Motion element estimation method and device based on target orientation and storage medium

Abstract

The invention relates to the technical field of underwater target tracking, in particular to a target orientation-based motion element estimation method and device and a storage medium. The motion element estimation method comprises the following steps: observing a to-be-measured target, and obtaining measurement data of the to-be-measured target; resolving the measurement data by adopting an analytic mathematical model to obtain an initial distance parameter of the to-be-measured target; and correcting the azimuth information and the initial distance parameter in the measurement data based on a filtering algorithm to obtain an optimal prediction result of the to-be-measured target. The relative calibration initial value is defined for the target orientation of the to-be-measured target by using the analytical method, and then the initial value is iteratively corrected by using the filtering method, so that on one hand, the disadvantage that the actual situation cannot be considered by the analytical method is avoided, and on the other hand, the dependence of the filtering method on the initial value is made up; and more accurate state estimation can be obtained.

Description

Technical Field

[0001] The present application relates to the technical field of underwater target tracking, and particularly relates to a method, device and storage medium for estimating motion elements based on target azimuth. Background Art

[0002] In recent years, with the rapid development of underwater navigation technology, applications in aspects such as ocean exploration, environmental monitoring, underwater rescue, and resource investigation have become increasingly widespread. In a complex underwater environment, accurately predicting the motion azimuth of underwater targets has become a current research hotspot and difficulty.

[0003] In related technologies, the methods for solving motion elements are generally divided into the analytical method and the filtering method. The analytical method is based on an accurate mathematical model, uses known physical laws and geometric relationships, and solves the target motion elements through mathematical operations on measurement data such as the position, azimuth, and distance of the target. The filtering method is mainly used to process measurement data containing noise to estimate the true motion state of the target. It is based on probability and statistics theory, and by processing a series of measurement data with noise, on the basis of considering the target motion model, filters out the interference of noise, thereby obtaining the optimal estimate of the target motion elements. Traditional underwater target tracking filtering technologies generally use the Kalman filtering method, etc.

[0004] However, although the Kalman filtering method is an extension of the least squares method, it has limitations due to its dependence on the establishment of the pre - system model. The Kalman filter assumes that the target observation noise is Gaussian - distributed, while the actual underwater environment is complex, and the distribution form of the observation noise is usually uncertain and time - varying. An incorrect observation model will lead to the failure or even divergence of the filter, and thus it is difficult to accurately solve the target information and meet the actual needs. Summary of the Invention

[0005] Aiming at the problem in related technologies that the underwater target tracking filtering technology has limitations due to its dependence on the establishment of the pre - system model, ignores the complexity of the actual underwater environment, leads to the failure or even divergence of the filter, and thus is unable to accurately solve the target information.

[0006] In a first aspect, an embodiment of the present application provides a method for estimating motion elements based on target azimuth, and the motion element estimation method includes: Observing a target to be measured and obtaining measurement data of the target to be measured; Using an analytical mathematical model to solve the measurement data to obtain an initial distance parameter of the target to be measured; Based on a filtering algorithm, correcting the azimuth information and the initial distance parameter in the measurement data to obtain an optimal prediction result of the target to be measured.

[0007] In combination with the first aspect, in one embodiment, the method of correcting the azimuth information and the initial distance parameter in the measurement data based on a filtering algorithm to obtain the optimal prediction result of the target to be measured includes: Correcting the azimuth information and the initial distance parameter in the measurement data based on the iterative weighted least squares method to obtain the optimal prediction result of the target to be measured.

[0008] In combination with the first aspect, in one embodiment, after obtaining the initial distance parameter of the target to be measured, it further includes: Evaluating the accuracy of the initial distance parameter. If the initial distance parameter does not meet the preset requirements, an exception is reported and the measurement data is checked.

[0009] In combination with the first aspect, in one embodiment, the evaluating the accuracy of the initial distance parameter includes: Solving the uncertainty of the distance of the target to be measured and the correlation coefficient between the distance of the target to be measured and the azimuth according to the initial distance parameter; Evaluating the accuracy of the initial distance parameter according to the uncertainty of the distance of the target to be measured and the correlation coefficient between the distance and the azimuth.

[0010] In combination with the first aspect, in one embodiment, the method of using an analytical mathematical model to solve the measurement data to obtain the initial distance parameter of the target to be measured includes: When the measurement data is obtained by a single observation platform on a single movement path, obtaining the target speed, the instantaneous aspect angle, and the target azimuth change rate of the target to be measured according to the measurement data; Estimating the initial estimated distance r between the target to be measured and the observation platform according to the target speed, the instantaneous aspect angle, and the target azimuth change rate:

[0011] In the formula, is the target speed, X is the instantaneous aspect angle, is the target azimuth change rate.

[0012] In combination with the first aspect, in one embodiment, the method of using an analytical mathematical model to solve the measurement data to obtain the initial distance parameter of the target to be measured includes: Obtaining the azimuth data of the target to be measured observed by the observation platform before and after turning according to the measurement data; Estimating the initial estimated distance between the target to be measured and the observation platform according to the azimuth change and the distance change of the target to be measured at two moments.

[0013] In combination with the first aspect, in one embodiment, the method of using an analytical mathematical model to solve the measurement data to obtain the initial distance parameter of the target to be measured includes: When the observation platform can move and turn, obtain the azimuth data of the target to be measured at three different time nodes according to the measurement data; Construct the geometric relationship of the target to be measured at different moments according to the azimuth data of the target to be measured at three different time nodes, and estimate the azimuth data of the target to be measured at the fourth time node according to the geometric relationship; Calculate the initial estimated distance between the target to be measured and the observation platform according to the azimuth data of the target to be measured at the fourth time node.

[0014] Combined with the first aspect, in one implementation, the use of an analytical mathematical model to solve the measurement data to obtain the initial distance parameter of the target to be measured includes: Obtain the frequency information of the target to be measured according to the measurement data, and analyze the geometric relationship between the frequency offset of the echo signal of the target to be measured and the change of the azimuth angle of the target to be measured according to the frequency information of the target to be measured; Calculate the real-time initial estimated distance between the target to be measured and the observation platform according to the geometric relationship between the frequency offset and the azimuth angle change.

[0015] In the second aspect, an embodiment of the present application provides a motion element estimation device based on the target azimuth, which includes: An observation unit, which is used to observe the target to be measured and obtain the measurement data of the target to be measured; An analysis unit, which is used to solve the measurement data by using an analytical mathematical model to obtain the initial distance parameter of the target to be measured; A correction unit, which is used to correct the azimuth information and the initial distance parameter based on a filtering algorithm to obtain the optimal prediction result of the target to be measured.

[0016] In the third aspect, an embodiment of the present application provides a computer-readable storage medium, characterized in that a motion element estimation program is stored on the computer-readable storage medium, and when the motion element estimation program is executed by a processor, the steps of the motion element estimation method as described in any one of the above are implemented.

[0017] The beneficial effects brought by the technical solutions provided by the embodiments of the present application include: In the present application, by using the analytical method to define a relatively calibrated initial value for the azimuth of the target to be measured, and then using the filtering method to iteratively correct the initial value, on the one hand, it avoids the disadvantages of the analytical method that cannot consider the actual situation, and on the other hand, it makes up for the dependence of the filtering method on the initial value. The combination of the two can obtain a more accurate state estimation. Description of the Drawings

[0018] Figure 1 It is a schematic flowchart of the motion element estimation method in the embodiment of the present application; Figure 2Flow chart of the motion element estimation method in a specific embodiment of the present application; Figure 3 Motion geometric situation diagram of the target to be measured in an embodiment of the present application; Figure 4 Schematic diagram of the root mean square error of distance and azimuth in an embodiment of the present application; Figure 5 Schematic diagram of the root mean square error of speed and course in an embodiment of the present application; Figure 6 Schematic diagram of the hardware structure of the motion element estimation device involved in the solution of the embodiment of the present application. Specific embodiments

[0019] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0020] In view of the problem in the related art that the underwater target tracking and filtering technology is limited by the establishment of the pre-system model, ignoring the complexity of the actual underwater environment, resulting in the filtering failure or even divergence, and thus unable to accurately solve the target information.

[0021] In a first aspect, as Figure 1 shown, the embodiment of the present application provides a motion element estimation method based on the target azimuth, and the motion element estimation method includes: Step S1: Observe the target to be measured and obtain the measurement data of the target to be measured.

[0022] It can be understood that the measurement data generally includes data such as the position, azimuth, and distance of the target to be measured, and generally, the observation platform for underwater target tracking is a moving ship. The number and moving mode of the observation platform sites will also affect the type of measurement data obtained subsequently and affect the subsequent calculation method.

[0023] Step S2: Use an analytical mathematical model to calculate the measurement data to obtain the initial distance parameter of the target to be measured.

[0024] It should be noted that the analytical method is usually based on certain assumptions and simplified conditions, but for actual situations beyond these assumptions, the calculation results may be inaccurate or even completely inapplicable.

[0025] In some specific embodiments, such as Figure 2As shown in the figure, during the calculation process of the above step S2 parsing method, the corresponding parsing mathematical model can be adopted according to different measurement data and actual situations. Therefore, step S2 includes the following calculation strategies: Case 1: When the measurement data obtained in step S1 is acquired by a single observation platform on a single movement path and there is no need for multi-station collaboration or multi-segment data fusion, the single-segment ranging method is used to estimate the distance of the target to be measured.

[0026] It should be noted that the single-segment ranging method is a measurement method that calculates the current distance between the target and the observation point based on the target movement parameters and geometric relationships within a short time period.

[0027] The specific calculation process of Case 1 includes: Step a: As Figure 3 shown, taking the observation station as the coordinate origin O, the due north direction as the y-axis, and the due east direction as the x-axis, the target speed and heading are respectively , . The target movement geometric situation is as Figure 3 shown.

[0028] Step b: Let the time change amount be , the azimuth change amount be , the moving distance of the target to be measured within time be , the current distance between the target to be measured and the sonar be , is the target instantaneous aspect angle. Then as Figure 3 shown, according to the sine theorem of a triangle, in , there is

[0029] Furthermore, since the change tends to be infinitesimal, there is:

[0030] From this, it can be obtained that:

[0031] Step c: When the instantaneous aspect angle and the current distance are both known, both sides of the equation are differentiated with respect to t to obtain the target azimuth change rate :

[0032] It should be noted that the azimuth change rate is the radian system of the azimuth change per second.

[0033] It can be seen from the above formula that the target azimuth change rate is related to the target speed , the instantaneous aspect angle is related to the initial estimated distance That is

[0034] In the formula is the target speed, X is the instantaneous aspect angle, is the target azimuth change rate

[0035] Case 2: When the observed azimuths before and after the ship where the observation platform is located turns can be obtained from the measurement data in step S1, the initial estimated distance of the target to be measured can be estimated by using the turning method between two navigation segments

[0036] It can be understood that the turning method between two navigation segments is a method of estimating the distance between the target and the ship by obtaining the target azimuth information observed before and after the ship turns, and using the geometric relationship between the azimuth change and the distance. Its core is to establish a mathematical model based on the azimuth data before and after the ship maneuvers (turns) to calculate the target distance, without the need for complex multi-station cooperation, and the ranging can be achieved by relying on the azimuth observation of a single platform

[0037] The specific calculation process of Case 2 includes Step a: Obtain the azimuth data of the target to be measured observed by the observation platform before and after turning according to the measurement data

[0038] Specifically, given the range, azimuth and observation line and between the body and the target to be measured at time and the movement of and In each route, two azimuths are observed at time

[0039] It should be noted that DTA is the target tangential movement distance, and DOA is the observer tangential movement distance. The tangential direction is perpendicular to the line connecting the observer and the target, and the speed in this direction is called the tangential speed, and the distance moved in this direction is called the tangential distance

[0040] Step b: By dividing the time and making approach approach the radian number and approach BR, the azimuth angle at time is determined. When STA and SOA are determined by the speeds of the target to be measured and the ship where the observation platform is located passing through the observation line respectively, approach Therefore ​

[0041] In the formula, BR is the azimuth change rate, STA is the tangential velocity of the target, and SOA represents the tangential motion velocity of the observer.

[0042] Step c: Estimate the initial estimated distance between the target to be measured and the observation platform according to the azimuth change and distance change of the target to be measured at two moments.

[0043] Specifically, assuming an ideal state, our ship instantaneously turns at a certain moment, and at this point, BR and SOA can be regarded as being derived at a moment before this moment. Correspondingly, after turning, BR' and SOA' are corresponding. Assuming that the target does not change its course and speed, then STA remains the same before and after turning. Substituting into the above formula in the same way, we get

[0044] After simplification, we get:

[0045] Among them:

[0046] By transposing terms, the distance information of the target to be measured can be obtained through the above formula .

[0047] Case 3: When the ship where the observation platform is located can move and turn, and the measurement data can obtain the azimuth data of the target to be measured at 3 different time nodes, the fourth leg turning method is used to estimate the distance data of the target to be measured.

[0048] It should be noted that the fourth leg turning method is a geometric calculation method based on multi-moment azimuth data. Its core is to construct the geometric relationship of multi-moment observations and inversely deduce the target distance. When the tracking state of the target to be measured is known at the positions of three moments, and our ship can turn at the fourth moment, when calculating the position at the fourth moment, the fourth leg turning method is used to calculate the target distance.

[0049] The specific calculation process of Case 3 includes: Step a: First, assume the observed azimuth at a certain moment . Establish a rectangular coordinate system. By fixing the position of our ship at a certain moment, the y-axis is the direction, and the x-axis is . the target position at a certain moment .

[0050] Step b: Construct the geometric relationship of the target to be measured at different times based on the azimuth data of the target to be measured at three different time nodes, and estimate the azimuth data of the target to be measured at the fourth time node according to the geometric relationship.

[0051] Specifically, assume , , At times, there are azimuths , , At the time, there is the target position, all four are known quantities. Then, the target position is a straight-line equation passing through . The analytical formula of the equation is:

[0052] By transposing terms, the position can be obtained through the above formula.

[0053] Step C: Calculate the initial estimated distance between the target to be measured and the observation platform according to the azimuth data of the target to be measured at the fourth time node.

[0054] Specifically, according to the above position , the distance information r of the target to be measured is calculated as:

[0055] Case 4: When the measurement data can obtain the frequency information and azimuth information of the target to be measured, the azimuth-frequency joint method is used to estimate the distance data of the target to be measured.

[0056] It should be noted that the azimuth-frequency joint method is a passive measurement method based on the joint solution of the target azimuth angle and Doppler frequency for distance. Its core is to analyze the geometric relationship between the frequency shift (Doppler effect) of the target echo signal and the change in azimuth angle, and inversely deduce the real-time distance between the target and the observation platform, which is applicable to the scenario where there is relative motion between the platform and the target to be measured.

[0057] Specifically, obtain the frequency information of the target to be measured according to the measurement data, and analyze the geometric relationship between the frequency shift of the target echo signal and the change in the azimuth angle of the target to be measured according to the frequency information of the target to be measured; then calculate the real-time initial estimated distance between the target to be measured and the observation platform according to the geometric relationship between the frequency shift and the change in azimuth angle. The calculation formula for its initial distance parameter r is:

[0058] In the formula, is the azimuth Doppler frequency, is the platform moving speed, is the wavelength of the transmitted signal, is the azimuth angle of the target relative to the platform motion direction, is the distance. In practical applications, this formula may vary due to specific system models and signal processing methods.

[0059] Step S3: Evaluate the accuracy of the initial distance parameter. If the initial distance parameter does not meet the preset requirements, report an anomaly and check the measurement data.

[0060] It should be noted that, as mentioned above, for the initial distance parameter calculated by the analytical method in Step S2, for actual situations beyond these assumptions, the solution results may be inaccurate or even completely inapplicable. For example, many analytical models assume that the system is linear and the noise is Gaussian distributed, but actual motion systems are often non-linear, and the noise characteristics may be more complex. Therefore, before performing iterative correction using the filtering method, it is necessary to exclude parameters that are significantly inconsistent with the actual situation, report anomalies, and check the calculation of the initial value and the acquisition of measurement data.

[0061] Specifically, the above Step S3 includes: Step S3a: Solve the uncertainty of the distance to the target to be measured and the distance-bearing correlation coefficient based on the initial distance parameter.

[0062] Specifically, the uncertainty of the distance The calculation formula is:

[0063] Where: is the x-position of the sensor in the first leg; is the x-position of the sensor in the second leg; is the y-position of the sensor in the first leg; is the y-position of the sensor in the second leg; is the uncertainty of the x-position between the two legs; is the uncertainty of the y-position between the two legs; is the distance between the two sensors.

[0064] The distance-bearing correlation coefficient The calculation formula is:

[0065] Where: is the bearing of the first leg; is the bearing of the second leg; is the bearing error; is the distance from the sensor to the target to be measured.

[0066] It should be noted that for the distance - azimuth correlation coefficient, the value range of the correlation coefficient is [-1, 1]. When the correlation coefficient is 1, it indicates a perfect positive correlation between the distance and the azimuth angle, that is, as the distance increases, the azimuth angle increases according to a certain fixed rule; when the correlation coefficient is -1, it indicates a perfect negative correlation, and the azimuth angle decreases according to a fixed rule as the distance increases; when the correlation coefficient is 0, it indicates that there is no linear relationship between the distance and the azimuth angle. If the correlation coefficient is not within [-1, 1], it indicates that there is an abnormality in the calculation of this index or the calculation of the distance. And when the uncertainty of the distance information exceeds ±3σ, it can be determined that the error is too large.

[0067] Step S3b: Evaluate the accuracy of the initial distance parameter based on the uncertainty of the distance to the target to be measured and the distance - azimuth correlation coefficient.

[0068] It should be noted that the uncertainty of the distance information is used to describe the uncertainty degree of the calculated number. When the uncertainty coefficient is small, it indicates that the distance information calculated by the analytical method is relatively accurate, and a relatively small covariance matrix can be given for initialization in the following least - squares calculation. If the uncertainty coefficient is large, it indicates that the distance information calculated by the analytical method is inaccurate, and this observation can be determined as an outlier or a relatively large covariance matrix can be given for initialization. And if the distance - azimuth correlation coefficient is not within the set interval, it indicates that the calculation result is abnormal.

[0069] Specifically, when the uncertainty of the distance is greater than the preset coefficient 3 , then it is determined that the calculated distance is an outlier, and the subsequent steps are aborted and the abnormality is reported. Otherwise, continue to the next step to judge the distance - azimuth correlation coefficient. When the distance - azimuth correlation coefficient , continue to the next step, otherwise it is determined that the calculated initial distance parameter is an outlier. If both of the above meet the preset requirements, it is determined that the initial distance parameter conforms to the actual situation Step S4: Based on the filtering algorithm, correct the azimuth information and the initial distance parameter in the measurement data to obtain the optimal prediction result of the target to be measured.

[0070] In some preferred embodiments, the iterative weighted least - squares method is used to correct the azimuth information and the initial distance parameter in the measurement data to obtain the optimal prediction result of the target to be measured.

[0071] Combined with the above - mentioned preferred embodiments, step S4 includes: Step S4a: Find the best - matching path of the target by minimizing the sum of the squares of the errors. When the system is non - linear, the observation equation can be written as:

[0072] Where: is a non - linear function.

[0073] Step S4b. According to the principle of Least Square Estimate (SLE), the target state is the value to be minimized. Then, linearize and approximate At the k-th iteration, use Taylor series expansion to expand around the current estimated value as follows: : ,

[0074] where: is the Jacobian matrix at .

[0075] At this time, the observation equation can be approximated as a linear equation .

[0076] Let , , then the above least squares method can be used to solve for the updated value of .

[0077] The iterative formula for Iterative Least Squares (ILS) is:

[0078] where , is the observation noise covariance matrix,

[0079] This process is repeated continuously until a certain convergence condition is met, such as , where is a preset very small threshold.

[0080] It can be understood that in Step S4, the filtering method is used to correct the azimuth parameter in the measurement data and the initial distance parameter obtained by the analytical method. The azimuth information is used for subsequent calculations after the distance information is calculated.

[0081] It should be noted that the present invention first uses the analytical method to calculate the target relative position information from the azimuth information in the pure azimuth tracking of different situations; secondly, combines the analytical method to solve the distance information to solve the distance uncertainty and the distance-azimuth correlation coefficient, and evaluates whether the solution result conforms to the actual situation; finally, uses the least squares method that does not need to consider the form of the observation noise distribution and the magnitude of the observation noise to perform iterative estimation on the target, and further obtains the target estimated position. This method has no complex calculations, fast solution speed and can improve the filtering accuracy.

[0082] Furthermore, to verify the technical effect of the estimation method of the present application, the present application analyzes the error of the solution result of the motion elements based on the root mean square error (RMSE) and the average root mean square error (ARMSE), and the calculation method is as follows:

[0083]

[0084] In the formula, is the number of Monte Carlo times, m is the number of prediction samples, is the predicted value, is the true value.

[0085] For the method for solving the target motion elements based on the analytical method and the least squares proposed in this solution, the parameters shown in Table 1 are set for the simulation experiment.

[0086] Table 1 Initial parameter settings

[0087] To improve the accuracy of the solution result of the target motion elements by the above method, using the above simulation parameters, the present invention estimates the target distance. The formula for calculating the error change rate is: (Dtrue - Dmeasured) / Dtrue * 100%).

[0088] The maximum error rate and the minimum error rate of the above four analytical mathematical model methods and the predicted distance by category are shown in Table 2: Table 2 Distance error rate of the motion element solution of the four methods

[0089] Calculate the distance information after 30 groups of estimations combined with the known azimuth information. The RMSE corresponding results of the distance, azimuth, speed and heading calculated by the proposed analytical least squares (Analytical Method - least square estimate, AM - SLE) are as Figure 4 、 Figure 5 shown.

[0090] From Figure 4 、 Figure 5 it can be seen that the method proposed in this solution has lower errors compared with the observations. And as can be seen from Table 3, the method proposed in this paper has smaller ARMSE in terms of distance, azimuth, speed and heading. It can be seen from Table 4 that although the calculation time of the algorithm proposed in the present application is increased compared with the pure azimuth Kalman filtering calculation method in the traditional technology, it is only at the millisecond level.

[0091] Table 3 ARMSE

[0092] Table 4 Algorithm calculation time

[0093] It can be seen that the motion element estimation method of the present application can effectively reduce the ARMSE of distance, azimuth, speed and heading, and reduce the calculation time.

[0094] In summary, the present invention first uses the analytical method to calculate the target relative position information from the azimuth information in the pure azimuth tracking of different situations; secondly, combines the analytical method to solve the distance information to solve the distance uncertainty and the distance-azimuth correlation coefficient, and evaluates whether the solution result conforms to the actual situation; finally, uses the least squares method that does not need to consider the form and magnitude of the observation noise to iteratively estimate the target, and further obtains the target estimated position. This method has no complex calculations, fast solution speed and can improve the filtering accuracy.

[0095] In a second aspect, the present application provides a motion element estimation device based on the target azimuth, which includes: an observation unit, an analysis unit and a correction unit; wherein, The observation unit is used to perform on the target to be measured and obtain the measurement data of the target to be measured; the analysis unit is used to solve the measurement data by using an analytical mathematical model to obtain the initial distance parameter of the target to be measured; the correction unit is used to correct the azimuth information and the initial distance parameter based on a filtering algorithm to obtain the optimal prediction result of the target to be measured.

[0096] Wherein, the function implementation of each module in the above-mentioned motion element estimation device corresponds to each step in the above-mentioned motion element estimation method embodiment, and its function and implementation process will not be described in detail here.

[0097] In a third aspect, the embodiments of the present application provide a motion element estimation device. The motion element estimation device can be a device with data processing functions such as a personal computer (PC), a laptop computer, a server, etc.

[0098] Refer to Figure 6 , Figure 6 is a schematic hardware structure diagram of the motion element estimation device involved in the embodiment solution of the present application. In the embodiment of the present application, the motion element estimation device may include a processor, a memory, a communication interface, and a communication bus.

[0099] Wherein, the communication bus can be of any type and is used to interconnect the processor, the memory, and the communication interface.

[0100] The communication interface includes interfaces such as input / output (I / O) interfaces, physical interfaces, and logical interfaces for implementing the interconnection of components inside the motion element estimation device, as well as interfaces for implementing the interconnection between the motion element estimation device and other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, a fiber optic interface, an ATM interface, etc.; the user device can be a display, a keyboard, etc.

[0101] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical memory, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0102] The processor can be a general-purpose processor, which can call the motion element estimation program stored in the memory and execute the motion element estimation method provided in the embodiments of the present application. For example, the general-purpose processor can be a central processing unit (CPU). Among them, the method executed when the motion element estimation program is called can refer to the various embodiments of the motion element estimation method of the present application, which will not be elaborated here.

[0103] Those skilled in the art can understand that Figure 6 the hardware structure shown in

[0104] does not constitute a limitation to the present application, and may include more or fewer components than shown in the figure, or combine certain components, or arrange different components.

[0105] On the readable storage medium of the present application, a motion element estimation program is stored, and when the motion element estimation program is executed by a processor, the steps of the motion element estimation method as described above are implemented.

[0106] Among them, the method implemented when the motion element estimation program is executed can refer to the various embodiments of the motion element estimation method of the present application, which will not be elaborated here.

[0107] It should be noted that the serial numbers of the above embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0108] Through the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases, the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium as described above (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to enable a terminal device to execute the methods described in various embodiments of the present application.

[0109] The terms "including" and "having" and any variations thereof in the specification, claims and drawings of the present application are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally further include steps or units not listed, or may optionally further include other steps or units inherent to these processes, methods, products or devices. The descriptions with terms such as "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequential order, nor do they limit that "first", "second" and "third" are different types.

[0110] In the description of the embodiments of the present application, "exemplary", "for example" or "for instance" etc. are used to indicate examples, illustrations or explanations. Any embodiment or design solution described as "exemplary", "for example" or "for instance" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of words such as "exemplary", "for example" or "for instance" is intended to present relevant concepts in a specific manner.

[0111] In the description of the embodiments of the present application, unless otherwise specified, " / " means "or". For example, A / B may represent A or B; the "and / or" in the text is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of the present application, "a plurality of" means two or more than two.

[0112] In some processes described in the embodiments of the present application, there are multiple operations or steps that appear in a specific order. However, it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or may be executed in parallel. The serial numbers of the operations are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0113] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A method for estimating motion elements based on target orientation, characterized in that: The motion element estimation method comprises: Observe the target to be measured and obtain the measurement data of the target to be measured; The analytical mathematical model is used to solve the measurement data to obtain the initial distance parameters of the target to be measured; Based on the filtering algorithm, the orientation information and initial distance parameters in the measurement data are corrected to obtain the optimal prediction result of the target to be measured.

2. The method for estimating motion elements based on target orientation according to claim 1, characterized in that: The method of correcting the orientation information and the initial distance parameters in the measurement data based on the filtering algorithm to obtain the optimal prediction result of the target to be measured includes: Based on the iterative weighted least squares method, the orientation information and initial distance parameters in the measurement data are corrected to obtain the optimal prediction result of the target to be measured.

3. The method for estimating motion elements based on target orientation according to claim 1, characterized in that: After obtaining the initial distance parameter of the target to be measured, the method further includes: Evaluate the accuracy of the initial distance parameters. If the initial distance parameters do not meet the preset requirements, report an exception and check the measurement data.

4. The method for estimating motion elements based on target orientation as claimed in claim 3, characterized in that: The step of evaluating the accuracy of the initial distance parameter comprises: The uncertainty of the distance of the target to be measured and the correlation coefficient between the distance and the orientation of the target to be measured are solved according to the initial distance parameters; The accuracy of the initial distance parameters is evaluated based on the uncertainty of the target distance and the correlation coefficient between distance and azimuth.

5. The method for estimating motion elements based on target orientation according to claim 1, characterized in that: The method of using an analytical mathematical model to solve the measurement data to obtain the initial distance parameters of the target to be measured includes: When the measurement data is obtained by a single observation platform in a single motion path, the target speed, instantaneous side angle and target azimuth change rate of the target to be measured are obtained according to the measurement data; Estimate the initial estimated distance r between the target and the observation platform based on the target speed, instantaneous side angle and target azimuth change rate: In the formula, is the target speed, X is the instantaneous side angle, is the target position change rate.

6. The method for estimating motion elements based on target orientation according to claim 1, characterized in that: The method of using an analytical mathematical model to solve the measurement data to obtain the initial distance parameters of the target to be measured includes: Obtaining the azimuth data of the target to be measured observed by the observation platform before and after the turn according to the measurement data; The initial estimated distance between the target to be measured and the observation platform is estimated according to the azimuth change and distance change of the target to be measured at two moments.

7. The method for estimating motion elements based on target orientation according to claim 1, characterized in that: The method of using an analytical mathematical model to solve the measurement data to obtain the initial distance parameters of the target to be measured includes: When the observation platform is movable and turns, the position data of the target to be measured at three different time nodes are obtained according to the measurement data; Constructing the geometric relationship of the target to be measured at different times according to the position data of the target to be measured at three different time nodes, and estimating the position data of the target to be measured at a fourth time node according to the geometric relationship; An initial estimated distance between the target to be measured and the observation platform is calculated according to the azimuth data of the target to be measured at the fourth time node.

8. The method for estimating motion elements based on target orientation according to claim 1, characterized in that: The method of using an analytical mathematical model to solve the measurement data to obtain the initial distance parameters of the target to be measured includes: Acquire frequency information of the target to be measured according to the measurement data, and analyze the geometric relationship between the frequency offset of the echo signal of the target to be measured and the change of the azimuth angle of the target to be measured according to the frequency information of the target to be measured; The real-time initial estimated distance between the target to be measured and the observation plane is calculated based on the geometric relationship between the frequency offset and the azimuth angle change.

9. A motion element estimation device based on target orientation, characterized in that: include: An observation unit, which is used to observe the target to be measured and obtain measurement data of the target to be measured; An analytical unit, which is used to solve the measurement data using an analytical mathematical model to obtain initial distance parameters of the target to be measured; The correction unit is used to correct the orientation information and the initial distance parameters based on the filtering algorithm to obtain the optimal prediction result of the target to be measured.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a motion element estimation program, wherein when the motion element estimation program is executed by a processor, the steps of the motion element estimation method according to any one of claims 1 to 8 are implemented.

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