Alignment method for vehicle-mounted radar and related equipment

Abstract

The invention discloses an alignment method for a vehicle-mounted radar and related equipment, and relates to the technical field of navigation calibration, and the method comprises the following steps: obtaining inertial measurement unit data and horizontal tilt angle sensor data of the vehicle-mounted radar; generating an initial attitude matrix based on the inertial measurement unit data; determining a horizontal attitude data difference based on the horizontal tilt angle sensor data and the initial attitude matrix; and obtaining an alignment result of the vehicle-mounted radar based on the horizontal attitude data difference and a Kalman filtering algorithm. The initial attitude matrix is generated by fusing the inertial measurement unit data and the horizontal tilt angle sensor data, fine alignment is performed by combining the horizontal attitude data difference and the Kalman filtering algorithm, the azimuth angle alignment precision of the vehicle-mounted radar in a dynamic environment is improved, dependence on external satellite signals is not needed, and the accuracy of the azimuth angle alignment is improved. High-precision autonomous alignment can be realized under complex working conditions such as shaking and vibration, meanwhile, the hardware cost and the system complexity are reduced, and the method has wide applicability.

Description

Technical Field

[0001] This application relates to the technical field of navigation calibration, and particularly to an alignment method and related devices for vehicle-mounted radar. Background Art

[0002] During the initial alignment process of vehicle-mounted radar, leveling and north-seeking operations need to be completed to ensure the positioning accuracy of the geographical coordinate system of the detected target. Traditional methods rely on a horizontal tilt sensor and a north-seeking instrument, but the north-seeking instrument needs to work in a stationary state and has poor anti-dynamic interference ability. When the vehicle-mounted radar base shakes due to hydraulic leveling, engine vibration, or driving bumps, the azimuth error output by the north-seeking instrument increases significantly, resulting in target positioning deviation. In addition, in the prior art, a method using a satellite navigation system (such as GNSS) to assist in the alignment of a moving base can improve the accuracy in a dynamic environment, but it cannot work stably in tunnels, urban dense areas, or signal-interfered scenarios, and its applicability is limited. Therefore, there is an urgent need for an alignment method for vehicle-mounted radar to solve the above-mentioned technical problems. Summary of the Invention

[0003] A series of simplified concepts are introduced in the Summary of the Invention section, which will be further described in detail in the Detailed Description section. The Summary of the Invention section of this application does not mean to attempt to limit the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the protection scope of the claimed technical solution.

[0004] In a first aspect, this application provides an alignment method for vehicle-mounted radar, the method comprising:

[0005] Obtaining inertial measurement unit data and horizontal tilt sensor data of the vehicle-mounted radar;

[0006] Generating an initial attitude matrix based on the inertial measurement unit data;

[0007] Determining the horizontal attitude data difference based on the horizontal tilt sensor data and the initial attitude matrix;

[0008] Obtaining the alignment result of the vehicle-mounted radar based on the horizontal attitude data difference and the Kalman filter algorithm.

[0009] In some embodiments, the inertial measurement unit data includes a gyro angle increment vector, local latitude, earth's angular velocity of rotation, and accelerometer-measured specific force. Generating an initial attitude matrix based on the inertial measurement unit data includes:

[0010] Determining a first direction cosine matrix from the current vehicle coordinate system to the initial vehicle coordinate system based on the gyro angle increment vector and the skew-symmetric matrix of the gyro angle increment vector;

[0011] Based on the specific force measured by the accelerometer, determine the second direction cosine matrix from the initial vehicle coordinate system to the inertial coordinate system;

[0012] Based on the earth's angular velocity of rotation, determine the third direction cosine matrix from the inertial coordinate system to the earth-fixed coordinate system;

[0013] Based on the local latitude, determine the fourth direction cosine matrix from the earth-fixed coordinate system to the navigation coordinate system;

[0014] Based on the first, second, third, and fourth direction cosine matrices, determine the initial attitude matrix from the current vehicle coordinate system to the navigation coordinate system.

[0015] In some embodiments, determining the second direction cosine matrix from the initial vehicle coordinate system to the inertial coordinate system based on the specific force measured by the accelerometer includes:

[0016] Integrate the specific force measured by the accelerometer over a preset time period to generate the velocity increment vector of the initial vehicle coordinate system;

[0017] Based on the earth's angular velocity of rotation and the local latitude, construct the theoretical velocity component matrix of the inertial coordinate system;

[0018] Perform projection matching between the velocity increment vector and the theoretical velocity component matrix to generate the second direction cosine matrix from the initial vehicle coordinate system to the inertial coordinate system.

[0019] In some embodiments, the horizontal tilt sensor data includes the transformed roll angle and the transformed pitch angle, and the horizontal attitude data difference includes the pitch angle difference and the roll angle difference. Determining the horizontal attitude data difference based on the horizontal tilt sensor data and the initial attitude matrix includes:

[0020] Based on the initial attitude matrix, calculate the relative attitude angles from the current vehicle coordinate system to the navigation coordinate system. The relative attitude angles include the initial roll angle, the initial pitch angle, and the initial azimuth angle;

[0021] Based on the transformed roll angle, the transformed pitch angle, and the initial azimuth angle, generate the corrected attitude matrix;

[0022] Extract the corrected pitch angle and the corrected roll angle from the corrected attitude matrix;

[0023] Based on the difference between the corrected pitch angle and the initial pitch angle, determine the pitch angle difference;

[0024] Based on the difference between the corrected roll angle and the initial roll angle, determine the roll angle difference.

[0025] In some embodiments, obtaining the alignment result of the vehicle-mounted radar based on the horizontal attitude data difference and the Kalman filtering algorithm includes:

[0026] Construct the observation equation of the Kalman filter, where the observation variable of the observation equation is the horizontal attitude data difference;

[0027] Construct the state equation of the Kalman filter, where the state variables of the state equation include the attitude error angle vector, the velocity error vector, the gyro zero bias vector, and the accelerometer zero bias vector;

[0028] Input the observation equation and the state equation into the Kalman filter, and calculate the correction amount of the attitude error angle vector by iteratively updating the state variables;

[0029] Based on the correction amount, perform a secondary correction on the initial attitude matrix to generate a secondary corrected attitude matrix;

[0030] Extract the corrected azimuth angle from the secondary corrected attitude matrix as the alignment result of the vehicle-mounted radar.

[0031] In some embodiments, it further includes:

[0032] Real-time monitor the sway amplitude and sway frequency of the vehicle-mounted radar base;

[0033] Based on the sway amplitude and sway frequency, adaptively adjust the observation noise parameter and the state noise parameter of the Kalman filter algorithm to suppress the influence of dynamic interference on the alignment result.

[0034] In some embodiments, it further includes:

[0035] Perform timestamp synchronization processing on the inertial measurement unit data and the horizontal inclination sensor data to generate synchronized sensor data;

[0036] Perform denoising and fusion processing on the synchronized sensor data through a preset low-pass filter algorithm to generate a fused sensor data stream;

[0037] Based on the fused sensor data stream, adjust the calculation frequencies of the initial attitude matrix and the horizontal attitude data difference to optimize the alignment continuity in a dynamic environment.

[0038] In a second aspect, an alignment device for a vehicle-mounted radar according to the present application includes:

[0039] A data acquisition unit for acquiring inertial measurement unit data and horizontal inclination sensor data of the vehicle-mounted radar;

[0040] An attitude matrix construction unit for generating an initial attitude matrix based on the inertial measurement unit data;

[0041] A data difference determination unit for determining the horizontal attitude data difference based on the horizontal inclination sensor data and the initial attitude matrix;

[0042] An alignment result generation unit obtains the alignment result of the vehicle-mounted radar based on the horizontal attitude data difference and the Kalman filtering algorithm.

[0043] In a third aspect, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program stored in the memory, the steps of the alignment method for the vehicle-mounted radar according to any one of the first aspects are implemented.

[0044] In a fourth aspect, the present application proposes a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the alignment method for the vehicle-mounted radar according to any one of the first aspects is implemented.

[0045] In summary, the present application improves the robustness and accuracy of the initial alignment of the vehicle-mounted radar by fusing the data of the inertial measurement unit and the horizontal inclination sensor data and combining the Kalman filtering algorithm. First, an initial attitude matrix is generated based on the rough alignment of the inertial measurement unit, providing a reliable basis for subsequent calculations; second, the attitude deviation under dynamic interference is effectively extracted by calculating the difference between the horizontal inclination sensor data and the initial attitude matrix; finally, the Kalman filter is used to iteratively correct the attitude deviation, suppressing noise accumulation and optimizing the azimuth estimation. The present application does not rely on external satellite signals, can achieve high-precision autonomous alignment under complex working conditions such as shaking and vibration, and at the same time reduces the hardware cost and system complexity, having wide applicability.

[0046] For the alignment method for the vehicle-mounted radar proposed in the present application, other advantages, objectives, and features of the present application will be partially reflected by the following description, and partially will also be understood by those skilled in the art through the research and practice of the present application. Description of the Drawings

[0047] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to limit the present specification. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0048] Figure 1 It is a schematic flowchart of an alignment method for a vehicle-mounted radar provided by an embodiment of the present application;

[0049] Figure 2 It is a schematic installation structure diagram of a vehicle-mounted radar provided by an embodiment of the present application;

[0050] Figure 3 It is an error curve diagram of the prior art provided by an embodiment of the present application;

[0051] Figure 4The error curve graph of the present application provided by the embodiments of the present application;

[0052] Figure 5 The structural schematic diagram of an alignment device for vehicle-mounted radar provided by the embodiments of the present application;

[0053] Figure 6 The structural diagram of an alignment electronic device for vehicle-mounted radar provided by the embodiments of the present application;

[0054] Among them, the corresponding relationship between the reference numerals in the figure and the component names is as follows:

[0055] 101 is the vehicle, 102 is the vehicle-mounted radar, 103 is the north-seeking instrument, 104 is the horizontal inclination sensor, and 105 is the inertial measurement unit. Specific embodiments

[0056] The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and above-mentioned drawings of the present application are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order different from that shown or described herein. In addition, the terms "include" and "have" 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 does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments.

[0057] Please refer to Figure 1 , which is a schematic flow chart of an alignment method for vehicle-mounted radar provided by the embodiments of the present application, and specifically may include:

[0058] S110. Obtain the data of the inertial measurement unit and the data of the horizontal inclination sensor of the vehicle-mounted radar;

[0059] Exemplarily, this step synchronously collects basic sensor data in a dynamic environment through the inertial measurement unit (IMU) and horizontal tilt sensor built into the vehicle-mounted radar. The inertial measurement unit provides key parameters such as gyroscope angle increment and accelerometer specific force to reflect the vehicle's motion state and attitude changes; the horizontal tilt sensor measures the inclination of the radar base in the X-axis and Y-axis directions in real time, that is, the transformed pitch angle and the transformed roll angle, which are used to characterize the attitude deviation in the horizontal plane. The synchronous acquisition of the two types of data provides the original input for the subsequent attitude matrix construction, ensuring the sensitivity of the initial alignment algorithm to dynamic interference.

[0060] The inertial measurement unit captures the angular velocity and acceleration information of the vehicle in three-dimensional space through multi-axis sensor fusion, providing a kinematic basis for coarse alignment; the horizontal tilt sensor directly quantifies the base leveling error to compensate for the accumulated drift of the inertial sensor in the horizontal plane. The collaborative work of the two realizes the perception of vehicle shaking, vibration and other interference, laying a physical foundation for data fusion for subsequent attitude correction and filtering optimization.

[0061] S120, generating an initial attitude matrix based on the inertial measurement unit data;

[0062] Exemplarily, by integrating the multi-source sensor data of the IMU, an initial attitude matrix from the carrier coordinate system to the navigation coordinate system is constructed to provide a reference for subsequent alignment. Specifically, the instantaneous rotation relationship of the carrier coordinate system is determined by using the gyro angle increment vector, and the conversion relationship from the carrier coordinate system to the inertial coordinate system, the ground-fixed coordinate system, and the navigation coordinate system is derived in turn by combining the accelerometer specific force integration result and the earth's rotation parameters. Finally, a matrix model representing the initial attitude of the vehicle is generated through the serial calculation of multi-level direction cosine matrices.

[0063] The generation of the initial attitude matrix is ​​the core foundation of dynamic alignment. It preliminarily calculates the attitude angles (pitch, roll and azimuth) of the vehicle in three-dimensional space by fusing the angular motion information of the gyroscope and the gravity component of the accelerometer, providing a reliable initial reference for the subsequent difference calculation of the horizontal tilt sensor and the Kalman filter correction. This process effectively solves the problem of insufficient accuracy of traditional north finders in dynamic environments, ensuring that subsequent algorithms can be iteratively optimized based on a unified attitude reference.

[0064] S130, determining a horizontal attitude data difference based on the horizontal tilt sensor data and the initial attitude matrix;

[0065] Exemplarily, by comparing the real-time inclination angle measured by the horizontal inclination sensor with the attitude angles in the initial attitude matrix, the attitude deviation in the horizontal plane is calculated. Specifically, the horizontal inclination sensor provides the inclination angle data of the X-axis and Y-axis (i.e., the transformed pitch angle and the transformed roll angle), while the initial attitude matrix contains the initial roll angle, the initial pitch angle, and the initial azimuth angle solved by the inertial measurement unit. By constructing a corrected attitude matrix and extracting the corrected angle values, the pitch angle difference and the roll angle difference are finally generated as the horizontal attitude data difference characterizing the attitude error under dynamic interference.

[0066] The horizontal attitude data difference reflects the deviation between the calculation result of the inertial sensor and the measured data of the horizontal inclination sensor, and is essentially a direct quantification of dynamic interferences such as vehicle shaking and vibration. This difference serves as the key observation input for the Kalman filter, providing an error correction basis for subsequent fine alignment, effectively suppressing the cumulative drift of the inertial sensor and the influence of external interferences on attitude estimation, thereby enhancing the robustness of azimuth alignment.

[0067] S140. Based on the horizontal attitude data difference and the Kalman filter algorithm, obtain the alignment result of the vehicle-mounted radar.

[0068] Exemplarily, through the Kalman filter algorithm, taking the horizontal attitude data difference as the observation variable and combining with the dynamic model of the inertial measurement unit, iterative correction and optimization of the attitude error are realized. Specifically, the Kalman filter constructs a state equation based on the attitude error angle vector, the velocity error vector, and the sensor zero bias, establishes an observation equation using the horizontal attitude data difference, and corrects the error components in the initial attitude matrix in real time by recursively updating the state estimation value. Finally, a high-precision azimuth angle is extracted through the twice-corrected attitude matrix to complete the initial alignment of the vehicle-mounted radar.

[0069] The Kalman filter effectively suppresses the influence of noise accumulation and dynamic interference by fusing the dynamic prediction of the inertial sensor and the measured deviation of the horizontal inclination sensor. It adaptively adjusts the observation noise and state noise parameters, optimizes the error correction weight, thereby significantly improving the accuracy and stability of azimuth alignment under complex working conditions such as shaking and vibration, and ensuring the reliable operation of the vehicle-mounted radar without relying on external signals.

[0070] In summary, in the embodiments of the present application, by fusing the data of the inertial measurement unit of the vehicle-mounted radar and the data of the horizontal inclination sensor, and combining with the Kalman filtering algorithm, the azimuth alignment accuracy and system robustness in a dynamic environment are significantly improved. Traditional methods rely on north-seeking instruments or satellite signal assistance, which are prone to large errors in the case of pedestal shaking, engine vibration or complex environments, resulting in target positioning deviation. This solution uses the inertial measurement unit to capture the dynamic motion characteristics of the vehicle, combines the low-frequency attitude correction information provided by the horizontal inclination sensor, and dynamically corrects the attitude error angle and velocity error through Kalman filtering to effectively suppress the cumulative deviation caused by interference. At the same time, this method completely relies on vehicle-mounted sensor data and does not require external communication signal support, and can still operate stably in signal-limited scenarios such as tunnels and urban building groups, solving the dependence of traditional technologies on static conditions or satellite signals, and significantly enhancing the anti-interference ability and adaptability in complex working conditions.

[0071] Please refer to Figure 2 , which is a schematic diagram of the installation structure of the vehicle-mounted radar provided by the embodiments of the present application. The initial alignment system of the vehicle-mounted radar usually consists of a vehicle-mounted radar 102, a north-seeking instrument 103, a horizontal inclination sensor 104 and an inertial measurement unit 105. The vehicle-mounted radar 102 is installed at the center position of the vehicle 101 to ensure the maximum detection range; the north-seeking instrument 103, the horizontal inclination sensor 104 and the inertial measurement unit 105 are rigidly connected to the vehicle pedestal and are used to sense the vehicle attitude and motion state in real time. In traditional methods, the north-seeking instrument 103 is responsible for azimuth alignment, and the horizontal inclination sensor 104 measures the pitch and roll angles. However, the north-seeking instrument 103 needs to work in a stationary state. When the vehicle shakes due to hydraulic leveling, engine vibration or driving bumps, the azimuth output error increases significantly. In addition, in the existing technology, the scheme of using the satellite navigation system to assist the dynamic pedestal alignment can improve the accuracy in a dynamic environment, but it has insufficient stability in tunnels, urban dense areas or signal-interfered scenarios, resulting in target positioning deviation. The present application constructs a highly robust alignment system without external signal support by optimizing the sensor layout and data fusion mechanism, combining the dynamic motion capture ability of the inertial measurement unit 105 and the low-frequency attitude correction characteristics of the horizontal inclination sensor 104, effectively solving the dependence of traditional technologies on static conditions or satellite signals.

[0072] In some examples, the inertial measurement unit data includes the gyro angle increment vector, local latitude, earth's angular velocity of rotation and accelerometer-measured specific force. Based on the inertial measurement unit data, an initial attitude matrix is generated, including:

[0073] Based on the gyro angle increment vector and the skew-symmetric matrix of the gyro angle increment vector, determine the first direction cosine matrix from the current vehicle coordinate system to the initial vehicle coordinate system;

[0074] Based on the specific force measured by the accelerometer, determine the second direction cosine matrix from the initial vehicle coordinate system to the inertial coordinate system;

[0075] Based on the angular velocity of the Earth's rotation, determine the third direction cosine matrix from the inertial coordinate system to the Earth-fixed coordinate system;

[0076] Based on the local latitude, determine the fourth direction cosine matrix from the Earth-fixed coordinate system to the navigation coordinate system;

[0077] Based on the first, second, third, and fourth direction cosine matrices, determine the initial attitude matrix from the current vehicle coordinate system to the navigation coordinate system.

[0078] Exemplarily, based on the gyro angle increment vector and the skew-symmetric matrix of the gyro angle increment vector, determining the first direction cosine matrix from the current vehicle coordinate system to the initial vehicle coordinate system includes:

[0079] Characterize the instantaneous angular motion of the vehicle through the gyro angle increment vector, and construct the first direction cosine matrix from the current vehicle coordinate system b (right-front-up vehicle coordinate system) to the initial vehicle coordinate system b0 based on the skew-symmetric matrix The gyro angle increment vector reflects the angular velocity change of the vehicle in three-dimensional space. Its data is collected in real time by the inertial measurement unit, providing dynamic rotation information for the initial alignment of the attitude matrix. The gyro angle increment vector Δθ k Represents the angular motion change at the discrete sampling point k. Its physical meaning is the integration result of the angular velocity measured by the three-axis gyroscope at the discrete sampling point, and it is the core input parameter for subsequent attitude calculation.

[0080] The first direction cosine matrix Can be solved through attitude update and is expressed as:

[0081]

[0082] where b1, b2,... bk-1 are the intermediate states passed from the initial vehicle coordinate system b0 to the current vehicle coordinate system b, k = 1, 2,... represents the discrete sampling point at the kth moment; the direction cosine matrix at each step can be obtained through the gyro angle increment vector Δθ k And is expressed as:

[0083]

[0084] where Δθ k = |Δθ k |, (Δθ k ×) is the skew-symmetric matrix of the corresponding vector Δθ k ; I is the 3rd order identity matrix; is the direction cosine matrix at the current moment; the above formula converts the discrete angular increment into the update of the continuous rotation matrix through the approximate expansion of the Rodriguez rotation formula. Its physical meaning is to successively correct the direction relationship of the vehicle coordinate system relative to the initial coordinate system through the instantaneous angular motion information measured by the gyroscope, ensuring the orthogonality and iterative stability of the matrix.

[0085] By iteratively updating the direction cosine matrix of all sampling points, the complete first direction cosine matrix from the current vehicle coordinate system to the initial vehicle coordinate system is obtained. This matrix, as the core component of the initial attitude matrix, provides an accurate initial attitude reference for the subsequent conversion of the inertial coordinate system, the earth-fixed coordinate system, and the navigation coordinate system. The entire process strictly depends on the mathematical properties of the gyro angular increment vector and its skew-symmetric matrix, ensuring the accuracy and real-time performance of attitude calculation in a dynamic environment.

[0086] Exemplarily, based on the accelerometer-measured specific force, determining the second direction cosine matrix from the initial vehicle coordinate system to the inertial coordinate system includes:

[0087] During the initial alignment process, the accelerometer measures the specific force f b which is the core input for constructing the second direction cosine matrix from the initial vehicle coordinate system b0 to the inertial coordinate system i (earth-centered inertial coordinate system). The accelerometer specific force contains the measured value of the specific force in the vehicle coordinate system, and its physical meaning is the vector sum of the linear acceleration and the gravitational acceleration. The specific force data f is collected by the accelerometer of the inertial measurement unit

[0088] and integrated within a preset time period to generate the velocity increment vector of the initial vehicle coordinate system b which is expressed as:

[0089]

[0090] where is the transformation matrix from the vehicle coordinate system to the initial vehicle coordinate system, f b is the specific force measured by the accelerometer; t is the sampling time interval; kt is the total sampling duration; the above formula represents the integration result corresponding to each sampling point k, reflecting the velocity change of the vehicle in the initial coordinate system.

[0091] Based on the earth's angular velocity ω ie and the local latitude L, the theoretical velocity component matrix of the inertial coordinate system is constructed which is expressed as:

[0092]

[0093] ​Among them, g is the acceleration due to gravity; this matrix describes the spatio-temporal evolution law of the theoretical velocity components in the inertial coordinate system through the coupling relationship between the earth's rotation effect and the gravity field.

[0094] Project the velocity increment vector onto the theoretical velocity component matrix and solve for the second direction cosine matrix from the initial body coordinate system to the inertial coordinate system through least squares or geometric orthogonality relations which is expressed as:

[0095]

[0096] where and are two linearly independent vectors selected from the theoretical velocity component matrix, and are the corresponding velocity increment vectors, 0 < k1 < k2; the above equation realizes the spatial alignment of the velocity increment through matrix inversion and multiplication operations, and finally generates the second direction cosine matrix This matrix accurately characterizes the rotation relationship between the body coordinate system and the inertial coordinate system, providing key parameters for subsequent navigation coordinate system transformation.

[0097] Exemplarily, based on the earth's angular velocity ω ie , determine the third direction cosine matrix from the inertial coordinate system i to the earth-fixed coordinate system e (earth-centered earth-fixed coordinate system) which includes:

[0098]

[0099] The earth's angular velocity ω ie is the core parameter for the transformation from the inertial coordinate system i to the earth-fixed coordinate system e. According to the definition of the earth-fixed coordinate system, it rotates around the earth's axis of rotation with angular velocity ω ie while the inertial coordinate system is fixed at the earth's center of mass and does not rotate with the earth. Therefore, the transformation between the two coordinate systems needs to be realized through a time-dependent rotation matrix to ensure the unitarity and orthogonality of the coordinate axes during the rotation process. The construction of the matrix elements needs to satisfy the orthogonality constraint. By updating the matrix parameters in real time, the coordinate system offset caused by the earth's rotation can be accurately compensated, providing a high-precision reference for subsequent navigation coordinate system transformation.

[0100] Exemplarily, based on the local latitude L, determine the fourth direction cosine matrix from the earth-fixed coordinate system e to the navigation coordinate system n (north-east-up coordinate system) which includes:

[0101]

[0102] Its physical meaning is to project the position or velocity vector in the Earth-fixed coordinate system onto the navigation coordinate system with the vehicle's current position as the origin. Specifically, it rotates 90° - L around the Y-axis of the Earth-fixed coordinate system to achieve latitude alignment; the X-axis points east, the Y-axis points north, and the Z-axis points vertically upward. Among the matrix elements, the -sinL and cosL terms represent the projection relationship of latitude on the coordinate axes in the horizontal plane, while the -cosL and sinL terms correct the components in the vertical direction. This matrix strictly depends on the local latitude parameter to ensure the accurate conversion between the Earth-fixed coordinate system and the navigation coordinate system, providing a geographical reference for the subsequent generation of the initial attitude matrix.

[0103] Exemplarily, based on the first direction cosine matrix The second direction cosine matrix The third direction cosine matrix And the fourth direction cosine matrix To determine the initial attitude matrix from the current vehicle coordinate system to the navigation coordinate system, including:

[0104] The initial attitude matrix The construction of is achieved by concatenating four groups of direction cosine matrices to convert the current vehicle coordinate system b to the navigation coordinate system n, and the expression is:

[0105]

[0106] Where, (The first direction cosine matrix) is generated from the gyro angle increment vector and the skew-symmetric matrix, reflecting the instantaneous rotation of the vehicle coordinate system; (The second direction cosine matrix) is determined by matching the accelerometer specific force integration with the theoretical velocity components to establish the conversion from the initial vehicle coordinate system to the inertial coordinate system; (The third direction cosine matrix) is constructed based on the Earth's angular velocity of rotation to achieve the rotational alignment from the inertial coordinate system to the Earth-fixed coordinate system; (The fourth direction cosine matrix) depends on the local latitude to complete the projection from the Earth-fixed coordinate system to the navigation coordinate system. Each matrix is multiplied in sequence, strictly following the chain rule of coordinate transformation to ensure that the initial attitude matrix accurately represents the three-dimensional attitude of the vehicle in the navigation coordinate system.

[0107] In some examples, the horizontal tilt sensor data includes the transformed roll angle and the transformed pitch angle, and the horizontal attitude data difference includes the pitch angle difference and the roll angle difference. Based on the horizontal tilt sensor data and the initial attitude matrix, to determine the horizontal attitude data difference, including:

[0108] Based on the initial attitude matrix, calculate the relative attitude angles from the current vehicle coordinate system to the navigation coordinate system. The relative attitude angles include the initial roll angle, the initial pitch angle, and the initial azimuth angle;

[0109] Exemplarily, based on the initial attitude matrix can solve for the which is expressed as:

[0110]

[0111] where is determined based on the above attitude; can be jointly obtained from the angular velocity of the Earth's rotation and the local latitude, and is expressed as:

[0112]

[0113] From obtain the coarse alignment result at the k-th sampling, including the initial roll angle φ yk , the initial pitch angle φ xk and the initial azimuth angle φ zk , which is expressed as:

[0114]

[0115] where arctan(a, b) is the arctangent function of the sign quadrant of a and b; C(I, j) is the element in the I-th row and J-th column of matrix C; the pitch angle (φ xk ) reflects the tilt angle of the carrier around the transverse axis (Y-axis) and is determined by the projection relationship of the vertical axis (Z-axis) of the navigation coordinate system in the carrier coordinate system; the roll angle (φ yk ) represents the rotation angle of the carrier around the longitudinal axis (X-axis) and is solved by the arcsine function of the element in the third row and first column of the matrix. The above formula clearly defines the calculation logic of the pitch angle and the roll angle to ensure the physical consistency of the attitude angle in a dynamic environment. The azimuth angle (φ zk ) represents the rotation angle of the carrier around the vertical axis (Z-axis), that is, the angle between the geographical north and the forward axis of the carrier. It is calculated by the ratio of the X-axis and Y-axis components in the horizontal plane of the navigation coordinate system, and the arctangent function is used to avoid direction ambiguity. This angle provides a reference for the subsequent difference calculation of the horizontal tilt sensor to ensure the stability of the azimuth reference during the dynamic correction process.

[0116] Based on the transformed roll angle, transformed pitch angle and initial azimuth angle, generate a correction attitude matrix, including:

[0117] Through the initial azimuth angle φ zk , construct a rotation matrix around the Z-axis of the navigation coordinate system, which is expressed as:

[0118]

[0119] Through the transformed pitch angle , construct a rotation matrix around the Y-axis of the navigation coordinate system, which is expressed as:

[0120]

[0121] By changing the roll angle Construct the rotation matrix about the X-axis of the navigation coordinate system, expressed as:

[0122]

[0123] Correct the attitude matrix Generated by the product of the above three rotation matrices, the expression is:

[0124]

[0125] The physical meaning of this matrix is to fuse the roll angle, pitch angle measured by the horizontal inclination sensor and the initial azimuth angle, and correct the attitude deviation caused by dynamic interference in the initial attitude matrix. Through the cascaded operation of axis-by-axis rotation, it accurately characterizes the actual attitude of the vehicle base in the navigation coordinate system, providing a reliable intermediate result for the subsequent extraction of the horizontal attitude data difference and the Kalman filter correction.

[0126] From the corrected attitude matrix Extract the corrected pitch angle and the corrected roll angle including:

[0127]

[0128] where, and are the elements of the second column, third column and first column of the third row of the corrected attitude matrix respectively. The above formula accurately calculates the corrected attitude angle by analyzing the geometric relationship between the matrix elements and the rotation about the Y-axis (pitch angle) and X-axis (roll angle). Its physical meaning is consistent with the calculation of the initial attitude angle, ensuring the mathematical continuity of the dynamic correction process.

[0129] Based on the difference between the corrected pitch angle and the initial pitch angle φ xk determine the pitch angle difference The pitch angle difference quantifies the pitch angle deviation caused by the base shaking or dynamic interference, providing a direct measure of the attitude error in the horizontal plane for the observation input of the Kalman filter.

[0130] Based on the difference between the corrected roll angle and the initial roll angle φ yk determine the roll angle difference The roll angle difference reflects the influence of the dynamic disturbance in the vehicle's lateral plane on the attitude estimation, and together with the pitch angle difference, constitutes the core observation variable of the horizontal attitude data difference, supporting the subsequent dynamic suppression of the attitude error by the Kalman filter.

[0131] In some instances, based on the horizontal attitude data difference and the Kalman filtering algorithm, the alignment result of the vehicle-mounted radar is obtained, including:

[0132] Construct an observation equation for Kalman filtering, where the observation variable of the observation equation is the horizontal attitude data difference;

[0133] Exemplarily, the observation equation of Kalman filtering is expressed as:

[0134] Z k =H k X k +V k

[0135] Among them, the observation equation of Kalman filtering is constructed through the linear relationship between the horizontal attitude data difference (pitch angle difference and roll angle difference) and the state variable.

[0136]

[0137] Among them, Z k is the Kalman filtering observation variable, which directly reflects the deviation between the attitude calculated by the inertial sensor and the attitude measured by the horizontal inclination sensor, and provides a dynamic correction basis for Kalman filtering; 0 3×1 is a three-dimensional zero vector, which is used to fill the observation vacancy of the velocity error in the state variable to ensure that the observation dimension matches the state dimension; H k is the Kalman filtering observation transfer matrix; O 3×3 is a 3-order all-zero matrix; O 2×2 is a 2-order all-zero matrix; I 3×3 is a 3-order identity matrix; I 2×2 is a 2-order identity matrix; X k is the Kalman filtering state variable; the state variable includes the attitude error angle vector δφ k 、the velocity error vector δv k 、the gyro zero bias vector ε k and the accelerometer zero bias vector V is the Kalman filtering observation noise.

[0138] Construct a state equation for Kalman filtering, which is expressed as:

[0139] X k =A k X k-1 +B k-1 W k-1

[0140] Among them, X k is the Kalman filtering state variable; A k is the Kalman filtering state transition matrix; B k-1 is the Kalman filtering control transfer matrix; W k-1is the state noise of the Kalman filter;

[0141]

[0142] wherein, is the gyro noise; is the accelerometer noise; The above equation accurately describes the evolution law of state variables in a dynamic environment by coupling the Earth's rotation, the motion of the carrier, and the sensor error model. Specifically, based on the angular velocity measurement and specific force measurement of inertial sensors, the propagation laws of the attitude error angle vector, velocity error vector, gyro bias vector, and accelerometer bias vector are dynamically modeled, and combined with the process noise covariance matrix, the recursive update of state variables is realized. The equation describes the error propagation mechanism under dynamic disturbances through the anti-symmetric operation term of the Earth's angular velocity, the dynamic term related to specific force, and the projection relationship of the direction cosine matrix, provides a mathematical basis for error correction, and ensures the stability and noise resistance of the recursive process.

[0143] The Kalman filter receives the observation equation and the state equation as inputs, and iteratively updates the state variables through a recursive algorithm. The horizontal attitude data difference (pitch angle difference and roll angle difference) in the observation equation is used as the key observation input, and is dynamically associated with the attitude error angle, velocity error, and sensor bias in the state equation. The filter calculates the optimal correction amount of the attitude error angle vector through a prediction and correction mechanism, combining the dynamic model of inertial sensors and the measured deviation of the horizontal inclination sensor. In each iteration, the filter adjusts the Kalman gain according to the difference between the current state estimate and the observation input, optimizes the error weight, and gradually converges to the true state, ensuring the efficient suppression of attitude errors in a dynamic environment.

[0144] Based on the correction amount, the initial attitude matrix is corrected twice to generate the twice-corrected attitude matrix, expressed as:

[0145]

[0146] wherein, (δφ k ×) is the anti-symmetric matrix of the attitude error angle vector δφ k ; is the twice-corrected attitude matrix; The above formula corrects the initial attitude matrix twice based on the attitude error correction amount output by the Kalman filter. The correction amount is converted into an incremental adjustment term of the rotation matrix through a mathematical mapping, and acts on the initial attitude matrix in the form of a small rotation to eliminate the cumulative error and maintain the orthogonality of the matrix. The corrected attitude matrix accurately reflects the true attitude of the carrier under dynamic disturbances, providing a high-precision reference for subsequent azimuth angle calculation.

[0147] The corrected azimuth angle is extracted from the twice-corrected attitude matrix as the alignment result of the vehicle-mounted radar, expressed as:

[0148]

[0149] in, To correct the azimuth, that is, the alignment result of the vehicle-mounted radar; the above formula extracts the corrected azimuth from the attitude matrix after the second correction as the final alignment result of the vehicle-mounted radar. The azimuth is calculated by analyzing the geometric relationship of the coordinate axis components in the horizontal plane in the matrix to determine the angle between the geographic north and the forward axis of the carrier. This application uses the four-quadrant inverse tangent function to eliminate directional ambiguity, and combines the dynamically corrected attitude matrix elements to output a high-stability, low-noise azimuth estimate. This result effectively overcomes the limitations of traditional north finders in dynamic environments, without relying on external signals, and achieves autonomous and reliable alignment under complex working conditions, ensuring the target positioning accuracy and system reliability of the vehicle-mounted radar.

[0150] In some examples, it also includes:

[0151] Real-time monitoring of the shaking amplitude and frequency of the vehicle-mounted radar base;

[0152] Based on the shaking amplitude and shaking frequency, the observation noise parameters and state noise parameters of the Kalman filter algorithm are adaptively adjusted to suppress the influence of dynamic interference on the alignment results.

[0153] For example, the shaking amplitude and shaking frequency of the vehicle-mounted radar base are monitored in real time through the inertial measurement unit and the horizontal tilt sensor. The shaking amplitude reflects the displacement of the base from the equilibrium position, and the shaking frequency represents the periodic change rate of the shaking. When the vehicle is dynamically disturbed by hydraulic leveling, engine vibration or driving bumps, the system quantifies the shaking intensity and periodic characteristics in real time through sensor data, providing input basis for Kalman filter parameter adjustment.

[0154] Based on the monitored sway amplitude and sway frequency, the system adaptively adjusts the observation noise covariance matrix and state noise covariance matrix of the Kalman filter. When the sway amplitude increases or the sway frequency increases, the observation noise weight is increased to reduce the dependence on the accumulated error of the inertial sensor, while the state noise weight is reduced to enhance the confidence of the model prediction; conversely, the default parameters are restored under stable conditions to maintain conventional filtering performance. This mechanism optimizes the error correction strategy by dynamically balancing the contribution of observation data and model predictions, and suppresses the noise amplification effect caused by high-frequency sway.

[0155] Through the adaptive adjustment of noise parameters, the Kalman filter can effectively distinguish real motion signals from dynamic interference noise, reducing the azimuth estimation deviation caused by shaking. In scenarios of severe shaking or high-frequency vibration, the system maintains the convergence of the filter through real-time parameter optimization, avoiding the divergence problem caused by error accumulation. Vehicle-mounted radar achieves high-precision and high-robustness autonomous alignment in complex dynamic environments, overcoming the dependence of traditional methods on static conditions or external signals, and enhancing the reliability of target positioning and environmental adaptability.

[0156] In some instances, it further includes:

[0157] Perform timestamp synchronization processing on inertial measurement unit data and horizontal inclination sensor data to generate synchronized sensor data;

[0158] Perform denoising and fusion processing on the synchronized sensor data through a preset low-pass filtering algorithm to generate a fused sensor data stream;

[0159] Based on the fused sensor data stream, adjust the calculation frequencies of the initial attitude matrix and the horizontal attitude data difference to optimize the alignment continuity in a dynamic environment.

[0160] Exemplarily, through a timestamp synchronization mechanism, align the inertial measurement unit data and the horizontal inclination sensor data in time to eliminate the timing deviation caused by sensor sampling delay or clock drift. Specifically, mark a unified time reference for the two types of data, and use an interpolation algorithm to fill in the missing time point data to ensure that the attitude information and the inclination measurement value at the same moment strictly correspond. This step solves the timing mismatch problem of multi-source sensor data in a dynamic environment and provides consistent data input for subsequent fusion processing.

[0161] Adopt a preset low-pass filtering algorithm to perform denoising processing on the synchronized sensor data, filter out high-frequency vibration noise (such as engine vibration, road surface bumps, etc.), and retain low-frequency effective signals (such as vehicle leveling movement, slow steering, etc.). Further, perform weighted fusion on the filtered inertial data and horizontal inclination data to generate a fused sensor data stream. During the fusion process, dynamically adjust the weight coefficient according to the sensor confidence. For example, increase the weight of the horizontal inclination data during severe shaking to suppress the cumulative drift of the inertial sensor. This process improves the data quality and provides a high signal-to-noise ratio input for attitude calculation.

[0162] Based on the characteristics of the fused sensor data stream, adaptively adjust the calculation frequencies of the initial attitude matrix and the horizontal attitude data difference. In the stage with strong dynamic interference (such as high-frequency shaking of the base), increase the calculation frequency to quickly respond to attitude changes; in the stage with stable interference, reduce the calculation frequency to reduce the processor load. This mechanism ensures the continuous and stable operation of the alignment algorithm in complex working conditions by balancing real-time performance and resource occupancy.

[0163] The technical solution of the present application will be further described in detail below through specific embodiments.

[0164] In this embodiment, the effectiveness of the vehicle-mounted radar initial alignment method is verified through a simulation environment, and the experimental conditions simulate the process of the vehicle engine shutting down and the hydraulic leveling system slowly leveling. The vehicle-mounted radar system includes an antenna, a north finder, a horizontal inclination sensor, and an inertial measurement unit, and each component is rigidly connected to the vehicle base. As Figure 2 shown, the antenna is installed at the center position of the vehicle to ensure the maximum detection range, and the north finder, the horizontal inclination sensor, and the inertial measurement unit are sequentially fixed to the base for synchronously collecting attitude data. The sampling duration of the experimental data is 180 seconds, the sampling frequency is 20 Hz, and a dynamic interference environment with a simulated horizontal shaking amplitude of 5° and a period of 10 seconds is used to truly reflect the alignment challenges under complex working conditions.

[0165] During the simulation process, the inertial measurement unit records dynamic motion data such as gyro angle increments and accelerometer specific forces in real time, and the horizontal inclination sensor synchronously measures the roll angle and pitch angle of the base. To verify the advantages of the method of the present application, the experiment is carried out in two stages for comparison: in the first stage, only the velocity error matching algorithm is relied on to calculate the azimuth angle, as Figure 3 shown, Figure 3 is the error curve graph of the prior art provided by the embodiment of the present application; in the second stage, the data of the horizontal inclination sensor is introduced as the observation input of the Kalman filter, as Figure 4 shown, Figure 4 is the error curve graph of the present application provided by the embodiment of the present application; the error results of the two methods are quantitatively evaluated through the mean value and the mean square deviation, where the mean value reflects the systematic deviation between the calculated value and the true value, and the mean square deviation characterizes the degree of error fluctuation.

[0166] The experimental results show that when only the velocity error matching algorithm is used, the mean value of the azimuth angle error is 0.281°, and the mean square deviation is 0.033°; after introducing the data of the horizontal inclination sensor, the mean value of the error is reduced to 0.138°, and the mean square deviation is optimized to 0.029°. This improvement shows that the horizontal inclination sensor effectively suppresses the cumulative drift of the inertial sensor and the error amplification caused by dynamic interference by providing low-frequency attitude correction information. The Kalman filter algorithm dynamically balances the weights of observation and prediction by fusing multi-source sensor data, and finally realizes the dual improvement of the accuracy and stability of azimuth angle estimation.

[0167] This embodiment fully verifies the engineering applicability of the method of the present application through simulation experiments. In typical dynamic interference scenarios such as hydraulic leveling and engine vibration, an alignment scheme that introduces a horizontal inclination sensor and combines Kalman filtering reduces the mean and fluctuation range of the azimuth error compared with traditional methods. The present application does not rely on external satellite signals and can still operate stably in signal-limited environments such as tunnels and urban building groups, providing a reliable technical guarantee for high-precision target positioning of vehicle-mounted radars and having broad industrial application prospects.

[0168] Please refer to Figure 5 , which is a schematic structural diagram of an alignment device for a vehicle-mounted radar provided by an embodiment of the present application, including:

[0169] A data acquisition unit 21, configured to acquire inertial measurement unit data and horizontal inclination sensor data of the vehicle-mounted radar;

[0170] An attitude matrix construction unit 22, configured to generate an initial attitude matrix based on the inertial measurement unit data;

[0171] A data difference determination unit 23, configured to determine a horizontal attitude data difference based on the horizontal inclination sensor data and the initial attitude matrix;

[0172] An alignment result generation unit 24, configured to obtain an alignment result of the vehicle-mounted radar based on the horizontal attitude data difference and the Kalman filtering algorithm.

[0173] Please refer to Figure 6 , an embodiment of the present application further provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, it implements the steps of any method for aligning a vehicle-mounted radar.

[0174] Since the electronic device introduced in this embodiment is the device adopted for an alignment device for a vehicle-mounted radar in an embodiment of the present application, based on the method introduced in the embodiment of the present application, those skilled in the art can understand the specific implementation manners and various variations of the electronic device in this embodiment. Therefore, the specific implementation of how this electronic device implements the method in the embodiment of the present application will not be described in detail here. As long as it is the device adopted by those skilled in the art to implement the method in the embodiment of the present application, it falls within the scope of protection of the present application.

[0175] In the specific implementation process, when the computer program 311 is executed by the processor, it can implement any implementation manner in the corresponding embodiment of the first aspect.

[0176] It should be noted that in the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailedly described in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0177] Those skilled in the art should understand that the embodiments of the present application may provide a method, a system, or a computer program product. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application may take the form of a computer program product implemented on one or more computer-readable storage media that contain computer-readable program code.

[0178] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of flows and / or blocks in the flowchart and / or block diagram can also be implemented. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0179] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the specified functions in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0180] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the specified functions in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0181] The embodiments of the present application also provide a computer program product, which includes computer software instructions. When the computer software instructions run on a processing device, the processing device is caused to execute Figure 1 the process of a method for aligning an in-vehicle radar corresponding to an embodiment.

[0182] A computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless manner. The computer-readable storage medium may be any available medium that can be stored by a computer or a data storage device such as a server or data center that includes one or more integrated available media. The available medium may be a magnetic medium, an optical medium, or a semiconductor medium, etc.

[0183] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be repeated here.

[0184] In several embodiments provided in the present application, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division, and there may be other division methods in actual implementation. 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 couplings, direct couplings, or communication connections to each other may be indirect couplings or communication connections through some interfaces, devices, or units, and may be in electrical, mechanical, or other forms.

[0185] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or 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.

[0186] In addition, the functional units in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware and / or software functional units.

[0187] When the 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 storage medium. Based on such an understanding, the technical solution of this 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 storage medium and includes several instructions for causing a computer device to execute all or part of the steps of the methods in various embodiments of this application.

[0188] The above embodiments are only used to illustrate the technical solutions of this application, rather than to limit them; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of various embodiments of this application.

[0189] Although the preferred embodiments of this specification have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concept. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of this specification.

[0190] Obviously, those skilled in the art can make various changes and deformations to this specification without departing from the spirit and scope of this specification. In this way, if these modifications and deformations of this specification fall within the scope of the claims of this specification and their equivalent technologies, this specification also intends to include these modifications and deformations.

Claims

1. An alignment method for vehicle-mounted radar, characterized in that, Including: Obtaining the inertial measurement unit data and horizontal tilt sensor data of the vehicle-mounted radar; Generating an initial attitude matrix based on the inertial measurement unit data; Determining the horizontal attitude data difference based on the horizontal tilt sensor data and the initial attitude matrix; Obtaining the alignment result of the vehicle-mounted radar based on the horizontal attitude data difference and the Kalman filtering algorithm.

2. The method according to claim 1, characterized in that The inertial measurement unit data includes a gyro angle increment vector, local latitude, earth rotation angular velocity, and accelerometer-measured specific force. The generating an initial attitude matrix based on the inertial measurement unit data includes: Determining a first direction cosine matrix from the current body coordinate system to the initial body coordinate system based on the gyro angle increment vector and the skew-symmetric matrix of the gyro angle increment vector; Determining a second direction cosine matrix from the initial body coordinate system to the inertial coordinate system based on the accelerometer-measured specific force; Determining a third direction cosine matrix from the inertial coordinate system to the earth-fixed coordinate system based on the earth rotation angular velocity; Determining a fourth direction cosine matrix from the earth-fixed coordinate system to the navigation coordinate system based on the local latitude; Determining the initial attitude matrix from the current body coordinate system to the navigation coordinate system based on the first direction cosine matrix, the second direction cosine matrix, the third direction cosine matrix, and the fourth direction cosine matrix.

3. The method according to claim 2, characterized in that, The determining a second direction cosine matrix from the initial body coordinate system to the inertial coordinate system based on the accelerometer-measured specific force includes: Performing an integral calculation on the accelerometer-measured specific force for a preset time period to generate a velocity increment vector of the initial body coordinate system; Constructing a theoretical velocity component matrix of the inertial coordinate system based on the earth rotation angular velocity and the local latitude; Performing projection matching on the velocity increment vector and the theoretical velocity component matrix to generate a second direction cosine matrix from the initial body coordinate system to the inertial coordinate system.

4. The method according to claim 1, wherein The horizontal tilt sensor data includes a transformed roll angle and a transformed pitch angle. The horizontal attitude data difference includes a pitch angle difference and a roll angle difference. The determining the horizontal attitude data difference based on the horizontal tilt sensor data and the initial attitude matrix includes: Calculating a relative attitude angle from the current body coordinate system to the navigation coordinate system based on the initial attitude matrix, where the relative attitude angle includes an initial roll angle, an initial pitch angle, and an initial azimuth angle; Generating a corrected attitude matrix based on the transformed roll angle, the transformed pitch angle, and the initial azimuth angle; Extracting a corrected pitch angle and a corrected roll angle from the corrected attitude matrix; Determining the pitch angle difference based on the difference between the corrected pitch angle and the initial pitch angle; Determining the roll angle difference based on the difference between the corrected roll angle and the initial roll angle.

5. The method according to claim 1, characterized in that, The obtaining the alignment result of the vehicle-mounted radar based on the horizontal attitude data difference and the Kalman filtering algorithm includes: Constructing an observation equation of the Kalman filter, where the observed variable of the observation equation is the horizontal attitude data difference; Construct the state equation of the Kalman filter, where the state variables of the state equation include the attitude error angle vector, the velocity error vector, the gyro zero bias vector, and the accelerometer zero bias vector; Input the observation equation and the state equation into the Kalman filter, and calculate the correction amount of the attitude error angle vector by iteratively updating the state variables; Based on the correction amount, perform a secondary correction on the initial attitude matrix to generate a secondary corrected attitude matrix; Extract the corrected azimuth angle from the secondary corrected attitude matrix as the alignment result of the vehicle-mounted radar.

6. The method according to claim 1, wherein It further includes: Real-time monitor the shaking amplitude and shaking frequency of the vehicle-mounted radar base; Based on the shaking amplitude and the shaking frequency, adaptively adjust the observation noise parameter and the state noise parameter of the Kalman filter algorithm to suppress the influence of dynamic interference on the alignment result.

7. The method according to claim 1, wherein It further includes: Perform timestamp synchronization processing on the inertial measurement unit data and the horizontal inclination sensor data to generate synchronized sensor data; Perform denoising and fusion processing on the synchronized sensor data through a preset low-pass filter algorithm to generate a fused sensor data stream; Based on the fused sensor data stream, adjust the calculation frequency of the initial attitude matrix and the horizontal attitude data difference to optimize the alignment continuity in a dynamic environment.

8. An alignment device for vehicle-mounted radar, characterized in that, It includes: A data acquisition unit for acquiring inertial measurement unit data and horizontal inclination sensor data of the vehicle-mounted radar; An attitude matrix construction unit for generating an initial attitude matrix based on the inertial measurement unit data; A data difference determination unit for determining the horizontal attitude data difference based on the horizontal inclination sensor data and the initial attitude matrix; An alignment result generation unit for obtaining the alignment result of the vehicle-mounted radar based on the horizontal attitude data difference and the Kalman filter algorithm.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the steps of the alignment method for a vehicle-mounted radar according to any one of claims 1 to 7 when executing the computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program, when executed by the processor, implements the alignment method for a vehicle-mounted radar according to any one of claims 1 to 7.

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