Maritime moving target motion prediction method and device based on limited prior information

Abstract

The present application relates to the technical field of position estimation, in particular to a sea mobile target motion prediction method and device based on limited prior information. The method comprises the following steps: acquiring prior information; constructing a tangent plane coordinate system based on the prior information; calculating a grid side length; determining a unit time step; constructing a voyage transfer probability density distribution function and a heading transfer probability density distribution function; determining a task area, performing grid processing on the task area based on the grid side length, performing discrete point processing on each grid, and acquiring a discrete point one-dimensional vector of each grid; constructing a transfer distance matrix based on a starting grid and a target grid, constructing a transfer angle matrix based on the starting grid, the target grid and a destination point, and acquiring a transfer probability matrix based on the transfer distance matrix and the transfer angle matrix. In this way, the target key area can be quickly searched through observation under limited prior information, thereby improving the target search perception efficiency.

Description

Technical Field

[0001] This invention relates to the field of position estimation technology, and specifically to a method and apparatus for predicting the motion of a moving target at sea based on limited prior information. Background Technology

[0002] Position estimation of moving targets at sea is a key technology in fields such as maritime surveillance, maritime traffic management, military reconnaissance, search and rescue operations, fisheries regulation, and marine scientific research. Due to the unique characteristics of the marine environment (such as lack of fixed reference points, limited communication, and complex weather and sea conditions), rapid and efficient position estimation of maritime targets (such as ships, submarines, buoys, and unmanned surface vessels) has significant strategic and practical value. Against this backdrop, accurate position estimation of moving targets at sea (such as ships, unmanned surface vessels, buoys, and even underwater vehicles) has become a crucial foundational technology for marine sensing and search.

[0003] However, the marine environment is characterized by its wide reach, dynamism, and complexity, posing numerous challenges to target location estimation. On the one hand, the lack of continuous communication and navigation infrastructure in offshore areas makes Global Navigation Satellite System (GNSS) signals susceptible to interference or obstruction, and cooperative methods such as Automatic Identification System (AIS) suffer from data loss, delays, or even deliberate shutdowns. On the other hand, non-cooperative targets cannot actively provide location information, requiring detection by passive / active sensors such as radar, optical remote sensing, electronic reconnaissance, or underwater acoustics. These observations are often short-lived due to factors such as endurance, field of view, and orbit, resulting in significant uncertainty in the observation results.

[0004] To maximize the efficiency of perception and search, and to clearly identify the key areas for perception and search, it is necessary to estimate the position of moving targets. Traditional position estimation methods are mostly based on simplified uniform or uniformly accelerated motion models, constructing temporal state transition functions and employing linear estimation algorithms such as Kalman filtering, which are effective under ideal conditions. However, under conditions of limited target information, due to the large amount of uncertainty, it is difficult to obtain deterministic target trajectory data; furthermore, the target's motion intention is ambiguous, and the trajectory is difficult to describe by a clear function. Therefore, developing a target position estimation method for ambiguous scenarios has become a critical scientific and engineering problem that urgently needs to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a method and apparatus for predicting the motion of moving targets at sea based on limited prior information. This method can quickly search key areas of targets through observation under limited prior information, thereby improving the efficiency of target search and perception.

[0006] In a first aspect of the present invention, a method for predicting the motion of a moving target at sea based on limited prior information is provided, comprising:

[0007] Step 1. Obtain prior information; construct a tangent plane coordinate system based on the prior information; calculate the mesh edge length; determine the unit time step;

[0008] Step 2. Construct the range transfer probability density distribution function and the heading transfer probability density distribution function;

[0009] Step 3. Determine the task area, perform gridding on the task area based on the grid edge length, and perform discrete point processing on each grid to obtain a one-dimensional vector of discrete points for each grid.

[0010] Step 4. Construct a transfer distance matrix based on the starting grid and the target grid, construct a transfer angle matrix based on the starting grid, the target grid, and the destination point, and obtain the transfer probability matrix based on the transfer distance matrix and the transfer angle matrix; obtain the transfer probability from the starting grid to the target grid based on the transfer probability matrix;

[0011] Step 5. Obtain the prior weight of each target point based on prior information, calculate the global probability of each target grid based on the transition probability and prior weight of each target grid, and determine the key grid based on the global probability of each target grid.

[0012] Step 6. Observe the key grid. If the target is found, the task ends; if the target is not found, update the prior weights and return to step 1.

[0013] As a preferred embodiment of the present invention, in step 1, obtaining prior information specifically includes obtaining the target's last confirmed position, the target's maximum speed, the target's last confirmed time, the initial time of satellite overpass, possible destination points, and the prior weight of each destination point;

[0014] Step 1, calculating the grid side length specifically includes:

[0015] The grid side length is calculated using the following formula:

[0016]

[0017] Where is the grid side length. For an effective imaging swath aspect ratio, To observe the swath width of the satellite's nadir point;

[0018] In step 1, determining the unit time step specifically includes:

[0019] The allowable range for a unit time step is determined using the following formula:

[0020]

[0021] in, Let be the grid side length. To achieve the target maximum speed, The unit time step;

[0022] The unit time step is determined based on the allowable range of the unit time step and the following formula:

[0023]

[0024] in, For a unit time step, The initial moment of satellite transit. For the final confirmation of the target, This represents the minimum allowed number of prediction iterations.

[0025] As a preferred embodiment of the present invention, in step 2, the range transfer probability density distribution function is constructed using the following formula:

[0026]

[0027] in, Let be the probability density distribution function of the flight transfer. To achieve the target maximum speed, For the desired speed, For the speed standard deviation, The target's current speed, The standard score is The standard Gaussian cumulative distribution function, The standard score is The standard Gaussian cumulative distribution function;

[0028] The heading transition probability density distribution function is constructed using the following formula:

[0029]

[0030] in, Let be the probability density distribution function for the heading transition. For the desired heading angle, The standard deviation of the heading angle. For the target heading angle, The standard score is The standard Gaussian cumulative distribution function, The standard score is The standard Gaussian cumulative distribution function.

[0031] As a preferred embodiment of the present invention, step 4, constructing the transfer distance matrix based on the starting grid and the target grid, specifically includes:

[0032] Step 41. Obtain the i-th discrete point in the starting grid and the j-th discrete point in the target grid. Obtain the X-axis distance and Y-axis distance based on the i-th and j-th discrete points. Calculate the inter-point distance based on the X-axis distance and Y-axis distance. Use the inter-point distance as the data value in the i-th row and j-th column of the transfer distance matrix. Repeat step 41 until the transfer distance matrix is ​​constructed.

[0033] In a preferred embodiment of the present invention, step 4, constructing the transfer angle matrix based on the starting grid, the target grid, and the target point, specifically includes:

[0034] Step 421. Obtain the i-th discrete point in the starting grid, obtain the j-th discrete point in the target grid, obtain a vector based on the i-th and j-th discrete points, and use the vector as the data value in the i-th row and j-th column of the first vector matrix; repeat step 421 until the first vector matrix is ​​constructed.

[0035] Step 422. Obtain the i-th discrete point in the starting grid, obtain the destination point, and obtain a vector 2 based on the i-th discrete point and the destination point. Use the vector 2 as the data value of all columns in the i-th row of the second vector matrix; repeat step 422 until the second vector matrix is ​​constructed.

[0036] Step 423. Obtain vector one in the i-th row and j-th column of the first vector matrix, and obtain vector two in the i-th row and j-th column of the second vector matrix. Calculate an angle based on vector one and vector two, and use the angle as the data value in the i-th row and j-th column of the transfer angle matrix. Repeat step 423 until the transfer angle matrix is ​​constructed.

[0037] As a preferred embodiment of the present invention, step 4, obtaining the transfer probability matrix based on the transfer distance matrix and the transfer angle matrix, specifically includes:

[0038] Step 431. Obtain the distance probability matrix based on the transfer distance matrix, unit time step, and range transfer probability density distribution function;

[0039] Step 432. Obtain the angle probability matrix based on the transfer angle matrix and the heading transfer probability density distribution function;

[0040] Step 433. Obtain the transition probability matrix based on the distance probability matrix and the angle probability matrix.

[0041] In a preferred embodiment of the present invention, step 6, updating the prior weights, specifically includes:

[0042] Step 61. Obtain the transition probability from the starting grid to the critical grid based on different destination points;

[0043] Step 62. Calculate the likelihood of no targets being found in the key grid based on different target points;

[0044] Step 63. Update the prior weights of different target points based on their likelihood.

[0045] In a second aspect of the present invention, a device for predicting the motion of a moving maritime target based on limited prior information is provided, comprising:

[0046] The preprocessing module is configured to: acquire prior information; construct a tangent plane coordinate system based on the prior information; calculate the mesh edge length; and determine the unit time step.

[0047] The function construction module is configured to construct the range transfer probability density distribution function and the heading transfer probability density distribution function.

[0048] The discrete point processing module is configured to determine the task area, perform gridding on the task area based on the grid edge length, perform discrete point processing on each grid, and obtain a one-dimensional vector of discrete points for each grid.

[0049] The transfer probability acquisition module is configured to construct a transfer distance matrix based on the starting grid and the target grid, construct a transfer angle matrix based on the starting grid, the target grid, and the destination point, and obtain a transfer probability matrix based on the transfer distance matrix and the transfer angle matrix; and obtain the transfer probability from the starting grid to the target grid based on the transfer probability matrix.

[0050] The critical grid determination module is configured to obtain the prior weight of each target point based on prior information, calculate the global probability of each target grid based on the transition probability and prior weight of each target grid, and determine the critical grid based on the global probability of each target grid.

[0051] The prior weight update module is configured to observe the key grid. If the target is found, the task ends; if the target is not found, the prior weights are updated.

[0052] In a third aspect of the present invention, a computer program product is provided, including a computer program that, when executed by a processor, implements the method provided according to the first aspect.

[0053] In a fourth aspect of the present invention, an electronic device is provided, including one or more processors and a memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the method provided in the first aspect.

[0054] In summary, the present invention has the following beneficial effects:

[0055] The method of this invention is applicable to scenarios where the true intent of a target is unknown but a limited set of intent hypotheses can be constructed based on prior information, such as maritime search and rescue, traffic monitoring, military reconnaissance, fisheries monitoring, and marine scientific research. It can quickly search key areas of a target through observation under limited prior information, thereby improving the efficiency of target search and perception. By describing the target's motion as a Gaussian distribution along the direction of the destination point, combined with the target's fuzzy intent, a Bayesian intent update-based approach is used to iteratively update the target intent weights based on observation information, thereby searching for the target's true intent direction. In this process, to increase computational accuracy while reducing computational complexity, a gridded partitioning method is adopted to limit position iteration to a certain range. Simultaneously, a transition probability matrix is ​​constructed based on vectorized full points, transforming cyclic calculations into calculations of distance and angle probability matrices, thus improving computational efficiency without reducing computational accuracy.

[0056] Further or more detailed beneficial effects will be described in conjunction with specific embodiments in the detailed implementation. Attached Figure Description

[0057] Figure 1 A flowchart of the motion prediction method for moving targets at sea based on limited prior information according to an embodiment of the present invention is shown;

[0058] Figure 2 A block diagram of a maritime moving target motion prediction device based on limited prior information according to an embodiment of the present invention is shown.

[0059] Figure 3 A block diagram of an electronic device according to an embodiment of the present invention is shown;

[0060] Figure 4 This diagram illustrates a gridded processing of the task area according to an embodiment of the present invention.

[0061] Figure 5 This diagram illustrates a method for discretizing a mesh according to an embodiment of the present invention. Detailed Implementation

[0062] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the invention. It should be understood that the accompanying drawings and embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the invention.

[0063] In the description of embodiments of the present invention, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0064] This invention provides a method for predicting the motion of a moving maritime target based on limited prior information, such as... Figure 1 As shown, the method includes:

[0065] Step 1. Obtain prior information; construct a tangent plane coordinate system based on prior information; calculate the grid edge length; determine the unit time step.

[0066] In this embodiment, the prior information includes the target's last confirmed location, maximum target speed, last confirmed time, initial time of satellite overpass, possible destination points, and prior weights for each destination point. The maritime moving target motion prediction method in this embodiment has the following prerequisite: the moving target's motion is purposeful, i.e., the possible destination points of the target are known in advance. Initially, the prior weights for each destination point can be manually set based on experience; subsequently, if no target is detected, the prior weights are automatically updated.

[0067] This embodiment does not require precise knowledge of the moving target's final destination. Instead, it constructs a reasonable set of destination intent assumptions based on limited prior information. In the following typical scenarios, although the exact destination cannot be predicted, several possible destinations can be reasonably inferred based on domain knowledge, historical patterns, or environmental characteristics:

[0068] Maritime search and rescue scenario: Missing vessels or personnel may head to the nearest island, waterway, rescue station, port, or refuge. A reasonable set of destination intentions can be constructed based on the location of the missing vessel, ocean currents, wind direction, and historical search and rescue data.

[0069] Maritime traffic surveillance scenario: Non-cooperative targets (such as vessels with AIS disabled) may head towards nearby major ports, anchorages, supply points, fishing grounds, or sensitive waters. A reasonable set of destination intent hypotheses can be constructed through historical track statistics, route pattern recognition, and cargo type inference.

[0070] Marine science research scenario: Unmanned underwater vehicles or buoys may travel to pre-designated sampling points, recovery points, communication relay points, or obstacle avoidance areas. A reasonable set of destination intent hypotheses can be constructed based on mission planning, ocean models, and research objectives.

[0071] In the above scenario, although the target's true intention (i.e., the true destination) is unknown, a set of destination intention hypotheses can be constructed based on limited prior information. This embodiment is designed for this condition of "limited prior information + multiple intention hypotheses." Through a Bayesian update mechanism, it continuously adjusts the weights of each intention hypothesis using observed data, thereby achieving adaptive estimation of the probability distribution of the target location and significantly improving search and planning efficiency.

[0072] In this embodiment, assuming the final confirmed location of the target is "20°N, 115°E", then the point at latitude 20° and longitude 115° is taken as the center point of the tangent plane coordinate system. At this point, the tangent plane coordinate system is completed.

[0073] In this embodiment, calculating the grid side length specifically includes:

[0074] The grid side length is calculated using the following formula:

[0075]

[0076] in, Let be the grid side length. For an effective imaging swath aspect ratio, To observe the swath width of the satellite's nadir point.

[0077] Assuming the effective imaging swath ratio is 0.95 and the observation swath at the nadir of the observed satellite is 100km, then the grid side length is 13.4km.

[0078] In this embodiment, determining the unit time step specifically includes:

[0079] The allowable range for a unit time step is determined using the following formula:

[0080]

[0081] in, Let be the grid side length. To achieve the target maximum speed, The unit time step.

[0082] Assuming the target's maximum speed is 25 km / h and the grid side length is 15 km, then the allowable range for the unit time step is between 18 minutes and 72 minutes.

[0083] The unit time step is determined based on the allowable range of the unit time step and the following formula:

[0084]

[0085] in, For a unit time step, The initial moment of satellite transit. For the final confirmation of the target, This represents the minimum allowed number of prediction iterations.

[0086] Assuming the time difference between the initial satellite transit time and the final target confirmation time is 45 minutes, we will first take... The value is 1, so the unit time step is 45 minutes, which is within the allowable range. Therefore, the final unit time step is determined to be 45 minutes.

[0087] Assuming the time difference between the initial satellite transit time and the final target confirmation time is 80 minutes, first take... The value is 1, at which point the unit time step is 80 minutes, which is outside the allowable range; therefore, we need to take another value. The value is 2, and the unit time step is 40 minutes. Within the allowable range, the unit time step is finally determined to be 40 minutes.

[0088] Step 2. Construct the range transfer probability density distribution function and the heading transfer probability density distribution function.

[0089] In this embodiment, the range transfer probability density distribution function is constructed using the following formula:

[0090]

[0091] in, Let be the probability density distribution function of the flight transfer. To achieve the target maximum speed, For the desired speed, For the speed standard deviation, The target's current speed, The standard score is The standard Gaussian cumulative distribution function, The standard score is The standard Gaussian cumulative distribution function.

[0092] In this embodiment, the heading change probability density distribution function is constructed using the following formula:

[0093]

[0094] in, Let be the probability density distribution function for the heading transition. For the desired heading angle, The standard deviation of the heading angle. For the target heading angle, The standard score is The standard Gaussian cumulative distribution function, The standard score is The standard Gaussian cumulative distribution function.

[0095] The range transfer probability density distribution function and the heading transfer probability density distribution function constructed in this step will be used to obtain the transfer probability matrix later.

[0096] Step 3. Determine the task area. Based on the grid edge length, the task area is gridded. Each grid is then discretized to obtain a one-dimensional vector of the discrete points of each grid.

[0097] In this embodiment, a circular region with a suitable radius is determined with the center point of the tangent plane coordinate system as the center, and then the task region is meshed (the mesh side length is the mesh side length calculated in step 1). For details, please refer to... Figure 4 As shown.

[0098] Taking a single grid as an example, assuming a grid yields 4 discrete points, refer to... Figure 5 As shown, the discrete points include a1(X1, Y1); a2(X2, Y1); a3(X1, Y2); and a4(X2, Y2). The resulting one-dimensional vector of discrete points from this grid is: (X1, Y1), (X2, Y1), (X1, Y2), (X2, Y2). The same operation applies to other grids, yielding the corresponding one-dimensional vectors of discrete points.

[0099] Step 4. Construct a transfer distance matrix based on the starting grid and the target grid, construct a transfer angle matrix based on the starting grid, the target grid and the destination point, and obtain the transfer probability matrix based on the transfer distance matrix and the transfer angle matrix; obtain the transfer probability from the starting grid to the target grid based on the transfer probability matrix.

[0100] This embodiment assumes that the current starting grid is the grid including the center point of the tangent plane coordinate system (referred to as grid G1), and the current target grid is the grid adjacent to the starting grid and located to the upper right of the starting grid (referred to as grid G2). This embodiment assumes that the one-dimensional vector of discrete points in grid G1 is: (G1X1, G1Y1), (G1X2, G1Y1), (G1X1, G1Y2), (G1X2, G1Y2); and the one-dimensional vector of discrete points in grid G2 is: (G2X1, G2Y1), (G2X2, G2Y1), (G2X1, G2Y2), (G2X2, G2Y2).

[0101] Let's assume the current target point is m1, with coordinates "m1X, m1Y".

[0102] In step 4 of this embodiment, constructing the transfer distance matrix based on the starting grid and the target grid specifically includes:

[0103] Step 41. Obtain the i-th discrete point in the starting grid and the j-th discrete point in the target grid. Obtain the X-axis distance and Y-axis distance based on the i-th and j-th discrete points. Calculate the inter-point distance based on the X-axis distance and Y-axis distance. Use the inter-point distance as the data value in the i-th row and j-th column of the transfer distance matrix. Repeat step 41 until the transfer distance matrix is ​​constructed.

[0104] For example, obtain the first discrete point "G1X1, G1Y1" in grid G1 and the first discrete point "G2X1, G2Y1" in grid G2. The distance in the X direction is "G2X1-G1X1" and the distance in the Y direction is "G2Y1-G1Y1". Then, sum the squares of the X-direction distance and the squares of the Y-direction distance and take the square root to obtain the distance between the points. Use this distance between the points as the data value in the first row and first column of the transition distance matrix.

[0105] For example, obtain the 4th discrete point "G1X2, G1Y2" in grid G1 and the 4th discrete point "G2X2, G2Y2" in grid G2. The distance in the X direction is "G2X2-G1X2" and the distance in the Y direction is "G2Y2-G1Y2". Then, sum the squares of the X-direction distance and the squares of the Y-direction distance and take the square root to obtain the distance between the points. Use this distance between the points as the data value in the 4th row and 4th column of the transition distance matrix.

[0106] The number of rows in the transition distance matrix corresponds to the number of discrete points in the one-dimensional vector of the starting grid; similarly, the number of columns in the transition distance matrix corresponds to the number of discrete points in the one-dimensional vector of the target grid. When both the starting and target grids have 4 discrete points in their one-dimensional vectors, the transition distance matrix is ​​a 4x4 matrix.

[0107] In step 4 of this embodiment, constructing the transfer angle matrix based on the starting mesh, the target mesh, and the target point specifically includes:

[0108] Step 421. Obtain the i-th discrete point in the starting grid, obtain the j-th discrete point in the target grid, obtain a vector 1 based on the i-th and j-th discrete points, and use the vector 1 as the data value in the i-th row and j-th column of the first vector matrix; repeat step 421 until the first vector matrix is ​​constructed.

[0109] For example, if we obtain the first discrete point "G1X1, G1Y1" in grid G1 and the first discrete point "G2X1, G2Y1" in grid G2, then the obtained vector one is "G2X1-G1X1, G2Y1-G1Y1". This vector one is used as the data value in the first row and first column of the first vector matrix.

[0110] For example, if we obtain the 4th discrete point "G1X2, G1Y2" in grid G1 and the 4th discrete point "G2X2, G2Y2" in grid G2, then the obtained vector 1 is "G2X2-G1X2, G2Y2-G1Y2". This vector 1 is used as the data value in the 4th row and 4th column of the first vector matrix.

[0111] The number of rows in the first vector matrix corresponds to the number of discrete points in the one-dimensional vector of the starting grid; the number of columns in the first vector matrix corresponds to the number of discrete points in the one-dimensional vector of the target grid. When both the starting and target grids have 4 discrete points in their one-dimensional vector, the first vector matrix is ​​a 4x4 matrix.

[0112] Step 422. Obtain the i-th discrete point in the starting grid, obtain the destination point, obtain a vector II based on the i-th discrete point and the destination point, and use the vector II as the data value of all columns in the i-th row of the second vector matrix; repeat step 422 until the second vector matrix is ​​constructed.

[0113] For example, if we obtain the first discrete point "G1X1, G1Y1" in grid G1 and the destination point "m1X, m1Y", then the obtained vector two is "m1X-G1X1, m1Y-G1Y1". This vector two is used as the data value of the first row and first column, the first row and second column, the first row and third column, and the first row and fourth column in the second vector matrix.

[0114] For example, if we obtain the 4th discrete point "G1X2, G1Y2" in grid G1 and the destination point "m1X, m1Y", then the obtained vector 2 is "m1X-G1X2, m1Y-G1Y2". This vector 2 is used as the data value of the 4th row and 1st column, the 4th row and 2nd column, the 4th row and 3rd column, and the 4th row and 4th column in the second vector matrix.

[0115] The number of rows in the second vector matrix corresponds to the number of discrete points in the one-dimensional vector of the starting grid; the number of columns in the second vector matrix corresponds to the number of discrete points in the one-dimensional vector of the target grid. When both the starting and target grids have 4 discrete points in their one-dimensional vector, the second vector matrix will be a 4x4 matrix.

[0116] Step 423. Obtain vector one in the i-th row and j-th column of the first vector matrix, and obtain vector two in the i-th row and j-th column of the second vector matrix. Calculate an angle based on vector one and vector two, and use the angle as the data value in the i-th row and j-th column of the transfer angle matrix. Repeat step 423 until the transfer angle matrix is ​​constructed.

[0117] When the first vector matrix is ​​a matrix with a certain number of rows and columns, the transition angle matrix is ​​also a matrix with a certain number of rows and columns. For example, when the first vector matrix is ​​a 4x4 matrix, the transition angle matrix is ​​also a 4x4 matrix.

[0118] In addition, the data value in the i-th row and j-th column of the transfer angle matrix (i.e. the angle between the two vectors) is calculated from vector one in the i-th row and j-th column of the first vector matrix and vector two in the i-th row and j-th column of the second vector matrix.

[0119] For example, to obtain the vector "G2X1-G1X1, G2Y1-G1Y1" in the first row and first column of the first vector matrix (assuming it is used as...) (represented), obtain the vector "m1X-G1X1, m1Y-G1Y1" in the first row and first column of the second vector matrix (assuming it is used). (represented by the following formula) The angle between two vectors can be calculated. :

[0120]

[0121] This allows us to obtain the data value in the first row and first column of the transition angle matrix.

[0122] In step 4 of this embodiment, obtaining the transition probability matrix based on the transition distance matrix and the transition angle matrix specifically includes:

[0123] Step 431. Obtain the distance probability matrix based on the transfer distance matrix, unit time step, and range transfer probability density distribution function.

[0124] In this embodiment, the unit time step has been obtained in step 1, and the range transfer probability density distribution function has been obtained in step 2.

[0125] For example, obtain the distance value in the first row and first column of the transfer distance matrix, divide the distance value by the unit time step to get the target's current speed, and finally substitute the target's current speed into the range transfer probability density distribution function to get a distance probability value. Use this distance probability value as the data value in the first row and first column of the distance probability matrix.

[0126] For example, obtain the distance value in the first row and second column of the transfer distance matrix, divide the distance value by the unit time step to get the target's current speed, and finally substitute the target's current speed into the range transfer probability density distribution function to get a distance probability value. Use this distance probability value as the data value in the first row and second column of the distance probability matrix.

[0127] By repeatedly performing step 431, a distance probability matrix based on grid G1, grid G2, and target point m1 can be obtained.

[0128] Step 432. Obtain the angle probability matrix based on the transfer angle matrix and the heading transfer probability density distribution function.

[0129] In this embodiment, the heading transition probability density distribution function has been obtained in step 2.

[0130] For example, by obtaining the angle value in the first row and first column of the transfer angle matrix, and then substituting the included angle value into the heading transfer probability density distribution function, an angle probability value can be obtained. This angle probability value can then be used as the data value in the first row and first column of the angle probability matrix.

[0131] For example, by obtaining the angle value in the first row and second column of the transfer angle matrix, and then substituting the included angle value into the heading transfer probability density distribution function, an angle probability value can be obtained. This angle probability value can then be used as the data value in the first row and second column of the angle probability matrix.

[0132] By repeatedly performing step 432, we can obtain the angle probability matrix based on grid G1, grid G2, and target point m1.

[0133] Step 433. Obtain the transition probability matrix based on the distance probability matrix and the angle probability matrix.

[0134] For example, obtain the distance probability value in the first row and first column of the distance probability matrix, obtain the angle probability value in the first row and first column of the angle probability matrix, multiply the distance probability value and the angle probability value to obtain a transition probability value, and use the transition probability value as the data value in the first row and first column of the transition probability matrix.

[0135] For example, obtain the distance probability value in the first row and second column of the distance probability matrix, obtain the angle probability value in the first row and second column of the angle probability matrix, multiply the distance probability value and the angle probability value to obtain a transition probability value, and use the transition probability value as the data value in the first row and second column of the transition probability matrix.

[0136] By repeatedly performing step 433, the transition probability matrix based on grid G1, grid G2, and target point m1 can be obtained.

[0137] In step 4 of this embodiment, obtaining the transition probability from the starting mesh to the target mesh based on the transition probability matrix specifically includes:

[0138] Construct the weight matrix. The weight matrix will have the same number of rows and columns as the transition probability matrix. For example, if the transition probability matrix is ​​4 rows and 4 columns, the weight matrix will also be 4 rows and 4 columns. Each data value in the weight matrix can be 1 / n, where n is the number of data values ​​in the weight matrix. For example, when the number of data values ​​is 16, the data value is 1 / 16.

[0139] A transition probability can be obtained by performing the Frobenius inner product between the transition probability matrix and the weight matrix. If the starting grid is grid G1, the target grid is grid G2, and the destination point is m1, then a transition probability is obtained based on grid G1 - grid G2 - destination point m1.

[0140] If there are other destination points, such as destination point m2, then step 4 needs to be re-executed to obtain the transition probability based on grid G1-grid G2-destination point m2.

[0141] In this embodiment, the target grid can also be a grid adjacent to the starting grid and located to the right of the starting grid (referred to as grid G3). Therefore, for the target point m1, step 4 needs to be executed again to obtain the transition probability based on grid G1 - grid G3 - target point m1. For the target point m2, step 4 needs to be executed again to obtain the transition probability based on grid G1 - grid G3 - target point m2.

[0142] In this embodiment, the target grid can also be a grid adjacent to the starting grid and located to the lower right of the starting grid (referred to as grid G4). Therefore, for target point m1, step 4 needs to be executed again to obtain the transition probability based on grid G1 - grid G4 - target point m1. For target point m2, step 4 needs to be executed again to obtain the transition probability based on grid G1 - grid G4 - target point m2.

[0143] In addition, the target grid can also be a grid that is adjacent to the starting grid and located below the starting grid (referred to as grid G5), a grid that is adjacent to the starting grid and located to the lower left of the starting grid (referred to as grid G6), a grid that is adjacent to the starting grid and located to the left of the starting grid (referred to as grid G7), a grid that is adjacent to the starting grid and located to the upper left of the starting grid (referred to as grid G8), or a grid that is adjacent to the starting grid and located above the starting grid (referred to as grid G9).

[0144] In this embodiment, the target mesh can also be the same as the starting mesh, that is, the target mesh is also mesh G1. Therefore, for the target point m1, step 4 needs to be repeated to obtain the transition probability based on mesh G1 - mesh G1 - target point m1. For the target point m2, step 4 needs to be repeated to obtain the transition probability based on mesh G1 - mesh G1 - target point m2.

[0145] Step 5. Obtain the prior weight of each target point based on prior information, calculate the global probability of each target grid based on the transition probability and prior weight of each target grid, and determine the key grid based on the global probability of each target grid.

[0146] In this embodiment, it is assumed that the prior weight of the destination point m1 is 0.7 and the prior weight of the destination point m2 is 0.3.

[0147] In this embodiment, it is assumed that the starting grid is grid G1 and there are 9 target grids, namely grid G1, grid G2, grid G3, grid G4, grid G5, grid G6, grid G7, grid G8, and grid G9.

[0148] Taking grid G9 as an example, in step 4, the transition probability (let's say p1) of grid G1-grid G9-target point m1 and the transition probability (let's say p2) of grid G1-grid G9-target point m2 have been obtained. Therefore, "0.7×p1+0.3×p2" is the global probability of grid G9.

[0149] Taking grid G2 as an example again, in step 4, the transition probability of grid G1-grid G2-target point m1 (let's assume it's p3) and the transition probability of grid G1-grid G2-target point m2 (let's assume it's p4) have already been obtained. Therefore, "0.7×p3+0.3×p4" is the global probability of grid G2.

[0150] Repeating step 5 yields the global probability for each target grid. In this embodiment, the target grid with the highest global probability is identified as the critical grid.

[0151] Step 6. Observe the key grid. If the target is found, the task ends; if the target is not found, update the prior weights and return to step 1.

[0152] In this embodiment, grid G8 is assumed to be the critical grid. When an observation arrives, grid G8 is observed. If a target is found in grid G8, the task ends. If no target is found in grid G8, the prior weights are updated.

[0153] In step 6 of this embodiment, updating the prior weights specifically includes:

[0154] Step 61. Obtain the transition probability from the starting grid to the critical grid based on different destination points.

[0155] In step 4, the transition probabilities based on grid G1-grid G8-target point m1 (assumed to be p5) and the transition probabilities based on grid G1-grid G8-target point m2 (assumed to be p6) have been obtained. In addition, in this embodiment, it is assumed that the old prior weight of target point m1 is 0.7 and the old prior weight of target point m2 is 0.3.

[0156] Step 62. Calculate the likelihood of no target being found in the key grid based on different target points.

[0157] For target point m1, there are two situations in which the target will not be detected:

[0158] Scenario 1. The target is not present (probability is "1-p5") and the observation sensor does not give a false alarm (probability is "1-Pw", assuming the false alarm rate of the observation sensor is Pw).

[0159] Scenario 2. The target is actually present (probability "p5") and the observation sensor missed the report (probability "1-Pd", assuming the detection rate of the observation sensor is Pd).

[0160] Therefore, for the target point m1, the likelihood that the target was not found in grid G8 is "(1-p5)×(1-Pw)+p5×(1-Pd)". In this embodiment, we assume that this likelihood is S1.

[0161] Similarly, for target point m2, the likelihood that no target was found in grid G8 is calculated as "(1-p6)×(1-Pw)+p6×(1-Pd)". In this embodiment, this likelihood is assumed to be S2.

[0162] Step 63. Update the prior weights of different target points based on their likelihood.

[0163] First, multiply the likelihood S1 by the old prior weight of 0.7 to obtain the first score (assume it's 0.66185). Then, multiply the likelihood S2 by the old prior weight of 0.3 to obtain the second score (assume it's 0.0834). Add the first score to the second score to obtain the total score (assume it's 0.74525). Divide the first score by the total score to obtain the new prior weight for target point m1 (assume it's 0.888), and divide the second score by the total score to obtain the new prior weight for target point m2 (assume it's 0.112). At this point, the prior weights for different target points have been updated; the prior weight for target point m1 becomes 0.888, and the prior weight for target point m2 becomes 0.112.

[0164] After the prior weights are updated, return to step 1, causing the prior information in step 1 to change, and then re-execute steps 1 to 6.

[0165] This invention provides a device for predicting the motion of a moving maritime target based on limited prior information, such as... Figure 2 As shown, the device includes:

[0166] Preprocessing module 801 is configured to: acquire prior information; construct a tangent plane coordinate system based on the prior information; calculate the mesh edge length; and determine the unit time step.

[0167] Function construction module 802 is configured to construct the range transfer probability density distribution function and the heading transfer probability density distribution function;

[0168] The discrete point processing module 803 is configured to determine the task area, perform gridding processing on the task area based on the grid edge length, perform discrete point processing on each grid, and obtain a one-dimensional vector of discrete points of each grid.

[0169] The transfer probability acquisition module 804 is configured to construct a transfer distance matrix based on the starting grid and the target grid, construct a transfer angle matrix based on the starting grid, the target grid and the destination point, and obtain a transfer probability matrix based on the transfer distance matrix and the transfer angle matrix; and obtain the transfer probability from the starting grid to the target grid based on the transfer probability matrix.

[0170] The critical grid determination module 805 is configured to obtain the prior weight of each target point based on prior information, calculate the global probability of each target grid based on the transition probability and prior weight of each target grid, and determine the critical grid based on the global probability of each target grid.

[0171] The prior weight update module 806 is configured to observe the key grid. If the target is found, the task ends; if the target is not found, the prior weights are updated.

[0172] Figure 3 A block diagram of an electronic device 900 according to some embodiments of the present invention is shown. The device 900 includes a processor 901, which performs various appropriate actions and processes according to computer program instructions loaded into random access memory (RAM) 903 based on computer program instructions stored in read-only memory (ROM) 902. Various programs and data required for the operation of the device 900 may also be stored in RAM 903. The processor 901, ROM 902, and RAM 903 are interconnected via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0173] The various processes and procedures described above can be executed by processor 901. For example, in some embodiments, the methods can be implemented as software programs tangibly contained in a machine-readable medium. In some embodiments, part or all of the software program can be loaded into and / or installed onto device 900 via ROM 902. When the software program is loaded into RAM 903 and executed by processor 901, one or more actions of the methods described above can be performed.

[0174] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload programmable logic devices (CPLDs), and so on.

[0175] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0176] This invention can be a method, apparatus, system, and / or program product. The program product may include a machine-readable storage medium on which machine-readable program instructions for performing various aspects of the invention are loaded. The machine-readable program instructions described herein can be downloaded from the machine-readable storage medium to various computing / processing devices, or downloaded via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the machine-readable program instructions from the network and forwards them for storage in the machine-readable storage medium of the respective computing / processing device.

[0177] Machine program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The machine-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the machine-readable program instructions. This electronic circuitry can execute the machine-readable program instructions to implement various aspects of the invention.

[0178] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. Furthermore, although the operations are depicted in a specific order, this should be understood as requiring that such operations be performed in the specific order shown or in sequential order, or requiring that all illustrated operations be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the invention. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0179] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for predicting the motion of a moving maritime target based on limited prior information, characterized in that, include: Step 1. Obtain prior information; Construct a tangent plane coordinate system based on the prior information; calculate the mesh edge length; Determine the unit time step; Step 2. Construct the range transfer probability density distribution function and the heading transfer probability density distribution function; Step 3. Determine the task area, perform gridding on the task area based on the grid edge length, and perform discrete point processing on each grid to obtain a one-dimensional vector of discrete points for each grid. Step 4. Construct a transfer distance matrix based on the starting grid and the target grid, construct a transfer angle matrix based on the starting grid, the target grid, and the destination point, and obtain a transfer probability matrix based on the transfer distance matrix and the transfer angle matrix; The transition probability from the starting grid to the target grid is obtained based on the transition probability matrix; Step 5. Obtain the prior weight of each target point based on prior information, calculate the global probability of each target grid based on the transition probability and prior weight of each target grid, and determine the key grid based on the global probability of each target grid. Step 6. Observe the key grid. If the target is found, the mission ends. If no target is found, update the prior weights and return to step 1; Specifically, step 4, which involves constructing the transfer angle matrix based on the starting grid, the target grid, and the destination point, includes: Step 421. Obtain the i-th discrete point in the starting grid, obtain the j-th discrete point in the target grid, obtain a vector based on the i-th and j-th discrete points, and use the vector as the data value in the i-th row and j-th column of the first vector matrix; repeat step 421 until the first vector matrix is ​​constructed. Step 422. Obtain the i-th discrete point in the starting grid, obtain the destination point, obtain a vector II based on the i-th discrete point and the destination point, and use the vector II as the data value of all columns in the i-th row of the second vector matrix; repeat step 422 until the second vector matrix is ​​constructed. Step 423. Obtain vector one in the i-th row and j-th column of the first vector matrix, obtain vector two in the i-th row and j-th column of the second vector matrix, calculate an angle based on vector one and vector two, and use the angle as the data value in the i-th row and j-th column of the transfer angle matrix; repeat step 423 until the transfer angle matrix is ​​constructed.

2. The method according to claim 1, characterized in that, In step 1, obtaining prior information specifically includes obtaining the target's last confirmed position, the target's maximum speed, the target's last confirmed time, the initial time of satellite overpass, possible destination points, and the prior weight of each destination point; Step 1, specifically calculating the grid side length, includes: The grid side length is calculated using the following formula: ； in, Let be the grid side length. For an effective imaging swath aspect ratio, To observe the swath width of the satellite's nadir point; In step 1, determining the unit time step specifically includes: The allowable range for a unit time step is determined using the following formula: ； in, Let be the grid side length. To achieve the target maximum speed, The unit time step; The unit time step is determined based on the allowable range of the unit time step and the following formula: ； in, For a unit time step, The initial moment of satellite transit. For the final confirmation of the target, This represents the minimum number of prediction iterations allowed.

3. The method according to claim 1, characterized in that, In step 2, the range transfer probability density distribution function is constructed using the following formula: ； in, Let be the probability density distribution function of the flight transfer. To achieve the target maximum speed, For the desired speed, For the speed standard deviation, The target's current speed, The standard score is The standard Gaussian cumulative distribution function, The standard score is The standard Gaussian cumulative distribution function; The heading transition probability density distribution function is constructed using the following formula: ； in, Let be the probability density distribution function for the heading change. For the desired heading angle, The standard deviation of the heading angle. For the target heading angle, The standard score is The standard Gaussian cumulative distribution function, The standard score is The standard Gaussian cumulative distribution function.

4. The method according to claim 1, characterized in that, Step 4, which involves constructing the transfer distance matrix based on the starting and target grids, specifically includes: Step 41. Obtain the i-th discrete point in the starting grid, obtain the j-th discrete point in the target grid, obtain the X-axis distance and Y-axis distance based on the i-th and j-th discrete points, calculate the inter-point distance based on the X-axis distance and Y-axis distance, and use the inter-point distance as the data value in the i-th row and j-th column of the transfer distance matrix; repeat step 41 until the transfer distance matrix is ​​constructed.

5. The method according to claim 1, characterized in that, Step 4, obtaining the transfer probability matrix based on the transfer distance matrix and the transfer angle matrix, specifically includes: Step 431. Obtain the distance probability matrix based on the transfer distance matrix, unit time step, and range transfer probability density distribution function; Step 432. Obtain the angle probability matrix based on the transfer angle matrix and the heading transfer probability density distribution function; Step 433. Obtain the transition probability matrix based on the distance probability matrix and the angle probability matrix.

6. The method according to claim 1, characterized in that, Step 6, updating the prior weights, specifically includes: Step 61. Obtain the transition probability from the starting grid to the critical grid based on different destination points; Step 62. Calculate the likelihood of no targets being found in the key grid based on different target points; Step 63. Update the prior weights of different target points based on their likelihood.

7. A device for predicting the motion of a moving maritime target based on limited prior information, characterized in that, include: The preprocessing module is configured to acquire prior information; Construct a tangent plane coordinate system based on the prior information; calculate the mesh edge length; Determine the unit time step; The function construction module is configured to construct the range transfer probability density distribution function and the heading transfer probability density distribution function. The discrete point processing module is configured to determine the task region, perform gridding processing on the task region based on the grid edge length, perform discrete point processing on each grid, and obtain a one-dimensional vector of discrete points for each grid. The transfer probability acquisition module is configured to construct a transfer distance matrix based on the starting grid and the target grid, construct a transfer angle matrix based on the starting grid, the target grid and the destination point, and acquire a transfer probability matrix based on the transfer distance matrix and the transfer angle matrix; The transition probability from the starting grid to the target grid is obtained based on the transition probability matrix; The critical grid determination module is configured to obtain the prior weight of each target point based on prior information, calculate the global probability of each target grid based on the transition probability and prior weight of each target grid, and determine the critical grid based on the global probability of each target grid. The prior weight update module is configured to observe key grids; if a target is detected, the task ends. If the target is not found, the prior weights are updated. The construction of the transfer angle matrix based on the starting grid, target grid, and destination point specifically includes: Step 421. Obtain the i-th discrete point in the starting grid, obtain the j-th discrete point in the target grid, obtain a vector based on the i-th and j-th discrete points, and use the vector as the data value in the i-th row and j-th column of the first vector matrix; repeat step 421 until the first vector matrix is ​​constructed. Step 422. Obtain the i-th discrete point in the starting grid, obtain the destination point, obtain a vector II based on the i-th discrete point and the destination point, and use the vector II as the data value of all columns in the i-th row of the second vector matrix; repeat step 422 until the second vector matrix is ​​constructed. Step 423. Obtain vector one in the i-th row and j-th column of the first vector matrix, obtain vector two in the i-th row and j-th column of the second vector matrix, calculate an angle based on vector one and vector two, and use the angle as the data value in the i-th row and j-th column of the transfer angle matrix; repeat step 423 until the transfer angle matrix is ​​constructed.

8. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1-6.

9. An electronic device, characterized in that, It includes one or more processors and memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method according to any one of claims 1-6.

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