Target positioning and real-time clustering method based on foresight sonar detection

Abstract

The invention belongs to the technical field of underwater multi-target positioning and real-time clustering of unmanned surface vehicles, and particularly relates to a target positioning and real-time clustering method based on foresight sonar detection, which can realize simultaneous positioning and clustering of underwater multi-target information, realize real-time detection and positioning of underwater multiple targets during navigation of an unmanned surface vehicle, and improve the positioning accuracy of the underwater multiple targets. And the underwater target positioning precision is ensured through a clustering method. By fusing the high-precision space-time reference provided by the unmanned ship inertial navigation system and the distance and azimuth information of the multiple targets obtained by foresight sonar detection, the geographic coordinates of the multiple underwater targets can be calculated at a time. Compared with a traditional underwater acoustic positioning mode in which a special transponder needs to be arranged for a single target, the method greatly improves the efficiency of multi-target detection and the integrity of information acquisition, and provides a key technical support for comprehensively mastering the underwater situation.

Description

Technical Field

[0001] This invention belongs to the field of underwater multi-target localization and real-time clustering technology for unmanned surface vessels, specifically involving a target localization and real-time clustering method based on forward-looking sonar detection. Background Technology

[0002] Unmanned surface vessel (USV) underwater multi-target localization and clustering technology is mainly applied to shallow water underwater target detection missions. It typically has two modes: direct localization and indirect localization. The direct localization mode mainly uses underwater acoustic positioning devices, such as ultra-short baseline positioning systems, to calculate the target position by measuring the distance between the target position and multiple known reference points. Indirect localization, on the other hand, uses equipment such as sonar and underwater cameras to measure the orientation and distance of the target on the bottom or in the water, and combines this with the platform's position information to calculate the target position.

[0003] Patent CN115760982B studies an underwater target localization method based on sonar images. The method calculates the azimuth angle of the target based on the sonar images to obtain the positioning coordinates of the underwater target in the sonar coordinate system, and uses this as the input parameter for underwater robot operation.

[0004] In existing technologies, underwater target detection using underwater acoustic positioning devices requires the deployment of acoustic arrays or transponders. Sound wave reflection is susceptible to multipath interference, and the information obtained is only location information.

[0005] In addition, underwater cameras acquire underwater target images and perform image processing for positioning and analysis. However, this method is greatly affected by the turbidity of the water and has a short operating range, only a few meters in a clear field of view.

[0006] In shallow ocean exploration, there are numerous underwater targets. Installing underwater acoustic transponders on these targets is complex, costly, and highly susceptible to the influence of the acoustic wave transmission environment. In addition, water quality varies greatly with location and tides, and underwater light decays rapidly, limiting the effectiveness of underwater cameras. Summary of the Invention

[0007] In view of this, the present invention provides a target localization and real-time clustering method based on forward-looking sonar detection, which can simultaneously locate and cluster underwater multi-target information, enabling unmanned surface vessels to detect and locate multiple underwater targets in real time during navigation, and ensuring the accuracy of underwater target localization through clustering methods.

[0008] To achieve the objectives of this invention, the following technical solutions are provided.

[0009] A target localization and real-time clustering method based on forward-looking sonar detection includes the following steps: Acquire inertial navigation data and forward-looking sonar detection data from unmanned surface vessels; The geographical location of the underwater target is calculated based on the inertial navigation data and sonar detection data; A target data group list is constructed and maintained for real-time clustering. Each element in the group list is a cluster group. Each cluster group has an upper limit on the number of clustering targets and is divided based on a predetermined clustering distance threshold. For each newly calculated target location data, the following clustering process is performed: If the grouping list is empty, a new clustering group is created with this target location and used as the cluster center; if the grouping list is not empty, the distance between the target location and the cluster centers of each existing clustering group in the list is calculated; if the distance is less than the clustering distance threshold, the target location is added to the corresponding clustering group; otherwise, a new clustering group is created with this target location; when the number of target locations in a clustering group reaches its preset upper limit, the cluster center of the group is output and the group is removed from the grouping list.

[0010] The calculation method for determining the geographical location of an underwater target based on the inertial navigation data and sonar detection data is as follows: based on time matching or selecting the most recent inertial navigation data, combined with the sonar detection distance and the absolute azimuth synthesized from the bow and relative azimuth, the distance is decomposed and converted into latitude and longitude changes through spherical geometry, and then superimposed on the latitude and longitude of the unmanned surface vessel to obtain the latitude and longitude of the target location.

[0011] The specific steps for calculating the geographical location of underwater targets based on inertial navigation data and sonar detection data include: Add the relative bearing detected by sonar to the bow direction of the unmanned surface vessel to obtain the absolute bearing with geographic north as the reference. The detection distance is decomposed into changes in longitude and changes in latitude based on the absolute orientation; Based on the Earth's average radius, the changes in longitude and latitude are converted into longitude and latitude values, respectively. The longitude and latitude changes are added to the longitude and latitude of the current position of the unmanned surface vessel to obtain the target's position in longitude and latitude.

[0012] The method for calculating the distance between the target location and the cluster center is as follows: Obtain the latitude and longitude coordinates of two points; The spherical distance is calculated based on the difference in latitude and longitude between the two locations and the average radius of the Earth.

[0013] The data structure of each cluster group in the target data grouping list includes at least: the latitude and longitude of the cluster center of the group, a list of all target location data belonging to the group, and an upper limit on the number of target locations that the group can accommodate.

[0014] The clustering process is executed cyclically for each input target location data to achieve real-time dynamic clustering of newly detected targets.

[0015] The inertial navigation data includes time, location latitude and longitude, and heading, while the sonar detection data includes time and the detection distance and relative orientation of multiple targets.

[0016] Beneficial effects First, this invention enables simultaneous localization of multiple underwater targets. By fusing the high-precision spatiotemporal reference provided by the unmanned surface vessel's inertial navigation system with the distance and azimuth information of multiple targets acquired by forward-looking sonar, the geographical coordinates of multiple underwater targets can be calculated simultaneously. Compared to traditional underwater acoustic localization methods that require the deployment of dedicated transponders for individual targets, this method significantly improves the efficiency of multi-target detection and the completeness of information acquisition, providing key technical support for a comprehensive understanding of the underwater situation.

[0017] Second, this invention enables real-time online clustering of target location information. The method employs a dynamically maintained grouping list data structure and an efficient clustering judgment process, allowing for real-time assignment and grouping updates for each newly detected target location during unmanned surface vessel (USV) navigation.

[0018] Third, this invention can output cluster centers in real time when certain conditions are met. This real-time processing capability enables underwater target information to be extracted and reported instantly, effectively supporting online decision-making and rapid response of unmanned surface vessel platforms, and overcoming the lag problem of traditional offline processing methods.

[0019] Fourth, this invention effectively improves the final accuracy of target detection through real-time clustering. Individual sonar detection data is susceptible to interference from noise, multipath effects, and other factors, resulting in biases. This method aggregates multiple discrete detection points belonging to the same physical target in real time and uses its geometric center as the final reported position of the target. This process smooths out random errors and filters out some outliers, thus obtaining a more stable and reliable target position estimate than that of a single detection data point, significantly improving the accuracy and robustness of underwater target localization results. Attached Figure Description

[0020] Figure 1 This is a schematic diagram illustrating the implementation steps of the method of the present invention.

[0021] Figure 2 This is a schematic diagram of the target data grouping list in the method of the present invention.

[0022] Figure 3 This is a schematic diagram of the target data grouping and cluster center calculation process in an embodiment of the method of the present invention. Detailed Implementation

[0023] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0024] This invention provides a target localization and real-time clustering method based on forward-looking sonar detection, the implementation steps of which are as follows: Figure 1 As shown. The method of the present invention includes the following steps: Step S1: Based on the inertial navigation output information of the unmanned surface vessel, including time T1, longitude x, latitude y, and heading, and the sonar detection information, including time T2, distance L, and azimuth. The sonar detection information contains multiple distance and azimuth values ​​at the same time.

[0025] Step S2: First, based on the time, search for the inertial navigation data corresponding to the sonar detection information from the inertial navigation output information. If the corresponding time does not exist, take the inertial navigation data closest to the time of the sonar detection information.

[0026] Then, based on the unmanned surface vessel's position (x, y) at that moment, its sonar detection range L, and its absolute bearing... and the average radius of the Earth Calculate the target position (x', y'). The unit is degrees, x' is the target longitude, and y' is the target latitude. The calculation method is shown in formulas (1) to (3).

[0027]

[0028] Approximate the Earth as a sphere and establish a rectangular coordinate system with due east and due north. First, convert the azimuth angle into an angle in the rectangular coordinate system. Then, decompose the distance along the coordinate axes of the rectangular coordinate system. The decomposed distance is converted into the changes in longitude and latitude at that location. Finally, these changes are superimposed on the latitude and longitude of the unmanned surface vessel to obtain the latitude and longitude of the target location.

[0029] In step S3, the target clustering data structure is designed, and a grouping list is created. The elements in the grouping list are cluster groups. Each cluster group has a maximum number of target clusters. Cluster groups are divided based on cluster distance. The target data grouping list is as follows: Figure 2 As shown.

[0030] The process of grouping target data and calculating cluster centers is as follows: Figure 3 As shown, the details are as follows: Input the target location data, check if the group list is empty. If empty, use the target location as the cluster center and proceed to the next iteration. If not empty, iterate through the group list, calculate the distance between the target location and the cluster centers of each group. If the distance is less than a predetermined cluster distance, add the target location to that cluster group; if the distance is greater than the predetermined cluster distance, add the target location to a new cluster group. If the number of elements in a cluster group reaches the predetermined target number of clusters, output the cluster center.

[0031] Given the latitude and longitude of two points respectively , The distance between the two points is D, and the distance calculation formula is as shown in formulas (4) to (6).

[0032]

[0033] Approximating the Earth as a sphere, we establish a rectangular coordinate system with due east and due north. First, we calculate the difference in latitude and longitude between two points and convert it to radians. Then, we calculate the sine of this difference in the spherical coordinate system and determine its radian value. Finally, we calculate the arc length based on the Earth's radius, which is the distance between the two points.

[0034] In step S4, based on the predetermined cluster radius and the predetermined number of cluster targets, the cluster center is output and the group is removed from the group list.

[0035] This invention includes, but is not limited to, the above embodiments. Any equivalent substitutions or partial improvements made under the spirit and principles of this invention shall be considered within the scope of protection of this invention.

Claims

1. A target localization and real-time clustering method based on forward-looking sonar detection, characterized in that, Includes the following steps: Acquire inertial navigation data and forward-looking sonar detection data from unmanned surface vessels; The geographical location of the underwater target is calculated based on the inertial navigation data and sonar detection data; A target data group list is constructed and maintained for real-time clustering. Each element in the group list is a cluster group. Each cluster group has an upper limit on the number of clustering targets and is divided based on a predetermined clustering distance threshold. For each newly calculated target location data, the following clustering process is performed: If the grouping list is empty, a new clustering group is created with this target location and used as the cluster center; if the grouping list is not empty, the distance between the target location and the cluster centers of each existing clustering group in the list is calculated; if the distance is less than the clustering distance threshold, the target location is added to the corresponding clustering group; otherwise, a new clustering group is created with this target location; when the number of target locations in a clustering group reaches its preset upper limit, the cluster center of the group is output and the group is removed from the grouping list.

2. The target localization and real-time clustering method based on forward-looking sonar detection according to claim 1, characterized in that, The calculation method for determining the geographical location of an underwater target based on the inertial navigation data and sonar detection data is as follows: according to time matching or selecting the most recent inertial navigation data, combined with the sonar detection distance and the absolute azimuth synthesized from the bow and relative azimuth, the distance is decomposed and converted into latitude and longitude changes through spherical geometry, and then superimposed on the latitude and longitude of the unmanned surface vessel to obtain the latitude and longitude of the target position.

3. The target localization and real-time clustering method based on forward-looking sonar detection according to claim 1, characterized in that, The specific steps for calculating the geographical location of underwater targets based on inertial navigation data and sonar detection data include: Add the relative bearing detected by sonar to the bow direction of the unmanned surface vessel to obtain the absolute bearing with geographic north as the reference. The detection distance is decomposed into changes in longitude and changes in latitude based on the absolute orientation; Based on the Earth's average radius, the changes in longitude and latitude are converted into longitude and latitude values, respectively. The longitude and latitude changes are added to the longitude and latitude of the current position of the unmanned surface vessel to obtain the target's position in longitude and latitude.

4. The target localization and real-time clustering method based on forward-looking sonar detection according to claim 1, characterized in that, The method for calculating the distance between the target location and the cluster center is as follows: Obtain the latitude and longitude coordinates of two points; The spherical distance is calculated based on the difference in latitude and longitude between the two locations and the average radius of the Earth.

5. The target localization and real-time clustering method based on forward-looking sonar detection according to claim 1, characterized in that, Each cluster group data structure in the target data grouping list includes at least: the latitude and longitude of the cluster center of the group, a list of all target location data belonging to the group, and an upper limit on the number of target locations that the group can accommodate.

6. The target localization and real-time clustering method based on forward-looking sonar detection according to claim 1, characterized in that, The clustering process is executed cyclically for each input target location data, achieving real-time dynamic clustering of newly detected targets.

7. A target localization and real-time clustering method based on forward-looking sonar detection according to any one of claims 1-6, characterized in that, The inertial navigation data includes time, location latitude and longitude, and heading, while the sonar detection data includes time and the detection distance and relative orientation of multiple targets.

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