] Step 130: Collect inertial sensor data of the target object in motion to obtain dynamic inertial sensor data.

[0027] This application collects inertial sensor data of a target object in motion, where the motion state of the target object indicates whether the object is undergoing uniform or variable motion. The collected inertial sensor data in motion is denoted as:

[0028] ;

[0029] in, Inertial sensor data representing an object in motion. Represents inertial sensor data The acceleration and angular velocity data of the object at N consecutive monitoring times.

[0030] Step 140: Extract the zero-bias drift signal from the dynamic inertial sensor data to obtain the dynamic zero-bias drift signal.

[0031] This application analyzes dynamic inertial sensor data to extract the zero-bias drift signal, thereby obtaining the dynamic zero-bias drift signal.

[0032] Step 150: Use the static zero-bias estimation model to predict the zero-bias error of the dynamic zero-bias drift signal and obtain the dynamic zero-bias error prediction result.

[0033] This application utilizes a static zero-bias estimation model to predict the zero-bias error of a dynamic zero-bias drift signal, thereby obtaining the corresponding dynamic zero-bias error prediction results.

[0034] Step 160: Calculate the error factor based on the dynamic zero bias error prediction results and the static zero bias error prediction results.

[0035] This application combines the prediction results of dynamic zero bias error and static zero bias error to calculate the error factor for correcting dynamic inertial sensor data.

[0036] Step 170: Correct the dynamic inertial sensor data using the error factor to obtain the corrected dynamic inertial sensor data.

[0037] After calculating the error factor, this application uses the error factor to correct the dynamic inertial sensor data, thus obtaining corrected dynamic inertial sensor data.

[0038] This application provides an adaptive inertial navigation error correction method. It obtains static inertial sensor data by collecting inertial sensor data of a target object in equilibrium; calculates the static zero-bias error prediction result corresponding to the static inertial sensor data based on a static zero-bias estimation model; collects inertial sensor data of the target object in motion to obtain dynamic inertial sensor data; extracts the zero-bias drift signal from the dynamic inertial sensor data to obtain the dynamic zero-bias drift signal; uses the static zero-bias estimation model to predict the zero-bias error of the dynamic zero-bias drift signal to obtain the dynamic zero-bias error prediction result; calculates the error factor based on the dynamic and static zero-bias error prediction results; and corrects the dynamic inertial sensor data using the error factor to obtain corrected dynamic inertial sensor data. By collecting inertial sensor data of the target object in equilibrium and motion states respectively, and using the static zero-bias estimation model to calculate the zero-bias error prediction in equilibrium and motion states respectively, the error factor is determined by combining the zero-bias error predictions in both states, and the sensor data is corrected according to the error factor to alleviate the error accumulation problem of the inertial navigation system and improve the long-term accuracy of the inertial navigation system.

[0039] In one embodiment, step 110 can be implemented by: collecting inertial sensor data of the target object under uniform linear motion to obtain static inertial sensor data; wherein, the static inertial sensor data includes acceleration data and angular velocity data.

[0040] This application collects inertial sensor data of a target object in an equilibrium state, where the equilibrium state of the target object indicates that the target object is undergoing uniform linear motion. The collected inertial sensor data in the equilibrium state is denoted as:

[0041] ;

[0042] in, This application uses static inertial sensor data to represent the static inertial sensor data of a target object in equilibrium. The static inertial sensor data of the target object is collected at multiple consecutive monitoring moments by setting an inertial sensor. The inertial sensor includes an accelerometer and a gyroscope. The static inertial sensor data includes acceleration data and angular velocity data. This represents the acceleration and angular velocity data of the target object collected at the nth monitoring time. N This represents the total number of monitoring moments. , These represent the acceleration and angular velocity data of the target object, respectively.

[0043] In one embodiment, step 110 can be implemented as follows: acquiring inertial sensor data of the target object in equilibrium to obtain initial inertial data; filtering the initial inertial data using a low-pass filter and a high-pass filter to obtain multi-scale low-frequency filter coefficients and multi-scale high-frequency filter coefficients; filtering the multi-scale low-frequency filter coefficients and multi-scale high-frequency filter coefficients to obtain filtered multi-scale low-frequency filter coefficients and filtered multi-scale high-frequency filter coefficients; reconstructing the filtered multi-scale low-frequency filter coefficients and filtered multi-scale high-frequency filter coefficients to obtain static inertial sensor data.

[0044] After acquiring the initial inertial data, this application constructs a low-pass filter and a high-pass filter of length Len, and uses the low-pass filter to filter the initial inertial sensor data. Filtering is performed to obtain multi-scale low-frequency filter coefficients, where the initial inertial sensor data... The multi-scale low-frequency filter coefficients are , Represents initial inertial sensor data In the low-frequency filter coefficients at the m-th scale, M represents the preset maximum scale, where...

[0045] ;

[0046] ;

[0047] in, express Low-frequency filter weights at scale m This represents an exponential function with the natural constant as its base. It represents the imaginary unit.

[0048] Similarly, a high-pass filter is used to process the initial inertial sensor data. Filtering is performed to obtain multi-scale high-frequency filter coefficients, including the initial inertial sensor data. The multi-scale high-frequency filter coefficients are , Represents initial inertial sensor data The high-frequency filter coefficients at the m-th scale, where,

[0049] ;

[0050] ;

[0051] in, express High-frequency filtering weights at scale m.

[0052] Then, the multi-scale low-frequency filter coefficients and multi-scale high-frequency filter coefficients are filtered to obtain the filtered multi-scale low-frequency filter coefficients and multi-scale high-frequency filter coefficients.

[0053] Among them, the low-frequency filter coefficient The filtering results are :

[0054] ;

[0055] in, Represents a symbolic function. This represents the threshold value for low-frequency filter coefficients. Indicates selection The larger value, This represents the function that takes the absolute value.

[0056] High-frequency filter coefficients The filtering results are :

[0057] ;

[0058] in, Describing the L2 norm, This represents the threshold value for the high-frequency filtering coefficients.

[0059] Finally, the static inertial sensor data is reconstructed from the filtered multi-scale low-frequency filter coefficients and multi-scale high-frequency filter coefficients. :

[0060] ;

[0061] ;

[0062] in, express The noise reduction results.

[0063] In one embodiment, step 120 can be implemented as follows: calculating the prior distribution of static inertial sensor data; updating the prior distribution to obtain the posterior distribution; and calculating the static zero bias error prediction result based on the prior distribution and the posterior distribution.

[0064] This application constructs a static zero-bias estimation model, which includes an input layer, a prior distribution calculation layer, a state update layer, and an estimation layer. Static inertial sensor data is then calculated using this static zero-bias estimation model. The corresponding static zero-bias error prediction results, where the zero-bias error prediction adopts the dynamic Bayesian method, and the specific calculation process is as follows:

[0065] First, the static inertial sensor data The input layer receives data from the static inertial sensor. Subsequently, the prior distributed computation layer calculates the static inertial sensor data. The prior distribution of any denoising result in the given data, where the denoising result is... The prior distribution is :

[0066] ;

[0067] in, Represents a sequence The mean, Represents a sequence The standard deviation.

[0068] Then, the state update layer updates the prior distribution to obtain the posterior distribution of the denoised result, where the denoised result... The posterior distribution is :

[0069] ;

[0070] Where T represents transpose. This represents the time interval between adjacent monitoring moments in an inertial sensor.

[0071] Finally, the estimation layer combines the prior and posterior distributions to generate static inertial sensor data. Corresponding static zero bias error prediction results :

[0072] ;

[0073] ;

[0074] in, Indicates the result of noise reduction processing The zero-biased estimate.

[0075] In one embodiment, step 140 can be implemented as follows: frequency decomposition of the dynamic inertial sensor data to obtain windowed spectrograms of the dynamic inertial sensor data at different frequency indices; dividing the windowed spectrograms into high-frequency windowed spectrograms and low-frequency windowed spectrograms; using the low-frequency windowed spectrogram as a low-frequency potential zero-bias drift signal; extracting the zero-bias drift signal from the low-frequency potential zero-bias drift signal to obtain the dynamic zero-bias drift signal.

[0076] First, the dynamic inertial sensor data Frequency decomposition was performed to obtain dynamic inertial sensor data. Windowed spectrograms at different frequency indices, including dynamic inertial sensor data. The windowed spectrum of frequency index u is as follows :

[0077] ;

[0078] in, , j Represents the imaginary unit. This represents an exponential function with the natural constant as its base. Represents dynamic inertial sensor data The windowing coefficients of the frequency index u.

[0079] Then, the windowed spectrum is divided into high-frequency windowed spectrum and low-frequency windowed spectrum. Specifically, if If it exceeds the preset threshold, then confirm. For high-frequency windowed spectrum, otherwise determine. Windowed spectrum of low frequencies.

[0080] Finally, a set of high-frequency windowed spectrograms and a set of low-frequency windowed spectrograms are constructed separately. The high-frequency windowed spectrograms in the high-frequency windowed spectrogram set are used as high-frequency motion signals, and the low-frequency windowed spectrograms in the low-frequency windowed spectrogram set are used as low-frequency potential zero-bias drift signals. The high-frequency windowed spectrogram set is... , Represents the first high-frequency windowed spectrogram in the set. A high-frequency windowed spectrum diagram This represents the total number of high-frequency windowed spectrograms in the high-frequency windowed spectrogram set, and the low-frequency windowed spectrogram set is... , This represents the b-th low-frequency windowed spectrum in the set of low-frequency windowed spectrum graphs. This represents the total number of low-frequency windowed spectrograms in the low-frequency windowed spectrogram set. Furthermore, by extracting the zero-bias drift signal from the potential zero-bias drift signal in the low-frequency range, the dynamic zero-bias drift signal is obtained.

[0081] In one embodiment, step 140 can be implemented as follows: calculate the power spectrum of the low-frequency windowed spectrum corresponding to the low-frequency potential zero-bias drift signal; calculate the power spectral density based on the power spectrum; calculate the corresponding density estimation weight based on the power spectral density; and calculate the dynamic zero-bias drift signal based on the density estimation weight and the low-frequency windowed spectrum.

[0082] First, the power spectrum of the low-frequency windowed spectrum characterizing the low-frequency potential zero-bias drift signal is calculated, where the low-frequency windowed spectrum... The power spectrum is :

[0083] ;

[0084] in, Low-frequency windowed spectrum diagram The amplitude.

[0085] Then, the power spectrum is converted into a power spectral density, where the power spectrum... The corresponding power spectral density is :

[0086] ;

[0087] in, This represents the time interval between adjacent monitoring moments in an inertial sensor.

[0088] Then, the power spectral density is converted into density estimation weights, where the power spectral density... The corresponding density estimation weights are :

[0089] ;

[0090] Finally, the low-frequency windowed spectrogram is weighted using density estimation weights to obtain the zero-bias drift signal. :

[0091] ;

[0092] ;

[0093] in, Low-frequency windowed spectrum diagram The corresponding frequency index, This indicates the frequency index for retrieving the low-frequency windowed spectrogram. j Represents the imaginary unit. Indicates the object in the first position n The zero-bias drift signal value at each monitoring moment.

[0094] After calculating the dynamic zero-bias drift signal, the static zero-bias estimation model is used to evaluate the dynamic zero-bias drift signal. Perform zero-bias error prediction and use the prediction results As the result of dynamic zero bias error prediction, , Indicates zero bias drift signal The object in the middle of the first n Zero-bias drift signal value at each monitoring time The zero-biased estimate.

[0095] In one embodiment, step 160 can be implemented as follows: calculating the similarity probability of the zero-bias estimate in the dynamic zero-bias prediction result and the static zero-bias prediction result; calculating the distance between the dynamic zero-bias prediction result and the static zero-bias prediction result based on the similarity probability of the zero-bias estimate; and calculating the error factor based on the distance between the dynamic zero-bias prediction result and the static zero-bias prediction result.

[0096] First, the static zero bias error prediction result is calculated. With dynamic zero bias error prediction results The similarity probability of the zero-biased estimator, where, and The similarity probability between them is , :

[0097] ;

[0098] Then, the similarity probabilities of the zero-biased estimates are converted into distances to obtain the distance values ​​between the zero-biased estimates, where, and The distance between them is :

[0099] ;

[0100] in, This means selecting the minimum value among them and obtaining it through iterative calculation. And used as the static zero bias error prediction result. With dynamic zero bias error prediction results The distance value.

[0101] Finally, the static zero bias error prediction results With dynamic zero bias error prediction results Distance value Converted to inertial sensor data error factor :

[0102] ;

[0103] in, Indicates the dynamic zero bias error prediction result standard deviation Indicates the static zero bias error prediction result standard deviation Indicates the dynamic zero bias error prediction result The mean, Indicates the static zero bias error prediction result The mean.

[0104] The error factor was calculated. Then, using the error factor Inertial sensor data in motion Corrections are made to obtain the error-corrected inertial sensor data. .

[0105] Specifically, inertial sensor data in motion. The corrected formula is:

[0106] ;

[0107] ;

[0108] in, Represents dynamic inertial sensor data The error correction results express Error correction value, This represents the L1 norm.

[0109] Figure 2 This is a schematic diagram of the structure of an adaptive inertial navigation error correction device provided in an exemplary embodiment of this application. Figure 2As shown, the adaptive inertial navigation error correction device 20 includes: a static data acquisition module 21, used to acquire inertial sensor data of the target object in equilibrium state to obtain static inertial sensor data; a static error prediction module 22, used to calculate the static zero-bias error prediction result corresponding to the static inertial sensor data based on the static zero-bias estimation model; a dynamic data acquisition module 23, used to acquire inertial sensor data of the target object in motion state to obtain dynamic inertial sensor data; a dynamic drift extraction module 24, used to extract the zero-bias drift signal from the dynamic inertial sensor data to obtain the dynamic zero-bias drift signal; a dynamic error prediction module 25, used to perform zero-bias error prediction on the dynamic zero-bias drift signal using the static zero-bias estimation model to obtain the dynamic zero-bias error prediction result; an error factor calculation module 26, used to calculate the error factor based on the dynamic zero-bias error prediction result and the static zero-bias error prediction result; and a dynamic data correction module 27, used to correct the dynamic inertial sensor data using the error factor to obtain the corrected dynamic inertial sensor data.

[0110] This application provides an adaptive inertial navigation error correction device. The device comprises: a static data acquisition module 21 acquiring inertial sensor data of a target object in equilibrium, obtaining static inertial sensor data; a static error prediction module 22 calculating the static zero-bias error prediction result corresponding to the static inertial sensor data based on a static zero-bias estimation model; a dynamic data acquisition module 23 acquiring inertial sensor data of the target object in motion, obtaining dynamic inertial sensor data; a dynamic drift extraction module 24 extracting the zero-bias drift signal from the dynamic inertial sensor data, obtaining the dynamic zero-bias drift signal; and a dynamic error prediction module 25 using the static zero-bias estimation model to predict the zero-bias error of the dynamic zero-bias drift signal. The dynamic zero-bias error prediction result is obtained; the error factor calculation module 26 calculates the error factor based on the dynamic zero-bias error prediction result and the static zero-bias error prediction result; the dynamic data correction module 27 corrects the dynamic inertial sensor data using the error factor to obtain the corrected dynamic inertial sensor data; by collecting inertial sensor data of the target object in equilibrium and motion states respectively, and using the static zero-bias estimation model to calculate the zero-bias error prediction in equilibrium and motion states respectively, the error factor is determined by combining the zero-bias error prediction in the two states, and the sensor data is corrected according to the error factor to alleviate the error accumulation problem of the inertial navigation system and improve the long-term accuracy of the inertial navigation system.

[0111] In one embodiment, the static data acquisition module 21 can be further configured to: acquire inertial sensor data of the target object under uniform linear motion to obtain static inertial sensor data; wherein, the static inertial sensor data includes acceleration data and angular velocity data.

[0112] In one embodiment, the static data acquisition module 21 can be further configured to: acquire inertial sensor data of the target object in equilibrium state to obtain initial inertial data; filter the initial inertial data using a low-pass filter and a high-pass filter respectively to obtain multi-scale low-frequency filter coefficients and multi-scale high-frequency filter coefficients; filter the multi-scale low-frequency filter coefficients and multi-scale high-frequency filter coefficients to obtain filtered multi-scale low-frequency filter coefficients and filtered multi-scale high-frequency filter coefficients; reconstruct the filtered multi-scale low-frequency filter coefficients and filtered multi-scale high-frequency filter coefficients to obtain static inertial sensor data.

[0113] In one embodiment, the static error prediction module 22 can be further configured to: calculate the prior distribution of static inertial sensor data; update the prior distribution to obtain the posterior distribution; and calculate the static zero bias error prediction result based on the prior distribution and the posterior distribution.

[0114] In one embodiment, the dynamic drift extraction module 24 can be further configured to: perform frequency decomposition on the dynamic inertial sensor data to obtain windowed spectrograms of the dynamic inertial sensor data at different frequency indices; divide the windowed spectrograms into high-frequency windowed spectrograms and low-frequency windowed spectrograms; use the low-frequency windowed spectrogram as a low-frequency potential zero-bias drift signal; extract the zero-bias drift signal from the low-frequency potential zero-bias drift signal to obtain the dynamic zero-bias drift signal.

[0115] In one embodiment, the dynamic drift extraction module 24 can be further configured to: calculate the power spectrum of the low-frequency windowed spectrum corresponding to the low-frequency potential zero-bias drift signal; calculate the power spectral density based on the power spectrum; calculate the corresponding density estimation weight based on the power spectral density; and calculate the dynamic zero-bias drift signal based on the density estimation weight and the low-frequency windowed spectrum.

[0116] In one embodiment, the error factor calculation module 26 can be further configured to: calculate the similarity probability of the zero-biased estimates in the dynamic zero-biased error prediction result and the static zero-biased error prediction result; calculate the distance between the dynamic zero-biased error prediction result and the static zero-biased error prediction result based on the similarity probability of the zero-biased estimates; and calculate the error factor based on the distance between the dynamic zero-biased error prediction result and the static zero-biased error prediction result.

[0117] Below, for reference Figure 3 This application describes an electronic device according to embodiments thereof. The electronic device may be either or both of a first device and a second device, or a standalone device independent of them, which may communicate with the first device and the second device to receive acquired input signals from them.

[0118] Figure 3A block diagram of an electronic device according to an embodiment of this application is illustrated.

[0119] like Figure 3 As shown, the electronic device 10 includes one or more processors 11 and memory 12.

[0120] The processor 11 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.

[0121] The memory 12 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 11 may execute the program instructions to implement the methods of the various embodiments of this application described above and / or other desired functions. Various contents such as input signals, signal components, and noise components may also be stored in the computer-readable storage medium.

[0122] In one example, the electronic device 10 may also include an input device 13 and an output device 14, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0123] When the electronic device is a standalone device, the input device 13 can be a communication network connector for receiving the collected input signals from the first device and the second device.

[0124] In addition, the input device 13 may also include, for example, a keyboard, a mouse, etc.

[0125] The output device 14 can output various information to the outside, including determined distance information, direction information, etc. The output device 14 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0126] Of course, for the sake of simplicity, Figure 3 Only some of the components of the electronic device 10 relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the electronic device 10 may include any other suitable components depending on the specific application.

[0127] In addition to the methods and apparatus described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0128] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0129] Furthermore, embodiments of this application may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0130] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0131] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0132] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0133] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0134] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0135] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A self-adapting inertial navigation error correction method, characterized in that, The method comprises the following steps: Collecting inertial sensor data of a target object in a balanced state to obtain static inertial sensor data; Based on a static zero bias estimation model, calculating a static zero bias error prediction result corresponding to the static inertial sensor data; Collecting inertial sensor data of the target object in a moving state to obtain dynamic inertial sensor data; Extracting a zero bias drift signal from the dynamic inertial sensor data to obtain a dynamic zero bias drift signal; Using the static zero bias estimation model to predict the zero bias error of the dynamic zero bias drift signal to obtain a dynamic zero bias error prediction result; Based on the dynamic zero bias error prediction result and the static zero bias error prediction result, an error factor is calculated; Using the error factor to correct the dynamic inertial sensor data to obtain corrected dynamic inertial sensor data; The error factor is calculated based on the dynamic zero bias error prediction result and the static zero bias error prediction result, which comprises: Calculating the similarity probability of the zero bias estimation value in the dynamic zero bias error prediction result and the static zero bias error prediction result; Based on the similarity probability of the zero bias estimation value, the distance value between the dynamic zero bias error prediction result and the static zero bias error prediction result is calculated; Based on the distance value between the dynamic zero bias error prediction result and the static zero bias error prediction result, the error factor is calculated.

2. The adaptive INS error correction method of claim 1, wherein, The method comprises the following steps: Collecting inertial sensor data of a target object in a balanced state to obtain static inertial sensor data; 3. The adaptive INS error correction method of claim 1, wherein, Collecting inertial sensor data of the target object in a uniform linear motion to obtain the static inertial sensor data; wherein the static inertial sensor data comprises acceleration data and angular velocity data. The method comprises the following steps: Collecting inertial sensor data of a target object in a balanced state to obtain initial inertial data; Using a low-pass filter and a high-pass filter to filter the initial inertial data respectively to obtain multi-scale low-frequency filter coefficients and multi-scale high-frequency filter coefficients; Filtering the multi-scale low-frequency filter coefficients and the multi-scale high-frequency filter coefficients to obtain filtered multi-scale low-frequency filter coefficients and filtered multi-scale high-frequency filter coefficients; 4. The adaptive INS error correction method of claim 1, wherein, Reconstructing the filtered multi-scale low-frequency filter coefficients and the filtered multi-scale high-frequency filter coefficients to obtain the static inertial sensor data. The method comprises the following steps: Calculating the prior distribution of the static inertial sensor data; Updating the prior distribution to obtain a posterior distribution; 5. The adaptive INS error correction method of claim 1, wherein, Based on the prior distribution and the posterior distribution, the static zero bias error prediction result is calculated. The method comprises the following steps: Frequency-decomposing the dynamic inertial sensor data to obtain a windowed frequency spectrum diagram of the dynamic inertial sensor data at different frequency indexes; Dividing the windowed frequency spectrum diagram into a high-frequency windowed frequency spectrum diagram and a low-frequency windowed frequency spectrum diagram; windowed low-frequency spectrum of the low-frequency potential zero bias drift signal as a dynamic zero bias drift signal. extracting a zero bias drift signal in the low-frequency potential zero bias drift signal to obtain the dynamic zero bias drift signal.

6. The adaptive INS error correction method of claim 5, wherein, The extracting a zero bias drift signal in the low-frequency potential zero bias drift signal to obtain the dynamic zero bias drift signal comprises: calculating a power spectrum of the low-frequency windowed spectrum corresponding to the low-frequency potential zero bias drift signal; based on the power spectrum, calculating a power spectrum density; based on the power spectrum density, calculating a corresponding density estimation weight; based on the density estimation weight and the low-frequency windowed spectrum, calculating the dynamic zero bias drift signal.

7. An adaptive inertial navigation error correction device, characterized in that, comprise: a static data acquisition module configured to acquire inertial sensor data of a target object in a balanced state to obtain static inertial sensor data; a static error prediction module configured to calculate a static zero bias error prediction result corresponding to the static inertial sensor data based on a static zero bias estimation model; a dynamic data acquisition module configured to acquire inertial sensor data of the target object in a moving state to obtain dynamic inertial sensor data; a dynamic drift extraction module configured to extract a zero bias drift signal in the dynamic inertial sensor data to obtain a dynamic zero bias drift signal; a dynamic error prediction module configured to perform zero bias error prediction on the dynamic zero bias drift signal using the static zero bias estimation model to obtain a dynamic zero bias error prediction result; an error factor calculation module configured to calculate an error factor based on the dynamic zero bias error prediction result and the static zero bias error prediction result; a dynamic data correction module configured to correct the dynamic inertial sensor data using the error factor to obtain corrected dynamic inertial sensor data. The error factor calculation module is further configured to: calculate a similarity probability of zero bias estimation values in the dynamic zero bias error prediction result and the static zero bias error prediction result; based on the similarity probability of the zero bias estimation values, calculate a distance value between the dynamic zero bias error prediction result and the static zero bias error prediction result; based on the distance value between the dynamic zero bias error prediction result and the static zero bias error prediction result, calculate the error factor.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and the computer program is used to execute the method in any one of claims 1-6.

9. An electronic device, comprising: comprise: a processor; a memory for storing instructions executable by the processor; the processor is configured to execute the method in any one of claims 1-6.

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