Large inertia radar tracking method and system based on time-correlation information fitting

Abstract

The present invention provides a large-inertia radar tracking method and system based on time information fitting, and relates to the field of radar precision tracking technology. The present invention performs time processing to obtain the absolute time of the servo subsystem, and then uses the obtained target position related information to set the constraints and key parameters of the seven-segment curve; then performs real-time seven-segment curve fitting, and interpolates an accurate smooth curve between the current radar actual position and the predicted starting position of the task, and finally realizes the tracking control of the large-inertia radar based on the accurate smooth curve servo subsystem. The present invention achieves the purpose of realizing accurate tracking of large-inertia radar without changing the radar vehicle-mounted platform and the overall structure of the vehicle body.

Description

Technical Field

[0001] The present invention relates to the field of radar precision tracking technology, and in particular to a large inertia radar tracking method and system based on time-correlation information fitting. Background Art

[0002] Since vehicle-mounted radar needs to perform rapid and precise measurement and target identification of distant space targets, it is necessary to consider the high mobility and structural stability of the radar vehicle-mounted platform at the same time. When the servo system of the vehicle-mounted radar adopts the traditional single PID control method, there will inevitably be random errors between the input target initial angle and the current servo angle position when the task starts or when switching to a new task during the task. This is equivalent to directly inputting a step signal to the system. When the large inertia radar turntable is in a higher position loop servo bandwidth working mode, the impact of the rapid response of the step signal is related to the step signal amplitude. The larger the step signal amplitude, the greater the impact of the response instant. This will inevitably have a huge impact on the stability and even levelness of the vehicle-mounted platform, and seriously affect the accuracy of radar tracking, measurement or identification tasks.

[0003] Existing solutions that take into account both the high maneuverability and structural stability of vehicle-mounted radar platforms are mostly to limit the number of vehicle-mounted radar array units, minimize the turntable antenna counterweight to reduce the moment of inertia, or increase the supporting structure. This obviously limits the design of the radar array size, places more stringent requirements on the supporting structure, and is more difficult to operate in practice.

[0004] Therefore, it is urgent to propose a technology that can rationally fit the initial random error control process introduced at the moment the radar tracking task is issued without changing the radar vehicle-mounted platform and the overall structure of the vehicle body, so that the large inertia radar can perform accurate tracking. Summary of the invention

[0005] 1. Technical issues to be solved

[0006] In view of the shortcomings of the prior art, the present invention provides a large-inertia radar tracking method and system based on time-correlation information fitting, which solves the problem that the prior art cannot achieve accurate tracking of large-inertia radars without changing the radar vehicle-mounted platform and the overall structure of the vehicle body.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] In a first aspect, the present invention first proposes a large inertia radar tracking method based on time-synchronous information fitting, the method comprising:

[0010] S1. Synchronize the servo subsystem with the radar main control subsystem to obtain the absolute time of the servo subsystem;

[0011] S2, acquiring target position related information in chronological order, and sending the target position related information to the servo subsystem;

[0012] S3, setting constraint conditions of the seven-segment curve based on the target position related information, and determining key parameters of the seven-segment curve;

[0013] S4, performing real-time seven-segment curve fitting based on the key parameters and the current absolute time of the servo subsystem, and interpolating an accurate smooth curve between the current actual position of the radar and the predicted position at the start of the mission;

[0014] S5. Implement tracking control of the large inertia radar based on the precise smooth curve servo subsystem.

[0015] Preferably, in said S1, synchronizing the time of the servo subsystem and the radar main control subsystem comprises: synchronizing the time of the servo subsystem and the radar main control subsystem by using a synchronization second pulse.

[0016] Preferably, the target position related information includes two-dimensional angle information of the target relative to the radar site and corresponding timestamp information.

[0017] Preferably, in S2, acquiring target position related information in chronological order and sending the target position related information to the servo subsystem includes:

[0018] S21, converting the target position from the geocentric coordinate system to the station-centric coordinate system;

[0019] S22, using the UKF filter to extrapolate the target motion state to obtain a series of two-dimensional information of the target relative to the radar;

[0020] S23, sending the two-dimensional information of a series of targets relative to the radar station sites obtained by extrapolation and the timestamp information corresponding to the two-dimensional information to the servo subsystem.

[0021] Preferably, the constraint conditions include: setting the values ​​of the starting angle, the starting angular velocity, the ending angle, the ending angular velocity, the maximum angular velocity, the maximum angular acceleration, and the maximum angular acceleration.

[0022] Preferably, the step S3, setting the constraint conditions of the seven-segment curve based on the target position related information, and determining the key parameters of the seven-segment curve includes:

[0023] S31, setting the task starting point of the seven-segment curve to the servo angle position and angular velocity at the start time of the task, and setting the task end point to the angle position and angular velocity of the first prediction point;

[0024] S32, performing a seven-segment analysis from the task starting point to the task end point to determine whether there is a uniform acceleration in the acceleration segment and the deceleration segment;

[0025] S33, by calculating the uniform speed time, verify whether the assumption in S32 is valid, and finally determine the time required for each segment and related parameters according to the calculation result of the uniform speed time.

[0026] Preferably, the key parameters include: the actual achievable boundary acceleration of the acceleration segment, the actual achievable boundary acceleration of the deceleration segment, the actual achievable maximum angular velocity, and the total time required for the entire process from the starting point to the end point of the mission.

[0027] Preferably, the step of interpolating an accurate smooth curve between the actual position of the current radar and the predicted position at the start of the mission in S4 further includes:

[0028] Judgment (T 0 -T s ) and T:

[0029] If T 0 -T s <T, the next prediction point will replace the original first prediction point, and S3 and S4 will be executed again;

[0030] If T 0 -T s >T, the current servo state is maintained until the two are equal;

[0031] If T 0 -T s =T, then interpolation is performed;

[0032] Among them, T 0 Indicates the timestamp of the first prediction point; T s Indicates the absolute time of the current servo subsystem; indicates the total time of the seven-segment curve fitting of the entire process from the starting point to the end point.

[0033] Preferably, the S5, realizing tracking control of the large inertia radar based on the precise smooth curve servo subsystem, includes: realizing tracking control of the large inertia radar by using a PID control algorithm based on the precise smooth curve servo subsystem.

[0034] In a second aspect, the present invention further proposes a large inertia radar tracking system based on time-synchronous information fitting, the system comprising a computer, the computer comprising:

[0035] at least one storage unit;

[0036] at least one processing unit;

[0037] Among them, at least one instruction is stored in the at least one storage unit, and the at least one instruction is loaded and executed by the at least one processing unit as a program corresponding to the large inertia radar tracking method based on time-correlation information fitting as described above.

[0038] (III) Beneficial effects

[0039] The present invention provides a large inertia radar tracking method and system based on time-correlation information fitting. Compared with the prior art, it has the following beneficial effects:

[0040] 1. The present invention first performs time synchronization processing between the servo subsystem and the radar main control system to obtain the absolute time of the servo subsystem, and then uses the obtained target position related information to set the constraint conditions of the seven-segment curve and determine the key parameters of the seven-segment curve; then performs real-time seven-segment curve fitting, and interpolates an accurate smooth curve between the current radar actual position and the predicted position at the start of the task, and finally implements tracking control of the large inertia radar based on the accurate smooth curve servo subsystem. The present invention overcomes the problem that when the vehicle-mounted large inertia radar turntable performs a target tracking task in a higher position loop bandwidth working mode, the instantaneous impact of the step signal input response generated by the initial random error is too large, thereby causing damage to the horizontality of the vehicle platform and the impact on the structural stability, and achieves the purpose of realizing accurate tracking of the large inertia radar without changing the radar vehicle-mounted platform and the overall structure of the vehicle body.

[0041] 2. The present invention performs seven-segment curve fitting on the initial random error amount, softens the control process on the basis of fully utilizing the power of the turntable, and can maximize the performance of the power system.

[0042] 3. The present invention combines time system information to seamlessly connect the mission start transition phase and the target tracking phase, thereby achieving the mission requirement of high-precision and stable tracking of the vehicle-mounted large-inertia radar turntable. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 It is an overall flow chart of the large inertia radar tracking method based on time-correlation information fitting of the present invention;

[0045] Figure 2 Detailed description of the embodiment of the large inertia radar tracking method based on time-correlation information fitting in the embodiment of the present invention;

[0046] Figure 3 This is a seven-segment curve fitting result diagram in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] The embodiments of the present application provide a large-inertia radar tracking method and system based on time-correlation information fitting, thereby solving the problem that the prior art cannot achieve accurate tracking of large-inertia radars without changing the radar vehicle-mounted platform and the overall structure of the vehicle body, thereby achieving the purpose of accurate tracking of large-inertia radars.

[0049] The technical solution in the embodiment of the present application is to solve the above technical problems, and the overall idea is as follows:

[0050] In order to achieve the purpose of accurate tracking of large inertia radar as much as possible without changing the radar vehicle-mounted platform and the overall structure of the vehicle body, the present invention first performs time synchronization to obtain the absolute time of the servo subsystem, then obtains the target position related information, and uses the target position related information to set the constraint conditions of the seven-segment curve, and determines the key parameters of the seven-segment curve, and then performs real-time seven-segment curve fitting, and interpolates an accurate smooth curve between the current radar actual position and the mission start predicted position, and finally based on the accurate smooth curve servo subsystem to achieve tracking control of the large inertia radar.

[0051] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0052] Embodiment 1:

[0053] In the first aspect, the present invention first proposes a large inertia radar tracking method based on time-series information fitting, see Figure 1 , the method comprising:

[0054] S1. Synchronize the servo subsystem with the radar main control subsystem to obtain the absolute time of the servo subsystem;

[0055] S2, acquiring target position related information in chronological order, and sending the target position related information to the servo subsystem;

[0056] S3, setting constraint conditions of the seven-segment curve based on the target position related information, and determining key parameters of the seven-segment curve;

[0057] S4, performing real-time seven-segment curve fitting based on the key parameters and the current absolute time of the servo subsystem, and interpolating an accurate smooth curve between the current actual position of the radar and the predicted position at the start of the mission;

[0058] S5. Implement tracking control of the large inertia radar based on the precise smooth curve servo subsystem.

[0059] It can be seen that the present embodiment first performs time synchronization processing between the servo subsystem and the radar main control system to obtain the absolute time of the servo subsystem, and then uses the obtained target position related information to set the constraint conditions of the seven-segment curve and determine the key parameters of the seven-segment curve; then performs real-time seven-segment curve fitting, and interpolates an accurate smooth curve between the current radar actual position and the predicted position at the start of the task, and finally implements tracking control of the large inertia radar based on the accurate smooth curve servo subsystem. The present invention overcomes the problem that when the vehicle-mounted large inertia radar turntable performs a target tracking task in a higher position loop bandwidth working mode, the instantaneous impact of the step signal input response generated by the initial random error is too large, thereby causing damage to the horizontality of the vehicle platform and the influence on the structural stability, and achieves the purpose of realizing accurate tracking of the large inertia radar without changing the radar vehicle-mounted platform and the overall structure of the vehicle body.

[0060] The following is combined with Figure 1-3 , and explanations of the specific steps S1-S5, to describe in detail the implementation process of an embodiment of the present invention. Figure 1-2 The specific implementation process of a large inertia radar tracking method based on time information fitting in this embodiment is as follows:

[0061] S1. Synchronize the servo subsystem with the radar main control subsystem to obtain the absolute time of the servo subsystem.

[0062] Before the large inertia radar starts to perform precision tracking tasks, the servo subsystem performs time synchronization with the radar main control subsystem based on the time system information. In this embodiment, the synchronization second pulse is used to ensure that the absolute time of the servo subsystem and the radar main control subsystem is consistent. Specifically, the servo subsystem obtains the second of the day as the reference time by synchronizing with the timing control board, and maintains the servo internal offset time based on the synchronization second pulse; the sum of the reference time and the offset time is the absolute time of the servo subsystem, which is expressed by T sFinally, the servo subsystem and the timing control board use the same path to synchronize the time and second pulses.

[0063] S2. Acquire a series of target position related information in chronological order, and send the target position related information to the servo subsystem.

[0064] When the radar tracking task starts, that is, when the target needs to be tracked, the radar main control subsystem transforms the target position to obtain the two-dimensional angle information of the target relative to the radar station. The specific coordinate transformation steps are as follows:

[0065] S21, first converting the target position from the geocentric coordinate system to the station-centric coordinate system;

[0066] Assuming that the latitude and longitude of the target are (Lon, Lat, Alt), Lon, Lat, Alt are the longitude, latitude and altitude of the target respectively, the position of the target in the radar array coordinate system can be marked as (Est, Nor, Up) by formula (1), where Est represents the distance of the target relative to the due east direction of the radar, Nor represents the distance of the target relative to the due north direction of the radar, and Up represents the distance of the target relative to the directly above the radar.

[0067]

[0068] In the formula, L is the semi-major axis of the Earth and E is the eccentricity of the Earth.

[0069] S22, use UKF filter to extrapolate the motion state of the observed target to obtain a series of two-dimensional information of the target relative to the radar

[0070] The position, velocity and acceleration of the target are selected as the target state vector, and the state conversion equation and observation equation of the system are shown in formula (2);

[0071]

[0072] Where x is the motion state vector, z is the radar measurement vector, f is the motion state transfer matrix, h is the observation matrix, q is the state noise, and r is the observation noise.

[0073] UKF approximates the PDF of the state vector and selects a series of σ sampling points. The sampling point calculation method is shown in formula (3), and the sampling point weight calculation method is shown in formula (4). After these sampling points are weighted, the nonlinear state estimate obtained is better than the state estimate of the second-order linear expansion.

[0074]

[0075]

[0076] In the formula, κ is the scale parameter, n x is the latitude of the state vector, (n χ +κ)≠0, is the state mean, P x is the state covariance.

[0077] S23, obtaining information related to the target position relative to the radar, and sending the information related to the target position to the servo subsystem.

[0078] The polar coordinate position of the target relative to the radar (Rng, Azi, Ele) can be obtained by formula (5), where θ Azi and θ Ele is the azimuth and elevation angle of the target relative to the radar;

[0079]

[0080] By extrapolating, we can obtain a series of two-dimensional information of the target relative to the radar, namely the azimuth angle θ Azi and pitch angle θ Ele After that, these angle information and corresponding timestamps are immediately sent to the servo subsystem, and the servo subsystem performs seven-segment fitting processing.

[0081] S3. Setting constraint conditions of the seven-segment curve based on the target position related information, and determining key parameters of the seven-segment curve.

[0082] According to the target position information relative to the radar obtained in S2, that is, the azimuth angle θ Azi and pitch angle θ Ele And the corresponding timestamp information, determine the constraints of the seven-segment curve, and calculate the key parameters of the seven-segment curve. Among them, the constraints of the seven-segment curve include the starting angle, starting angular velocity, end angle, end angular velocity, maximum angular velocity, maximum angular acceleration, and maximum angular jerk. The calculation of the key parameters of the seven-segment curve specifically includes the following steps:

[0083] S31, the servo subsystem records a series of extrapolated predicted position angles, angular velocities and corresponding timestamps in the order of reception time, the task starting point is set to the servo angle position and angular velocity at the start time of the task, and the task end point is set to the angle position and angular velocity of the first predicted point;

[0084] The maximum angular velocity, maximum angular acceleration and maximum angular jerk are indicative requirements of the radar subsystem. They are the maximum critical values ​​required by the radar to observe various space targets and ensure that the tracking accuracy is within the error range, under the premise that the servo subsystem drive control motor can achieve them. They are fixed values ​​set inside the servo subsystem.

[0085] S32. Perform a seven-segment analysis from the task starting point set in S31 to the end point. Assuming that there are acceleration and deceleration segments in the process, and that the set maximum angular velocity can be reached according to the set constraints, a judgment must be made as to whether there is a uniform acceleration in both the acceleration and deceleration segments.

[0086] For the acceleration segment, the maximum angular velocity is denoted by ω max , the starting angular velocity is represented by ω s , the maximum angular acceleration is represented by a max , the maximum angular acceleration is denoted by j max , then when it satisfies When the acceleration stage cannot reach the set maximum angular acceleration a max , let the actual boundary acceleration be alima, the acceleration period and the deceleration period are inverse processes, and the time is equal and represented as T j1 The entire acceleration period is expressed as T a , then:

[0087]

[0088] Otherwise, when When , it means that the acceleration section can reach the set maximum acceleration a max , then:

[0089]

[0090] For the deceleration stage, the terminal angular velocity is expressed as ω e , when satisfied, it means that the deceleration stage cannot reach the set maximum acceleration -a max , let the actual boundary acceleration be alimd, the acceleration and deceleration time is the inverse process of the deceleration and acceleration time, and the time is equal to that of the deceleration and acceleration time, which is expressed as T j2 The entire deceleration period is expressed as T d ,when Sometimes:

[0091]

[0092] Otherwise, when When the deceleration stage can reach the set maximum acceleration -a max ,have:

[0093]

[0094] S33. By calculating the uniform speed time, verify whether the assumption of S32 is valid. Based on the calculation result of the uniform speed time, discuss the actual time of the acceleration and deceleration sections again, so as to finally determine the time required for each section and related parameters.

[0095] By calculating the uniform speed period, verify whether the assumption of S32 is valid, and discuss the uniform speed period by classification.

[0096] The starting angle, end angle, and acceleration and deceleration process angle satisfy the relationship shown in formula (10):

[0097]

[0098] Among them, θ s is the starting angle, θ e is the end point angle, θ a is the rotation angle during the entire acceleration process, θ d is the rotation angle during the whole deceleration process, T υ is the entire uniform speed period, and from (10) we can get:

[0099]

[0100] Analyze the result of (11), if T υ ≥0, it means there is a uniform speed segment. If the assumption in S32 holds, then:

[0101]

[0102] Among them, ω lim is the maximum angular velocity that can actually be achieved, T is the total time required for the entire process of fitting the curve from the starting point to the end point;

[0103] If T υ <0, it means there is no uniform speed segment, and the assumption in S32 is not true, then:

[0104]

[0105] The T obtained by formula (13) a 、T d The value of is used to judge, if T a <0, indicating that there can only be one deceleration stage, then:

[0106]

[0107] If T d <0, indicating that there can only be one acceleration segment, then:

[0108]

[0109] If 0≤T a <2T j1 or 0≤T d <2T j2 , indicating that the set maximum angular velocity and the set maximum angular acceleration cannot be achieved. At this time, the maximum angular acceleration a is set by decreasingmax , and then iterate the calculation according to formula (13) until T is satisfied. a ≥2T j1 And T d ≥2T j2 until;

[0110] If T a ≥2T j1 And T d ≥2T j2 , indicating that the set maximum angular velocity cannot be reached at this time, but the set maximum angular acceleration can be reached. At this time, there are:

[0111]

[0112] Due to T υ <0 means that there is no uniform speed segment in the process, so the total time T = T a +T d .

[0113] At this point, the time required for each segment and related parameters are obtained, including the actual boundary acceleration that can be achieved in the acceleration segment is alima, the actual boundary acceleration that can be achieved in the deceleration segment is alima, and the actual maximum angular velocity ω that can be achieved lim , and the total time T required for the entire process from the starting point to the end point, including the acceleration time and the deceleration time, both of which are T j1 , acceleration time T a The acceleration and deceleration time and the deceleration and acceleration time are both T j2 .

[0114] S4. Perform real-time seven-segment curve fitting based on the key parameters and the current absolute time of the servo subsystem to interpolate an accurate smooth curve between the current actual position of the radar and the predicted position at the start of the mission.

[0115] In this embodiment, the seven-segment fitting curve interpolates a smooth curve between the mechanical stop angle position at the moment of task start and the task start predicted angle.

[0116] Before interpolation, first determine the first prediction point timestamp T obtained in S2 0 With the current servo subsystem absolute time T s The relationship between the time difference and the total time T of the seven-segment curve fitting from the starting point to the end point obtained in S3 is: 0 -T s <T, it means that the first prediction point is too close to fit the transition. At this time, the next prediction point replaces the original first prediction point, and then S3 and S4 are executed again; if T 0 -T s>T, then keep the current servo state until the two are equal; if T 0 -T s =T, then interpolation is performed to start interpolating a smooth curve between the starting point and the end point. The specific method is as follows:

[0117] The interpolation time is set to t. As the absolute time of the radar main control subsystem advances, the interpolation time also advances. The angle function θ(t), angular velocity function ω(t), angular acceleration function a(t) and angular jerk function j(t) in each period of the curve fitting can be expressed in sections as formulas (17)-(20):

[0118]

[0119]

[0120]

[0121]

[0122] At this point, the smooth transition seven-segment curve from the starting point to the prediction point of the radar tracking task is determined. The seven-segment curve fitting result is shown in the figure below: Figure 3 As shown, the whole process greatly reduces the impact of the large inertia turntable starting point error signal input on the supporting structure, and can maximize the performance of the power system.

[0123] S5. Implement tracking control of the large inertia radar based on the precise smooth curve servo subsystem.

[0124] After the above step S4 obtains an accurate smooth curve, the predicted point trajectory can be smoothly entered for tracking. At this time, the predicted point is the input of the servo subsystem position loop. During the whole process, the control system (i.e., the servo subsystem) uses the PID control algorithm to achieve high-precision dynamic tracking of the entire large inertia turntable. After reasonable parameter configuration, the current servo system position loop is a type II system. The steady-state error of the input acceleration signal of the system after the closed loop is less than the accuracy requirement of the radar servo system, which can ensure high-precision dynamic tracking of the entire task.

[0125] In this embodiment, an incremental PID controller or a position controller is used to achieve high-precision dynamic tracking of the entire large inertia turntable. When an incremental PID controller is used, the calculation formula of the incremental PID controller is:

[0126]

[0127] Among them, ω tar is the target speed input to the motor driver, K p is the position loop proportional parameter, K iis the position loop integral parameter, K d is the differential parameter of the position loop, θ(k) is the discrete sampling value of the angle function of the seven-segment fitting curve at the current moment, α(k) is the servo mechanical angle value at the current moment, e(k) is the angle error at the current moment, e(k-1) is the angle error at the previous moment, and e(k-2) is the error at the previous moment;

[0128] Preferably, after reasonable parameter configuration, the current servo system position loop is a type II system, and the relationship between the input acceleration signal and the steady-state error of the system is e ss =a 0 / k a ;

[0129] Among them, e ss is the steady-state error, a 0 is the acceleration of the input signal, k a is the static error coefficient. As long as the constraint condition set in S3 is met, the maximum acceleration a max If the steady-state error is less than the accuracy requirement of the radar servo system, high-precision dynamic tracking of the entire mission can be ensured.

[0130] At this point, the entire process of the large inertia radar tracking method based on time-synchronous information fitting of the present invention is completed.

[0131] Embodiment 2:

[0132] In a second aspect, the present invention further provides a large inertia radar tracking system based on time-synchronous information fitting, the system comprising a computer, the computer comprising:

[0133] at least one storage unit;

[0134] at least one processing unit;

[0135] Among them, at least one instruction is stored in the at least one storage unit, and the at least one instruction is loaded and executed by the at least one processing unit as a program corresponding to the large inertia radar tracking method based on time-correlation information fitting as described above.

[0136] It can be understood that the large-inertia radar tracking system based on time-synchronous information fitting provided in an embodiment of the present invention corresponds to the large-inertia radar tracking method based on time-synchronous information fitting. The explanations, examples, beneficial effects and other aspects of the relevant contents can refer to the corresponding contents in the large-inertia radar tracking method based on time-synchronous information fitting, and will not be repeated here.

[0137] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0138] 1. The present invention first performs time synchronization processing between the servo subsystem and the radar main control system to obtain the absolute time of the servo subsystem, and then uses the obtained target position related information to set the constraint conditions of the seven-segment curve and determine the key parameters of the seven-segment curve; then performs real-time seven-segment curve fitting, and interpolates an accurate smooth curve between the current radar actual position and the predicted position at the start of the task, and finally implements tracking control of the large inertia radar based on the accurate smooth curve servo subsystem. The present invention overcomes the problem that when the vehicle-mounted large inertia radar turntable performs a target tracking task in a higher position loop bandwidth working mode, the instantaneous impact of the step signal input response generated by the initial random error is too large, thereby causing damage to the horizontality of the vehicle platform and the impact on the structural stability, and achieves the purpose of realizing accurate tracking of the large inertia radar without changing the radar vehicle-mounted platform and the overall structure of the vehicle body.

[0139] 2. The present invention performs seven-segment curve fitting on the initial random error amount, softens the control process on the basis of fully utilizing the power of the turntable, and can maximize the performance of the power system.

[0140] 3. The present invention combines time system information to seamlessly connect the mission start transition phase and the target tracking phase, thereby achieving the mission requirement of high-precision and stable tracking of the vehicle-mounted large-inertia radar turntable.

[0141] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0142] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A large inertia radar tracking method based on time-correlation information fitting, characterized in that: The method comprises: S1. Synchronize the servo subsystem with the radar main control subsystem to obtain the absolute time of the servo subsystem; S2, acquiring target position related information in chronological order, and sending the target position related information to the servo subsystem; S3, setting constraint conditions of the seven-segment curve based on the target position related information, and determining key parameters of the seven-segment curve; S4, performing real-time seven-segment curve fitting based on the key parameters and the current absolute time of the servo subsystem, and interpolating an accurate smooth curve between the current actual position of the radar and the predicted position at the start of the mission; S5. Implementing tracking control of a large inertia radar based on the precise smooth curve servo subsystem; Wherein, the S2 includes: S21, converting the target position from the geocentric coordinate system to the station-centric coordinate system; S22, using the UKF filter to extrapolate the target motion state to obtain a series of two-dimensional information of the target relative to the radar; S23, sending the two-dimensional information of a series of targets relative to the radar station site obtained by extrapolation and the timestamp information corresponding to the two-dimensional information to the servo subsystem; The S3 includes: S31, setting the task starting point of the seven-segment curve to the servo angle position and angular velocity at the start time of the task, and setting the task end point to the angle position and angular velocity of the first prediction point; S32, performing a seven-segment analysis from the task starting point to the task end point to determine whether there is a uniform acceleration in the acceleration segment and the deceleration segment; S33, by calculating the uniform speed time, verify whether the assumption in S32 is valid, and finally determine the time required for each segment and related parameters according to the calculation result of the uniform speed time.

2. The method according to claim 1, characterized in that In the above S1, the time synchronization process of the servo subsystem and the radar main control subsystem includes: using the time synchronization second pulse to synchronize the servo subsystem and the radar main control subsystem.

3. The method according to claim 1, characterized in that The target position related information includes two-dimensional angle information of the target relative to the radar site and corresponding timestamp information.

4. The method according to claim 1, characterized in that The constraint conditions include: setting the starting angle, starting angular velocity, end angle, end angular velocity, maximum angular velocity, maximum angular acceleration, and maximum angular acceleration values.

5. The method according to claim 1, characterized in that The key parameters include: the actual boundary acceleration that the radar can achieve in the acceleration segment, the actual boundary acceleration that the radar can achieve in the deceleration segment, the actual maximum angular velocity that can be achieved, and the total time required for the entire process from the starting point to the end point.

6. The method according to claim 1, characterized in that The S4 interpolation of the accurate smooth curve between the current radar actual position and the mission start predicted position also includes: judge T 0- T s and T The size relationship between them: like T 0- T s < T , then the next prediction point replaces the original first prediction point, and then S3 and S4 are re-executed; like T 0- T s > T , then keep the current servo state until the two are equal; like T 0- T s = T , then interpolation is performed; in, T 0 indicates the first prediction point timestamp; T s Indicates the current servo subsystem absolute time; T It represents the total time of the seven-segment curve fitting from the starting point to the end point.

7. The method according to claim 1, characterized in that The S5, realizing tracking control of the large inertia radar based on the precise smooth curve servo subsystem, includes: realizing tracking control of the large inertia radar by using a PID control algorithm based on the precise smooth curve servo subsystem.

8. A large inertia radar tracking system based on time-correlation information fitting, characterized in that: The system includes a computer, wherein the computer includes: at least one storage unit; at least one processing unit; Among them, at least one instruction is stored in the at least one storage unit, and the at least one instruction is loaded and executed by the at least one processing unit to execute a program corresponding to the large inertia radar tracking method based on time-correlation information fitting as described in any one of claims 1-7.

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