Marine multi-target observation method and device

Abstract

The application provides a marine multi-target observation method and device, and relates to the technical field of satellite observation. In the method, satellite information, typical route information of a plurality of target objects to be observed, historical route information and current state information of each target object are acquired; a to-be-observed region is divided into a plurality of grid regions, and based on the typical route information, the historical route information and the current state information, the motion trajectories of each target object in the plurality of grid regions within a preset future time period are predicted; based on attribute information, current state information and historical route information of each target object, the importance of each target object is determined; and based on the motion trajectory of each target object, the aggregation degree of the target objects in each grid region is determined; based on the importance, the aggregation degree and the satellite information, observation parameters of each satellite in each grid region are determined, and each satellite is controlled to observe the plurality of target objects according to the observation parameters after configuration.

Description

Technical Field

[0001] This application relates to the field of satellite observation technology, and more specifically, to a method and apparatus for multi-target observation at sea. Background Technology

[0002] With the continuous development of the marine economy and the increasing demand for maritime security, the continuous monitoring of moving targets at sea has become increasingly important. Low-Earth orbit (LEO) satellites, due to their advantages such as short revisit cycles and wide coverage, have become an important means of monitoring maritime targets.

[0003] Currently, there are two main problems with methods for multi-target observation at sea when resources such as satellites are limited:

[0004] The first problem is that relying on fixed cycles or human experience to scan the area to be observed uniformly or according to fixed priorities makes it difficult to accurately predict the spatial distribution of targets in the future, resulting in a lack of targeted allocation of satellite resources;

[0005] The second problem is that focusing only on the static attributes of a single target, or only on the number density of targets within a region, results in a large number of low-value targets occupying too many observation resources in densely populated areas, while high-value targets cannot obtain sufficient observation frequency. Summary of the Invention

[0006] In view of the above problems, this application provides a method and apparatus for multi-target observation at sea.

[0007] This application provides a method for multi-target observation at sea, comprising: acquiring satellite information, typical route information of multiple target objects to be observed, historical route information and current status information of each target object, wherein the typical route information represents the route information of multiple navigable routes in the area to be observed, the satellite information represents the attribute information of multiple satellites observing the area to be observed, and multiple target objects are navigating in the area to be observed; dividing the area to be observed into multiple grid areas, and predicting the movement trajectory of each target object in the multiple grid areas within a preset future time period based on the typical route information, historical route information and current status information; determining the importance of each target object based on the attribute information, current status information and historical route information of each target object; determining the degree of clustering of target objects in each grid area based on the movement trajectory of each target object; determining the observation parameters of each satellite in each grid area based on the importance, degree of clustering and satellite information, and controlling each satellite to observe multiple target objects according to the configuration of the observation parameters.

[0008] According to embodiments of this application, based on typical route information, historical route information, and current status information, the motion trajectory of each target object in multiple grid areas within a preset future time period is predicted, including: for any target object among multiple target objects, determining the current coordinates and current heading of the target object based on the current status information of the target object; determining the target grid area based on the current coordinates of the target object and the grid coordinates of each grid area; determining the target navigation route that meets preset conditions from multiple navigable routes based on typical route information, the target grid area, and the current heading; and predicting the motion trajectory of the target object within a future time period based on the target navigation route and historical route information.

[0009] According to an embodiment of this application, a target navigation route that meets preset conditions is determined from multiple navigable routes based on typical route information, a target grid area, and the current heading. This includes: for any navigable route among the multiple navigable routes, determining the grid area to which the navigable route belongs and the regional heading of the grid area based on typical route information; and if the target grid area falls within the grid area to which the target route belongs and the angle between the current heading and the regional heading is less than a preset angle threshold, then the navigable route is taken as the target navigation route.

[0010] According to embodiments of this application, the current status information includes the current position coordinates and the current movement speed, and the historical route information includes multiple historical position coordinates. Based on the attribute information, current status information, and historical route information of each target object, the importance of each target object is determined, including: for any target object among multiple target objects, determining the first importance of the target object based on the target object's attribute information; determining the route deviation degree of the target object based on the multiple historical position coordinates and the current coordinates, and determining the second importance of the target object based on the route deviation degree; determining the third importance of the target object based on the target object's current movement speed; and determining the overall importance of the target object based on the first, second, and third importance degrees.

[0011] According to an embodiment of this application, determining the degree of deviation of a target object from its flight path based on multiple historical location coordinates and its current coordinates includes: determining the historical trajectory of the target object based on multiple historical location coordinates; calculating the shortest distance from the current target to the historical trajectory; and determining the degree of deviation of the flight path based on the shortest distance, wherein the shortest distance and the degree of deviation of the flight path are positively correlated.

[0012] According to an embodiment of this application, determining the degree of clustering of target objects in each grid region based on the motion trajectory of each target object includes: for any grid region among multiple grid regions, determining each target object passing through the grid region in a future time period and the time period of each target object passing through the grid region based on the motion trajectory of each target object; determining a first degree of clustering of the grid region based on the number of each target object; determining the degree of overlap of the time periods of each target object based on the time period of each target object passing through the grid region, and determining a second degree of clustering based on the degree of overlap; and determining the degree of clustering of the grid region based on the first degree of clustering and the second degree of clustering.

[0013] According to embodiments of this application, the observation parameters of each satellite in each grid region are determined based on importance, clustering degree, and satellite information, including: for any grid region among multiple grid regions, the clustering degree of the grid region is weighted based on the importance of each target object in the grid region to obtain the clustering weight of the grid region; the observation frequency of the grid region is determined based on the clustering weight of the grid region and a pre-set regional weight; the target satellite and its trajectory are determined from multiple satellites based on satellite information; and the observation window for the target satellite to observe the grid region is determined based on the satellite trajectory, and the observation frequency and observation window are used as the observation parameters of the target satellite in the grid region.

[0014] According to an embodiment of this application, after determining the observation frequency of a grid region based on the clustering weight and a pre-set regional weight, the method further includes: obtaining task information of the region to be observed, and determining the task type of the grid region from the task information; determining a first adjustment coefficient and a second adjustment coefficient based on the task type; adjusting the clustering weight using the first adjustment coefficient, and adjusting the regional weight using the second adjustment coefficient; adjusting the observation frequency based on the adjusted clustering weight and regional weight; and returning to the step of using the observation frequency and observation window as observation parameters of the target satellite in the grid region based on the adjusted observation frequency.

[0015] According to an embodiment of this application, the task type includes a first type and a second type; when determining the first adjustment coefficient and the second adjustment coefficient based on the task type, the method further includes: when the task type is the first type, making the first adjustment coefficient greater than the second adjustment coefficient; when the task type is the second type, making the first adjustment coefficient less than the second adjustment coefficient.

[0016] This application also provides a multi-target observation device for the sea, comprising: an acquisition module for acquiring satellite information, typical route information of multiple target objects to be observed, historical route information and current status information of each target object, wherein the typical route information represents the route information of multiple navigable routes in the area to be observed, the satellite information represents the attribute information of multiple satellites observing the area to be observed, and the multiple target objects are navigating in the area to be observed; a prediction module for dividing the area to be observed into multiple grid areas, and predicting the motion trajectory of each target object in the multiple grid areas within a preset future time period based on the typical route information, historical route information and current status information; a determination module for determining the importance of each target object based on the attribute information, current status information and historical route information of each target object, and determining the degree of clustering of target objects in each grid area based on the motion trajectory of each target object; and an observation module for determining the observation parameters of each satellite in each grid area based on the importance, degree of clustering and satellite information, and controlling each satellite to observe multiple target objects according to the configuration of the observation parameters.

[0017] The multi-target observation method and apparatus for the sea provided in this application can achieve the following beneficial effects:

[0018] (1) By dividing the area to be observed into multiple grid areas and predicting the trajectory of the target object based on typical route information, historical route information and current status information, the allocation of satellite resources can predict the future spatial distribution of the target in advance, avoiding the invalid observation of the satellite in areas without targets or with low density, and enabling the limited satellite resources to focus on areas with dense targets, thereby improving the utilization efficiency of satellite observation resources.

[0019] (2) By determining the importance of each target object and the degree of clustering of target objects in each grid area, and comprehensively determining the observation parameters based on the importance and degree of clustering, the allocation of observation resources can take into account both the observation needs of high-value individual targets and the coverage needs of high-density group areas, thus maintaining a balance between high-value targets and high-density areas. Attached Figure Description

[0020] The above and other objects, features and advantages of this application will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0021] Figure 1 A flowchart illustrating a method for multi-target observation at sea according to an embodiment of this application is shown schematically.

[0022] Figure 2 A schematic diagram of a multi-target marine observation device according to an embodiment of this application is shown. Detailed Implementation

[0023] The embodiments of this application will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of this application. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of this application for ease of explanation. However, it will be apparent that one or more embodiments may be implemented without these specific details. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0024] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0025] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0026] Figure 1 A flowchart illustrating a method for multi-target observation at sea according to an embodiment of this application is shown.

[0027] like Figure 1 As shown, the multi-target observation method at sea in this embodiment includes steps S110 to S140.

[0028] In step S110, satellite information, typical route information of multiple target objects to be observed, historical route information and current status information of each target object are acquired. Typical route information represents the route information of multiple navigable routes in the area to be observed, and satellite information represents the attribute information of multiple satellites observing the area to be observed. Multiple target objects navigate in the area to be observed.

[0029] The area to be observed can be a maritime geographic area that requires satellite observation. The target object can be a moving target navigating within the area to be observed.

[0030] For example, the area to be observed could be a waterway or the sea area surrounding a port.

[0031] For example, the target object can be a ship, a buoy, etc.

[0032] For example, satellite information can include orbital parameters and sensor parameters; orbital parameters can be used to determine the satellite's spatial position at any given time, while sensor parameters can be used to determine the coverage and data quality of the satellite's Earth observations.

[0033] For example, orbital parameters may include orbital altitude, orbital inclination, and perigee argument; sensor parameters may include sensor type (such as optical sensor or infrared sensor), spatial resolution, and spectral band.

[0034] For example, different types of sensors can be used for different observation scenarios. Optical sensors are suitable for high-resolution imaging under good daylight conditions, while infrared sensors are suitable for observation at night or under adverse weather conditions.

[0035] Typical flight path information reflects the common path information of multiple target objects moving within the observed area. Historical flight path information can be a record of the actual flight path of the target object over a past period, including its position coordinates at historical moments. Current status information can be relevant information generated by the target object's movement at the current moment or the most recent moment, such as its current position coordinates, current direction of movement, or current speed.

[0036] For example, K-means clustering and DBSCAN clustering based on dynamic time-normalized distance or Hausdorff distance can be used to group the motion trajectories of targets with similar spatial orientations into one class; the spatial centerline of multiple motion trajectories in each class can then be taken to obtain the possible routes corresponding to that class of motion trajectories. Alternatively, recommended channels marked on electronic charts can also be used as possible routes.

[0037] In step S120, the area to be observed is divided into multiple grid areas, and based on typical route information, historical route information and current status information, the movement trajectory of each target object in multiple grid areas is predicted within a preset future time period.

[0038] According to the preset division rules, the continuous observation area is discretized into several non-overlapping grid regions. The grid regions can be square grids, rectangular grids, hexagonal grids, or latitude and longitude grids; the granularity of the grid regions can be determined according to the actual observation requirements, and can also be based on the spatial resolution and swath width of the sensors in the satellite, to ensure that the size of a single grid region matches the coverage area of ​​a single satellite observation.

[0039] For example, the grid coordinates of a grid area can be represented by a grid location code, which can be designed in a hierarchical manner to identify and index spatial locations.

[0040] For example, a grid location code can consist of a basic grid code, an extended grid code, and an additional information code. The basic grid code is used to identify the area where the target object is located, the extended grid code is used to identify the specific grid area location, and the additional information code is used to characterize the time attribute, attribute characteristics, or other auxiliary information of the grid area. The basic grid code and the extended grid code together constitute the spatial code of the grid area, and the additional information code is optional.

[0041] For example, a grid location code can be a three-dimensional grid location code, totaling 32 bits, composed of an interleaved two-dimensional encoding part and a height dimension encoding part. The two-dimensional encoding part is used to represent the position of the grid region in the longitude and latitude directions, while the height dimension encoding part is used to represent the height level of the grid region.

[0042] In step S130, the importance of each target object is determined based on its attribute information, current status information, and historical flight path information; and the degree of clustering of target objects in each grid area is determined based on the movement trajectory of each target object.

[0043] The higher the importance of the target object, the more priority or higher frequency of observation is required. Clustering can be a quantitative indicator of the degree to which each grid area will be of interest or influence by the target object in the future. The higher the clustering of the grid area, the more target objects will pass through or stay in the grid area.

[0044] In step S140, based on importance, clustering degree and satellite information, the observation parameters of each satellite in each grid area are determined, and each satellite is controlled to observe multiple target objects after being configured according to the observation parameters.

[0045] Observation parameters can be the quantifiable indicators and operational instructions that a satellite needs to configure when performing observation missions.

[0046] The maritime multi-target observation method based on the embodiments of this application predicts the target's trajectory by fusing typical route information, historical route information, and current status information, thus achieving accurate prediction of the target's future spatial distribution. By comprehensively considering the importance of the target object and the degree of clustering of the grid area, it achieves adaptive allocation of observation resources under limited satellite resources, effectively avoiding waste of observation resources and improving the pertinence of maritime multi-target observation and the success rate of observation mission execution.

[0047] In the embodiments of this application, based on typical route information, historical route information, and current status information, the motion trajectory of each target object in multiple grid areas within a preset future time period is predicted, including: for any target object among multiple target objects, determining the current coordinates and current heading of the target object based on the current status information of the target object; determining the target grid area based on the current coordinates of the target object and the grid coordinates of each grid area; determining the target navigation route that meets preset conditions from multiple navigable routes based on typical route information, the target grid area, and the current heading; and predicting the motion trajectory of the target object within a future time period based on the target navigation route and historical route information.

[0048] For example, if there is noise or delay in the current coordinates or current heading in the current status information, Kalman filtering or mean filtering can be used to smooth the current coordinates, and sliding window averaging can be used to smooth the current heading.

[0049] For example, the current coordinates can be converted into the corresponding grid position code. The converted grid position code is then compared with the grid position codes of each grid region. The grid region that matches successfully is the target grid region.

[0050] For example, if the current coordinates happen to be located on the boundary of two or more grid regions, the target grid region is determined according to preset boundary processing rules. Boundary processing rules may include: selecting the grid region with the smallest grid position code covering the current coordinates as the target grid region, or selecting the grid region closest to the current heading direction as the target grid region, or using a fuzzy membership method to assign the current coordinates to multiple grid regions simultaneously.

[0051] The maritime multi-target observation method based on the embodiments of this application achieves rapid positioning by mapping the current coordinates of the target object to a grid area, and filters the target navigation route from typical route information based on the target grid area and the current heading, so that the future path of the target object is limited by the group of routes; at the same time, it combines historical route information to determine the speed distribution of the target object on the target navigation route to estimate the arrival time of each grid area, thereby realizing accurate prediction of the future trajectory of the target object.

[0052] In the embodiments of this application, a target navigation route that meets preset conditions is determined from multiple navigable routes based on typical route information, target grid area and current heading. This includes: for any navigable route among multiple navigable routes, determining the grid area to which the navigable route belongs and the regional heading of the grid area to which the grid area belongs based on typical route information; and taking the navigable route as the target navigation route if the target grid area falls into the grid area to which the grid area belongs and the angle between the current heading and the regional heading is less than a preset angle threshold.

[0053] The regional heading can be the direction in which the navigable route extends within its respective grid area.

[0054] For example, the regional heading can be the vector direction of the navigable route from the previous adjacent grid region to the current grid region, or the vector direction from the current grid region to its next adjacent grid region.

[0055] The angle between the current heading and the area heading represents the angular difference between the target object's current direction of motion and the extension direction of the navigable route within the target grid area. This angle reflects the consistency between the target object's actual direction of motion and the direction of the navigable route. The smaller the angle, the more consistent the target object's actual direction of motion is with the direction of the navigable route, and the higher the probability that the target object will travel along the navigable route; conversely, the larger the angle, the greater the deviation between the target object's actual direction of motion and the direction of the navigable route, and the lower the probability that the target object will travel along the navigable route.

[0056] The maritime multi-target observation method based on the embodiments of this application ensures that the selected navigation route is consistent with the current direction of movement of the target object by comparing the heading angle, thereby improving the accuracy of the target navigation route.

[0057] In the embodiments of this application, the current state information includes the current position coordinates and the current movement speed, and the historical route information includes multiple historical position coordinates. Based on the attribute information, current state information, and historical route information of each target object, the importance of each target object is determined, including: for any target object among multiple target objects, determining the first importance of the target object based on the target object's attribute information; determining the route deviation degree of the target object based on the multiple historical position coordinates and the current coordinates, and determining the second importance of the target object based on the route deviation degree; determining the third importance of the target object based on the target object's current movement speed; and determining the overall importance of the target object based on the first, second, and third importance degrees.

[0058] For example, the attribute information of the target object may include ship type and ship size.

[0059] For example, the fourth level of importance of a target object can be determined based on the region it belongs to. The overall importance of a target object can then be determined based on its first, second, third, and fourth levels of importance.

[0060] For example, the Analytic Hierarchy Process (AHP) and the entropy weight method can be combined to determine the weights of the first, second, third, and fourth levels of importance. First, an AHP judgment matrix is ​​constructed, and the first weight of each element in the matrix is ​​preset. Then, the entropy weight method is used to determine the second weight based on the degree of variation in the data. Finally, a linear weighting method is used to combine the first and second weights to obtain the comprehensive weight.

[0061] For example, the importance of a target object can be calculated using the following formula;

[0062]

[0063] In the formula, As to the importance of the target object, This is the transpose of the weight vector. To evaluate the indicator vector, Weights for ship types, Weighting for ship size, As the weight of the current speed of motion, The weight of the region, The weighting of the degree of deviation from the flight path. Based on the importance of ship type, The importance of ship size, Given the importance of current speed of movement, Depending on the importance of the region, The importance of the degree of deviation from the flight path.

[0064] The maritime multi-target observation method based on the embodiments of this application determines the first importance level based on attribute information to reflect the inherent value of the target object, determines the course deviation based on multiple historical position coordinates and current coordinates to reflect the trajectory anomaly of the target object, and determines the third importance level based on the current speed to reflect the speed anomaly of the target object. This avoids the one-sidedness of single-dimensional evaluation and improves the accuracy and reliability of importance assessment.

[0065] In the embodiments of this application, determining the degree of deviation of the target object's flight path based on multiple historical location coordinates and the current coordinates of the target object includes: determining the historical movement trajectory of the target object based on multiple historical location coordinates; calculating the shortest distance from the current target to the historical movement trajectory; and determining the degree of deviation of the flight path based on the shortest distance, wherein the shortest distance and the degree of deviation of the flight path are positively correlated.

[0066] For example, when the historical trajectory is a continuous line, calculate the perpendicular distance from the current coordinate to that trajectory line. The minimum distance among all points on the trajectory line from the current coordinate is the shortest distance.

[0067] For example, when the historical trajectory is represented as a grid sequence, the current coordinates are first mapped to the grid region to obtain the current grid region; then, it is determined whether the current grid region belongs to the grid sequence of the historical trajectory. If the current grid region belongs to the grid sequence, the shortest distance is determined to be zero; if the current grid region does not belong to the grid sequence, the shortest spatial distance from the current grid region to each grid region in the grid sequence of the historical trajectory is calculated, and the minimum value is taken as the shortest distance.

[0068] In the embodiments of this application, determining the degree of clustering of target objects in each grid region based on the motion trajectory of each target object includes: for any grid region among multiple grid regions, determining each target object passing through the grid region in a future time period and the time period of each target object passing through the grid region based on the motion trajectory of each target object; determining a first degree of clustering of the grid region based on the number of each target object; determining the degree of overlap of the time periods of each target object based on the time periods of each target object passing through the grid region, and determining a second degree of clustering based on the degree of overlap; and determining the degree of clustering of the grid region based on the first degree of clustering and the second degree of clustering.

[0069] For example, density-based spatial clustering algorithms can be used to perform cluster analysis on target objects within a grid region. Density-based spatial clustering algorithms use parameters... The density of the sample set is described by the neighborhood radius and the threshold number of samples within the neighborhood. By calculating the density distribution of target objects within the grid area, dense and sparse regions in each grid area can be identified.

[0070] In the embodiments of this application, the observation parameters of each satellite in each grid region are determined based on importance, clustering degree, and satellite information. This includes: for any grid region among multiple grid regions, weighting the clustering degree of the grid region based on the importance of each target object in the grid region to obtain the clustering weight of the grid region; determining the observation frequency of the grid region based on the clustering weight of the grid region and a pre-set region weight; determining the target satellite and its trajectory for observing the grid region from multiple satellites based on satellite information; and determining the observation window for the target satellite to observe the grid region based on the satellite trajectory, and using the observation frequency and observation window as the observation parameters of the target satellite in the grid region.

[0071] For example, by introducing weights based on importance, the clustering degree of grid regions can be weighted to obtain the clustering weight of the grid regions, which can be calculated using the following formula;

[0072]

[0073] In the formula, To aggregate weights, To determine the importance of the i-th target object, The density contribution is given to the i-th target object, where N is the total number of target objects within the grid region. , where i and N are both positive integers.

[0074] The maritime multi-target observation method based on the embodiments of this application weights the degree of clustering based on the importance of each target object within the grid area, so that the clustering weight can reflect the comprehensive value of the target object group and avoid high-value target objects being overwhelmed by a large number of low-value target objects.

[0075] In the embodiments of this application, after determining the observation frequency of a grid region based on the clustering weight and a pre-set regional weight, the method further includes: obtaining task information of the region to be observed, and determining the task type of the grid region from the task information; determining a first adjustment coefficient and a second adjustment coefficient based on the task type; adjusting the clustering weight using the first adjustment coefficient, and adjusting the regional weight using the second adjustment coefficient; adjusting the observation frequency based on the adjusted clustering weight and regional weight; and returning to the step of using the observation frequency and observation window as observation parameters of the target satellite in the grid region based on the adjusted observation frequency.

[0076] For example, the cluster weights can be adjusted using the first adjustment factor, and the region weights can be adjusted using the second adjustment factor, using the following formula;

[0077]

[0078]

[0079] In the formula, The adjusted observation frequency, The first adjustment factor is... This is the second adjustment factor. Let g be the region weight of the g-th grid region. To aggregate weights, The importance of the i-th target object. The region to which the i-th target object belongs is the g-th grid region.

[0080] For example, observation parameters may include observation time intervals (such as average time interval and maximum time interval), and the observation time interval can be determined based on the adjusted observation frequency, specifically using the following formula;

[0081]

[0082]

[0083] In the formula, The average time interval, For the maximum time interval, This is the adjusted observation frequency.

[0084] For example, considering the satellite's observation window and the motion characteristics of the target object, the following constraints also need to be considered when determining the observation time of the observation window: the visible time window of the satellite over the grid area, the minimum time interval between adjacent observations (considering the satellite attitude transition time), the maximum number of satellite power-on / off cycles, and the dwell time of the target object within the grid area.

[0085] For example, to ensure the most balanced use of satellites, an objective optimization function can be employed. ,in, To observe coverage targets, The target number of power on / off cycles is... For the purpose of satellite load balancing, The weights for the observation coverage target, The weight of the target number of power on / off cycles, The weights for satellite load balancing objectives.

[0086] For example, the observation coverage target can be determined based on the required number of observations for a grid area and the actual number of observations arranged, i.e. ,in, The required number of observations for the g-th grid region. The actual number of observations arranged for the g-th grid region. Let g be the set of all grid regions, where g and G are both positive integers.

[0087] For example, the target for the number of power on / off cycles could be based on the number of times the satellite powers on and off within a predetermined time period. and maximum allowed number of power on / off cycles Determined, that is ,in, Let s be the s-th satellite, and S be the set of all satellites capable of performing observation missions, where s and S are both positive integers.

[0088] For example, the satellite load balancing target can be based on the average load of all satellites. The mission payload of the s-th satellite is determined, i.e. .

[0089] For example, the satellite power-on / off time needs to meet the duration of a single power-on cycle. , To minimize boot time, This is the longest boot time.

[0090] For example, the number of times the satellite needs to be switched on and off must meet the following requirements. The time interval between two adjacent observation tasks needs to meet the following requirements. , This refers to the time required for the satellite to adjust from its current attitude to the target attitude; the satellite's cumulative daily operating time needs to meet certain requirements. , This is the longest duration of a single satellite observation.

[0091] The maritime multi-target observation method based on the embodiments of this application dynamically adjusts the aggregation weight and regional weight and adjusts the observation frequency by adjusting the mission information, so that the observation frequency can be flexibly adapted to the changes in mission requirements, avoiding the problem of fixed observation parameters not matching the mission scenario, and realizing adaptive optimization of observation parameters.

[0092] In embodiments of this application, the task type includes a first type and a second type; when determining the first adjustment coefficient and the second adjustment coefficient based on the task type, the method further includes: when the task type is the first type, making the first adjustment coefficient greater than the second adjustment coefficient; when the task type is the second type, making the first adjustment coefficient less than the second adjustment coefficient.

[0093] Based on the above-mentioned method for multi-target observation at sea, this application also provides a multi-target observation device for sea, which will be described below in conjunction with... Figure 2 The device is described in detail.

[0094] Figure 2 A schematic diagram of a multi-target marine observation device according to an embodiment of this application is shown.

[0095] like Figure 2 As shown, the marine multi-target observation device 200 of this embodiment includes an acquisition module 210, a prediction module 220, a determination module 230, and an observation module 240.

[0096] The acquisition module 210 is used to acquire satellite information, typical flight path information of multiple target objects to be observed, historical flight path information and current status information of each target object. The typical flight path information represents the flight path information of multiple navigable routes in the area to be observed, and the satellite information represents the attribute information of multiple satellites observing the area to be observed. Multiple target objects are navigating in the area to be observed. In one embodiment, the acquisition module 210 can be used to perform step S110 described above, which will not be repeated here.

[0097] The prediction module 220 is used to divide the area to be observed into multiple grid areas, and based on typical flight path information, historical flight path information, and current status information, predict the movement trajectory of each target object in the multiple grid areas within a preset future time period. In one embodiment, the prediction module 220 can be used to perform step S120 described above, which will not be repeated here.

[0098] The determination module 230 is used to determine the importance of each target object based on its attribute information, current status information, and historical flight path information; and to determine the degree of clustering of target objects in each grid area based on the movement trajectory of each target object. In one embodiment, the determination module 230 can be used to perform step S130 described above, which will not be repeated here.

[0099] The observation module 240 is used to determine the observation parameters of each satellite in each grid region based on importance, clustering degree, and satellite information, and to control each satellite to observe multiple target objects according to the configured observation parameters. In one embodiment, the observation module 240 can be used to execute step S140 described above, which will not be repeated here.

[0100] According to embodiments of this application, any multiple modules among the acquisition module 210, prediction module 220, determination module 230, and observation module 240 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module. According to embodiments of this application, at least one of the acquisition module 210, prediction module 220, determination module 230, and observation module 240 can be at least partially implemented as hardware circuits, such as field-programmable gate arrays, programmable logic arrays, systems-on-a-chip, systems-on-a-substrate, systems-on-package, application-specific integrated circuits, or any other reasonable means of integrating or packaging circuits, or implemented in software, hardware, or firmware, or in any appropriate combination of any of these three implementation methods. Alternatively, at least one of the acquisition module 210, prediction module 220, determination module 230, and observation module 240 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0101] Those skilled in the art will understand that the features described in the various embodiments of this application can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments of this application can be combined or combined in various ways without departing from the spirit and teachings of this application. All such combinations or combinations fall within the scope of this application.

[0102] The embodiments of this application have been described above. However, these embodiments are merely illustrative and not intended to limit the scope of this application. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Without departing from the scope of this application, those skilled in the art can make various substitutions and modifications, all of which should fall within the scope of this application.

Claims

1. A method for multi-target observation at sea, characterized in that, include: The system acquires satellite information, typical flight path information of multiple target objects to be observed, historical flight path information and current status information of each target object. The typical flight path information represents the flight path information of multiple navigable routes in the area to be observed. The satellite information represents the attribute information of multiple satellites that are observing the area to be observed. The multiple target objects navigate in the area to be observed. The area to be observed is divided into multiple grid areas, and based on the typical flight path information, the historical flight path information, and the current status information, the movement trajectory of each target object in the multiple grid areas is predicted within a preset future time period. Based on the attribute information, current status information, and historical route information of each target object, the importance of each target object is determined; Based on the motion trajectory of each target object, the degree of aggregation of target objects in each grid region is determined; Based on the importance, the degree of aggregation, and the satellite information, the observation parameters of each satellite in each grid region are determined, and each satellite is controlled to observe multiple target objects according to the observation parameters.

2. The method according to claim 1, characterized in that, Based on the typical flight path information, the historical flight path information, and the current status information, predict the movement trajectory of each target object in the multiple grid areas within a preset future time period, including: For any one of the multiple target objects, determine the current coordinates and current heading of the target object based on the current state information of the target object; The target grid region is determined based on the current coordinates of the target object and the grid coordinates of each grid region; Based on the typical route information, the target grid area, and the current heading, a target navigation route that meets the preset conditions is determined from the multiple navigable routes; Based on the target navigation route and the historical route information, the trajectory of the target object is predicted within the future time period.

3. The method according to claim 2, characterized in that, Based on the typical route information, the target grid area, and the current heading, a target navigation route that meets preset conditions is determined from the multiple navigable routes, including: For any navigable route among the multiple navigable routes, based on the typical route information, determine the grid region to which the navigable route belongs and the regional heading of the grid region to which it belongs; If the target grid area falls within the grid area to which it belongs and the angle between the current heading and the heading of the area is less than a preset angle threshold, the navigable route is taken as the target navigation route.

4. The method according to claim 1, characterized in that, The current status information includes the current location coordinates and current movement speed, and the historical route information includes multiple historical location coordinates; based on the attribute information, current status information, and historical route information of each target object, the importance of each target object is determined, including: For any one of the multiple target objects, the first importance of the target object is determined based on the attribute information of the target object; Based on multiple historical location coordinates and current coordinates of the target object, the degree of flight path deviation of the target object is determined, and the second importance of the target object is determined based on the degree of flight path deviation; Based on the current speed of the target object, the third degree of importance of the target object is determined; The importance of the target object is determined based on the first importance level, the second importance level, and the third importance level.

5. The method according to claim 4, characterized in that, Based on multiple historical location coordinates and current coordinates of the target object, the degree of flight path deviation of the target object is determined, including: Based on the multiple historical location coordinates, the historical movement trajectory of the target object is determined; Calculate the shortest distance from the current target to the historical trajectory; The degree of deviation of the route is determined based on the shortest distance, wherein the shortest distance and the degree of deviation of the route are positively correlated.

6. The method according to claim 1, characterized in that, Based on the motion trajectory of each target object, the degree of clustering of target objects in each grid region is determined, including: For any one of the plurality of grid regions, based on the movement trajectory of each target object, determine each target object that passes through the grid region in the future time period and the time period for each target object to pass through the grid region; Based on the number of each target object, determine the first degree of aggregation of the grid region; Based on the time period of each target object passing through the grid area, the degree of overlap of the time periods of each target object is determined, and a second degree of aggregation is determined based on the degree of overlap; The degree of aggregation of the grid region is determined based on the first degree of aggregation and the second degree of aggregation.

7. The method according to claim 1, characterized in that, Based on the importance, the clustering degree, and the satellite information, the observation parameters of each satellite in each grid region are determined, including: For any one of the multiple grid regions, the clustering degree of the grid region is weighted based on the importance of each target object in the grid region to obtain the clustering weight of the grid region; The observation frequency of the grid region is determined based on the clustering weight of the grid region and the pre-set region weight. Based on the satellite information, the target satellite for observing the grid area and the satellite trajectory of the target satellite are determined from the plurality of satellites; Based on the satellite trajectory, the observation window for the target satellite to observe the grid area is determined, and the observation frequency and the observation window are used as the observation parameters of the target satellite in the grid area.

8. The method according to claim 7, characterized in that, After determining the observation frequency of the grid region based on the clustering weight of the grid region and a pre-set region weight, the method further includes: Obtain the task information of the area to be observed, and determine the task type of the grid area from the task information; Based on the task type, determine the first adjustment coefficient and the second adjustment coefficient; The clustering weight is adjusted using the first adjustment coefficient, and the region weight is adjusted using the second adjustment coefficient; The observation frequency is adjusted based on the adjusted clustering weight and region weight; Based on the adjusted observation frequency, return to the step of using the observation frequency and the observation window as the observation parameters of the target satellite in the grid area.

9. The method according to claim 8, characterized in that, The task types include a first type and a second type; when determining the first adjustment coefficient and the second adjustment coefficient based on the task types, the method further includes: When the task type is the first type, the first adjustment coefficient is made greater than the second adjustment coefficient; When the task type is the second type, the first adjustment coefficient is made smaller than the second adjustment coefficient.

10. A multi-target observation device for marine environments, characterized in that, include: The acquisition module is used to acquire satellite information, typical flight path information of multiple target objects to be observed, historical flight path information and current status information of each target object. The typical flight path information represents the flight path information of multiple navigable routes in the area to be observed. The satellite information represents the attribute information of multiple satellites that are observing the area to be observed. The multiple target objects are navigating in the area to be observed. The prediction module is used to divide the area to be observed into multiple grid areas, and based on the typical flight path information, the historical flight path information and the current status information, predict the movement trajectory of each target object in the multiple grid areas within a preset future time period. The determination module is used to determine the importance of each target object based on its attribute information, current status information, and historical flight route information. Based on the motion trajectory of each target object, the degree of aggregation of target objects in each grid region is determined; An observation module is used to determine the observation parameters of each satellite in each grid region based on the importance, the aggregation degree, and the satellite information, and to control each satellite to observe multiple target objects after being configured according to the observation parameters.

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