A real-time measurement method for stubble height of a wheel-type harvester based on RTK

Abstract

This invention belongs to the field of agricultural machinery automation technology, specifically relating to a method based on RTK This invention relates to a real-time stubble height measurement method for wheeled harvesters. The method involves measuring the machine's pitch angle and altitude using a positioning and navigation system; measuring the header's altitude using a header positioning antenna mounted on the header; measuring the header's rotation angle relative to the machine body using an angle sensor installed in the grain conveying channel; and optimizing the header's ground clearance using a Kalman filter based on a mathematical model, based on the machine's pitch status. The stubble height is then output to the operation control system. The sensor technology is mature, highly accurate, and has a reliable installation structure, allowing direct measurement of the header's ground clearance (stubble height). Simultaneously, for harvesters with... GPS - RTK For autonomous harvesters with positioning and navigation systems, only the installation of a positioning antenna and an angle sensor at the header is needed to calculate the real-time stubble height of the agricultural machine and feed it back to the operation control system.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of agricultural machinery automation, and particularly relates to a real-time stubble height measurement method for a wheeled harvester based on RTK. BACKGROUND

[0002] The harvester driver needs to adjust the height of the cutting platform from the ground in real time during operation to avoid damaging the mechanism by touching the ground and the ridge, and to avoid causing loss of crops due to the cutting platform being too high, and at the same time, it is also important to stabilize the stubble at an appropriate height to reduce the load of the harvester and improve the effect of subsequent straw returning to the field.

[0003] With the development of agricultural machinery automatic driving technology, some intelligent harvesters currently do not need to be operated by drivers, but personnel cannot be freed from the control of the working mechanism such as the cutting platform, and labor is not saved. Therefore, the autonomous operation system is an important research direction for the subsequent development of intelligent agricultural machinery. For the research and development of the stubble control system of the harvester, it is urgent to develop a low-cost and reliable cutting platform height measurement device.

[0004] Currently, the cutting platform height is generally measured by measuring the angle between the cutting platform grain conveying channel and the machine body, the stroke of the cutting platform lifting hydraulic rod, and the like, and the relative position of the cutting platform to the machine body is calculated through geometric relationship, but considering the change of the vehicle body posture, the relative height of the cutting platform to the machine body is not equal to the stubble height. For direct measurement of the stubble height, the cutting platform height from the ground is generally measured by designing a mechanical ground profiling device, or a cutting platform height measurement system based on additional sensors such as ultrasonic waves, laser radars, and machine vision is developed. However, the above measurement methods have a series of problems such as low precision, low utilization rate of sensors, high redundant cost of the system, complex mechanical installation, and easy damage in harsh agricultural environments.

[0005] Therefore, based on the above technical problems, a new real-time stubble height measurement method for a wheeled harvester based on RTK is designed. SUMMARY

[0006] The purpose of the present application is to provide a real-time stubble height measurement method for a wheeled harvester based on RTK.

[0007] In order to solve the above technical problems, the present application provides a real-time stubble height measurement method for a wheeled harvester based on RTK, which comprises:

[0008] measuring the pitch angle and the altitude of the vehicle body through the agricultural machinery positioning and navigation system;

[0009] measuring the altitude of the cutting platform through the cutting platform positioning antenna installed on the cutting platform;

[0010] measuring the rotation angle of the cutting platform relative to the machine body through the cutting platform angle sensor installed on the grain conveying channel;

[0011] Based on the pitch status of the agricultural machinery, the optimal estimate of the current cutter height above the ground is made using a Kalman filter according to a mathematical model.

[0012] Output the stubble height to the operation control system.

[0013] Furthermore, the measurement of the vehicle's pitch angle and altitude via the agricultural machinery positioning and navigation system includes:

[0014] Obtain the altitude h1 of the agricultural machinery roof and the pitch angle α between the vehicle body and the relative horizontal ground.

[0015] Furthermore, the measurement of the elevation of the cutter platform using a cutter platform positioning antenna mounted on the platform includes:

[0016] Obtain the elevation h2 of the cutting platform.

[0017] Furthermore, the measurement of the header angle relative to the machine body using a header angle sensor installed in the grain conveying channel includes:

[0018] Obtain the rotation angle β of the cutting table relative to the machine body.

[0019] Furthermore, the mathematical model used to optimally estimate the current header height above the ground through a Kalman filter, based on the pitch state of the agricultural machinery, includes:

[0020] Motion model of a wheeled harvester when it is tilted back:

[0021]

[0022] Wherein, H is the vertical distance from the bottom B of the cutter to the ground; l AC The height of the cutting platform center A perpendicular to the ground; l DC From the center of the rear axle of the agricultural machinery to l AC distance; l AB l1 is the distance from the rotation center A of the cutting table to the bottom B of the cutter; l2 is the vertical distance from the rotation center of the cutting table to the mounting plane of the top positioning antenna; l AE H1 is the distance from the rotation center A of the cutting platform to the single positioning antenna E installed on the cutting platform; H1 is the height of the cutting platform above the ground calculated using GPS elevation.

[0023] H2=l AC ·cosα+l DC ·sinα-l AB ·sinβ;

[0024] H2 represents the height of the cutter platform above the ground, calculated using the angle of the cutter platform.

[0025] Furthermore, the motion model of the wheeled harvester when it is tilted down:

[0026]

[0027] H2 = [l AC - (l DC - l DG ) tan α] · cos α - l AB · sin β;

[0028] wherein, l DG is the agricultural vehicle wheelbase; l GC is the distance from the agricultural vehicle front axle center to l AC .

[0029] Further, the controller performs data fusion on the final value H final of the header height above ground by taking the GPS elevation involved in the calculation of the header height above ground H1 and the header angle involved in the calculation of the header height above ground H2 as system observation values, and making optimal estimation on the final value H

[0030] A state space model is established, and H1 is regarded as the state quantity of the system, and the state vector is:

[0031]

[0032] In the formula, δ represents the deviation quantity involved in the calculation of the GPS elevation.

[0033] A state transition equation and an observation equation are established to describe the dynamics and observation process of the system.

[0034] The state transition equation is:

[0035] In the formula, X k is the state vector at time k, X k-1 is the state vector at time k-1, dt is the sampling time interval, w k is the process noise, and w k obeys normal distribution with a mean of 0 and a covariance of Q.

[0036] The observation equation is:

[0037] In the formula, Z k is the observation value at time k, v k is the measurement noise, and v k obeys normal distribution with a mean of 0 and a covariance of R.

[0038] The covariance matrix of the state vector is:

[0039] In the formula, σ H 2 and σ δ 2variances of the header height above ground and the bias, respectively;

[0040] The covariance matrices of the process noise w and the observation noise v are:

[0041]

[0042] where σ w 2 is the variance of the process noise, and are the variances of the GPS elevation and the header angle observation noise, respectively.

[0043] Further, the optimal estimation of the header height above ground is performed using Kalman filtering;

[0044] The predicted value at time k is obtained according to the optimal estimation value at time k-1:

[0045]

[0046] The predicted value covariance at time k is obtained according to the optimal estimation value covariance at time k-1 and the covariance of the process noise:

[0047]

[0048] where, and are the priori estimation value of the state vector and the state covariance matrix at time k, respectively, is the posteriori estimation value of the state covariance matrix at time k-1;

[0049] The Kalman gain K is calculated: k

[0050]

[0051] The optimal estimation value at time k is obtained according to the predicted value at time k, the observation value at time k, and the Kalman gain:

[0052]

[0053] The optimal estimation value covariance at time k is obtained according to the predicted value covariance at time k and the Kalman gain:

[0054]

[0055] where I is the identity matrix.

[0056] The prediction update loop is repeated to obtain the final value H final of the header height above ground at each time.

[0057] ​Further, the outputting the stubble height to the work control system comprises:

[0058] According to the stubble height terminal value H of the header at each moment final The stubble height of the wheeled harvester is regulated.

[0059] In another aspect, the application also provides a wheeled harvester using the above RTK-based real-time measurement method of the stubble height of the wheeled harvester, comprising:

[0060] The positioning and navigation system, the header positioning antenna, the header angle sensor and the controller;

[0061] The double positioning antennas of the positioning and navigation system are installed on the roof in front and back of the vehicle in parallel to the vehicle center axis, and are connected to the controller;

[0062] The header positioning antenna is installed on the side of the header, and is connected to the controller;

[0063] The header angle sensor is installed at the connection between the header grain conveying channel and the vehicle body;

[0064] The controller is adapted to regulate the stubble height of the wheeled harvester using the above RTK-based real-time measurement method of the stubble height of the wheeled harvester.

[0065] The beneficial effects of the application are that the vehicle body pitch angle and the altitude are measured by the agricultural machinery positioning and navigation system; the altitude of the header is measured by the header positioning antenna installed on the header; the rotation angle of the header relative to the vehicle body is measured by the header angle sensor installed on the grain conveying channel; the current header height is optimally estimated by the Kalman filter according to the mathematical model based on the pitch state of the agricultural machinery; the stubble height is output to the work control system; the sensor technology is mature, the precision is high, the installation structure is reliable, and the header height (stubble height) can be directly measured. At the same time, for the automatic driving harvester with the GPS-RTK positioning and navigation system, only one positioning antenna and one angle sensor need to be installed on the header to calculate the real-time stubble height of the agricultural machinery and feed back to the work control system.

[0066] Other features and advantages of the application will be set forth in the following description, and in part will become apparent to those skilled in the art upon examination of the following or can be learned by practice of the application. The objects and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

[0067] In order to make the above-mentioned objects, features and advantages of the application more obvious and easy to understand, the following preferred embodiments are specifically described below, and the accompanying drawings are described in detail as follows. BRIEF DESCRIPTION OF DRAWINGS

[0068] In order to more clearly illustrate the technical solutions in the specific embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the description of the specific embodiments or the prior art. Obviously, the drawings described below are some embodiments of the present application, and all other drawings obtained by those of ordinary skill in the art without creative labor based on these drawings also belong to the protection scope of the present application.

[0069] Figure 1 is a flow chart of a RTK-based stubble height real-time measurement method of a wheeled harvester of the present application;

[0070] Figure 2 is a schematic diagram of an unmanned wheeled harvester of the present application;

[0071] Figure 3 is a schematic diagram of the state of the wheeled harvester of the present application when passing through a horizontal flat road surface;

[0072] Figure 4 is a schematic diagram of the motion state model of the wheeled harvester of the present application when raised;

[0073] Figure 5 is a schematic diagram of the motion state model of the wheeled harvester of the present application when lowered;

[0074] Figure 6 is a flow chart of a Kalman optimal estimation running method of the present application;

[0075] Figure 7 is a flow chart of a header height measurement running method of the present application.

[0076] In the drawings:

[0077] Double-positioning antenna 1, control cabinet 2, header positioning antenna 3, header angle sensor 4. DETAILED DESCRIPTION

[0078] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the present application will be described clearly and completely below in conjunction with the drawings. Obviously, the described embodiments are some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor also belong to the protection scope of the present application.

[0079] Embodiment 1

[0080] As Figures 1 to 7As shown, the embodiment 1 provides a RTK-based wheeled harvester stubble height real-time measurement method, which comprises: measuring the pitch angle and altitude of the vehicle body through the agricultural machinery positioning and navigation system; measuring the altitude of the header through the header positioning antenna installed on the header; measuring the rotation angle of the header relative to the vehicle body through the header angle sensor installed on the grain conveying channel; performing optimal estimation on the current header ground clearance through the Kalman filter according to the mathematical model based on the pitch state of the agricultural machinery; outputting the stubble height to the operation control system; compared with the existing header measurement method, the RTK-based wheeled harvester stubble height real-time measurement method adopts mature sensor technology, has high precision, and has reliable installation structure, which can directly measure the header ground clearance (stubble height). At the same time, for the automatic driving harvester with GPS-RTK positioning and navigation system, only one positioning antenna and one angle sensor need to be installed on the header to calculate the real-time stubble height of the agricultural machinery and feed back to the operation control system.

[0081] In the embodiment, the vertically arranged double positioning antennas of the GPS-RTK automatic navigation system (positioning and navigation system) are installed on the front and rear of the roof and connected with the control cabinet for measuring the altitude and pitch angle of the vehicle body. The header positioning antenna is installed on the right side of the header and connected with the control cabinet for measuring the altitude of the header. The header angle sensor is installed on the grain conveying channel of the header and connected with the vehicle body for measuring the rotation angle of the header relative to the vehicle body. The control cabinet is installed on the right side of the cab, and based on the main control chip built in the control cabinet, the header ground clearance is calculated in real time through the given mathematical model and Kalman optimal estimation, and the height information is fed back to the operation control system of the automatic driving harvester.

[0082] In the embodiment, the measurement of the pitch angle and altitude of the vehicle body through the agricultural machinery positioning and navigation system comprises: obtaining the roof altitude h1 of the agricultural machinery measured by the navigation system and the pitch angle a of the vehicle body relative to the horizontal ground measured by the navigation system.

[0083] In the embodiment, the measurement of the altitude of the header through the header positioning antenna installed on the header comprises: obtaining the header altitude h2 measured by the single positioning antenna on the header.

[0084] In the embodiment, the measurement of the rotation angle of the header relative to the vehicle body through the header angle sensor installed on the grain conveying channel comprises: obtaining the rotation angle β of the header relative to the vehicle body measured by the header angle sensor.

[0085] In the embodiment, the optimal estimation on the current header ground clearance through the Kalman filter according to the mathematical model based on the pitch state of the agricultural machinery comprises: the motion state model when the wheeled harvester is raised, the mathematical relationship between the header ground clearance H (the vertical distance from the bottom B of the cutter to the ground), the pitch angle a of the vehicle body (the pitch angle of the vehicle body relative to the horizontal ground), the roof altitude h1 of the agricultural machinery, and the header altitude h2:

[0086]

[0087] Wherein, H is the vertical distance from the bottom of the cutter B to the ground; l AC H is the height of the center of the cutting platform A from the ground; l DC l is the distance from the center of the rear axle of the agricultural machine to l AC ; l AB is the distance from the center of rotation of the cutting platform A to the bottom of the cutter B; l AE 2 is the vertical distance from the center of rotation of the cutting platform to the installation plane of the top positioning antenna; l AE is the distance from the center of rotation of the cutting platform A to the single positioning antenna E installed on the cutting platform; H1 is the cutting platform height calculated with GPS elevation;

[0088] The cutting platform height H is related to the body elevation angle α, the cutting platform relative to the body rotation angle β

[0089] H2 = l AC ·cosα + l DC ·sinα - l AB ·sinβ

[0090] Wherein, H2 is the cutting platform height calculated with the cutting platform angle.

[0091] In this embodiment, the motion state model of the wheeled harvester when it is lowered:

[0092]

[0093] H2 = [l AC -(l DC -l DG )tanα]·cosα - l AB ·sinβ

[0094] Wherein, l DG is the wheelbase of the agricultural vehicle; l GC is the distance from the center of the front axle of the agricultural machine to l AC .

[0095] In this embodiment, the controller performs data fusion on the cutting platform height final value H final by taking the cutting platform height H1 calculated with GPS elevation and the cutting platform height H2 calculated with the cutting platform angle as system observations and making optimal estimation through a Kalman filter unit: first, a state space model is established, and H1 is regarded as the state quantity of the system, and the state vector is:

[0096]

[0097] In the formula, δ represents the deviation amount calculated with GPS elevation.

[0098] The state transition equation and the observation equation are established to describe the dynamics of the system and the observation process:

[0099] The state transition equation is:

[0100] The observation equation is:

[0101] In the formula, X k is the state vector at time k, X k-1 is the state vector at time k-1, dt is the sampling time interval, w k is the process noise, w k obeys the normal distribution with a mean of 0 and a covariance of Q.

[0102] The covariance matrix of the state vector is:

[0103] In the formula, σ H 2 and σ δ 2 are the variances of the cutting platform height and the deviation, respectively.

[0104] The covariance matrices of the process noise w and the observation noise v are:

[0105]

[0106] In the formula, Z k is the observation value at time k, v k is the measurement noise, v k obeys the normal distribution with a mean of 0 and a covariance of R.

[0107] In this embodiment, the Kalman filter is used to perform optimal estimation of the cutting platform height; the process of the Kalman filter includes two steps of prediction and update: the prediction step: according to the optimal estimation value at time k-1, the predicted value at time k is obtained:

[0108]

[0109] According to the optimal estimation value covariance at time k-1 and the process noise covariance, the predicted value covariance at time k is obtained:

[0110]

[0111] Among them, and are the prior estimation values of the state vector and the state covariance matrix at time k, respectively, is the posterior estimation value of the state covariance matrix at time k-1.

[0112] Update step: calculate Kalman gain K k :

[0113]

[0114] According to the prediction value at time k, the observation value at time k, the Kalman gain, the optimal estimation value at time k is obtained:

[0115]

[0116] According to the prediction value covariance at time k, the Kalman gain, the optimal estimation value variance / covariance at time k is obtained:

[0117]

[0118] Wherein, I is the unit matrix;

[0119] The prediction update loop is repeated to obtain the harvester ground height final value H final .

[0120] In this embodiment, the outputting the stubble height to the operation control system comprises: according to the harvester ground height final value H final of each moment, the stubble height of the wheeled harvester is regulated; the real-time harvester ground height is calculated by the established mathematical model and fed back to the operation control system, so as to realize the stubble height regulation. The installation cost and difficulty of the harvester measuring mechanism are greatly reduced, the utilization rate of the existing sensor is improved, and the data processing load of the controller is reduced; at the same time, because the GPS antenna packaging is reliable and there is no additional mechanical structure, the reliability of the system in harsh agricultural environment is also greatly increased.

[0121] Embodiment 2

[0122] Based on the embodiment 1, the embodiment 2 further provides a wheeled harvester adopting the RTK-based real-time measurement method of the stubble height of the wheeled harvester in the embodiment 1, comprising: a positioning and navigation system, a harvester positioning antenna, a harvester angle sensor and a controller; the double positioning antennas of the positioning and navigation system are installed on the top of the vehicle in parallel to the vehicle center axis and connected to the controller; the harvester positioning antenna is installed on the side of the harvester and connected to the controller; the harvester angle sensor is installed at the connection between the harvester grain conveying channel and the vehicle body; and the controller is adapted to regulate the stubble height of the wheeled harvester by using the above-mentioned RTK-based real-time measurement method of the stubble height of the wheeled harvester.

[0123] In summary, this invention measures the pitch angle and altitude of the harvester using an agricultural machinery positioning and navigation system; measures the altitude of the cutter using a cutter positioning antenna mounted on the cutter head; measures the angle of rotation of the cutter head relative to the machine body using a cutter angle sensor installed in the grain conveying channel; and, based on the machine's pitch status, optimizes the current cutter head height above the ground using a Kalman filter according to a mathematical model; finally, it outputs the stubble height to the operation control system. Compared with existing cutter head measurement methods, this method uses mature and highly accurate sensor technology with a reliable installation structure, and can directly measure the cutter head height above the ground (stubble height). Furthermore, for autonomous harvesters equipped with a GPS-RTK positioning and navigation system, only one additional positioning antenna and one angle sensor need to be installed at the cutter head to calculate the real-time stubble height of the harvester and feed it back to the operation control system.

[0124] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0125] In addition, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0126] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the 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 to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0127] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A method for real-time measurement of stubble height for a RTK-based wheeled harvester, characterized in that, Comprise: Measuring the pitch angle and the altitude of the vehicle body through the agricultural machinery positioning navigation system; Measuring the altitude of the header through the header positioning antenna installed on the header; Measuring the rotation angle of the header relative to the vehicle body through the header angle sensor installed on the grain conveying channel of the header; Optimally estimating the current header height from the ground through Kalman filter according to the mathematical model based on the pitch state of the agricultural machinery; Outputting the stubble height to the operation control system; The measuring the pitch angle and the altitude of the vehicle body through the agricultural machinery positioning navigation system comprises: Obtaining the altitude h1 of the vehicle roof and the pitch angle a generated by the vehicle body relative to the horizontal ground; The measuring the altitude of the header through the header positioning antenna installed on the header comprises: Obtaining the altitude h2 of the header; The measuring the rotation angle of the header relative to the vehicle body through the header angle sensor installed on the grain conveying channel of the header comprises: Obtaining the rotation angle β of the header relative to the vehicle body; The optimally estimating the current header height from the ground through Kalman filter according to the mathematical model based on the pitch state of the agricultural machinery comprises: The motion state model when the wheeled harvester is raised: Where H is the vertical distance from the bottom of the cutter B to the ground; l AC is the height of the center of the cutting platform A from the ground; l DC is the distance from the center of the rear axle of the agricultural machine to the AC cutting platform; l AB is the distance from the center of rotation of the cutting platform A to the bottom of the cutter B; l2 is the vertical distance from the center of rotation of the cutting platform to the installation plane of the top positioning antenna; l AE is the distance from the center of rotation of the cutting platform A to the single positioning antenna E installed on the cutting platform; H1 is the GPS elevation involved in calculating the height of the cutting platform from the ground; H2 = 1 AC • cos a + 1 DC • sin a - 1 AB • sin b; Wherein, H2 is the header height from the ground calculated by the header angle.

2. The RTK-based real-time measurement method for the stubble height of the wheeled harvester according to claim 1, wherein, The motion state model when the wheeled harvester is lowered: H2= [l AC - (l DC - l DG ) tan a] · cos a - l AB · sin b; wherein, l DG is the wheelbase of the agricultural vehicle; l GC is the distance from the front axle center of the agricultural vehicle to l AC .

3. The RTK-based real-time measurement method for the stubble height of the wheeled harvester according to claim 2, wherein, The controller takes the GPS elevation involved in calculating the header height H1 and the header angle involved in calculating the header height H2 as system observation values, performs data fusion on the header height final value H final Make the best estimate: A state space model is established, and H1 is regarded as the state quantity of the system, and the state vector is: In the formula, δ represents the deviation quantity calculated by the GPS elevation; A state transition equation and an observation equation are established to describe the dynamics and observation process of the system: The state transition equation is: where X k is the state vector at time k, X k-1 is the state vector at time k - 1, dt is the sampling time interval, w k is the process noise, w k is normally distributed with mean 0 and covariance Q. The observation equation is: where Z k is the observation at time k, v k is the measurement noise, v k is normally distributed with mean 0 and covariance R; The covariance matrix of the state vector is: where σ H 2 and σ δ 2 are the variance of the header height above ground and the deviation, respectively. The covariance matrices of the process noise w and the observation noise v are respectively: where σ w 2 σ2p is the variance of the process noise, and σ2h and σ2a are the observation noise variances for GPS elevation and swath angle, respectively.

4. The RTK-based real-time measurement method for the stubble height of the wheeled harvester according to claim 3, wherein, Kalman filter is used for optimal estimation of the header height from the ground; The predicted value at time k is obtained according to the optimal estimation value at time k-1: The predicted value covariance at time k is obtained according to the optimal estimation value covariance at time k-1 and the process noise covariance: wherein, and are the prior estimates of the state vector and the state covariance matrix at time k, respectively, is the posterior estimate of the state covariance matrix at time k-1. Computing the Kalman gain K k : The optimal estimation value at time k is obtained according to the predicted value at time k, the observation value at time k and the Kalman gain: The optimal estimation value variance / covariance at time k is obtained according to the predicted value covariance at time k and the Kalman gain: Wherein, I is the unit matrix; The prediction update loop iterates to obtain the final value of the header ground clearance H at each time instant final .

5. The RTK-based real-time measurement method for the stubble height of the wheeled harvester according to claim 4, wherein, The outputting the stubble height to the operation control system comprises: According to the end value H of the ground clearance of the header at each instant final The stubble height of a wheeled harvester is regulated.

6. A wheel-type harvester employing the real-time stubble height measuring method for a wheel-type harvester based on RTK according to claim 1, characterized by, Comprise: The positioning navigation system, the header positioning antenna, the header angle sensor and the controller; The double positioning antennas of the positioning navigation system are installed on the roof of the vehicle in parallel to the central axis of the vehicle and are connected to the controller; The header positioning antenna is installed on the side of the header and is connected to the controller; The header angle sensor is installed at the connection between the grain conveying channel of the header and the vehicle body; The controller is adapted to regulate and control the stubble height of the wheeled harvester by using the RTK-based real-time measurement method for the stubble height of the wheeled harvester according to claim 1.

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