A method, device and storage medium for estimating motion elements based on target orientation

Abstract

The present application relates to the technical field of underwater target tracking, and specifically to a method, device and storage medium for motion element estimation based on target orientation. The motion element estimation method comprises: observing the target to be measured and obtaining measurement data of the target to be measured; solving the measurement data using an analytical mathematical model to obtain the initial distance parameters of the target to be measured; and correcting the orientation information and initial distance parameters in the measurement data based on a filtering algorithm to obtain the optimal prediction result of the target to be measured. The present application defines a relatively calibrated initial value for the orientation of the target to be measured by an analytical method, and then uses a filtering method to iteratively correct the initial value. On the one hand, it avoids the disadvantage that the analytical method cannot take into account the actual situation, and on the other hand, it makes up for the reliance of the filtering method on the initial value. The combination of the two can obtain a more accurate state estimation.

Description

Technical Field

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

[0002] In recent years, with the rapid development of underwater navigation technology, its applications in ocean exploration, environmental monitoring, underwater rescue, and resource survey have become increasingly widespread. In complex underwater environments, accurately predicting the motion direction of underwater targets has become a hot topic and a difficult problem in current research.

[0003] In related technologies, motion factor calculation methods are generally categorized into analytical and filtering methods. Analytical methods rely on precise mathematical models, utilizing known physical laws and geometric relationships to perform mathematical operations on measurement data, such as the target's position, orientation, and distance, to determine the target's motion factors. Filtering methods are primarily used to process noisy measurement data to estimate the target's true motion state. Based on the theory of probability and statistics, they process a series of noisy measurement data, filter out noise interference, and obtain an optimal estimate of the target's motion factors while considering the target's motion model. Traditional underwater target tracking filtering techniques generally employ methods such as the Kalman filter.

[0004] However, as an extension of the least squares method, the Kalman filter has limitations due to its reliance on the establishment of a pre-established system model. Kalman filtering assumes that target observation noise is Gaussian distributed. However, the actual underwater environment is complex, and the distribution of observation noise is often uncertain and time-varying. An incorrect observation model can lead to filter failure or even divergence, making it impossible to accurately determine target information and thus failing to meet practical needs. Summary of the Invention

[0005] Among the related technologies, underwater target tracking filtering technology relies on the establishment of the early system model and has limitations. It ignores the complexity of the actual underwater environment, resulting in filtering failure or even divergence, and thus the inability to accurately solve the target information problem.

[0006] In a first aspect, an embodiment of the present application provides a method for estimating motion elements based on target orientation, the method comprising:

[0007] Observe the target to be measured and obtain measurement data of the target to be measured;

[0008] The measurement data is solved using an analytical mathematical model to obtain the initial distance parameters of the target to be measured;

[0009] 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.

[0010] In conjunction with the first aspect, in one embodiment, the correcting the orientation information and 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:

[0011] 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.

[0012] In combination with the first aspect, in one embodiment, after obtaining the initial distance parameter of the target to be measured, the method further includes:

[0013] 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.

[0014] In conjunction with the first aspect, in one embodiment, evaluating the accuracy of the initial distance parameter includes:

[0015] 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;

[0016] 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.

[0017] In conjunction with the first aspect, in one embodiment, 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:

[0018] 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;

[0019] 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:

[0020]

[0021] Where, is the target speed, X is the instantaneous side angle, is the target direction change rate.

[0022] In conjunction with the first aspect, in one embodiment, 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:

[0023] Obtaining the azimuth data of the target to be measured observed by the observation platform before and after the turn based on the measurement data;

[0024] The initial estimated distance between the target and the observation platform is estimated based on the azimuth change and distance change of the target at two moments.

[0025] In conjunction with the first aspect, in one embodiment, 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:

[0026] When the observation platform is movable and turns, the orientation data of the target to be measured at three different time nodes are obtained according to the measurement data;

[0027] Constructing the geometric relationship of the target at different times based on the position data of the target at three different time nodes, and estimating the position data of the target at a fourth time node based on the geometric relationship;

[0028] An initial estimated distance between the target to be measured and the observation platform is calculated based on the orientation data of the target to be measured at the fourth time node.

[0029] In conjunction with the first aspect, in one embodiment, 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:

[0030] Obtaining frequency information of the target to be measured based on the measurement data, and analyzing 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 based on the frequency information of the target to be measured;

[0031] 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.

[0032] In a second aspect, an embodiment of the present application provides a motion element estimation device based on target orientation, comprising:

[0033] An observation unit, which is used to observe the target to be measured and obtain measurement data of the target to be measured;

[0034] An analytical unit, which is used to solve the measurement data using an analytical mathematical model to obtain the initial distance parameters of the target to be measured;

[0035] The correction unit is used to correct the orientation information and initial distance parameters based on the filtering algorithm to obtain the optimal prediction result of the target to be measured.

[0036] In a 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, wherein 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 items are implemented.

[0037] The beneficial effects of the technical solutions provided in the embodiments of the present application include:

[0038] This application uses an analytical method to define a relatively calibrated initial value for the orientation of the target to be measured, and then uses a filtering method to iteratively correct the initial value. On the one hand, it avoids the disadvantage that the analytical method cannot take into account the actual situation, and on the other hand, it makes up for the reliance of the filtering method on the initial value. The combination of the two can obtain a more accurate state estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 Schematic diagram of the flow of the motion element estimation method in an embodiment of the present application;

[0040] Figure 2 This is a flowchart of a motion element estimation method in a specific embodiment of the present application;

[0041] Figure 3 The motion geometry diagram of the target to be measured in the embodiment of the present application;

[0042] Figure 4 Schematic diagram of the root mean square error of distance and azimuth in the embodiment of the present application;

[0043] Figure 5 Schematic diagram of the root mean square error of speed and heading in the embodiment of the present application;

[0044] Figure 6 This is a schematic diagram of the hardware structure of the motion element estimation device involved in the embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 this application.

[0046] Among the related technologies, underwater target tracking filtering technology relies on the establishment of the early system model and has limitations. It ignores the complexity of the actual underwater environment, resulting in filtering failure or even divergence, and thus the inability to accurately solve the target information problem.

[0047] First, as Figure 1 As shown, an embodiment of the present application provides a method for estimating motion elements based on target orientation, the method comprising:

[0048] Step S1: Observe the target to be measured and obtain measurement data of the target to be measured.

[0049] It is understandable that the measurement data generally includes data such as the position, orientation, and distance of the target to be measured, and the observation platform for underwater target tracking is generally a mobile ship. The number of observation platform sites and the movement method will also affect the subsequent measurement data type and the subsequent solution method.

[0050] Step S2: using an analytical mathematical model to solve the measurement data to obtain the initial distance parameters of the target to be measured.

[0051] It is worth noting that analytical methods are usually based on certain assumptions and simplified conditions, but for actual situations beyond these assumptions, the solution results may be inaccurate or even completely inapplicable.

[0052] In some specific implementations, such as Figure 2 As shown, in the analytical solution process of step S2, the corresponding analytical mathematical model can be used for solution according to the measurement data and actual conditions. Therefore, step S2 includes the following solution strategies:

[0053] Case 1: When the measurement data obtained in step S1 is obtained by a single observation platform on a single motion path and does not require multi-station coordination or multi-segment data fusion, the single-segment ranging method is used to estimate the distance to the target to be measured.

[0054] It is worth noting 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 period of time.

[0055] The specific solution process of Case 1 includes:

[0056] Step a: Figure 3 As shown, the observation station is taken as the coordinate origin O, the north direction is the y-axis, the east direction is the x-axis, and the target speed and heading are , The target motion geometry is as follows: Figure 3 shown.

[0057] Step b: Let the time variation be , the azimuth change is , the target to be measured is The distance traveled in time is , the current distance between the target to be measured and the sonar is , is the instantaneous side angle of the target. Figure 3 As shown in the triangle sine theorem, In

[0058]

[0059] Furthermore, due to The change tends to infinitesimal, so:

[0060]

[0061] From this we can get:

[0062]

[0063] Step c: When the instantaneous side angle and current distance Knowing that both sides of the equation are derived with respect to t, we can get the target direction change rate :

[0064]

[0065] It should be noted that the azimuth change rate is expressed in radians per second.

[0066] It can be seen from the above formula that the target azimuth change rate is related to the target speed. , instantaneous side angle and the initial estimated distance Related, that is

[0067]

[0068] Where, is the target speed, X is the instantaneous side angle, is the target direction change rate.

[0069] Case 2: When the observed direction of the ship where the observation platform is located before and after turning can be obtained from the measurement data of step S1, the dual-segment turning method can be used to estimate the initial estimated distance of the target to be measured.

[0070] The dual-segment turn method, as it's understood, uses the geometric relationship between azimuth changes and distance to estimate the distance between the target and the ship by acquiring the target's azimuth information observed before and after the ship's turn. Its core is to establish a mathematical model to calculate the target's distance based on the ship's azimuth data before and after the maneuver (turn). This method eliminates the need for complex multi-station coordination and relies on single-platform azimuth observations to achieve ranging.

[0071] The specific solution process of Case 2 includes:

[0072] Step a: obtaining the azimuth data of the target to be measured observed by the observation platform before and after the turn based on the measurement data.

[0073] Specifically, it is known that and The range, orientation, and observation line of the main body and the target to be measured between the times In each route, and Two directions are observed at all times:

[0074]

[0075] It should be noted that DTA is the target's tangential distance, and DOA is the observer's tangential distance. The tangential direction is perpendicular to the line connecting the observer and the target. The speed in this direction is called the tangential velocity, and the distance traveled in this direction is called the tangential distance.

[0076] Step b: By dividing the time and make , approaching , Approximate radians and Approaching BR, confirmed When STA and SOA are respectively formed by the ship where the target to be measured and the observation platform are located, they cross the observation line. Determined by the speed, Approach ,therefore

[0077]

[0078] Where BR is the azimuth change rate, STA is the target tangential velocity, and SOA represents the observer's tangential motion velocity.

[0079] Step c: estimating the initial estimated distance between the target to be measured and the observation platform according to the change in the azimuth and distance of the target to be measured at two moments.

[0080] Specifically, assuming that under ideal conditions, the ship At this point, BR and SOA can be seen as The time before the turn is derived. Correspondingly, the BR' and SOA' correspond to the time after the turn. Assuming that the target does not change the course and speed, the STA is the same before and after the turn. Substituting the same logic into the above formula, we get

[0081]

[0082] After simplification, we get:

[0083]

[0084] in:

[0085]

[0086] By shifting the terms, the distance information of the target to be measured can be obtained through the above formula .

[0087] 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 three different time nodes, the fourth-segment turning method is used to estimate the distance data of the target to be measured.

[0088] It's worth noting that the fourth-segment turn method is a geometric solution based on multi-time bearing data. Its core is to infer the target range by constructing geometric relationships between multi-time observations. When the target tracking state is known at three times, the ship can turn at the fourth time. To determine the fourth-time position, the fourth-segment turn method is used to calculate the target range.

[0089] The specific solution process for case three includes:

[0090] Step a: First, assume Always observe your direction . Create a rectangular coordinate system by The ship's position is fixed at this moment, and the y-axis is direction, the x-axis is . Target position at any moment .

[0091] Step b: constructing the geometric relationship of the target to be measured at different moments 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 the fourth time node according to the geometric relationship.

[0092] Specifically, assuming , , Each time has its own direction 、 , There is a target position at all times, and the four times are all known quantities. Then, the target position is a The equation of the line is:

[0093]

[0094] By moving the term, you can get the position through the above formula .

[0095] Step C: Calculate the initial estimated distance between the target to be measured and the observation platform based on the orientation data of the target to be measured at the fourth time node.

[0096] Specifically, according to the above position Calculate the distance information r of the target to be measured as:

[0097]

[0098] 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.

[0099] It should be noted that the azimuth-frequency method is a passive measurement method that calculates distance based on the target's azimuth angle and Doppler frequency. Its core is to infer the real-time distance between the target and the observation platform by analyzing the geometric relationship between the frequency offset (Doppler effect) of the target's echo signal and the azimuth angle change. This method is suitable for scenarios where the platform and the target are in relative motion.

[0100] Specifically, the frequency information of the target to be measured is obtained based on the measurement data. The geometric relationship between the frequency offset of the target's echo signal and the change in the target's azimuth angle is analyzed based on the frequency information of the target to be measured. Then, 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 change in azimuth angle. The calculation formula for the initial distance parameter r is:

[0101]

[0102] Where, is the azimuth Doppler frequency, is the platform moving speed, is the wavelength of the transmitted signal, is the azimuth of the target relative to the platform's motion direction, is the distance. In practical applications, this formula may vary depending on the specific system model and signal processing method.

[0103] Step S3: Evaluate the accuracy of the initial distance parameters. If the initial distance parameters do not meet the preset requirements, report an abnormality and check the measurement data.

[0104] It's worth noting that, as previously mentioned, the initial distance parameters calculated using analytical methods in step S2 may be inaccurate or even completely unsuitable for situations that exceed these assumptions. For example, many analytical models assume a linear system and Gaussian noise distribution, but actual motion systems are often nonlinear, and noise characteristics can be more complex. Therefore, before applying filtering iterative corrections, it is necessary to exclude parameters that clearly do not conform to the actual situation, report any anomalies, and verify the calculation of the initial values and the acquisition of the measurement data.

[0105] Specifically, the above step S3 includes:

[0106] Step S3a: solving the uncertainty of the distance to the target to be measured and the correlation coefficient between the distance to the target to be measured and the orientation according to the initial distance parameter.

[0107] Specifically, the uncertainty of the distance The calculation formula is:

[0108]

[0109] in: is the x position of the sensor in the first flight segment; is the x position of the sensor in the second segment; is the y position of the sensor in the first flight segment; is the y position of the sensor in the second segment; The uncertainty in the x position between the two flight segments; Uncertainty in the y position between the two flight segments; is the distance between the two sensors.

[0110] Range-azimuth correlation coefficient The calculation formula is:

[0111]

[0112] in: is the bearing of the first leg; is the bearing of the second leg; is the azimuth error; The distance from the sensor to the target to be measured.

[0113] It's worth noting that the range of the range-azimuth correlation coefficient is [-1, 1]. A correlation coefficient of 1 indicates a perfect positive correlation between distance and azimuth, meaning that as distance increases, azimuth increases according to a fixed pattern. A correlation coefficient of -1 indicates a perfect negative correlation, meaning that as distance increases, azimuth decreases according to a fixed pattern. A correlation coefficient of -0 indicates no linear relationship between distance and azimuth. If the correlation coefficient is not in the range [-1, 1], it indicates an anomaly in the calculation of the indicator or the distance. If the uncertainty in distance information exceeds ±3σ, it can be considered an excessive error.

[0114] Step S3b: Evaluate the accuracy of the initial distance parameter based on the uncertainty of the target distance and the correlation coefficient between the distance and the orientation.

[0115] It's worth noting that the uncertainty of distance information is used to describe the degree of uncertainty in the calculated values. When the uncertainty coefficient is small, the distance information calculated by the analytical method is relatively accurate, and a smaller initial covariance matrix can be used in the following least squares calculation. If the uncertainty coefficient is large, the distance information calculated by the analytical method is inaccurate, and the observation can be identified as an outlier or a larger initial covariance matrix can be used. If the distance and azimuth correlation coefficients are outside the specified range, the calculated results are abnormal.

[0116] Specifically, when the uncertainty of the distance Greater than the preset coefficient 3 , the calculated distance is determined to be an abnormal value, and the subsequent steps are terminated to report the abnormality. Otherwise, continue to the next step to judge the distance-azimuth correlation coefficient. , continue to the next step, otherwise the calculated initial distance parameter is determined to be an abnormal value. If both of the above two items meet the preset requirements, the initial distance parameter is determined to be in line with the actual situation

[0117] Step S4: Correct the orientation information and initial distance parameters in the measurement data based on a filtering algorithm to obtain an optimal prediction result of the target to be measured.

[0118] In some preferred implementations, an iterative weighted least squares method is used to correct the orientation information and initial distance parameters in the measurement data to obtain an optimal prediction result of the target to be measured.

[0119] In combination with the above preferred embodiment, step S4 includes:

[0120] Step S4a, find the best matching path to the target by minimizing the sum of squares of the errors. When the system is nonlinear, the observation equation can be written as:

[0121]

[0122] in: is a nonlinear function.

[0123] Step S4b: According to the principle of least squares estimate (SLE), the target state is the minimized value. Perform linear approximation and use Taylor series expansion at the kth iteration to calculate the current estimate Expand nearby :

[0124] ,

[0125]

[0126] in: is The Jacobian matrix at .

[0127] At this point, the observation equation can be approximated as a linear equation .

[0128] make , , then the least squares method mentioned above can be used to solve The updated value of .

[0129] The iterative formula of iterative least squares (ILS) is:

[0130]

[0131] in, , is the observation noise covariance matrix,

[0132]

[0133] This process is repeated until certain convergence conditions are met, such as ,in It is a very small threshold that is set in advance.

[0134] It can be understood that in step S4, the filtering method is used to correct the orientation parameters in the measurement data and the initial distance parameters obtained by the analytical method, and the orientation information is used for subsequent sequential calculations after the distance information is calculated.

[0135] It is worth noting that the present invention first uses the analytical method to calculate the target relative position information from the azimuth information in pure bearing tracking under different situations; secondly, the analytical method is combined to solve the distance information to solve the distance uncertainty and the distance-azimuth correlation coefficient, and evaluate whether the solution is consistent with the actual situation; finally, the least squares method is used to iteratively estimate the target without considering the distribution form and size of the observation noise, and further obtain the estimated position of the target. This method does not require complex calculations, has a fast solution speed and can improve the filtering accuracy.

[0136] Furthermore, in order to verify the technical effect of the estimation method of this application, this application analyzes the error of the motion element solution result based on the root mean square error (RMSE) and average root mean square error (ARMSE). The calculation method is as follows:

[0137]

[0138]

[0139] Where, is the Monte Carlo number, m is the number of prediction samples, is the predicted value, is the true value.

[0140] For the target motion element solution method based on analytical method and least squares proposed in this scheme, the parameters shown in Table 1 are set to carry out simulation experiments.

[0141] Table 1 Initial parameter settings

[0142]

[0143] To ensure the accuracy of the target motion element solution, the present invention uses the above simulation parameters to estimate the target distance. The error change rate is calculated as: (Dreal - Dmeasured) / Dreal * 100%).

[0144] The maximum and minimum error rates of the above four analytical mathematical model methods and the distance prediction by class are shown in Table 2:

[0145] Table 2 Error rates of motion element distance calculations using four methods

[0146]

[0147] The RMSE of distance, azimuth, velocity and heading calculated by the proposed Analytical Method-least square estimate (AM-SLE) after calculating 30 sets of estimated distance information and combining it with the known azimuth information is shown in the following figure: Figure 4 、 Figure 5 shown.

[0148] Depend on Figure 4 、 Figure 5 As can be seen, the proposed method has lower errors compared to observations. Table 3 also shows that the proposed method has smaller ARMSEs for range, bearing, velocity, and heading. Table 4 shows that the computation time of the proposed algorithm is slightly longer than that of the traditional bearing-only Kalman filter method, but only in milliseconds.

[0149] Table 3 ARMSE

[0150]

[0151] Table 4 Algorithm calculation time

[0152]

[0153] 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.

[0154] In summary, the present invention first uses an analytical method to calculate the relative position information of the target from the azimuth information in pure bearing tracking under different situations; secondly, the distance information is solved by combining the analytical method to solve the distance uncertainty and the distance-azimuth correlation coefficient, and evaluate whether the solution is consistent with the actual situation; finally, the target is iteratively estimated using the least squares method that does not need to consider the distribution form and size of the observation noise, and the target estimated position is further obtained. This method does not require complex calculations, has a fast solution speed and can improve the filtering accuracy.

[0155] In a second aspect, the present application provides a motion element estimation device based on target orientation, which includes: an observation unit, an analysis unit and a correction unit; wherein,

[0156] The observation unit is used to observe 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 using an analytical mathematical model to obtain the initial distance parameters of the target to be measured; the correction unit is used to correct the orientation information and initial distance parameters based on the filtering algorithm to obtain the optimal prediction result of the target to be measured.

[0157] Among them, the functional 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 functions and implementation processes are no longer repeated here.

[0158] In a third aspect, an embodiment of the present application provides a motion element estimation device, which may be a device with data processing capabilities, such as a personal computer (PC), a laptop computer, or a server.

[0159] Reference Figure 6 , Figure 6 FIG2 is a schematic diagram of the hardware structure of the motion element estimation device involved in the embodiment 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.

[0160] The communication bus may be of any type and is used to interconnect the processor, memory, and communication interface.

[0161] Communication interfaces include input / output (I / O), physical, and logical interfaces, which interconnect components within the motion element estimation device and other devices (such as other computing devices or user devices). Physical interfaces can include Ethernet, fiber, and ATM interfaces; user devices can include displays and keyboards.

[0162] 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 storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0163] The processor may be a general-purpose processor that can invoke a motion element estimation program stored in a memory and execute the motion element estimation method provided in the embodiments of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the motion element estimation program is invoked can be referenced from the various embodiments of the motion element estimation method of the present application and will not be further described here.

[0164] Those skilled in the art will understand that Figure 6 The hardware structure shown in the figure does not constitute a limitation to the present application and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0165] In a fourth aspect, an embodiment of the present application also provides a readable storage medium.

[0166] The readable storage medium of the present application 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 described above are implemented.

[0167] 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, and will not be repeated here.

[0168] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0169] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, or the part that contributes to the existing technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above and includes a number of instructions for enabling a terminal device to execute the methods described in each embodiment of this application.

[0170] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. 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 optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit the "first", "second" and "third" to different types.

[0171] In the description of the embodiments of this application, the words "exemplary," "for example," or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary," "for example," or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "for example," or "for example" is intended to present the relevant concepts in a concrete manner.

[0172] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0173] In some processes described in the embodiments of the present application, multiple operations or steps are included that appear in a specific order. However, it should be understood that these operations or steps may not be performed in the order in which they appear in the embodiments of the present application or may be performed in parallel. The sequence numbers of the operations are only used to distinguish between different operations, and the sequence numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations or steps may be performed in sequence or in parallel, and these operations or steps may be combined.

[0174] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also 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 measurement data of the target to be measured; The measurement data is solved using an analytical mathematical model 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; 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, obtaining the orientation data of the target to be measured at three different time nodes based on the measurement data; constructing the geometric relationship of the target to be measured at different times based on the orientation data of the target to be measured at the three different time nodes, and estimating the orientation data of the target to be measured at a fourth time node based on the geometric relationship; and calculating the initial estimated distance between the target to be measured and the observation platform based on the orientation data of the target to be measured at the fourth time node.

2. The method for estimating motion elements based on target orientation according to claim 1, wherein: The method of correcting the orientation information and 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, wherein: 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 according to claim 3, wherein: The accuracy of the evaluation initial distance parameter includes: 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, wherein: The analytical mathematical model is used to solve the measurement data to obtain the initial distance parameters of the target to be measured, including: 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: Where, is the target speed, X is the instantaneous side angle, is the target direction change rate.

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

7. The method for estimating motion elements based on target orientation according to claim 1, wherein: The analytical mathematical model is used to solve the measurement data to obtain the initial distance parameters of the target to be measured, including: Obtaining frequency information of the target to be measured based on the measurement data, and analyzing 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 based on 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.

8. 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 the initial distance parameters of the target to be measured; 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 is movable and turns, obtaining the orientation data of the target to be measured at three different time nodes based on the measurement data; constructing the geometric relationship of the target to be measured at different times based on the orientation data of the target to be measured at the three different time nodes, and estimating the orientation data of the target to be measured at a fourth time node based on the geometric relationship; and calculating the initial estimated distance between the target to be measured and the observation platform based on the orientation data of the target to be measured at the fourth time node; The correction unit is used to correct the orientation information and initial distance parameters based on the filtering algorithm to obtain the optimal prediction result of the target to be measured.

9. 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 7 are implemented.

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