Vehicle-mounted integrated navigation method and device for laser Doppler velocimeter

Abstract

The invention discloses a vehicle-mounted integrated navigation method and device for a laser Doppler velocimeter, and the method comprises the steps: obtaining navigation initial information, carrying out the system initialization and alignment of an inertial sensor based on the navigation initial information, then executing inertial navigation, obtaining the velocimeter data of the laser Doppler velocimeter after an inertial navigation result is obtained, and carrying out the vehicle-mounted integrated navigation of the laser Doppler velocimeter. Combining the inertial navigation result with the tachymeter data to obtain an initial navigation result; and correcting the initial navigation result through a Kalman filtering algorithm to obtain a target navigation result. By comprehensively utilizing the data of the Doppler velocimeter and the IMU, high-precision navigation in a complex environment is realized, and the reliability and robustness of a navigation system are effectively improved. Meanwhile, the corrected attitude, position and speed are output through a Kalman filtering model, the advantages of the two sensors are integrated, the respective errors are reduced, and the method is particularly suitable for navigation in a complex environment, such as the conditions of bumpiness of a road and undulation of the ground.

Description

Technical Field

[0001] This application relates to the technical field of vehicle integrated navigation, and more specifically, to a method and device for vehicle integrated navigation using a laser Doppler velocimeter. Background Art

[0002] In the field of vehicle navigation, the commonly used vehicle inertial integrated navigation methods at present mainly include SINS / GPS combination and SINS / odometer combination. On the one hand, although the SINS / GPS integrated navigation can well solve the problem of error accumulation, GPS has disadvantages such as poor dynamic response ability, susceptibility to electronic interference, and easy signal occlusion, and the integrated system is non-autonomous. On the other hand, although the odometer can accurately measure the vehicle speed and mileage, the change of the odometer scale factor is large, which seriously restricts its speed measurement accuracy, and real-time calibration must be carried out during each use. This patent proposes a method for vehicle integrated navigation using a laser Doppler velocimeter for complex environments such as road bumps and ground undulations. It is applicable to complex environments such as road bumps and ground undulations. Summary of the Invention

[0003] In view of at least one defect or improvement requirement of the prior art, the present invention provides a method and device for vehicle integrated navigation using a laser Doppler velocimeter, which performs integrated navigation based on the output information of the Doppler velocimeter and outputs a navigation result, and can adapt to complex environments such as vehicle system bumps and ground undulations, improving navigation accuracy and adaptability.

[0004] To achieve the above object, according to the first aspect of the present invention, there is provided 1. A method for vehicle integrated navigation using a laser Doppler velocimeter, which is applied to a vehicle system. The vehicle navigation system includes a laser Doppler velocimeter and an inertial sensor, and is characterized by including:

[0005] Obtain initial navigation information, where the initial navigation information includes the status information of the laser Doppler velocimeter and the measurement data of the inertial sensor;

[0006] Based on the initial navigation information, initialize and align the inertial sensor and then perform inertial navigation to obtain an inertial navigation result;

[0007] Obtain the velocimeter data of the laser Doppler velocimeter, fuse the inertial navigation result with the velocimeter data to obtain an initial navigation result;

[0008] Correct the initial navigation result through the Kalman filter algorithm to obtain a target navigation result.

[0009] Further, the obtaining of the initial navigation information includes:

[0010] Obtain the initial position information of the navigation;

[0011] Obtain a status word from the outputs of a laser Doppler velocimeter and an inertial sensor based on the initial position information, encode the status word and output it to obtain a status code;

[0012] Determine the driving state of the vehicle based on the status code to obtain the status information of the laser Doppler velocimeter and the measurement data of the inertial sensor.

[0013] Furthermore, the obtaining of the velocimeter data of the laser Doppler velocimeter and the combination of the inertial navigation result and the velocimeter data to obtain an initial navigation result includes:

[0014] Construct a Kalman filter model;

[0015] Based on the inertial navigation result and the velocimeter data combined with the Kalman filter model, calculate to obtain an initial navigation result.

[0016] Furthermore, the calculating to obtain an initial navigation result based on the inertial navigation result and the velocimeter data combined with the Kalman filter model includes:

[0017] Obtain the speed data in the velocimeter data and decompose it according to the azimuth output to obtain a first set of speed components, and the first set of speed components includes the speed components of the velocimeter in different azimuths;

[0018] Obtain the speed data in the inertial navigation result and decompose it according to the azimuth output to obtain a second set of speed components, and the second set of speed components includes the speed components of the inertial sensor in different azimuths;

[0019] Compare and integrate the first set of speed components and the second set of speed components to obtain an initial navigation result.

[0020] Furthermore, the correcting the initial navigation result through the Kalman filter algorithm to obtain a navigation result includes:

[0021] Use the Kalman filter algorithm to estimate the error state vector of the velocimeter data, and determine the initial navigation result as the observable quantity of the Kalman filter; the error elements in the error state vector include position error, speed error, attitude angle error, accelerometer zero drift, gyroscope drift, and velocimeter scale factor error;

[0022] Based on the observable quantity and the error state vector, use the Kalman filter algorithm for closed-loop correction to obtain a navigation result.

[0023] Furthermore, the state equation for constructing the Kalman filter model is specifically:

[0024]

[0025] Among them, X(t) represents the error state vector; A(t) represents the system state transition matrix; W(t) represents the system measurement noise vector;

[0026] Define the error state vector; the error elements include position error, velocity error, attitude angle error, accelerometer zero drift, gyroscope drift, and tachometer scale factor error;

[0027] Determine the system state transition matrix based on the error state vector.

[0028] Furthermore, after correcting the initial navigation result through the Kalman filter algorithm to obtain the target navigation result, it further includes: correcting the zero bias of the inertial sensor based on the target navigation result for the next update of the inertial navigation result.

[0029] According to the second aspect of the present invention, there is also provided a vehicle-mounted combined navigation device for a laser Doppler velocimeter, including:

[0030] A processing module for obtaining initial navigation information, where the initial navigation information includes the status information of the laser Doppler velocimeter and the measurement data of the inertial sensor;

[0031] A first data acquisition module for performing inertial navigation after initializing and aligning the inertial sensor based on the initial navigation information to obtain an inertial navigation result;

[0032] A second data acquisition module for obtaining the velocimeter data of the laser Doppler velocimeter, combining the inertial navigation result with the velocimeter data to obtain an initial navigation result;

[0033] A correction module for correcting the initial navigation result through the Kalman filter algorithm to obtain the target navigation result.

[0034] According to the third aspect of the present invention, there is also provided a computer-readable storage medium, in which a computer program is stored, and the computer program is configured to execute the above-mentioned vehicle-mounted combined navigation method for a laser Doppler velocimeter when running.

[0035] According to the fourth aspect of the present invention, there is also provided an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the above-mentioned processor executes the above-mentioned vehicle-mounted combined navigation method for a laser Doppler velocimeter through the computer program.

[0036] Generally speaking, compared with the prior art through the above technical solutions conceived by the present invention, the following beneficial effects can be achieved:

[0037] The present invention provides a vehicle-mounted integrated navigation method for a laser Doppler velocimeter. By obtaining initial navigation information, the inertial sensor is system-initialized and aligned based on the initial navigation information, and then inertial navigation is performed. After obtaining the inertial navigation result, the velocimeter data of the laser Doppler velocimeter is acquired, and the inertial navigation result is combined with the velocimeter data to obtain an initial navigation result. The initial navigation result is corrected by the Kalman filter algorithm to obtain the target navigation result. Whenever the observation data of the velocimeter is obtained, using the coupling relationship between the velocity error and the attitude angle, the error state variables such as attitude, velocity, gyro zero drift, accelerometer zero drift, and position are estimated through Kalman filtering technology, and closed-loop correction is performed, which can effectively reduce the accumulation of navigation errors and achieve better positioning and orientation accuracy. The method proposed by the present invention combines the advantages of two different navigation technologies to improve navigation accuracy and reliability. By comprehensively utilizing the data of the Doppler velocimeter and the IMU, high-precision navigation in complex environments is realized, effectively improving the reliability and robustness of the navigation system. At the same time, the corrected attitude, position, and velocity are output through the Kalman filter model, and these output values are more accurate than the data of any single sensor alone, integrating the advantages of the two sensors and reducing their respective errors, which is particularly suitable for navigation in complex environments, such as the situation where the road bumps up and down and the ground undulates. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0039] Figure 1 FIG. 1 is one of the flow diagrams of an optional vehicle-mounted integrated navigation method for a laser Doppler velocimeter provided by an embodiment of the present application;

[0040] Figure 2 FIG. 2 is another flow diagram of an optional vehicle-mounted integrated navigation method for a laser Doppler velocimeter provided by an embodiment of the present application;

[0041] Figure 3 FIG. 3 is a flow diagram of an optional operation of a vehicle-mounted navigation system provided by an embodiment of the present application;

[0042] Figure 4 FIG. 4 is a structural diagram of an optional vehicle-mounted integrated navigation device for a laser Doppler velocimeter provided by an embodiment of the present application;

[0043] Figure 5 FIG. 5 is a structural diagram of an optional electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the following further describes the present invention in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0045] The terms "first", "second", "third", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0046] According to one aspect of the embodiments of this application, a vehicle-mounted integrated navigation method for a laser Doppler velocimeter is provided. The following combines Figure 1 Describe the vehicle-mounted integrated navigation method for a laser Doppler velocimeter provided by the embodiments of this application.

[0047] Figure 1 is a schematic flowchart of an optional vehicle-mounted integrated navigation method for a laser Doppler velocimeter provided by the embodiments of this application. As Figure 1 shown, the process of this method may include the following steps:

[0048] S102, obtain initial navigation information, where the initial navigation information includes the status information of the laser Doppler velocimeter and the measurement data of the inertial sensor;

[0049] S104, perform system initialization and alignment on the inertial sensor based on the initial navigation information, and then perform inertial navigation to obtain an inertial navigation result;

[0050] S106, obtain the velocimeter data of the laser Doppler velocimeter, combine the inertial navigation result with the velocimeter data to obtain an initial navigation result;

[0051] S108, correct the initial navigation result through the Kalman filter algorithm to obtain a target navigation result.

[0052] A vehicle-mounted integrated navigation method for a laser Doppler velocimeter provided by the embodiments of this application is applied to a vehicle-mounted system. The vehicle-mounted navigation system includes a laser Doppler velocimeter and an inertial sensor.

[0053] It should be noted that the vehicle speedometer of the present invention is a Laser Doppler Velocimetry (LDV), which is a high-precision speed measurement device based on the Doppler effect principle. The inertial sensor is a sensor that can provide information on the acceleration, speed, and position of an object. In a vehicle navigation system, the Laser Doppler Velocimetry provides speed measurement, and the inertial sensor provides accurate information about the vehicle's position and attitude. By viewing the data from these sensors, the device can gain a more comprehensive understanding of the vehicle's driving direction and motion state.

[0054] The Kalman filtering algorithm adopted in this application is an efficient recursive filter that can perform optimal state estimation in a dynamic system, even when the measurement data is contaminated by noise. This algorithm is based on two key steps: prediction and update. Through these two steps, it continuously and recursively estimates the system state.

[0055] In the prediction stage, the Kalman filter uses the estimate of the previous state to make an estimate of the current state. This process involves a state transition model, which assumes that the state of the system at the current moment is based on the previous state plus some control inputs and process noise. In the update stage, the filter uses the observed value of the current state to optimize the prediction value obtained in the prediction stage to obtain a more accurate new estimate value. This process involves an observation model, which relates the observed value to the state value and takes into account the observation noise.

[0056] In the present invention, the Kalman filtering algorithm is used to fuse the data from different sensors (such as the Laser Doppler Velocimetry and the inertial sensor) and provide an optimal state estimate to correct and improve the accuracy and reliability of the navigation system.

[0057] Figure 2 This is the second schematic diagram of the process of an optional method for combined vehicle navigation using a Laser Doppler Velocimetry provided by an embodiment of this application. As Figure 2 shown, first, after the product is powered on, a power-on self-check is performed. According to the state information output by the sensor, the state of the speedometer sensor is encoded. According to the output status code, the current navigation mode is provided for the vehicle to perform combined navigation;

[0058] Furthermore, Figure 3 This is the schematic diagram of the process of an optional vehicle navigation system operation provided by an embodiment of this application. As Figure 3 shown, when the inertial sensor outputs valid information, the navigation position information is input for initialization and alignment. After the initialization and alignment are completed, pure inertial navigation is performed, and the navigation result is updated in the order of speed update, position update, and attitude update;

[0059] Finally, when the output data of the Doppler velocimeter is valid, the Kalman filter is used to fuse the velocity result output by the Doppler velocimeter with the above navigation result, correct the error of the navigation system, and output the navigation result in real time. During the navigation process, when the data output of the laser Doppler velocimeter is fault-free, the above navigation result is fused through the Kalman filter; the navigation mode of the vehicle-mounted navigation system and the final navigation result are output.

[0060] In an exemplary embodiment, initial navigation information is obtained, including: obtaining the initial position information of the navigation; obtaining a status word from the outputs of the laser Doppler velocimeter and the inertial sensor based on the initial position information, encoding and outputting the status word to obtain a status code; determining the driving state of the vehicle based on the status code to obtain the status information of the laser Doppler velocimeter and the measurement data of the inertial sensor.

[0061] Specifically, the initial position information of the vehicle navigation is obtained, a status word is obtained from the outputs of different sensors, the status word is encoded and output, and according to the output of the status code, the driving state of the vehicle at this time is prompted, and the output information is saved.

[0062] Among them, the output data of different sensors include the measurement data of the inertial sensor and the measurement data of the laser Doppler velocimeter. The measurement data of the inertial sensor are, for example, the output values of the accelerometer and gyro of the inertial sensor. The measurement data of the laser Doppler velocimeter include the speed information of the velocimeter, etc.

[0063] In an exemplary embodiment, obtaining the velocimeter data of the laser Doppler velocimeter and combining the inertial navigation result with the velocimeter data to obtain an initial navigation result includes:

[0064] Constructing a Kalman filter model;

[0065] Based on the inertial navigation result and the velocimeter data combined with the Kalman filter model, an initial navigation result is calculated.

[0066] Based on the content disclosed in the above embodiments, in the vehicle-mounted integrated navigation method provided by the present application, the calculating the initial navigation result by combining the inertial navigation result with the velocimeter data and the Kalman filter model includes:

[0067] Obtaining the speed data in the velocimeter data and decomposing it according to the azimuth output to obtain a first set of speed components;

[0068] Obtaining the speed data in the inertial navigation result and decomposing it according to the azimuth output to obtain a second set of speed components;

[0069] Comparing and integrating the first set of speed components and the second set of speed components to obtain an initial navigation result.

[0070] Among them, the first set of velocity components includes the velocity components of the velocimeter in different azimuths. For example, the velocity of the velocimeter is decomposed into a northward velocity and an eastward velocity. The second set of velocity components includes the velocity components of the inertial sensor in different azimuths.

[0071] After decomposing the velocimeter velocity into the navigation coordinate system, it is compared with the velocity components of the second set of velocity components calculated by the pure inertial unit to form the observation quantity of the Kalman filter.

[0072] In an exemplary embodiment, the method of correcting the initial navigation result through the Kalman filter algorithm to obtain the navigation result includes:

[0073] The Kalman filter algorithm is used to estimate the error state vector of the velocimeter data, and the initial navigation result is determined as the observation quantity of the Kalman filter.

[0074] Based on the observation quantity and the error state vector, the Kalman filter algorithm is used for closed-loop correction to obtain the navigation result.

[0075] Among them, the error elements in the error state vector include position error, velocity error, attitude angle error, accelerometer zero drift, gyroscope drift, and velocimeter scale factor error. By establishing a comprehensive error state vector, the system can comprehensively consider various error sources, including position error, velocity error, attitude angle error, accelerometer zero drift, gyroscope drift, and velocimeter scale factor error. This comprehensiveness enables the system to provide more accurate navigation performance in the face of complex road conditions, such as bumps and terrain undulations.

[0076] Next, a specific embodiment is used to describe the present solution again.

[0077] (1) Figure 2 It is the second schematic diagram of the flow of an optional vehicle-mounted integrated navigation method using a laser Doppler velocimeter provided by an embodiment of the present application. As Figure 2 shown, when the vehicle-mounted navigation system is powered on, first, a power-on self-check is performed to ensure that each module works normally, and the initial navigation information of each sensor is obtained in real time. Among them, the initial navigation information includes the status information of the laser Doppler velocimeter and the measurement data of the inertial sensor. When the inertial sensor and the Doppler velocimeter output valid data, initialization and alignment are started, and the position and height of the product at the initial moment are input as the initial values to start alignment.

[0078] The coordinate system can be defined as follows:

[0079] Geocentric Earth Coordinate System e: The origin is located at the center of the earth, and the z e axis is along the direction of the earth's rotation, and the x eThe x-axis lies in the prime meridian plane and is parallel to the equatorial plane, and the y- e axis is determined by the right-hand rule.

[0080] Geocentric inertial coordinate system i: A supposed absolutely stationary coordinate system, which is formed by freezing the geocentric Earth coordinate system at the initial moment.

[0081] Navigation coordinate system n: That is, the geographic coordinate system, with the origin at the inertial navigation center, the x- e axis pointing east, the x- n axis pointing north, and the z- n axis pointing skyward.

[0082] Vehicle body coordinate system b: The origin is at the inertial navigation center, and the x- b , y- b , z- b axes point right, forward, and upward along the vehicle body respectively.

[0083] Base inertial coordinate system A coordinate system formed by freezing the vehicle body coordinate system through inertial freezing at the initial moment.

[0084] The alignment process is as follows:

[0085] Let the latitude of the alignment point be L, then the attitude matrix can be determined by the following formula:

[0086]

[0087] Obtain the attitude matrix determined by the alignment.

[0088] (2) After initializing and aligning the inertial sensors based on the initial navigation information, perform inertial navigation to obtain the inertial navigation result. When the alignment is completed, start inertial navigation. The inertial navigation is updated according to the following process and the navigation result is output.

[0089] The update process of inertial navigation is divided into velocity update, position update, and attitude update.

[0090] For example, the velocity update equation:

[0091]

[0092] In the formula: is the specific force output of the accelerometer; v- n is the velocity of the vehicle body in the navigation coordinate system; g- n is the gravitational acceleration at the position of the vehicle body.

[0093] For example, the position update equation:

[0094]

[0095] Where: φ, λ, and h are the longitude, latitude, and altitude of the carrier's location, respectively; are the velocity components of the carrier in the northeast-down directions in the navigation coordinate system; R M 、R N represent the radius of curvature of the meridian and the radius of curvature of the prime vertical at the carrier's location, respectively.

[0096] The attitude update equation is, for example:

[0097]

[0098] Where: is the attitude transformation matrix from the body coordinate system b to the navigation coordinate system n; is the angular velocity of the body coordinate system relative to the navigation coordinate system; × is the skew-symmetric matrix of.

[0099] (3) When both the laser Doppler velocimeter and inertial measurement unit data are valid, whenever the laser Doppler velocimeter data is updated, the inertial + velocity integrated navigation mode is performed. First, the state equation of the Kalman filter model is established as follows:

[0100]

[0101] X(t) is the error state vector; A(t) is the system state transition matrix; W(t) is the system measurement noise vector.

[0102] Error state vector For example:

[0103]

[0104] Where, δλ, δL, δh represent the latitude, longitude, and altitude errors, respectively; δv e , δv n , δv u represent the east, north, and down velocity errors; φ e , φ n , φ u represent the east, north, and down error angles, respectively; represent the zero offsets of the east, north, and down accelerometers, respectively; ε x , ε y , ε z represent the drifts of the east, north, and down gyroscopes, respectively; δk represents the scale factor error of the velocimeter.

[0105] System state transition matrix Specifically as follows:

[0106]

[0107] Among them,

[0108]

[0109] The system measurement noise vector W(t) is 16-dimensional, as follows:

[0110]

[0111] Among them, a x , a y , a z is the noise of the accelerometer in the vehicle coordinate system, ω x , ω y , ω z is the noise of the gyroscope in the vehicle coordinate system, and v is the noise generated by the speedometer. They are white noises with a mean of 0 and a normal distribution.

[0112] The speed sensed by the speedometer is the running speed of the navigation vehicle. Ignoring the influence of the vertical channel and outputting according to the azimuth angle in the horizontal plane, the speed of the speedometer can be decomposed into the northward speed and the eastward speed.

[0113] After decomposing the speed to the navigation coordinate system according to the output of the speedometer, it is compared with the speed component calculated by the pure inertial unit to form the observable quantity of the Kalman filter.

[0114] Furthermore, according to the selected state vector, the corresponding measurement equation is, for example:

[0115] Z(t) = H(t)X(t)+V(t) (8)

[0116] Among them, Z(t) is the measurement vector; H(t) is the measurement matrix; V(t) is the measurement noise vector.

[0117] Then, establish the error equation of the inertial sensor, for example:

[0118] The attitude error equation, for example:

[0119]

[0120] In the formula: φ n is the attitude error angle of the vehicle in the navigation coordinate system; is the angular velocity output of the gyroscope; is the projection of the angular velocity output of the gyroscope in the navigation coordinate system; is the attitude transformation matrix from the vehicle coordinate system b to the navigation coordinate system n, ε n is the projection of the gyroscope zero bias in the navigation coordinate system.

[0121] The speed error equation, for example:

[0122]

[0123] Where: δv n is the velocity error of the vehicle in the navigation system; f b is the specific force output of the accelerometer; f n is the projection of the specific force output of the accelerometer in the navigation coordinate system; v n is the velocity of the vehicle in the navigation coordinate system; and are the earth rotation rate and position rate respectively, is the projection of the accelerometer zero bias in the navigation coordinate system.

[0124] The position error equation is, for example:

[0125]

[0126] Where: δφ and δλ are the longitude and latitude errors of the position where the vehicle is located respectively; are the velocity components of the vehicle in the east and north directions in the navigation coordinate system respectively.

[0127] Assume that the zero bias of the gyroscope and the zero bias of the accelerometer are both first-order Gaussian-Markov processes.

[0128] The measurement vector Z can be expressed as:

[0129] Z = [δv e , δv n , δv u (12)

[0130] Where: δv e , δv n , δv u are the errors between the inertial navigation solution and the GNSS measurement in the east, north, and up velocity directions.

[0131] The measurement matrix can be expressed as:

[0132]

[0133] Discretize the state equations of equations (5) and (8) under continuous time conditions, and we can get:

[0134]

[0135] Where: X k-1 , X k are the state vectors of the system at times k-1 and k respectively; Φ k,k-1 is the state transition matrix of the system from time k-1 to time k; Γk-1 is the noise driving matrix of the system at time k-1; W k-1 is the noise vector of the system at time k-1; Z k is the measurement vector at time k; H k is the measurement matrix at time k; V k is the measurement noise at time k.

[0136] The main process of the Kalman filter includes two parts: time update and measurement update. The steps are as follows:

[0137] One-step state prediction is as follows:

[0138]

[0139] In the formula: is the predicted value of the system state from time k-1 to time k; is the estimated value of the system state at time k-1.

[0140] One-step state prediction mean square error matrix is as follows:

[0141]

[0142] In the formula: P k-1 is the covariance matrix of the system state at time k-1; P k,k-1 is the covariance matrix of; Q k-1 is the system noise covariance matrix at time k-1.

[0143] Filter gain is as follows:

[0144]

[0145] In the formula: K k is the gain matrix of the filter at time k; P k,k-1 is the covariance matrix of; R k is the covariance matrix of the measurement noise at time k.

[0146] State estimation is as follows:

[0147]

[0148] State estimation mean square error matrix is as follows:

[0149]

[0150] In summary, through the vehicle-mounted integrated navigation method of the laser Doppler velocimeter proposed by the present invention, whenever the observation data of the velocimeter is obtained, the coupling relationship between the velocity error and the attitude angle is utilized, and the error state variables such as attitude, velocity, gyro zero drift, accelerometer zero drift, and position are estimated through Kalman filtering technology, and closed-loop correction is performed to effectively reduce the accumulation of navigation errors and achieve better positioning and orientation accuracy.

[0151] According to another aspect of the embodiments of the present application, there is also provided a detection device for implementing the above vehicle-mounted integrated navigation method of the laser Doppler velocimeter. Figure 4 It is a schematic structural diagram of an optional vehicle-mounted integrated navigation device of the laser Doppler velocimeter according to the embodiments of the present application. As Figure 4 shown, the device may include:

[0152] A processing module 402, configured to obtain initial navigation information, where the initial navigation information includes the status information of the laser Doppler velocimeter and the measurement data of the inertial sensor;

[0153] A first data acquisition module 404, configured to perform inertial navigation after initializing and aligning the inertial sensor based on the initial navigation information to obtain an inertial navigation result;

[0154] A second data acquisition module 406, configured to obtain the velocimeter data of the laser Doppler velocimeter, combine the inertial navigation result with the velocimeter data to obtain an initial navigation result;

[0155] A correction module 408, configured to correct the initial navigation result through the Kalman filtering algorithm to obtain a target navigation result.

[0156] It should be noted that the processing module 402 in this embodiment may be used to execute the above step S102, the first data acquisition module 404 in this embodiment may be used to execute the above step S104, the second data acquisition module 406 in this embodiment may be used to execute the above step S106, and the correction module 408 in this embodiment may be used to execute the above step S108.

[0157] Through the above modules, for the vehicle-mounted integrated navigation method of the laser Doppler velocimeter proposed, whenever the observation data of the velocimeter is obtained, the coupling relationship between the velocity error and the attitude angle is utilized, and the error state variables such as attitude, velocity, gyro zero drift, accelerometer zero drift, and position are estimated through Kalman filtering technology, and closed-loop correction is performed to effectively reduce the accumulation of navigation errors and achieve better positioning and orientation accuracy.

[0158] It should be noted here that the implementation examples and scenarios of the above modules and the corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can run in a hardware environment, can be implemented by software, or can be implemented by hardware, where the hardware environment includes a network environment.

[0159] According to another aspect of the embodiments of the present application, a storage medium is also provided. Optionally, in this embodiment, the above storage medium can be used to execute the program code of any one of the above laser Doppler velocimeter vehicle integrated navigation methods in the embodiments of the present application.

[0160] Optionally, in this embodiment, the storage medium is set to store program code for executing the following steps:

[0161] S1. Obtain navigation initial information, where the navigation initial information includes the status information of the laser Doppler velocimeter and the measurement data of the inertial sensor;

[0162] S2. Based on the navigation initial information, perform system initialization and alignment on the inertial sensor and then execute inertial navigation to obtain an inertial navigation result;

[0163] S3. Obtain the velocimeter data of the laser Doppler velocimeter, combine the inertial navigation result with the velocimeter data to obtain an initial navigation result;

[0164] S4. Correct the initial navigation result through the Kalman filtering algorithm to obtain a target navigation result.

[0165] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments, and details are not described herein again.

[0166] Among them, the computer-readable storage medium can include but is not limited to any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, micro drives, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nano-systems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0167] According to another aspect of the embodiments of the present application, an electronic device for implementing the above laser Doppler velocimeter vehicle integrated navigation method is also provided, and the electronic device can be a server, a terminal, or a combination thereof.

[0168] Figure 5 is a schematic structural diagram of an optional electronic device according to an embodiment of the present application, as Figure 5As shown in the figure, it includes a processor 502, a communication interface 504, a memory 506, and a communication bus 508. Among them, the processor 502, the communication interface 504, and the memory 506 communicate with each other through the communication bus 508. Among them,

[0169] The memory 506 is used to store computer programs;

[0170] When the processor 502 is used to execute the computer program stored on the memory 506, the following steps are implemented:

[0171] S1, obtain initial navigation information, where the initial navigation information includes the status information of the laser Doppler velocimeter and the measurement data of the inertial sensor;

[0172] S2, based on the initial navigation information, perform system initialization and alignment on the inertial sensor and then execute inertial navigation to obtain an inertial navigation result;

[0173] S3, obtain the velocimeter data of the laser Doppler velocimeter, combine the inertial navigation result with the velocimeter data to obtain an initial navigation result;

[0174] S4, correct the initial navigation result through the Kalman filter algorithm to obtain a target navigation result.

[0175] Optionally, the communication bus can be a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This communication bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 5 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus. The communication interface is used for communication between the above-mentioned electronic device and other devices.

[0176] The memory can include RAM, and can also include non-volatile memory, for example, at least one disk memory. Optionally, the memory can also be at least one storage device located far from the aforementioned processor.

[0177] As an example, the above-mentioned memory 506 may but is not limited to include the processing module 402, the first data acquisition module 404, the second data acquisition module 406, and the correction module 408 in the above-mentioned vehicle-mounted integrated navigation device with a laser Doppler velocimeter. In addition, it may also include but is not limited to other module units in the above-mentioned vehicle-mounted integrated navigation device with a laser Doppler velocimeter, which will not be elaborated in this example.

[0178] The above-mentioned processor may be a general-purpose processor, including but not limited to: CPU (Central Processing Unit, central processing unit), NP (Network Processor, network processor), etc.; it may also be a DSP (Digital Signal Processing, digital signal processor), ASIC (Application Specific Integrated Circuit, application-specific integrated circuit), FPGA (Field-Programmable Gate Array, field-programmable gate array) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0179] Optionally, the specific examples in this embodiment may refer to the examples described in the above-mentioned embodiments, and will not be elaborated herein.

[0180] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps may be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0181] In the above-mentioned embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0182] In several embodiments provided by this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some service interfaces. The indirect coupling or communication connection of the device or unit may be in an electrical or other form.

[0183] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0184] In addition, in each embodiment of the present application, each functional unit can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0185] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned memory includes: USB flash drives, read-only memory (ROM), random access memory (RAM), mobile hard disks, magnetic disks, or optical disks, etc., which can store program codes.

[0186] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program. This program can be stored in a computer-readable memory, and the memory can include: flash drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, etc.

[0187] The above are only exemplary embodiments of the present disclosure, and the scope of the present disclosure cannot be limited thereby. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. After considering the specification and practicing the present disclosure, those skilled in the art will easily think of other embodiments of the present disclosure. The present application aims to cover any variations, uses, or adaptive changes of the present disclosure. These variations, uses, or adaptive changes follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not described in the present disclosure. The specification and embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

[0188] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as within the scope described in this specification.

[0189] Those skilled in the art can easily understand that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A laser Doppler velocimeter vehicle-mounted integrated navigation method, applied to a vehicle-mounted system, wherein the vehicle-mounted navigation system comprises a laser Doppler velocimeter and an inertial sensor, and is characterized in that: include: Acquiring initial navigation information, wherein the initial navigation information includes status information of a laser Doppler velocimeter and measurement data of an inertial sensor; Initializing and aligning the inertial sensor system based on the initial navigation information, and then performing inertial navigation to obtain an inertial navigation result; Acquire the velocimeter data of the laser Doppler velocimeter, combine the inertial navigation result with the velocimeter data, and obtain the initial navigation result; The initial navigation result is corrected by the Kalman filter algorithm to obtain the target navigation result and complete the navigation.

2. The laser Doppler velocimeter vehicle-mounted integrated navigation method according to claim 1, characterized in that: The obtaining of initial navigation information includes: Get the initial location information of navigation; Obtaining a status word from the output of the laser Doppler velocimeter and the inertial sensor based on the initial position information, encoding and outputting the status word to obtain a status code; The driving state of the vehicle is determined based on the state code, and the state information of the laser Doppler velocimeter and the measurement data of the inertial sensor are obtained.

3. The laser Doppler velocimeter vehicle-mounted integrated navigation method as claimed in claim 1, characterized in that: The method of obtaining the velocimeter data of the laser Doppler velocimeter and combining the inertial navigation result with the velocimeter data to obtain the initial navigation result includes: Construct a Kalman filter model; Based on the inertial navigation results and the speed meter data combined with the Kalman filter model, the initial navigation results are calculated.

4. The laser Doppler velocimeter vehicle-mounted integrated navigation method as claimed in claim 3, characterized in that: The initial navigation result is calculated based on the inertial navigation result and the speed meter data in combination with the Kalman filter model, including: Obtaining velocity data from the speed meter data and decomposing it according to the azimuth output to obtain a first velocity component set, wherein the first velocity component set includes velocity components of the speed meter at different azimuths; Obtaining velocity data in the inertial navigation result and decomposing it according to the azimuth output to obtain a second velocity component set, wherein the second velocity component set includes velocity components of the inertial sensor at different azimuths; The first velocity component set and the second velocity component set are compared and integrated to obtain an initial navigation result.

5. The laser Doppler velocimeter vehicle-mounted integrated navigation method as claimed in claim 1, characterized in that: The method of correcting the initial navigation result by using the Kalman filter algorithm to obtain the navigation result includes: The error state vector of the speedometer data is estimated by using a Kalman filter algorithm, and the initial navigation result is determined as an observation of the Kalman filter; the error elements in the error state vector include position error, velocity error, attitude angle error, accelerometer zero drift, gyroscope drift and speedometer scale coefficient error; A Kalman filter algorithm is used to perform closed-loop correction based on the observed quantity and the error state vector to obtain a navigation result.

6. The laser Doppler velocimeter vehicle-mounted integrated navigation method as claimed in claim 3, characterized in that: The state equation for constructing the Kalman filter model is specifically: Where X(t) represents the error state vector; A(t) represents the system state transfer matrix; W(t) represents the system measurement noise vector; Defining an error state vector; the error elements include position error, velocity error, attitude angle error, accelerometer zero drift, gyroscope drift and speedometer scale coefficient error; A system state transfer matrix is ​​determined based on the error state vector.

7. The laser Doppler velocimeter vehicle-mounted integrated navigation method according to claim 1, characterized in that: After the initial navigation result is corrected by the Kalman filter algorithm to obtain the target navigation result, the method further includes: correcting the zero bias of the inertial sensor based on the target navigation result for the next inertial navigation result update.

8. A laser Doppler speedometer vehicle-mounted integrated navigation device, characterized in that: include: A processing module, used for acquiring initial navigation information, wherein the initial navigation information includes status information of a laser Doppler velocimeter and measurement data of an inertial sensor; A first data acquisition module is used to perform inertial navigation after initializing and aligning the inertial sensor system based on the initial navigation information to obtain an inertial navigation result; A second data acquisition module is used to obtain the velocimeter data of the laser Doppler velocimeter, and combine the inertial navigation result with the velocimeter data to obtain an initial navigation result; The correction module is used to correct the initial navigation result through the Kalman filter algorithm to obtain the target navigation result.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 7 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 7 through the computer program.

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