A motion platform position calculation method and system, an electronic device, and a medium

Abstract

The application relates to a motion platform position calculation method and system, electronic equipment and a medium, and belongs to the technical field of search, wherein the method comprises the following steps: in an on-demand search process, performing cascade convolution on probability density matrices describing the distribution of a platform at each moment to obtain a target probability density matrix; the central element of each probability density matrix is a probability density value at the distribution expectation of the platform at the corresponding moment; the distribution expectations corresponding to each moment are superimposed to obtain a target distribution expectation; and the distribution of the platform is determined according to the target probability density matrix and the target distribution expectation. The application can more quickly and accurately determine the distribution of the platform.

Description

Technical Field

[0001] This invention relates to the field of caller search technology, and in particular to a method, system, electronic device, and medium for calculating the position of a motion platform. Background Technology

[0002] In the process of responding to a call for help, if a location distribution model can be quickly constructed based on the movement process of the target object, an efficient search strategy can be designed based on the location distribution, thereby increasing the probability of discovering the target object. For a moving platform that has undergone multiple complex maneuvers (such as straight flight, turning, and random movement), it is difficult to accurately describe the changes in its location and distribution characteristics over time using analytical methods. Therefore, accurately and quickly estimating the location distribution of a specific searched platform is a key technology that needs further breakthroughs in responding to a call for help scenarios.

[0003] Currently, the main solutions for implementing this technology are the Monte Carlo method and the empirical method.

[0004] The Monte Carlo method simulates the complex motion of the target as a random process, sampling and generating a scatter plot of the platform through numerous repeated simulations. The scatter plot indirectly reflects the platform's distribution characteristics. However, this method typically requires a large number of repeated simulations and has extremely poor timeliness. It is generally difficult to apply to call-to-response tasks with high real-time requirements.

[0005] To address the issue of poor real-time performance caused by the Monte Carlo method, people also delineate distribution areas based on various prior knowledge and experience. However, the empirical method is greatly influenced by human subjectivity, and the accuracy of the delineated areas is difficult to guarantee. Summary of the Invention

[0006] In view of this, it is necessary to provide a method, system, electronic device and medium for estimating the position of motion platforms, so as to solve the problem that existing methods for estimating the distribution of motion platforms cannot balance timeliness and accuracy.

[0007] To address the aforementioned problems, in a first aspect, the present invention provides a method for estimating the position of a motion platform, comprising:

[0008] During the call search process, the probability density matrices describing the platform's distribution at each time step are concatenated and convolved to obtain the target probability density matrix; wherein, the central element of each probability density matrix is ​​the probability density value of the platform's expected distribution at the corresponding time step.

[0009] The expected distributions at each time point are superimposed to obtain the expected target distribution.

[0010] The distribution of the platform is determined based on the target probability density matrix and the target distribution expectation.

[0011] In one possible implementation, the method further includes:

[0012] Based on the expected motion parameters of the platform at the first moment and the standard deviation of the expected motion parameters, combined with the 3σ criterion, the main distribution area of ​​the platform at the second moment is determined; wherein, the second moment is the moment following the first moment, and any moment other than the initial moment among the various moments;

[0013] The circumscribed quadrilateral of the main distribution area is used as the statistical range;

[0014] Generate a probability density matrix describing the distribution of the platform within the statistical range.

[0015] In one possible implementation, the method further includes:

[0016] The statistical range is adjusted according to the real-time requirements of recall search.

[0017] In one possible implementation, determining the main distribution region of the platform at the second prior time step based on the platform's expected motion parameters at the first time step and the standard deviation of the expected motion parameters, combined with the 3σ criterion, includes:

[0018] When the motion range of the platform exceeds a preset motion range threshold, the main distribution area of ​​the platform at the second time is determined based on the expected motion parameters of the platform at the first time and the standard deviation of the expected motion parameters, combined with the 3σ criterion.

[0019] In one possible implementation, generating the probability density matrix describing the distribution of the platform within the statistical range includes:

[0020] Based on the range of motion and / or the real-time requirements of the recall search, determine the matrix partitioning scale;

[0021] Based on the matrix partitioning scale, a probability density matrix describing the distribution of the platform within the statistical range is generated.

[0022] In one possible implementation, generating a probability density matrix describing the distribution of the platform within the statistical range based on the matrix partitioning scale includes:

[0023] Based on the matrix block scale and the statistical range, the geographical region mapped to each element in the matrix is ​​determined;

[0024] Determine the motion parameters of the platform when it moves to each of the geographical regions;

[0025] Based on the various motion parameters and their probability density functions, the probability density values ​​corresponding to each geographical region are calculated, and a probability density matrix is ​​generated.

[0026] In one possible implementation, the method further includes:

[0027] The target probability density matrix is ​​displayed in the form of a heatmap.

[0028] Secondly, the present invention also provides a motion platform position estimation system, comprising:

[0029] The convolution module is used to perform concatenated convolution on the probability density matrices describing the platform's distribution at various times during the call search process to obtain the target probability density matrix; wherein the central element of each probability density matrix is ​​the probability density value of the platform's expected distribution at the corresponding time.

[0030] The expectation overlay module is used to overlay the expected distributions at each time step to obtain the expected target distribution.

[0031] The distribution determination module is used to determine the distribution of the platform based on the target probability density matrix and the target distribution expectation.

[0032] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the motion platform position calculation method of any of the above-mentioned methods.

[0033] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the motion platform position estimation method described in any of the preceding claims.

[0034] The beneficial effects of this invention are:

[0035] In terms of mathematical principles, this invention treats various complex movements as independent stochastic processes. Therefore, during the calculation process, a probability density matrix describing the platform's distribution at each moment is constructed, and then the matrices are concatenated to superimpose the platform's position distribution after each complex maneuver. Compared to the Monte Carlo method, this invention does not require a large number of repeated deductions and can obtain the platform's distribution information more quickly. Compared to empirical methods, this invention can provide an objective and accurate distribution representation.

[0036] Meanwhile, considering that direct convolution typically requires expanding the size of each probability density matrix before convolution, the probability density matrix would significantly increase with the motion process, leading to a surge in computation. Therefore, this invention further standardizes the probability density matrix so that its central element corresponds to the probability density at the expected distribution location, used only to describe the probability density distribution around the expected distribution location. This eliminates the need for expansion during convolution, and the central element of the resulting target probability density matrix corresponds to the probability density value at the expected distribution location, effectively reducing the size of the probability density matrix. The expected distribution location is calculated using a separate superposition method. Finally, based on the superimposed target expected distribution location and the target probability density matrix obtained after convolution to describe the probability density around the expected distribution location, the specific distribution of the platform after multiple maneuvers can be clearly determined. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 is a flowchart of a method for calculating the position of a motion platform according to an embodiment of the present invention;

[0039] Figure 2 This invention provides a schematic diagram of the mapping between a centralized probability density matrix and an actual geographical region.

[0040] Figure 3 A flowchart illustrating another embodiment of the method for estimating the position of a motion platform provided by the present invention;

[0041] Figure 4 The heatmap of the centered probability density matrix in the initial state provided by this invention;

[0042] Figure 5 A schematic diagram showing the main distribution area and statistical range of the platform after a single maneuver, provided by the present invention.

[0043] Figure 6 Heatmap of the centralized probability density matrix of the platform after a single maneuver, provided by the present invention;

[0044] Figure 7 A heatmap of the target probability density matrix obtained after a single maneuver of the platform provided by this invention;

[0045] Figure 8 A schematic diagram of a structure of an embodiment of the motion platform position estimation system provided by the present invention;

[0046] Figure 9 A schematic diagram of an embodiment of the electronic device provided by the present invention. Detailed Implementation

[0047] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0049] In the description of the embodiments of this invention, unless otherwise stated, "a plurality of" means two or more. The terms "first," "second," etc., used in the embodiments of this invention are used to distinguish similar objects, and are not used to describe a specific order or sequence, nor to indicate or imply their relative importance or implicitly specify the number of indicated technical features. It should be understood that such data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class, and the number of objects is not limited; for example, a first object can be one or more.

[0050] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0051] Before demonstrating the implementation examples, we will first analyze the mathematical principles of the search process. For the searched platform, we assume that its initial position coordinates are two-dimensional random variables. Its probability density is Subsequently, its complex motion process can be broken down into several rounds of single motion, such as several rounds of uniform linear motion, turning motion, uniformly accelerated motion, etc. Each round of motion will cause the platform to displace; let the th round... The displacement is a two-dimensional random variable. Its probability density is After n movements, the final position of the platform is also a set of random variables, denoted as . Clearly, the displacements generated by each motion are independent of each other. Therefore, the random variable of the platform's final position can be expressed as:

[0052]

[0053] When synthesizing multiple complex probability densities, it is difficult to obtain analytical solutions of a specific form. This is especially true when the synthesized random variables... , When the variables are independent, numerical convolution can be used for probability synthesis.

[0054] Let random variable The probability density is ,random variable The probability density is ,and and If they are independent of each other, then the random variables .

[0055] Its probability density function for:

[0056]

[0057] The first moment (expectation) of a random variable satisfies:

[0058]

[0059] As can be seen from the above formula, the probability density of the combination of two independent random variables is the convolution of the probability densities of each random variable, and its expectation is the sum of the expectations of each random variable.

[0060] Based on the above mathematical principles, this invention designs a method for calculating the position of a motion platform, referring to... Figure 1 The methods include:

[0061] S101, during the call search process, the probability density matrices describing the platform's distribution at each time step are concatenated and convolved to obtain the target probability density matrix; where the central element of each probability density matrix is ​​the probability density value of the platform's expected distribution at the corresponding time step.

[0062] Call-to-call search refers to a search activity conducted after obtaining the platform's initial location and subsequent movement information, by calculating the platform's possible distribution area, and then heading to the area where the platform might be located.

[0063] The platform, or the object being searched, can be people, submarines, surface ships, anti-submarine patrol aircraft, drones, helicopters, etc.

[0064] A probability density matrix describes the numerical representation of a two-dimensional continuous probability distribution on a discretized grid. Each grid cell represents a real geographical region, and the value of each element in the matrix is ​​equal to the probability density value of the probability density function at the center point of that grid cell. In this embodiment, the central element of the probability density matrix at each time step is the probability density value of the platform at the expected distribution location at that time step. This type of matrix can be simply referred to as a centered probability density matrix.

[0065] Reference Figure 2 This diagram illustrates a mapping between a centralized probability density matrix and an actual geographical region, as provided by the present invention. Large squares represent the geographical region covered by the matrix, and smaller squares within the large squares represent smaller regions within the larger squares. The matrix represents the geographical area covered by each element. The blue ellipse represents the distribution area of ​​the platforms (taking a two-dimensional normal distribution as an example), and the black square represents the geographical area covered by the central element of the matrix, usually denoted by the expected value of the distribution (EX, EY). The probability density matrix can be obtained by calculating the probability density value at the center point of the small square by using the probability density function describing the platform distribution.

[0066] In one example, the various moments include , The corresponding probability density matrices are A, B, and C, respectively. The convolution process includes: A B C. By using cascaded convolution, the positional distribution of the platform after each motion can be superimposed.

[0067] In one example, the target probability density matrix obtained by convolution can be displayed in the form of a heatmap, so that the searcher can more intuitively understand the platform distribution.

[0068] S102, superimpose the expected distributions at each time point to obtain the expected distribution of the target distribution.

[0069] The expected distribution at the initial moment is the position expectation, while the expected distribution at subsequent moments is the displacement expectation. Therefore, by superimposing the expected distributions at each moment, we obtain the final position expectation of the platform, which is also the target distribution expectation.

[0070] S103. Determine the platform distribution based on the target probability density matrix and the target distribution expectation.

[0071] The center of the target probability density matrix corresponds to the probability density value at the expected target distribution. The target probability density matrix describes the probability density distribution of the platform around the expected target distribution. Therefore, based on the target probability density matrix and the expected target distribution, the platform's distribution can be determined. Consequently, during the call-to-response search process, the probability distribution at any given time can be calculated based on the event sequence triggered by the platform, assisting the searcher in conducting an effective search.

[0072] The motion platform position estimation method provided in this embodiment can be applied to a motion platform position estimation software system, which can run on a terminal device. The terminal device can be a tablet computer, vehicle-mounted device, aircraft-mounted device, augmented reality (AR) / virtual reality (VR) device, laptop computer, ultra-mobile personal computer (UMPC), netbook, personal digital assistant (PDA), mobile phone, etc. This embodiment does not impose any restrictions on the specific type of terminal device.

[0073] In summary, this embodiment constructs a probability density matrix describing the platform's distribution at various times, and then performs concatenated convolutions on the matrix to superimpose the platform's position distribution after each complex maneuver. Compared to the Monte Carlo method, it does not require a large number of repeated deductions and can obtain the platform's distribution information more quickly. Compared to empirical methods, it can provide an objective and accurate distribution representation.

[0074] Furthermore, this embodiment also standardizes the probability density matrix so that its central element corresponds to the probability density value at the expected distribution location, and is only used to describe the probability density distribution around the expected distribution. Therefore, it does not require outward expansion during convolution, effectively reducing the size of the probability density matrix after convolution. To better understand the effect of this embodiment, a specific example is provided below:

[0075] Suppose that for two probability density functions, the first random variable X1 has an expected probability density position of (0, 0) and its distribution range is a planar space of size 30m × 30m. The second random variable X2 has an expected probability density position of (100, 100) and its distribution range is a planar space of size 20m × 20m.

[0076] When calculating the distribution of X1+X2, if we do not emphasize centering, i.e. standardization, and directly construct the probability density matrix, the matrix needs to be able to cover both (0, 0) and (100, 100) positions. Taking each element in the matrix as representing a 1×1m space as an example, at least two 125×125 probability density matrices need to be constructed because the matrix needs to be expanded outward by (30+20) / 2 on the basis of 100×100.

[0077] If centering is performed, the probability density distribution of X1 can be described by a 30×30 matrix, and its expected value is recorded as (0, 0); the probability density distribution of X2 can be described by a 20×20 matrix, and its expected value is recorded as (100, 100). During convolution, only the 30×30 and 20×20 matrices need to be convolved, and the computational cost is significantly less than that of a 125×125 matrix convolution.

[0078] In terms of the calculation results, there will be no difference between the two calculation results, but centering can significantly reduce the matrix size required to describe the probability density function, thereby reducing the convolution calculation time.

[0079] In some embodiments of the present invention, reference is made to... Figure 3 The methods for estimating the location of the moving platform also include:

[0080] S301, based on the expected motion parameters of the platform at the first moment and the standard deviation of the expected motion parameters, combined with the 3σ criterion, determine the main distribution area of ​​the platform at the second moment; where the second moment is the moment following the first moment, and any moment other than the initial moment.

[0081] The 3σ criterion, also known as the three-standard-deviation criterion, states that in a normal distribution, approximately 99.73% of the data falls within three standard deviations before and after the mean. Based on the 3σ criterion, it can be determined that the distribution range of motion parameters is mainly concentrated in the desired motion parameters. Three times its standard deviation. Then, based on the main distribution range of the motion parameters, the main distribution area of ​​the platform after one maneuver can be determined.

[0082] Desired motion parameters may include desired speed, desired heading, etc.

[0083] Based on this step, the main distribution area of ​​the platform at each time point, except for the initial time point, can be calculated. For the initial time point, it can be assumed that the platform distribution follows a normal distribution, and the main distribution area of ​​the platform can be determined based on the characteristics of the normal distribution.

[0084] S302 defines the circumscribed quadrilateral of the main distribution area as the statistical range.

[0085] S303 generates a probability density matrix describing the distribution of the platform within the statistical range.

[0086] The main distribution area is usually irregular in shape. To facilitate the generation of the probability density matrix, its circumscribed quadrilateral can be used as the statistical range to generate the probability density matrix.

[0087] In summary, this embodiment determines the statistical range, i.e. the mapping range of the probability density matrix, based on the 3σ criterion, which can significantly reduce the size of the matrix and improve the speed of convolution operations.

[0088] In some embodiments of the present invention, the motion platform position estimation method further includes:

[0089] Adjust the statistical scope according to the real-time requirements of recall search.

[0090] For example, if the real-time requirement is high, the statistical scope should be appropriately narrowed; if the real-time requirement is low, the statistical scope should be appropriately expanded.

[0091] In some embodiments of the present invention, S301 further includes:

[0092] When the platform's motion range exceeds the preset motion range threshold, the main distribution area of ​​the platform at the second moment is determined based on the platform's expected motion parameters and the standard deviation of the expected motion parameters at the first moment, combined with the 3σ criterion.

[0093] Methods for estimating the position of a motion platform also include:

[0094] When the platform's motion range is less than or equal to the preset motion range threshold, the entire motion range will be used as the main distribution area.

[0095] The range of motion refers to the spatial area encompassing all positions that the platform can reach after a single movement.

[0096] This embodiment dynamically selects the main distribution range based on the platform's motion range, which can balance the requirements for real-time performance and distribution accuracy.

[0097] In some embodiments of the present invention, S303 includes:

[0098] The matrix partitioning scale is determined based on the range of motion and / or the real-time requirements of the recall search.

[0099] Based on the matrix block scale, a probability density matrix describing the distribution of the platform within the statistical range is generated.

[0100] Among them, the matrix partitioning scale can be used express, This represents the scale of the actual region represented by each element in the matrix along the x and y axes. When the range of motion is large, the matrix block scale can be set larger, and vice versa. When real-time requirements are low, the matrix block scale can be set larger, and vice versa.

[0101] Based on the matrix partitioning scale and statistical range, the number of elements in the probability density matrix and the position index of each element can be determined. Then, by combining the probability density function to calculate the probability density value of each element position, the probability density matrix can be obtained.

[0102] In some embodiments of the present invention, the step of generating a probability density matrix describing the distribution of the platform within a statistical range based on the matrix block scale includes:

[0103] Based on the matrix block scale and statistical range, determine the geographical region mapped to each element in the matrix;

[0104] Determine the motion parameters of the platform as it moves to different geographical areas;

[0105] Based on the various motion parameters and their probability density functions, the probability density values ​​for each geographical region are calculated, and a probability density matrix is ​​generated.

[0106] Referring to Table 1, a probability density matrix generation algorithm provided by this invention is shown.

[0107]

[0108] Regarding step 7 of the algorithm, the following example illustrates its reverse reasoning process and probability density calculation. Assume the platform moves at a constant linear velocity, and the velocity follows (v... min v max The uniform distribution of ) The heading follows a normal distribution ,

[0109] Solve for the probability density of the platform starting from the origin (0, 0) and reaching the position (x, y) after time t.

[0110] First, consider the platform's distribution in (x, y) as two events. The first event is that its heading needs to be arctan(y / x); the second event is that its speed needs to be sqrt(x^2+y^2) / t.

[0111] Then, substitute the speed event and heading event into... and Find its corresponding probability density and Then the probability density of the target distribution in (x, y) can be described as: .

[0112] It should be noted that, and It can be adapted to the actual situation, for example, It can also follow a normal distribution.

[0113] The present invention will be described in more detail below with an example.

[0114] Suppose we are performing a call-to-response search on a platform, and the expected initial location of the platform is... = (10, 100), the distribution coordinates follow a two-dimensional Gaussian distribution, with variances of 30 in both the x and y directions. The platform then moves, with the desired heading... 90°, desired speed 10 m / s, heading standard deviation The standard deviation of the speed is 0.2 rad. Given a speed of 1 m / s, we need to determine the probability density of the platform's location after 60 seconds to assist in designing the subsequent search scheme.

[0115] First, construct the centered probability density matrix of the initial position, given its expected position. Its distribution characteristics are Gaussian, therefore its probability density is:

[0116]

[0117] Let the matrix block size be 10m, then the position corresponding to the central element is (10, 100). The centered probability density matrix is ​​formed according to the method in Table 1, and its heatmap is shown below. Figure 4 As shown. Figure 4 The horizontal and vertical axes in the graph represent geographical coordinates, with the x-axis representing east and the y-axis representing north, in meters (m). The color represents the magnitude of the probability density value.

[0118] Furthermore, solve for the centered probability density matrix after one maneuver. Given that the desired heading is 90° and the desired speed is 10 m / s, then EX1 = t Expected speed cos expected heading = 60 10 sin( / 2) = 600, EY1 = t Expected speed cos expected heading = 60 10 cos( / 2)=0, the position of the central element of the centralized probability density matrix after the maneuver is Based on 3 In principle, only heading 3 is retained. and speed 3 The distribution within the range, then the main distribution area and statistical range are as follows: Figure 5 The partial sector and rectangular regions are shown in Table 1. The centered probability density matrix is ​​formed according to the method in Table 1, and the heatmap of the centered probability density matrix is ​​shown below. Figure 6 As shown.

[0119] Then, convolve these two probability density matrices to obtain the matrix as shown below. Figure 7 As shown, Figure 4 and Figure 6 It can represent the platform's relative position after a single round of movement in the form of a heatmap, and further form a matrix convolution. Figure 7 The corresponding probability heatmap shows that the superposition of the two distributions increases the distribution range of the platform and integrates the characteristics of the two distributions, proving the effectiveness of the present invention.

[0120] Finally, and The summation, combined with the probability density matrix obtained after convolution, yields the final probability distribution.

[0121] Referring to Table 2, a comparison table of multiple algorithm implementations of the above examples is shown. As can be seen from Table 2, the centered probability density matrix convolution method proposed in this invention can significantly reduce the matrix scale, shortening the computation time from 0.1802 to 0.0027. This significantly improves computational efficiency, enabling the technique to support real-time estimation of target distribution areas in call search.

[0122] Table 2 Algorithm Comparison

[0123]

[0124] Reference Figure 8 The diagram illustrates a structural schematic of an embodiment of the motion platform position estimation system provided by the present invention. The system 800 includes:

[0125] The convolution module 801 is used to perform concatenated convolution on the probability density matrix describing the distribution of the platform at each time step during the call search process to obtain the target probability density matrix; wherein, the central element of each probability density matrix is ​​the probability density value of the platform at the expected distribution at the corresponding time step.

[0126] The expectation superposition module 802 is used to superimpose the expected distributions at each time step to obtain the expected target distribution.

[0127] The distribution determination module 803 is used to determine the distribution of the platform based on the target probability density matrix and the target distribution expectation.

[0128] It should be noted that the implementation principles or processes of the above modules can be referred to the aforementioned implementation examples of the motion platform position estimation method, and will not be elaborated here.

[0129] Reference Figure 9 The present invention illustrates an electronic device 900. The electronic device 900 includes a processor 901, a memory 902, and a display 903. Figure 9 Only some components of the electronic device 900 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0130] In some embodiments, processor 901 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 902 or process data, such as the motion platform position estimation method of the present invention.

[0131] In some embodiments, processor 901 may be a single server or a group of servers. The server group may be centralized or distributed. In some embodiments, processor 901 may be local or remote. In some embodiments, processor 901 may be implemented on a cloud platform. In one embodiment, the cloud platform may include a private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, internal cloud, multi-cloud, etc., or any combination thereof.

[0132] In some embodiments, memory 902 may be an internal storage unit of electronic device 900, such as a hard disk or memory of electronic device 900. In other embodiments, memory 902 may also be an external storage device of electronic device 900, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on electronic device 900.

[0133] Furthermore, the memory 902 may include both internal storage units of the electronic device 900 and external storage devices. The memory 902 is used to store application software and various types of data installed on the electronic device 900.

[0134] In some embodiments, display 903 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 903 is used to display information from electronic device 900 and to display a visual user interface. Components 901-903 of electronic device 900 communicate with each other via a system bus.

[0135] In one embodiment, when the processor 901 executes the motion platform position calculation program in the memory 902, the following steps can be implemented:

[0136] During the call search process, the probability density matrices describing the platform's distribution at each time step are concatenated and convolved to obtain the target probability density matrix; where the central element of each probability density matrix is ​​the probability density value of the platform's expected distribution at the corresponding time step.

[0137] The expected distributions at each time point are superimposed to obtain the expected target distribution.

[0138] The platform distribution is determined based on the target probability density matrix and the expected value of the target distribution.

[0139] It should be understood that when the processor 901 executes the motion platform position calculation program in the memory 902, in addition to the functions mentioned above, it can also perform other functions, as can be found in the description of the corresponding method embodiments above.

[0140] Furthermore, this embodiment of the invention does not specifically limit the type of electronic device 900 mentioned. Electronic device 900 can be a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, aircraft-borne equipment, laptop computer, or other portable electronic devices. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic device can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the invention, electronic device 900 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0141] In one embodiment, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by the processor, implements the steps of any of the motion platform position estimation methods described above.

[0142] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0143] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for estimating the position of a motion platform, characterized in that, include: During the call search process, the probability density matrices describing the platform's distribution at each time step are concatenated and convolved to obtain the target probability density matrix; wherein, the central element of each probability density matrix is ​​the probability density value of the platform's expected distribution at the corresponding time step. The expected distributions at each time point are superimposed to obtain the expected target distribution. The distribution of the platform is determined based on the target probability density matrix and the target distribution expectation.

2. The method for calculating the position of a motion platform according to claim 1, characterized in that, The method further includes: Based on the expected motion parameters of the platform at the first moment and the standard deviation of the expected motion parameters, combined with the 3σ criterion, the main distribution area of ​​the platform at the second moment is determined; wherein, the second moment is the moment following the first moment, and any moment other than the initial moment among the various moments; The circumscribed quadrilateral of the main distribution area is used as the statistical range; Generate a probability density matrix describing the distribution of the platform within the statistical range.

3. The method for estimating the position of a motion platform according to claim 2, characterized in that, The method further includes: The statistical range is adjusted according to the real-time requirements of recall search.

4. The method for estimating the position of a motion platform according to claim 2, characterized in that, The determination of the platform's main distribution region at the second pre-second time step, based on the platform's expected motion parameters at the first moment and the standard deviation of those parameters, combined with the 3σ criterion, includes: When the motion range of the platform exceeds a preset motion range threshold, the main distribution area of ​​the platform at the second time is determined based on the expected motion parameters of the platform at the first time and the standard deviation of the expected motion parameters, combined with the 3σ criterion.

5. The method for estimating the position of a motion platform according to claim 2, characterized in that, The generation of the probability density matrix describing the distribution of the platform within the statistical range includes: Based on the range of motion and / or the real-time requirements of the recall search, determine the matrix partitioning scale; Based on the matrix partitioning scale, a probability density matrix describing the distribution of the platform within the statistical range is generated.

6. The method for calculating the position of a motion platform according to claim 5, characterized in that, The step of generating a probability density matrix describing the distribution of the platform within the statistical range based on the matrix block scale includes: Based on the matrix block scale and the statistical range, the geographical region mapped to each element in the matrix is ​​determined; Determine the motion parameters of the platform when it moves to each of the geographical regions; Based on the various motion parameters and their probability density functions, the probability density values ​​corresponding to each geographical region are calculated, and a probability density matrix is ​​generated.

7. The method for estimating the position of a motion platform according to claim 1, characterized in that, The method further includes: The target probability density matrix is ​​displayed in the form of a heatmap.

8. A motion platform position estimation system, characterized in that, include: The matrix convolution module is used to perform concatenated convolution on the probability density matrices describing the platform's distribution at various times during the call search process to obtain the target probability density matrix; wherein, the central element of each probability density matrix is ​​the probability density value of the platform's expected distribution at the corresponding time. The expectation overlay module is used to overlay the expected distributions at each time step to obtain the expected target distribution. The distribution determination module is used to determine the distribution of the platform based on the target probability density matrix and the target distribution expectation.

9. An electronic device, characterized in that, The invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the motion platform position calculation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the motion platform position calculation method as described in any one of claims 1 to 7.

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