CN111912295A - Trajectory drop point prediction system - Google Patents
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Abstract
The invention relates to a trajectory drop point prediction system.A pre-configuration strategy comprises the steps of providing a reference model coordinate system and configuring external equipment in the reference model coordinate system; processing the obtained detection data by a fusion orbit determination strategy through a preset fusion orbit determination algorithm to generate a fusion track; the guiding data reflects the corresponding predicted position and predicted speed of the fusion track; and the drop point prediction strategy acquires a plurality of guide data in real time and carries out drop point prediction according to the acquired guide data. Through the cooperation of various strategies, different external equipment is internally calibrated, meanwhile, tracks of different external data can be constructed in the same coordinate system, then fusion orbit determination processing is carried out to form a uniform track, real-time prediction and drop point reporting are carried out through an extrapolation algorithm, and the method is more accurate and higher in applicability.
Description
Technical Field
The invention relates to military application systems, in particular to a ballistic drop point prediction system.
Background
The drop point refers to a drop point or a bullet distribution center, and the drop point prediction refers to calculating drop point parameters from current ballistic parameters, namely predicting drop point positions and areas of ballistic trajectories according to ballistic information obtained through detection. The accuracy of the drop point forecast is crucial to quickly finding the drop point, determining a safe area of the drop point, safety control and the like. For a projectile with a large drop point spread range, the drop zone monitoring equipment is often out of work, and drop point prediction is generally carried out by means of ballistic radar data. When the existing radar is developed, the point-falling prediction is only an auxiliary function, a developing party does not make deep research on the point-falling prediction, only a simple fitting extrapolation function is given, the prediction precision is not high, and particularly when the data arc section is short, the prediction error is large, and reliable reference cannot be provided for finding the point-falling. The traditional forecasting method is to carry out fitting extrapolation according to ballistic coordinates measured by testing equipment, and the forecasting model is basically a pure data fitting extrapolation method, namely, arc-dropping section data is selected for extrapolation, and when the arc section of the data acquired by the equipment is long, the forecasted drop point is accurate. However, when the arc segment of the data is short or only the arc segment data is raised, the pure data fitting extrapolation method has large error and even fails, and other methods are needed for forecasting. Therefore, the accuracy of the existing point-drop forecasting technology is problematic when the technology is applied to the scene.
Disclosure of Invention
In view of the above, the present invention provides a ballistic landing point prediction system.
In order to solve the technical problems, the technical scheme of the invention is as follows: a trajectory drop point prediction system is provided with a plurality of pieces of external equipment which are used for detecting trajectories and generating detection data, and comprises a pre-configuration strategy, a fusion orbit determination strategy, a trajectory pre-reading strategy and a drop point prediction strategy;
the pre-configuration strategy is used for pre-configuring the external equipment, and the pre-configuration strategy comprises providing a reference model coordinate system and configuring the external equipment in the reference model coordinate system;
the fusion orbit determination strategy is used for generating a corresponding fusion track according to detection data of each piece of external equipment acquired in real time, and the fusion orbit determination strategy processes a plurality of detection data obtained by a preset fusion orbit determination algorithm to generate a fusion track;
the track pre-reading strategy is used for generating guide data according to the fusion track, and the guide data reflects the predicted position and the predicted speed corresponding to the fusion track;
the drop point prediction strategy acquires a plurality of guide data in real time and carries out drop point prediction according to the acquired guide data.
Further, the pre-configuration strategy comprises a dimension restoration step, a wild value elimination step, a space-time registration step and a system parameter correction step;
the dimension restoring step comprises configuring corresponding external equipment according to a preset dimension standard of each piece of detection data;
the wild value eliminating step comprises the steps of determining the tracking threshold value of each external device;
the space-time registration step comprises the steps of carrying out time registration on the external equipment through a time registration strategy and carrying out space registration on the external equipment through a space registration strategy;
the system parameter correcting step comprises correcting the error of the measured value of the external equipment.
Further, the reference model coordinate system is configured as a transmit coordinate system.
Further, the spatial registration strategy comprises a coordinate system transformation algorithm for spatial registration.
Further, the external equipment comprises radar detection equipment and photoelectric detection equipment.
Further, the fusion orbit determination algorithm comprises fusion filtering of each detection data through a least square algorithm to generate a fusion track.
Further, the fusion orbit determination algorithm comprises the steps of filtering each detection data through a Kalman filtering algorithm to obtain a filtering track, and then fusing the filtering track to obtain a fusion track.
Further, the track pre-reading strategy is subjected to extrapolation calculation through a Runge-Kutta algorithm to obtain a predicted position and a predicted speed of the fused track under a preset step length so as to generate guiding data.
Further, the guiding data obtained by the radar detection device guides the action of the photoelectric detection device or the guiding data obtained by the corresponding fusion track of the photoelectric detection device.
Further, the drop point prediction strategy includes a cutoff condition, when the guidance data meets the cutoff condition, a prediction result corresponding to the guidance data is generated, and the drop point prediction strategy includes performing drop point prediction through prediction results at different moments.
The technical effects of the invention are mainly reflected in the following aspects: through the arrangement, through the matching of various strategies, different external equipment is firstly subjected to internal calibration, meanwhile, tracks of different external data can be constructed in the same coordinate system, then fusion orbit determination processing is carried out to form a uniform track, real-time prediction is carried out through an extrapolation algorithm, and the drop point report is carried out, so that the method is more accurate and has stronger applicability.
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FIG. 1: the invention discloses a real-time data processing software architecture schematic diagram;
FIG. 2: the software flow schematic diagram of the invention;
FIG. 3: the invention is a schematic diagram of coordinate transformation;
FIG. 4: comparison of the odorless change with the linearization method of the invention;
FIG. 5: the invention discloses a multipoint integral extrapolation drop point forecast point selection data structure chart.
Detailed Description
The following detailed description of the embodiments of the present invention is provided in order to make the technical solution of the present invention easier to understand and understand.
A trajectory drop point prediction system is provided and associated with a plurality of external devices, the external devices are used for detecting trajectories and generating detection data, and the system is briefly described as follows according to the support of software program parts supporting the realization of the following functions and the principle of hardware topology: the real-time data processing mainly completes real-time processing of various test information received by the system, including external data processing, guidance data generation, flight track prediction and real-time parallel processing of multiple trajectories and tracks, and provides various measurement processing information for users inside and outside the command center. The real-time data processing mainly comprises the functions of task scheme loading, measurement data preprocessing, data fusion extrapolation, data forwarding processing, data auxiliary debugging and the like. The remote measurement and external measurement data sent by the collection and distribution software are received, the processes of data preprocessing, filtering, fusion and the like are carried out, information such as measurement trajectory, filtering data, fusion data, guidance data and the like is formed and is respectively sent to the data storage management software, the command display software, the measurement and control equipment and the like. As shown in fig. 1, the task scheme loading module mainly realizes acquisition of task scheme information before the test; the measurement data preprocessing module is used for carrying out primary processing on various test information received by the system, and comprises the processes of data validity judgment, dimension restoration, wild value elimination and the like; the data fusion extrapolation module is used for realizing fusion processing of measurement tracks of different external equipment, and simultaneously carrying out extrapolation processing of a certain frame number or time according to a measurement data processing result, and is used for guiding other equipment to track and measure target information and carrying out point-dropping pre-extrapolation on the basis; the processing data forwarding is used for forwarding the software receiving processing data, and comprises the steps of forwarding radar measurement data, optical measurement data, telemetering measurement data, GPS positioning data and the like to command display software, data storage management software, three-dimensional visual scene software and the like; the data auxiliary debugging module is mainly used for comparative analysis of original data and processed data in a task process, as shown in fig. 1.
For the projectile flying freely in the air, the motion law is quite complex. The rigid body trajectory equation of all aerodynamic force and moment is considered, although the rigid body trajectory equation has the characteristics of accurate motion description rule, high calculation precision and the like, the attitude parameters of the projectile required to be determined are many and are not easy to measure. And for a projectile with stable flight, the nutation angle is always small, so that the projectile target can be regarded as a particle, and the particle ballistic model is used for describing the motion track of the projectile. The data fusion extrapolation mainly comprises fusion positioning processing, guide extrapolation calculation and drop point prediction calculation, and in the data processing process, the calculation is respectively carried out according to task information and target information, so that the parallel processing of multiple trajectories and flight paths is realized. During fusion positioning, dynamic fusion processing is realized by setting the weighting coefficient of the measured data of the device, as shown in fig. 2.
The method comprises a pre-configuration strategy, a fusion orbit determination strategy, a track pre-reading strategy and a drop point prediction strategy; preferably, the external device includes a radar detection device and a photoelectric detection device. The pre-configuration strategy is used for pre-configuring the external equipment, and the pre-configuration strategy comprises providing a reference model coordinate system and configuring the external equipment in the reference model coordinate system; preferably, the reference model coordinate system is configured as a transmit coordinate system. The pre-configuration strategy comprises a dimension restoration step, a wild value elimination step, a space-time registration step and a system parameter correction step; the method comprises the steps of performing coordinate conversion and filtering smoothing processing according to track curves of different external equipment, wherein the processing comprises modules such as radar processing data, optical processing data, telemetering processing data, GPS positioning data and the like, firstly, performing conversion processing of corresponding coordinate systems according to equipment measurement data types, wherein the conversion processing comprises a Gaussian coordinate system, a geoid coordinate system, a transmitting coordinate system and the like, secondly, performing smoothing processing on data tracks of the measurement data by adopting a filtering processing method, and meanwhile, forwarding the processed data to a subsequent software module.
As shown in fig. 3, the coordinate transformation involves four coordinate systems in total: (1) a geocentric rectangular coordinate system O-XYZ; (2) a geocentric geodetic coordinate system O-BLH; (3) an emission coordinate system (o-xyz)0, the geodetic longitude and latitude height of an emission point (B0, L0, H0) and the shooting azimuth angle D0; (4) the measurement coordinate system (o-xyz) N, the station large warp and weft height (Bn, Ln, Hn), the x-axis pointing in the north direction of astronomy, N being 1, 2 …, N representing the number of measurement systems, is shown in fig. 3. The specific algorithm for coordinate system conversion is as follows: for the range, when the test equipment is set to the azimuth zero position, three azimuth standards are mainly used: the north of the target range, the north of the country and the north of the earth. The north of the target range is specified by the target range according to the south-north trend of the field, and the included angle between the direction of the target range and the north of the country is a certain value alpha; the national north is 54 coordinate system north (the shooting range adopts a new 54 coordinate system), the orientation of the north and the big-Earth north has a sub-noon convergence angle gamma, the gamma is not a fixed value and changes along with the change of point locations; the earth north is also called true north or magnetic north and points to the earth north pole, and the included angle between the true north and the north of the target field is alpha + gamma.
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And coordinate transformation is carried out on the measured data according to the requirement based on coordinate transformation among a Gaussian coordinate system, a geoid ellipsoid coordinate system and a transmitting coordinate system. Firstly, preprocessing such as dimension restoration, outlier elimination, space-time registration, system parameter correction and the like is carried out on original measurement data.
The dimension restoring step comprises configuring corresponding external equipment according to a preset dimension standard of each piece of detection data; in the dimension restoration process, the key points are subjected to numerical value conversion according to the predefinition of the dimension of each measured value;
the wild value eliminating step comprises the steps of determining the tracking threshold value of each external device; in the wild value eliminating process, an ellipsoid tracking gate is adopted for wild value judgment, and the threshold value of the tracking gate is determined according to the filter residual estimated in each step;
the space-time registration step comprises the steps of carrying out time registration on the external equipment through a time registration strategy and carrying out space registration on the external equipment through a space registration strategy; the spatial registration strategy comprises a coordinate system conversion algorithm to carry out spatial registration. In the process of space-time registration, increasing and decreasing sampling is carried out on non-equidistant sampling data of each measuring device, so that time registration of each measuring station is realized, and then data of each measuring station are uniformly converted to a transmitting system through coordinate system conversion, so that space registration of the measuring data of each measuring station is realized;
the system parameter correcting step comprises correcting the error of the measured value of the external equipment. And in the process of correcting the system parameters, correcting the errors of the measured values according to the inherent error empirical values of all the measuring stations. And for the heterogeneous sensor data received by each station, fusion orbit determination based on a distributed architecture or a centralized architecture can be performed.
The fusion orbit determination strategy is used for generating a corresponding fusion track according to detection data of each piece of external equipment acquired in real time, and the fusion orbit determination strategy processes a plurality of detection data obtained by a preset fusion orbit determination algorithm to generate a fusion track; the specific embodiments of fusion orbit determination include the following two types:
in one embodiment, the fusion orbit determination algorithm includes fusion filtering the respective detection data by a least squares algorithm to generate a fusion trajectory. If the centralized fusion orbit determination based on the least square is adopted, the measurement data of different heterogeneous sensors are unified to the same time space, then the least square filtering algorithm is utilized to carry out fusion filtering, and the key point of the processing is to carry out unified modeling on the radar measurement data and the photoelectric measurement data, so that the method can simultaneously adapt to the fusion requirements of radar-radar, radar-photoelectric and photoelectric.
At the moment k, under a transmitting coordinate system, a fusion orbit determination calculation formula of the multi-measuring station is as follows:
Xk＝Xk-1+ΔX＝Xk-1+(DTP-1D)-1DTP-1ΔL
in the formula, Xk＝(Xk Yk Zk)TFor the trajectory position coordinate at time k, the D matrix is the jacobian matrix of the partial derivatives of each station measurement to ballistic parameter X, Y, Z:
D＝(D1 D2...DN)
if the nth device is a radar device:
if the nth device is an optoelectronic device:
wherein
P is a measurement error covariance matrix of each measurement station: p ═ diag (P)1，P2，…PN)。
If the nth device is a radar device:
if the nth device is an optoelectronic device:
Δ L is a linear delta matrix of measurements: Δ L ═ Δ M1 ΔM2…ΔMN)T。
If the nth device is a radar device:
if the nth device is an optoelectronic device:
and completing the realization of the fusion orbit determination strategy based on the least square method.
In another embodiment, the fusion orbit determination algorithm includes filtering each detection data through a kalman filtering algorithm to obtain a filtered trajectory, and then fusing the filtered trajectory to obtain a fused trajectory. If the distributed fusion orbit determination based on Kalman filtering is adopted, firstly, Kalman filtering algorithm is used for processing the measurement data of each sensor to obtain respective filtering track, and then the final fusion result is obtained through weighted fusion. The processing key is that aiming at the nonlinear motion characteristic of a missile target, a nonlinear Kalman filtering algorithm is adopted to realize target tracking and orbit determination, and an odorless Kalman filtering (UKF) algorithm is adopted, and the statistic characteristics of random vectors are approximated through the nonlinear propagation of sigma points, so that the prediction and the update of the motion target state are completed. Compared with Extended Kalman Filtering (EKF) which is nonlinear Kalman filtering, the UKF avoids the Jacobian determinant solving process in the EKF processing process, reduces the calculation complexity and has higher nonlinear degree.
The tasteless transform can compute a statistical measure of random variables that pass through a nonlinear system. The basic idea is that for a known uncertain nonlinear mapping y ═ f (x), a series of reference points (sigma points) about x are selected, the points are mapped through a nonlinear function to obtain new sigma points, and then the new sigma points are used for estimating the statistical property of y
FIG. 4 is a graphical representation comparing the tasteless change to the linearization process. It can be seen that the method of using sigma points to estimate statistical measures of random variables through a nonlinear system has better accuracy than the linearized method.
2Nx +1 symmetric sigma point set: the selection of the initial sigma point set needs to keep the mean value and the variance of the sigma point set consistent with the mean value and the variance of the random variable x, and weight factors are set to adjust the proportion of different sigma points in the whole estimation process. If it is
Representing a weight factor, xiExpressing the value of sigma point, N expressing the number of point set, 2Nx +1 expressing symmetric sigma point set as
Tasteless transform: providing a non-linear mapping y ═ f (x), where
For n-dimensional random vector, obtained by a non-linear function f
Is an m-dimensional random vector. Designing the sigma according to the design scheme of the sigma pointma point set xiiI is 1, 2 … l, and each point has a weight coefficient of
The sigma point can be obtained by f propagationi＝f(ξi) Thus, the random property for y can be calculated as:
tasteless kalman filtering: consider the following nonlinear model:
xk+1＝fk(xk)+wk
zk＝hk(xk)+vkis process evolution noise, Zk∈RmIs the measurement vector of the k time to the system state, hk:Rm×Rm→RmIs a measurement map, and vk-N(0,Rk) Is the measurement noise.
Initial conditions
At the moment of k-1, the sigma point is used for obtaining state one-step prediction
And covariance matrix P of prediction errork|k-1
Adopting 2n +1 symmetrical sigma point set
Namely, it is
Wherein the content of the first and second substances,
representing a matrix (n + λ) Pk-1|k-1Ith column of the square root of (2). Computing
Sigma point propagated by equation of state, i.e. having
Wherein the weight coefficient takes the following values
λ＝α2(n + κ) -n, α determines the degree of dispersion of the sigma points, usually taking a small positive value (e.g., 0.01), κ usually takes 0, and β is used to describe the distribution information of x (β is the optimal value of 2 in the case of gaussian).
At the moment k, a new sigma point set is constructed according to the state predicted value
Then, the measurement equation is used for calculating the one-step advance prediction of the measurement, namely
After obtaining the new measurement zk, filter updating is performed
In conclusion, the track foundation of the fusion track providing extrapolation algorithm can be constructed in real time by fusing the orbit determination strategy.
The track pre-reading strategy is used for generating guide data according to the fusion track, and the guide data reflects the predicted position and the predicted speed corresponding to the fusion track; preferably, the track pre-reading strategy performs extrapolation calculation through a Runge-Kutta algorithm to obtain a predicted position and a predicted speed of the fused track under a preset step length to generate guidance data. Firstly, a track pre-reading strategy is explained, because a fusion orbit determination strategy can obtain a track under a launching coordinate system in real time, a trajectory position at the next moment or the next step can be obtained through extrapolation calculation based on the track, even a predicted track is formed, a drop point prediction can be performed according to the predicted track, and the guide extrapolation data processing focus is to perform track prediction according to a measurement track of a single measuring device or a fusion track of a plurality of measuring devices, so as to realize the tracking and the guidance of other devices. In a conventional shooting range, the flight distance of an uncontrolled projectile is generally dozens of kilometers, so that the influence of the curvature of the earth surface and the change of gravity acceleration can be ignored, but the impact of ballistic wind on the flight of the projectile is large, so that the impact needs to be considered. According to the theory related to the ballistic theory, the particle trajectory mathematical model of the system is established by combining the atmospheric parameters. The following assumptions are made for the projectile motion in air: (1) the nutation angle (or angle of attack) is zero throughout the movement of the projectile; (2) the projectile is a axisymmetric body; (3) the ground surface is a plane; (4) the gravity acceleration is unchanged in magnitude and is always vertically downward in direction; (5) the coriolis acceleration caused by the rotation of the earth is zero. Taking time t as an independent variable, and a shot particle motion equation set in a ground rectangular coordinate system is as follows:
in the formula:
x, y, z: the position coordinates of the projectile in the launching coordinate system; v. ofx，vy，vz: velocity component of the projectile in the launch coordinate system; w is ax，wz: a ballistic wind velocity component; c: the ballistic coefficient is calculated by the formula
Wherein d is the bullet diameter; i: the elastic coefficient; m: the weight of the bullet is increased; g: local gravitational acceleration; h (y): as a function of the density of the air,
rho is the air density at the position of the shot, rhoonIs the local ground air density; g (v, c)s): as a function of the air resistance as a function of,
wherein C isD(Ma) is a coefficient of resistance,
mach number, ys current speed of sound,
is the projectile tangential velocity. Integration initial conditions: when t is 0, vx＝v0 cos θ，vy＝v0 sin θ，vz＝0，x＝y＝z＝0。v0As the initial velocity, θ is the firing angle.
Preferably, the guiding data obtained by the radar detection device guides the action of the photoelectric detection device or the guiding data obtained by the corresponding fusion track of the photoelectric detection device. The method comprises the steps of tracking and guiding radar equipment according to a track processing result of photoelectric detection equipment, tracking and guiding the photoelectric equipment in a falling area according to the track processing result of the radar detection equipment, wherein the processing key lies in determining preparation time of the guide equipment to be tracked, and on the premise of meeting the requirement of the preparation time, appropriate tracking and guiding data are given through track prediction. The design adopts a fourth-order Runge-Kutta algorithm to reduce the error of an extrapolation stage and improve the accuracy of extrapolation estimation. In the missile extrapolation process, the missile moves in a free-fall motion mode, the influence of the earth attraction, the coriolis force, the traction force and the air resistance on the result is mainly considered, and therefore the mass center motion equation of the missile in the launching coordinate system is as follows:
in the formula, XD is air resistance, [ g ]x gx gz]TFor gravitational acceleration, [ a ]ex aey aez]TTo involve acceleration, [ a ]cx acy acz]TIs a coriolis acceleration.
Adopting a four-order Runge-Kutta algorithm to perform extrapolation calculation, setting tau as a value step length and setting initial values of position and speed as Xj、
The position and velocity at the next time are calculated as follows:
therefore, the guide data calculation based on the track extrapolation can be realized, and the guide data can be obtained.
The drop point prediction strategy acquires a plurality of guide data in real time and carries out drop point prediction according to the acquired guide data. The drop point prediction strategy comprises a cut-off condition, when the guide data meet the cut-off condition, a prediction result corresponding to the guide data is generated, and the drop point prediction strategy comprises the step of performing drop point prediction through the prediction results at different moments. Firstly, the drop point prediction aims at reporting the drop point, the specific content is that on the basis of a trajectory extrapolation data key processing method, a track extrapolation method is used for performing drop point prediction, the number of cutoff conditions can be two, the first one is stop time, Tf (obtained according to theoretical trajectory estimation) is set, namely Tj is calculated to be Tf to stop, and the extrapolation result at the moment is output as a drop point prediction result; the second is the stopping height, set to Hf (obtained from the theoretical trajectory), stopping when the extrapolated height at the moment tj is less than this value, and outputting the extrapolated result at this moment as the drop point forecast result. In engineering applications, as shown in fig. 5, the robustness of the processing algorithm is improved by adopting a multi-point integral extrapolation mode. And at the moment tj, respectively performing drop point prediction by using the former n points as extrapolation initial points, and finally solving and outputting a drop point mean value. And (4) sliding backwards by taking the N point data as a group by adopting the same method, and forecasting the next group of the drop points.
And fitting extrapolation is carried out on data according to the requirements of external guidance and frame synchronization among different external equipment, a drop point is predicted according to a theoretical trajectory and the real-time information of an external flight target, and the real-time drop point prediction is mainly used for carrying out real-time prediction calculation on the drop point of the aircraft according to real-time measurement data, so that the range distance of the drop point is provided, and support is provided for user decision. In order to solve the problems of theoretical target trajectory of a target range and real-time information prediction of a measured flying target, a trajectory extrapolation data processing method based on Runge-Kutta algorithm is provided, tracking guidance of photoelectric equipment is carried out according to a trajectory processing result of radar detection equipment, on the premise that preparation time requirements are met, appropriate tracking guidance data is given through trajectory prediction, and four-order Runge-Kutta algorithm is adopted for measured coordinate data to reduce extrapolation stage errors. When the measured data is used for optimal trajectory reconstruction, only initial conditions such as initial speed, bullet weight, firing angle and the like are used as parameters for optimization, other conditions (such as weather) are used as known quantities, and more parameters can be added for optimization subsequently, so that reconstruction accuracy is improved.
Of course, the above is only a typical example of the present invention, and besides, the present invention may have other embodiments, and all technical solutions formed by using equivalent substitutions or equivalent transformations fall within the scope of the claimed invention.
Claims (10)
1. A ballistic landing point prediction system configured with a number of peripheral devices associated therewith, the peripheral devices for detecting a ballistic trajectory and generating detection data, characterized by: the method comprises a pre-configuration strategy, a fusion orbit determination strategy, a track pre-reading strategy and a drop point prediction strategy;
the pre-configuration strategy is used for pre-configuring the external equipment, and the pre-configuration strategy comprises providing a reference model coordinate system and configuring the external equipment in the reference model coordinate system;
the fusion orbit determination strategy is used for generating a corresponding fusion track according to the detection data of each piece of external equipment acquired in real time, and the fusion orbit determination strategy is used for processing a plurality of detection data obtained by a preset fusion orbit determination algorithm to generate a fusion track;
the track pre-reading strategy is used for generating guide data according to the fusion track, and the guide data reflects the predicted position and the predicted speed corresponding to the fusion track;
the drop point prediction strategy acquires a plurality of guide data in real time and carries out drop point prediction according to the acquired guide data.
2. A ballistic drop point prediction system as defined in claim 1, wherein: the pre-configuration strategy comprises a dimension restoration step, a wild value elimination step, a space-time registration step and a system parameter correction step;
the dimension restoring step comprises configuring corresponding external equipment according to a preset dimension standard of each piece of detection data;
the wild value eliminating step comprises the steps of determining the tracking threshold value of each external device;
the space-time registration step comprises the steps of carrying out time registration on the external equipment through a time registration strategy and carrying out space registration on the external equipment through a space registration strategy;
the system parameter correcting step comprises correcting the error of the measured value of the external equipment.
3. A ballistic drop point prediction system as defined in claim 1, wherein: the reference model coordinate system is configured as a transmit coordinate system.
4. A ballistic drop point prediction system as defined in claim 1, wherein: the spatial registration strategy comprises a coordinate system conversion algorithm to carry out spatial registration.
5. A ballistic drop point prediction system as defined in claim 1, wherein: the external equipment comprises radar detection equipment and photoelectric detection equipment.
6. A ballistic drop point prediction system as defined in claim 5, wherein: the fusion orbit determination algorithm comprises the step of performing fusion filtering on each detection data through a least square algorithm to generate a fusion track.
7. A ballistic drop point prediction system as defined in claim 5, wherein: the fusion orbit determination algorithm comprises the steps of filtering all detection data through a Kalman filtering algorithm to obtain filtering tracks, and fusing the filtering tracks to obtain fusion tracks.
8. A ballistic drop point prediction system as defined in claim 5, wherein: and the track pre-reading strategy is subjected to extrapolation calculation through a Runge-Kutta algorithm to obtain the predicted position and the predicted speed of the fusion track under a preset step length so as to generate guide data.
9. A ballistic drop point prediction system as defined in claim 8, wherein: and guiding data obtained by the radar detection device is used for guiding the photoelectric detection device to act or guiding data obtained by the corresponding fusion track of the photoelectric detection device.
10. A ballistic drop point prediction system as defined in claim 1, wherein: the drop point prediction strategy comprises a cut-off condition, when the guide data meet the cut-off condition, a prediction result corresponding to the guide data is generated, and the drop point prediction strategy comprises the step of performing drop point prediction through the prediction results at different moments.
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