Underwater target positioning method and system based on acoustic-magnetic combined detection

Abstract

The invention provides an underwater target positioning method and system based on acoustic-magnetic combined detection, belongs to the technical field of underwater target detection, and aims at solving the problems of an existing method and system in the aspects of combined detection, accurate positioning, quick response and the like. The method comprises the following steps that sonar data and magnetic field data of a target water area are obtained, the sonar data are obtained through a plurality of sonar nodes, and the magnetic field data are obtained through a plurality of aircrafts; preprocessing the sonar data and the magnetic field data, and respectively generating a sonar response parameter and a magnetic compensation coefficient of the target water area; a plurality of sonar nodes are used for monitoring a target water area, and early warning signals of candidate targets are determined according to sonar response parameters; and determining a response aircraft from the plurality of aircrafts according to the early warning signal, collecting the current magnetic field data of the candidate target according to the position information through the response aircraft, processing the current magnetic field data based on the magnetic compensation coefficient, and determining the underwater target according to the processing result.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of underwater target detection, and in particular to an underwater target positioning method and system based on acoustic-magnetic joint detection, an electronic device, and a computer program product. BACKGROUND

[0002] With the deepening of tasks such as marine resource development, environmental protection, and emergency search and rescue, underwater target detection technology is facing increasing demand and challenges. At present, sonar technology, as the mainstream means of underwater detection, can achieve large-scale scanning, but is easily affected by multipath effect and noise interference in complex hydrological environments, resulting in insufficient positioning accuracy. Emerging geomagnetic detection technology has the characteristics of fast response, wide coverage, and high positioning accuracy, but has insufficient detection capability on weak magnetic or non-ferromagnetic targets, and the overall detection range and applicable objects are limited. In addition, existing technical solutions mostly rely on a single platform or fixed deployment, or simple sensor stacking, and generally have complex systems, slow response, low resource utilization efficiency, and lack of stable tracking capability for moving or low-speed targets. SUMMARY

[0003] Embodiments of the present application provide an underwater target positioning method and system based on acoustic-magnetic joint detection, an electronic device, and a computer program product, which can solve the problems of existing underwater target detection methods in joint detection, accurate positioning, and fast response.

[0004] In a first aspect, embodiments of the present application provide an underwater target positioning method based on acoustic-magnetic joint detection, which comprises the following steps: Obtaining sonar data and magnetic field data of a target water area, the sonar data being obtained by a plurality of sonar nodes, and the magnetic field data being obtained by a plurality of aircrafts; Preprocessing the sonar data and the magnetic field data to generate sonar response parameters and magnetic compensation coefficients of the target water area, respectively; Monitoring the target water area using the plurality of sonar nodes, determining a warning signal of a candidate target according to the sonar response parameters, the warning signal including position information; Determining a response aircraft from the plurality of aircrafts according to the warning signal, collecting current magnetic field data of the candidate target by the response aircraft according to the position information, processing the current magnetic field data based on the magnetic compensation coefficients, and determining the underwater target according to the processing result.

[0005] In a second aspect, embodiments of the present application provide an underwater target positioning system based on acoustic-magnetic joint detection, which comprises the following: deploying a plurality of sonar nodes and a plurality of aircrafts in the target water area; preprocessing the sonar data and the magnetic field data to generate a sonar response parameter and a magnetic compensation coefficient of the target water area, respectively; monitoring the target water area using the plurality of sonar nodes, determining a warning signal of a candidate target according to the sonar response parameter, the warning signal including position information; and determining a response aircraft from the plurality of aircrafts according to the warning signal, collecting current magnetic field data of the candidate target by the response aircraft according to the position information, processing the current magnetic field data based on the magnetic compensation coefficient, and determining the underwater target according to a processing result.

[0006] In a third aspect, an electronic device is provided, which includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, and the program or instructions, when executed by the processor, implement the steps of the method in the first aspect.

[0007] In a fourth aspect, a computer program product is provided, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions executable by a computer to implement the steps of the method in the first aspect.

[0008] The embodiments of the present application overcome the limitations of the detection range of a single platform, and can measure the magnetic field data by the aircraft when a suspected target is found, and effectively improve the positioning accuracy and identification reliability of a weak small target by combining the pre-calculated magnetic compensation coefficient. The wide-area patrol of the sonar nodes and the rapid maneuver of the airborne magnetic exploration help to shorten the response time from detection to identification. The multiple sonar nodes are used for normal monitoring, and the aircrafts fly on demand based on the monitoring results, which can avoid the high energy consumption of the continuous operation of the large platform in the traditional scheme, realize the dynamic optimization allocation of the sensor resources, and reduce the overall operation cost of the system. Through the complementation and combination of the two physical field information of acoustics and magnetism, the general detection capability of various underwater objects can be provided by the sonar, and the high specificity of the magnetic exploration in metal target identification can be utilized, which effectively improves the comprehensive detection capability of small debris, weak signal targets and other targets in the underwater complex environment. BRIEF DESCRIPTION OF DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0010] Figure 1 is a flowchart of an underwater target positioning method of a combined acoustic-magnetic detection provided by an embodiment of the present application; Figure 2 is a schematic diagram of a sonar node and an aircraft provided by an embodiment of the present application; Figure 3 is a flowchart of another underwater target positioning method of a combined acoustic-magnetic detection provided by an embodiment of the present application; Figure 4 is a flowchart of still another underwater target positioning method of a combined acoustic-magnetic detection provided by an embodiment of the present application; Figure 5 is a structural schematic diagram of an underwater target positioning system of a combined acoustic-magnetic detection provided by an embodiment of the present application; Figure 6 is a system framework schematic diagram of an underwater target positioning system of a combined acoustic-magnetic detection provided by an embodiment of the present application; Figure 7 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0011] In order to make the person skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0012] The terms "first", "second", and the like in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein. In addition, "and / or" in the specification and claims indicates at least one of the connected objects, and the character " / ", generally indicates that the front and rear associated objects are in an "or" relationship.

[0013] With the increasing emphasis on ocean resource development and environmental protection, underwater target detection technology has become an important support for ensuring sustainable use of the ocean, carrying out search and rescue missions, and conducting environmental monitoring. With the continuous expansion of global marine activities, there is an increasing demand for discovering, locating, and tracking underwater debris, marine garbage, and other targets.

[0014] Sonar-based detection methods are currently the mainstream means for carrying out underwater target discovery, location, and tracking. By emitting sound waves and receiving echoes, large-scale scanning and preliminary target detection can be achieved. However, the marine environment is extremely complex, and underwater and surface environments are affected by factors such as sound wave attenuation, multipath propagation, salinity temperature gradient interference, etc., resulting in sonar being susceptible to multipath effects and noise interference in complex hydrological conditions, with low positioning accuracy, limited detection range, and difficulty in effectively tracking low-speed or stationary targets, limiting its application in high-precision, real-time response, and robust detection tasks.

[0015] In recent years, magnetometer technology has made significant progress as a new underwater target detection method. This technology typically uses a magnetometer mounted on an aircraft to measure magnetic field anomalies for large-scale, rapid scanning, effectively covering a wide sea area. Compared to traditional sonar, magnetometer technology has a clear advantage in accuracy, with positioning errors controlled at the meter level, far superior to sonar's tens of meters in complex environments. In addition, high-speed air travel allows magnetometer technology to have fast response speed, wide coverage, and be unaffected by hydrological and meteorological disturbances. However, magnetometer technology also has limitations, as the compensation coefficients used to correct magnetic field interference need to be calculated in advance, and the high cost of equipment limits its deployment to a much smaller scale than sonar, restricting its large-scale application.

[0016] Current solutions that combine sonar and magnetometer technology generally involve integrating sonar devices and magnetometers on the same underwater vehicle or surface ship, following a predetermined route and simultaneously collecting sonar data and magnetic field data. This type of solution is typically used for large underwater object surveys such as submarine pipelines, cables, and wrecks. The main disadvantages of this type of solution include the following: 1. Limited detection range: Existing sonar has limited range in turbid water, complex seabed, and multipath environments, and magnetometer detection is insufficient for weak magnetic or non-ferromagnetic targets, resulting in limited overall detection range and applicability.

[0017] 2. Slow response speed: Most solutions rely on towed cable deployment or large platform operations, which are complex and have poor real-time performance, making them unsuitable for rapid response or emergency detection tasks.

[0018] 3. Insensitive to small targets and weak signals: Existing solutions are mainly for pipelines, shipwrecks or large targets. For small debris or marine garbage, the signals are weak, and the detection and recognition rates of existing sonar and magnetic detection combined solutions are insufficient.

[0019] 4. Low system complexity and resource utilization: Multi-sensor platforms are often highly integrated, consume a lot of power, and are bulky, resulting in high costs and limited battery life, which restricts their large-scale promotion in civilian applications.

[0020] 5. Lack of stable tracking capability for moving or low-speed targets: Existing solutions are mostly designed for stationary or buried objects, and lack the ability to continuously track slow-moving garbage or debris.

[0021] To address the aforementioned issues, this application proposes a method and system for underwater target detection, localization, and tracking that integrates sonar and geomagnetic detection technologies. By integrating the wide-area scanning capability of sonar with the high-precision characteristics of airborne geomagnetic detection, it achieves joint detection, rapid response, precise localization, and end-to-end tracking. In complex marine environments, sonar quickly locks onto the target area, while geomagnetic detection further achieves meter-level precision localization. Combined with real-time data fusion technology, it ensures accurate localization and stable tracking of low-speed or stationary targets. This application effectively overcomes the shortcomings of insufficient sonar accuracy and limited geomagnetic detection deployment, improving detection efficiency and robustness, and providing an efficient solution for marine resource development, search and rescue missions, and environmental monitoring.

[0022] The following is in conjunction with the appendix Figures 1 to 7 This paper provides a detailed description of an underwater target localization method and system using combined acoustic and magnetic detection, through specific embodiments and application scenarios.

[0023] Figure 1 This application illustrates an embodiment of an underwater target localization method using combined acoustic and magnetic detection. This method can be executed by an electronic device, which may include a server and / or terminal equipment. In other words, the method can be executed by software or hardware installed on the server and / or terminal equipment, and includes the following steps: Step 110: Acquire sonar data and magnetic field data of the target water area. The sonar data is obtained through multiple sonar nodes, and the magnetic field data is obtained through multiple aircraft.

[0024] The sonar data can be obtained from underwater acoustic detection data distributed among multiple sonar nodes deployed in the target waters. These sonar nodes form a detection network, and each sonar node generates raw data by actively emitting acoustic signals (e.g., linear frequency modulated signals) and receiving their echo signals. Sonar data generally includes information such as the echo intensity, time delay, and Doppler shift of the underwater target, which can be used to preliminarily determine the presence, approximate location, and distance of the target.

[0025] The magnetic field data can be obtained from the Earth's magnetic field strength data acquired by multiple aircraft conducting patrol missions over the target waters. Aircraft equipped with magnetic detection devices (e.g., magnetometers) measure the total intensity or vector of the Earth's magnetic field during low-altitude flight. To reduce magnetic interference from the aircraft itself, the magnetometer is typically mounted on a tail extension or wingtip pod. The magnetic field data reflects the absolute value of the Earth's magnetic field at the measurement point. When ferromagnetic targets are present underwater, they cause localized distortions in the Earth's magnetic field, known as "magnetic anomalies." By identifying these anomalous signals from the background field, the presence of underwater ferromagnetic objects can be confirmed, achieving meter-level accuracy in positioning.

[0026] See Figure 2 Sonar nodes are deployed on the surface of the target water area, and an aircraft equipped with a magnetometer is deployed in the airspace above the target water area. Both are used to detect underwater targets.

[0027] Step 120: Preprocess the sonar data and magnetic field data to generate sonar response parameters and magnetic compensation coefficients for the target water area.

[0028] The sonar response parameters are used to identify anomalous underwater targets during routine patrols. Raw data acquired from distributed sonar nodes is time-synchronized to ensure that subsequent data from multiple nodes are processed on a unified time reference. Digital signal processing techniques are used to filter the raw sonar echo signals to remove background noise from the marine environment and interference introduced by multipath effects. The judgment threshold in the sonar response parameters can be used by each sonar node to determine whether an anomalous echo exists.

[0029] The magnetic compensation coefficients are used to correct for magnetic interference from the aircraft itself during airborne magnetic surveys. An aircraft equipped with a magnetometer performs a series of pre-set maneuvers over the target water area to collect background magnetic field data at different spatial positions and flight attitudes. Based on the collected data, a compensation model is applied to calculate and characterize the aircraft's own magnetic interference as a set of coefficients related to its flight attitude, namely the magnetic compensation coefficients.

[0030] Step 130: Use the multiple sonar nodes to monitor the target water area, and determine the early warning signal of the candidate target based on the sonar response parameters. The early warning signal includes location information.

[0031] Multiple sonar nodes form a detection network to continuously monitor the target water area over a wide area. These nodes work collaboratively according to a unified time base and a predetermined patrol plan. Each node periodically emits sound signals and simultaneously collects echo signals, achieving all-weather, grid-like coverage of the target water area. Each sonar node independently collects data in real time and compares the collected data with a set threshold in the sonar response parameters. When the signal strength of any node continuously exceeds the set threshold, that node determines that a candidate target exists within its monitoring range and generates a corresponding warning signal.

[0032] The early warning signal can be a structured data packet, including location information, timestamp, confidence level, etc. The location information can be calculated based on the geographical coordinates or area of ​​the candidate target using the Global Positioning System (GPS) and time difference of arrival (TDOA) technology. The timestamp is the time information when the candidate target is confirmed. The confidence level can be the calculated collaborative confidence value of multiple sonar nodes.

[0033] Step 140: Based on the warning signal, determine a response aircraft from the plurality of aircraft, collect the current magnetic field data of the candidate target through the response aircraft according to the location information, process the current magnetic field data based on the magnetic compensation coefficient, and determine the underwater target based on the processing result.

[0034] The response aircraft can be determined based on a dynamic scheduling process. Based on multiple available aircraft and their real-time status information, a pre-defined optimization algorithm is used for global evaluation to obtain the optimal aircraft, which is then selected as the response aircraft for this mission. A path planning algorithm is employed to plan the optimal flight path for the response aircraft to reach the candidate target's location. The path cost comprehensively considers distance and environmental factors, ensuring the aircraft responds within a preset timeframe.

[0035] Once the responding aircraft arrives at the area indicated by the location information based on the optimal flight path, it can enter a low-altitude flight mode and perform coverage flight according to a predetermined scanning trajectory. The magnetometer on board the aircraft collects current magnetic field data to capture weak magnetic anomaly signals that may be caused by candidate targets.

[0036] The processing of the current magnetic field data may include: first, high-pass filtering the data to suppress low-frequency environmental noise; then, using a magnetic compensation coefficient to correct the data to remove magnetic interference generated by the aircraft itself, obtaining a magnetic anomaly signal only related to the candidate target. Based on the compensated current magnetic field data, if a magnetic anomaly signal significantly exceeding the background noise is identified near the candidate target's location, the candidate target can be confirmed as an underwater target and its geographic coordinates recorded. This information, along with relevant data, can be reported to the command center in real time via the communication system to trigger subsequent tracking, identification, or other tasks. If no magnetic anomaly signal is detected, the warning signal is classified as a false alarm for updating model parameters and adjusting patrol strategies.

[0037] This embodiment overcomes the limitations of a single platform's detection range by deploying multiple sonar nodes and aircraft in the target waters. When a suspected target is detected, the aircraft can measure magnetic field data, and combined with pre-calculated magnetic compensation coefficients, it can effectively improve the positioning accuracy and identification reliability of weak targets. Wide-area patrols by sonar nodes and rapid maneuverability of airborne magnetic detectors help shorten the response time from detection to identification. Using multiple sonar nodes for routine monitoring, with the aircraft flying on demand based on monitoring results, avoids the high energy consumption of continuous operation of large platforms in traditional solutions, enabling dynamic optimization of sensor resource allocation and reducing overall system operating costs. Through the complementarity and combination of acoustic and magnetic field information, sonar can provide general detection capabilities for various underwater objects, while magnetic detectors can leverage their high specificity in identifying metallic targets, effectively improving the comprehensive detection capabilities for small debris, weak-signal targets, and other objects in complex underwater environments.

[0038] In yet another exemplary embodiment, based on step 110 of the above embodiment, the method for acquiring sonar data and magnetic field data of the target water area, wherein the sonar data is obtained through multiple sonar nodes and the magnetic field data is obtained through multiple aircraft, may further include the following specific steps: Multiple sonar nodes are set up on the surface of the target water area. The multiple sonar nodes include a static node group and / or a dynamic node group. The static node group includes sonar nodes fixed on buoys, which form a grid array. The spacing between adjacent nodes is determined based on the area and depth of the target water area and a grid optimization algorithm. The dynamic node group includes sonar nodes set on a submersible. The submersible performs maneuvering cruises within the grid array formed by the static node group.

[0039] This embodiment uses a target sea area as an example. Multiple sonar nodes are distributed sonar devices deployed on the sea surface to achieve initial coverage of the target sea area. Ten to fifty commercial multibeam sonar nodes can be selected, operating at frequencies between 200 and 400 kHz, balancing detection range and resolution, suitable for medium- to long-range target detection. These nodes are fixed on buoys or underwater vehicles, forming a grid-like array with a distance of more than 5 kilometers between nodes. The underwater vehicle sonar moves in real time for maneuvering patrols, ensuring wide coverage and minimizing blind spots. The spacing between the buoy sonar nodes is adjusted using a grid optimization algorithm based on the area and depth of the target sea area.

[0040] In this embodiment, a grid-like array of static buoy nodes achieves basic, large-scale, and continuous coverage of the target water area, overcoming the limitations of a single platform's detection range. Furthermore, the node spacing can be dynamically optimized based on the water area and depth, effectively avoiding resource redundancy or insufficient coverage, and improving the scientific rigor and cost-effectiveness of the detection node layout. The static node group provides a stable monitoring foundation, while the dynamic node group can maneuver and patrol within the grid, covering detection blind spots between static nodes and allowing for focused detection of suspicious areas when necessary. This combined static and dynamic sonar node layout significantly enhances adaptability to complex underwater environments and increases the probability of target detection. The distributed, grid-like sonar node deployment also avoids the risk of system paralysis due to single-point failures. Even if individual nodes fail, the network can maintain basic functionality, and dynamic nodes can temporarily fill in for failed nodes, ensuring the continuity and integrity of the monitoring mission.

[0041] In yet another exemplary embodiment, based on step 110 of the above embodiment, the method for acquiring sonar data and magnetic field data of the target water area, wherein the sonar data is obtained through multiple sonar nodes and the magnetic field data is obtained through multiple aircraft, may further include the following specific steps: The system controls the multiple aircraft to reach the airspace above the target water area, and each aircraft is equipped with a magnetic detection device. The system controls the aircraft to perform maneuvering flight above the target water area and collects magnetic field data of the aircraft corresponding to different spatial positions and / or different flight attitudes through the magnetic detection device.

[0042] The maneuvering flight attitudes include at least one of roll, pitch, and sideslip, with roll angles ranging from ±10°, pitch angles from ±5°, and sideslip angles from ±5°. The aircraft extends a long boom from its tail, on which a magnetic detection device, such as a magnetometer mounted on a support extending from the tail, is deployed to reduce magnetic interference from the aircraft itself. The aircraft, equipped with this device, performs maneuvers, for example, using ±10° roll, ±5° pitch, and ±5° sideslip, to collect background magnetic field data of the ocean at different locations.

[0043] Based on the collected marine background magnetic field data, the magnetic compensation coefficient for a specific area can be calculated using the Tolles-Lawson model, providing a correction benchmark for subsequent aeromagnetic surveys.

[0044] In this embodiment, by controlling the aircraft to perform maneuvering flight actions such as roll, pitch, and sideslip, spatial magnetic field data at different altitudes and attitudes can be collected. This overcomes the limitation of traditional steady flight, which can only acquire two-dimensional planar data, and allows for the construction of a more accurate and three-dimensional background magnetic field model of the target water area, providing a comparison benchmark for subsequent identification of weak magnetic anomaly signals. Magnetic interference from the aircraft itself is a key factor affecting detection accuracy. By actively collecting magnetic field data from the aircraft in multiple flight attitudes, its own magnetic compensation coefficient can be determined more accurately. This magnetic compensation coefficient, obtained based on the aircraft's dynamic maneuvering flight, is more accurate than the result calculated under a single steady state. It can effectively filter out magnetic interference in subsequent detection, helping to improve the accuracy of identifying magnetic anomaly signals of underwater targets.

[0045] In yet another exemplary embodiment, based on step 110 of the above embodiment, the method for acquiring sonar data and magnetic field data of the target water area, wherein the sonar data is obtained through multiple sonar nodes and the magnetic field data is obtained through multiple aircraft, may further include the following specific steps: Build cloud server hardware; design cloud server software; establish 5G or satellite communication links; report and remotely transmit the acquired sonar and magnetic field data to the cloud server; the cloud server performs calculations on the data and provides user feedback.

[0046] The sonar and magnetic field data are based on the coordinated implementation of sonar deployment and aeromagnetic surveys, ensuring a stable data source and accurate environmental compensation conditions, laying the foundation for subsequent patrol, detection, and tracking missions. During this process, 5G or satellite communication links are simultaneously established to enable real-time reporting and remote transmission of sonar and aeromagnetic data, ensuring stable, low-latency communication support for subsequent resource scheduling and monitoring tasks.

[0047] In yet another exemplary embodiment, based on step 120 of the above embodiment, the method of preprocessing the sonar data and magnetic field data to generate the sonar response parameters and magnetic compensation coefficient of the target water area may further include the following specific steps: The sonar data is aligned using timestamps, and then filtered and denoised. Based on the filtered and denoised sonar data, an anomaly detection threshold is set for detecting sonar data from a single sonar node in the target water area, and a confidence threshold is set for detecting sonar data from multiple sonar nodes in the target water area. The anomaly detection threshold and the confidence threshold are used as sonar response parameters.

[0048] The data alignment process utilizes GPS timestamps to calibrate data from multiple sonar nodes, ensuring that the sonar data are on the same timeline. The filtering and noise reduction process employs digital signal processing techniques to remove marine environmental noise.

[0049] The anomaly detection threshold can be determined using the Ordered Statistical Constant False Alarm Rate (OS-CFAR) algorithm. The goal of CFAR is for radar or sonar systems to automatically and adaptively set a detection threshold when the background noise (or clutter) power is unknown and varies with time and location. This threshold dynamically adjusts with noise fluctuations to maintain a constant false alarm probability; that is, the system will not generate a large number of false alarms when the noise is strong, nor will it miss weak targets when the noise is weak. OS-CFAR is an advanced algorithm in the CFAR family, specifically designed to address the performance degradation problem of traditional CFAR in non-uniform clutter environments. Anomaly Detection Threshold ,in, Let K be the mean background noise, and K be a constant greater than 1, preferably 2.5. For each sonar node, its local anomaly detection threshold is calculated based on the OS-CFAR algorithm, and the signal strength of the sonar node is compared with the local anomaly detection threshold to obtain the local detection status of the sonar node. If the signal strength of the sonar node exceeds its local anomaly detection threshold, the local detection status of the sonar node is abnormal; otherwise, it is normal.

[0050] The confidence threshold is used to calculate the network collaborative confidence score. The confidence threshold is a value between 0.5 and 0.8, preferably 0.7. The number of nodes in the distributed sonar network whose local detection status is abnormal is counted. Based on the counted number of abnormal nodes and the total number of nodes, the network collaborative confidence score is calculated. For example, the confidence score is calculated through inter-node fusion. ,in This represents an abnormal number of sonar nodes. Let C be the total number of sonar nodes. Assuming a confidence threshold of 0.7, if C > 0.7, an alarm will be reported.

[0051] The aforementioned anomaly detection threshold and confidence threshold can be dynamically adjusted based on target characteristics. A candidate target is detected and an early warning signal is generated only when both of the following conditions are met simultaneously: the signal strength of at least one node exceeds its local anomaly detection threshold; and the network collaborative confidence level exceeds a preset confidence threshold.

[0052] In this embodiment, a dual-determination mechanism combining local anomaly detection and network-coordinated confidence verification avoids false alarms caused by occasional noise interference from a single node, temporary environmental disturbances, or local biological activity. Warning signals are generated only when two conditions are met, ensuring high reliability and enhancing the overall reliability of the detection results. The OS-CFAR algorithm dynamically calculates the anomaly threshold for each sonar node, adaptively tracking background noise changes and maintaining the optimal detection threshold for weak echo signals in complex and variable aquatic environments. This overcomes the drawback of fixed thresholds causing drastic performance degradation in low signal-to-noise ratio environments, thereby increasing the probability of detecting weakly scattering targets such as small debris and marine litter. Furthermore, the anomaly detection threshold and confidence threshold can be dynamically adjusted based on different target characteristics and real-time environmental conditions, exhibiting good flexibility and intelligence to ensure detection performance across various mission scenarios and aquatic conditions.

[0053] In yet another exemplary embodiment, based on step 120 of the above embodiment, the method of preprocessing the sonar data and magnetic field data to generate the sonar response parameters and magnetic compensation coefficient of the target water area may further include the following specific steps: The magnetic field data is corrected; based on the corrected magnetic field data and the magnetic interference compensation model, the magnetic compensation coefficient is calculated.

[0054] The correction of magnetic field data can be achieved by acquiring data from a three-axis magnetometer during maneuvering flight in advance and combining it with a model of the Earth's magnetic field, or by using filtering methods.

[0055] The magnetic interference compensation model can be a reverse Tolles-Lawson model. The traditional Tolles-Lawson model is used in the data compensation stage. The aircraft's own magnetism (hard iron, soft iron, eddy current effect) can interfere with high-precision magnetometers. This interference is closely related to the aircraft's attitude (heading, roll, pitch). The goal of the traditional Tolles-Lawson model is, given the magnetic compensation coefficient A and the current platform attitude data X, to subtract the platform's own magnetic interference from the observed total magnetic field B_obs, obtaining a clean signal B_comp representing the Earth's magnetic field and the target magnetic anomaly. The calculation formula for magnetic interference compensation is as follows:

[0056] in, The compensated magnetic field strength, A represents the observed magnetic field strength, A represents the pre-calculated magnetic compensation coefficient, and X represents the measurement data vector related to the aircraft's attitude.

[0057] The inverse Tolles-Lawson model is essentially the process of solving for the compensation coefficient A in the traditional model. It occurs during the calibration phase before system deployment. The goal of the inverse Tolles-Lawson model is to obtain that crucial magnetic compensation coefficient A. Specifically, the aircraft performs a series of specific maneuvers (such as ±10° roll, ±5° pitch, etc.) in a region with a known and uniform magnetic field background (usually a pre-surveyed calibration field, or a target area at sea where a uniform background is assumed through maneuvering flight), collecting a large amount of magnetic field data B_obs at different attitudes. Since the background field is known to be uniform, the variation in B_obs mainly comes from the platform's own disturbances changing with attitude. The optimal magnetic compensation coefficient A is then solved from this data using mathematical inversion algorithms (such as the least squares method).

[0058] The corrected magnetic field data can be further processed by filtering out low-frequency noise and compensating for magnetic interference based on the inverse Tolles–Lawson model to obtain the compensated magnetic field data. Low-frequency noise can be filtered out using a high-pass filter.

[0059] In addition, the compensation coefficient is updated regularly using periodic and major meteorological changes as critical points to adapt to environmental changes.

[0060] In this embodiment, the magnetic compensation coefficient is obtained by applying the inverse Tolles-Lawson model. This model considers the complex relationship between hard iron, soft iron, eddy current effects, and flight attitude, and can effectively eliminate the magnetic interference from the aircraft itself during subsequent detection, obtaining a pure magnetic signal caused only by the geomagnetic field and underwater targets. This is beneficial for detecting targets with weak magnetic anomalies. The magnetic compensation coefficient is not fixed or factory preset, but is calibrated on-site by the aircraft performing specific maneuvers over the target sea area. This allows for the acquisition of magnetic compensation coefficients that are more consistent with the current actual geomagnetic environment, effectively eliminating errors caused by regional differences and slow changes in the magnetic properties of the aircraft. Furthermore, by combining periodic or environmental event-triggered coefficient update strategies, it can ensure that the magnetic probe maintains optimal detection performance throughout the entire mission cycle and has good environmental adaptability.

[0061] Figure 3This illustration shows a flowchart of another underwater target localization method using combined acoustic and magnetic detection, provided by an embodiment of this application. This method can be executed by an electronic device, which may include a server and / or terminal equipment. In other words, the method can be executed by software or hardware installed on the server and / or terminal equipment, and includes the following steps: Step 210: Acquire sonar data and magnetic field data of the target water area. The sonar data is obtained through multiple sonar nodes, and the magnetic field data is obtained through multiple aircraft.

[0062] Step 220: Preprocess the sonar data and magnetic field data to generate sonar response parameters and magnetic compensation coefficients for the target water area.

[0063] Steps 210 and 220 can be found above. Figure 1 The specific descriptions of steps 110 and 120 in the illustrated embodiment are provided, and they achieve the same technical effect. To avoid repetition, they will not be repeated here.

[0064] Step 230: Use the multiple sonar nodes to monitor the target water area, and determine the early warning signal of the candidate target based on the sonar response parameters. The early warning signal includes location information. The method of this embodiment may also include the following specific steps: The multiple sonar nodes are synchronized in time; the multiple sonar nodes are controlled to transmit linear frequency modulated signals into the target water area; the echo signals of the linear frequency modulated signals are acquired by the multiple sonar nodes at a preset sampling rate; abnormal signals in the echo signals are identified according to the sonar response parameters, the abnormal signals identified by the multiple sonar nodes are compared, and an early warning signal for the candidate target is generated based on the comparison results.

[0065] The time synchronization calibration error can be controlled within 50 milliseconds, the pulse width of the linear frequency modulated signal is 0.1 milliseconds to 1 millisecond, and the center frequency of the linear frequency modulated signal is between 200 kHz and 400 kHz.

[0066] Multiple sonar nodes form a distributed sonar array, which, after deployment, enters a routine monitoring state, responsible for wide-area underwater target monitoring of the target waters. The distributed sonar array includes fixed buoy sonar nodes and submersible sonar nodes that patrol according to a predetermined plan. Continuous scanning through the distributed sonar array ensures efficient detection of abnormal targets, providing a foundation for subsequent high-precision positioning and tracking.

[0067] First, GPS timestamps are used to synchronize the time of each sonar node, with calibration errors controlled within 50 milliseconds to ensure the accuracy of multi-node collaborative detection. The submersible sonar node conducts regular patrols of the target sea area according to a predetermined plan, while continuously collecting buoy sonar detection data. Second, the sonar nodes transmit linear frequency modulation (LFM) signals with pulse widths ranging from 0.1 to 1 millisecond and center frequencies between 200 and 400 kHz, optimizing echo resolution and noise immunity to effectively address multipath effects and background noise in the marine environment. Subsequently, each sonar node acquires echo data at a sampling rate of 1 MHz, generating high-quality raw data through high-speed analog-to-digital conversion for anomaly detection and target identification by the data preprocessing module. Finally, signals exceeding a threshold are compared across multiple devices and reported in real time via a communication link.

[0068] Step 240: Based on the warning signal, determine a response aircraft from the plurality of aircraft, collect the current magnetic field data of the candidate target through the response aircraft according to the position information, process the current magnetic field data based on the magnetic compensation coefficient, and determine the underwater target based on the processing result.

[0069] Step 240 can be found above. Figure 1 The specific description of step 140 in the illustrated embodiment can achieve the same technical effect, and will not be repeated here to avoid repetition.

[0070] In this embodiment, the seamless integration of hardware (multibeam sonar nodes), commands (time synchronization and signal transmission control), signals (LFM optimized design) and data ensures the efficiency, reliability and robustness of daily detection, providing a stable anomaly detection capability for the overall system process.

[0071] Figure 4 This diagram illustrates a flowchart of another underwater target localization method using combined acoustic and magnetic detection, provided by an embodiment of this application. This method can be executed by an electronic device, which may include a server and / or terminal equipment. In other words, the method can be executed by software or hardware installed on the server and / or terminal equipment, and includes the following steps: Step 310: Acquire sonar data and magnetic field data of the target water area. The sonar data is obtained through multiple sonar nodes, and the magnetic field data is obtained through multiple aircraft.

[0072] Step 320: Preprocess the sonar data and magnetic field data to generate sonar response parameters and magnetic compensation coefficients for the target water area.

[0073] Step 330: Use the multiple sonar nodes to monitor the target water area, and determine the early warning signal of the candidate target based on the sonar response parameters. The early warning signal includes location information.

[0074] Steps 310-330 can be found above. Figure 1 The specific descriptions of steps 210 to 230 in the illustrated embodiment are provided, and the same technical effects can be achieved. To avoid repetition, they will not be repeated here.

[0075] Step 340: Based on the warning signal, a response aircraft is determined from the plurality of aircraft. The response aircraft collects the current magnetic field data of the candidate target based on the position information. The current magnetic field data is processed based on the magnetic compensation coefficient, and the underwater target is determined based on the processing result. The method of this embodiment may further include the following specific steps: Based on the location information and its environmental parameters, the multiple aircraft and their status information, and using a genetic algorithm and a path planning algorithm, the multiple aircraft are evaluated for response, and the aircraft with the best response evaluation result is determined as the responding aircraft. The responding aircraft is controlled to move to the corresponding low-altitude area according to the location information, and the current magnetic field data of the candidate target is collected by the magnetic detection device on the responding aircraft. The current magnetic field data is compensated according to the magnetic compensation coefficient. If the current magnetic field data after compensation is abnormal, the candidate target is taken as the underwater target.

[0076] The location information includes the geographic coordinates of candidate targets, which can be analyzed to redirect the nearest patrol aircraft, optimizing response time. The aircraft then rapidly flies to the area where the candidate target is located according to instructions. Upon arrival, the aircraft enters low-altitude flight to ensure its onboard magnetic field detectors can capture high-precision magnetic field data, while also covering the surrounding area of ​​the target region to improve the comprehensiveness of detection. The patrol aircraft can be one or more small civilian aircraft, each equipped with a flux-gate magnetometer with a resolution of 0.05 nT, regularly performing routine patrols over the monitored sea area. Before the patrol, the magnetic compensation coefficient, calculated in advance by the pilot module, provides a benchmark for subsequent data processing. Aircraft equipped with magnetic field detectors can detect problems during routine patrols and can quickly descend to a lower altitude for reconnaissance. Of course, in most cases, sonar remains the primary method for identifying anomalies in candidate target locations.

[0077] After the aircraft arrives at the designated location based on its position information, its onboard magnetometer begins collecting current magnetic field data. The sampling frequency is adapted to the target characteristics, typically recording the observed magnetic field strength at a high sampling rate. To highlight anomalous signals, high-pass filtering can be used, with a cutoff frequency (…). The Hz frequency was set to 10 Hz to effectively filter out low-frequency background noise while retaining magnetic field changes associated with the anomalous target. The collected data underwent preliminary processing, as described above, using the Tolles-Lawson model for noise filtering and magnetic field compensation. The calculation formula is as follows: This model effectively separates the magnetic field characteristics of anomalous targets, enabling preliminary target identification. The processed data and anomalous target information are then transmitted to the command center via a communication system, providing a basis for subsequent decision-making.

[0078] In this embodiment, multiple aircraft are dynamically evaluated based on the location information of the early warning signal, and the best responding aircraft is selected. This helps to achieve intelligent and optimized allocation of detection resources, ensuring that the closest and best-condition aircraft is deployed to perform the task in the shortest possible time. This upgrades the traditional passive survey mode to an active response and precise delivery mode, significantly shortening the response time from anomaly detection to on-site confirmation. Controlling the responding aircraft to approach and enter low-altitude flight shortens the detection distance between the sensor and the candidate target, enhances the strength of the magnetic anomaly signal, and avoids environmental interference such as high-altitude wind fields. This provides optimal conditions for the magnetometer to capture weak anomaly signals with high signal-to-noise ratios, providing a reliable data foundation for subsequent identification.

[0079] In yet another exemplary embodiment, based on step 340 of the above embodiment, the method of evaluating the response of the plurality of aircraft according to the location information and determining the aircraft with the best response evaluation result as the responding aircraft may further include the following specific steps: A resource pool is initialized, which includes aircraft and their status information. Based on the location information and its environmental parameters, as well as the aircraft and their status information in the resource pool, a response evaluation is performed on multiple aircraft using a genetic algorithm and a path planning algorithm.

[0080] The initialization resource pool contains at least one aircraft and its status information. It receives early warning signals about candidate targets and corresponding environmental parameters from the sonar node detection network. Based on the location information, environmental parameters, and the status information of the aircraft in the resource pool, it dynamically determines an optimal response aircraft from the resource pool. It then plans a flight path for the determined response aircraft to reach the area where the candidate target is located.

[0081] The status information may include the aircraft's current geographical location and remaining battery power.

[0082] The genetic algorithm used is employed to dynamically determine the responding aircraft. Specifically, it may include the following steps: constructing a fitness function that comprehensively considers the distance factor between the aircraft and candidate targets, as well as the aircraft's remaining energy factor. Based on this function, multiple aircraft in the resource pool are evaluated and selected. For example, only aircraft with a battery level higher than 20% can be included in the schedulable resources. The fitness function F can be specifically represented as follows: F = α × D + β × B Where D is the distance weight, B is the battery capacity weight, and α and β are weighting coefficients.

[0083] Preferably, α is 0.6 and β is 0.4.

[0084] The planned flight path is implemented using the A* algorithm.

[0085] The path planning algorithm can be a heuristic search algorithm, which introduces global information when examining each possible node in the shortest path, estimates the distance of the current node to the destination, and uses this estimate as a measure of the probability that the node is on the shortest path. For example, the path cost C of the A* search algorithm is calculated as: C = ∑(d i +w i ), where d i w represents the segmented path distance. i The wind and wave impact weights correspond to environmental parameters.

[0086] Based on the response assessment results, the total response time from receiving the warning signal to planning the completed path for the responding aircraft is less than a preset value, such as 10 seconds.

[0087] In this embodiment, by initializing a resource pool and using a genetic algorithm for global apples, the optimal responding aircraft with the best overall performance can be dynamically determined from multiple aircraft based on constraints such as distance and energy. The selection mechanism based on a fitness function overcomes the one-sidedness of the proximity principle, ensuring both response speed and mission sustainability, thus maximizing overall resource utilization efficiency. Employing a path planning algorithm combined with environmental parameters effectively improves flight safety, ensuring rapid aircraft response and data acquisition. Through algorithmic model and system integration, the total response time from receiving a warning to completing path planning is controlled within a preset time, facilitating rapid response and solving the problems of cumbersome deployment and slow response of traditional detection methods. This approach can be applied to emergency scenarios with extremely high time requirements.

[0088] In yet another exemplary embodiment, based on the above embodiments, the method of this embodiment may further include the following specific steps: The initial position coordinates of the underwater target are obtained; the aircraft is controlled to perform measurements in the low-altitude region corresponding to the initial position coordinates to obtain magnetic field gradient data at continuous time points; the current motion direction of the underwater target is calculated based on the magnetic field gradient data; and the movement trajectory of the underwater target is predicted and iteratively updated based on the aquatic environment information obtained from an external database and the calculated current motion direction of the underwater target, using a filtering algorithm.

[0089] The initial coordinates of the underwater target can be obtained through a communication link. An aeromagnetic survey vehicle is then dispatched to quickly reach the area where the underwater target is located and conducts detailed surveys to determine its precise location. The vehicle flies at low altitude along a pre-set trajectory, dynamically measuring changes in the magnetic field to determine the underwater target's direction of movement. Magnetic field gradients are continuously collected. B) Calculate the instantaneous direction of motion by analyzing the changes in the magnetic signal through time series analysis. The specific calculation formula is shown below:

[0090] Where Δt is the sampling interval, used to estimate the target's motion direction vector, distinguish between stationary and moving targets, and initially quantify the velocity direction.

[0091] This embodiment takes the target sea area as an example, further integrating ocean current information obtained from external databases for trajectory prediction. Real-time ocean current velocity fields (including velocity and direction) are obtained from the ocean database, and interpolation processing is used to ensure synchronization with magnetic field data. Combining the target's current direction and historical position sequence, particle filtering or Kalman filtering algorithms are employed to predict the future trajectory. The calculation formula for the state transition equation is shown below:

[0092] in, Let t be the position at time t. Due to the influence of ocean currents, The noise is Gaussian, and the system forward-predicts the potential travel path of the target.

[0093] Iterative updates can refresh the positioning and trajectory models every second based on extended Kalman filtering as the aircraft continues to cruise and new data is input, minimizing errors. When data is missing, a predictive model is used to temporarily fill in the gaps, ensuring tracking continuity.

[0094] In this embodiment, the location and tracking of underwater targets serve as a core component of subsequent underwater exploration. Precise location and dynamic tracking of underwater targets are achieved using sonar and airborne magnetic detection technologies. By utilizing the interaction between the magnetometer onboard the aircraft and anomalous magnetic signals, changes in the magnetic field are captured to infer the target's motion state. This is combined with external ocean current information for trajectory prediction, providing real-time guidance for decision-making. The entire process consists of four steps: initial location, direction determination, trajectory prediction, and iterative updates, ensuring the accuracy and continuity of tracking and enabling dynamic monitoring and forward-looking analysis of anomalous marine targets.

[0095] Corresponding to the underwater target localization method using combined acoustic and magnetic detection provided in the above embodiments, based on the same technical concept, this application also provides an underwater target localization system using combined acoustic and magnetic detection. See Figure 5 The system 400 includes a deployment module 410, a data preprocessing module 420, a sonar patrol module 430, and an aeromagnetic patrol and positioning module 440.

[0096] The deployment module 410 is used to acquire sonar data and magnetic field data of the target water area. The sonar data is obtained through multiple sonar nodes, and the magnetic field data is obtained through multiple aircraft. The data preprocessing module 420 is used to preprocess the sonar data and magnetic field data to generate sonar response parameters and magnetic compensation coefficients of the target water area. The sonar patrol module 430 is used to monitor the target water area using the multiple sonar nodes and determine the early warning signal of the candidate target based on the sonar response parameters. The early warning signal includes location information. The aeromagnetic patrol and positioning module 440 is used to determine a response aircraft from the multiple aircraft based on the early warning signal, collect the current magnetic field data of the candidate target through the response aircraft based on the location information, process the current magnetic field data based on the magnetic compensation coefficient, and determine the underwater target based on the processing result.

[0097] The deployment module is responsible for setting up sonar nodes and aeromagnetic vehicles, calibrating parameters, and establishing communication links to provide a stable foundation for subsequent missions. The data preprocessing module is responsible for cleaning and synchronizing sonar and aeromagnetic data to provide high-quality input. The sonar patrol module conducts wide-area patrols using a distributed sonar array to quickly detect underwater anomalies. The aeromagnetic patrol and positioning module utilizes small aircraft for auxiliary patrols and rapid response, covering sonar blind spots and achieving high-precision positioning.

[0098] Based on the above embodiments, the deployment module 410 is further configured to set up multiple sonar nodes on the target water surface, wherein the multiple sonar nodes include a static node group and / or a dynamic node group; the static node group includes sonar nodes fixed on buoys, the buoys forming a grid array, and the spacing between adjacent nodes is determined based on the area and depth of the target water area and a grid optimization algorithm; the dynamic node group includes sonar nodes set on a submersible, the submersible performing maneuvering cruises within the grid array formed by the static node group.

[0099] Based on the above embodiments, the underwater target positioning system may further include: a resource scheduling module, used to evaluate the responses of the multiple aircraft based on the location information and its environmental parameters, the multiple aircraft and their status information, and based on a genetic algorithm and a path planning algorithm, and to determine the aircraft with the best response evaluation result as the responding aircraft.

[0100] The resource scheduling module can dynamically optimize the allocation of air and sea sensor resources and paths to improve patrol and response efficiency.

[0101] Based on the above embodiments, the underwater target positioning system may further include: a target positioning and tracking module, used to acquire the initial position coordinates of the underwater target, control the aircraft to perform measurements in the low-altitude region corresponding to the initial position coordinates, obtain magnetic field gradient data at continuous time points, calculate the current motion direction of the underwater target based on the magnetic field gradient data, and predict and iteratively update the movement trajectory of the underwater target based on the aquatic environment information obtained from an external database and the calculated current motion direction of the underwater target, and based on a filtering algorithm.

[0102] The target localization and tracking module generates precise positioning and continuous trajectories based on fused data, supporting real-time target tracking. The output control module generates visual reports and provides feedback on scheduling commands, offering intuitive results and closed-loop control for civilian users. These modules work together to address the shortcomings of insufficient accuracy in traditional sonar and high costs in magnetic exploration, making them suitable for marine resource development, environmental protection, and search and rescue missions.

[0103] The detection system of this application embodiment is based on a collaborative method of wide-area sonar patrol and high-precision magnetic detection positioning. It utilizes a distributed sonar array to quickly detect anomalies and schedules an airborne magnetic detection platform to accurately locate and continuously track small, weak-signal targets. It also employs A* path planning algorithms to achieve dynamic optimization and allocation of multiple platforms, thereby completing the task switching from anomaly detection to magnetic detection intervention within seconds. It can detect and track irregular, weak-signal targets such as marine debris and small wreckage. For the first time, it has achieved full-process detection of complex targets such as marine debris and wreckage from detection and location to continuous tracking. It breaks through the bottleneck of existing technologies being insensitive to small targets, has a wider range of applications, and can serve environmental protection and emergency search and rescue, providing a new technical path for marine debris management, emergency search and rescue, and environmental protection.

[0104] The detection system of this application adopts a dual-sensor collaborative architecture of "sonar patrol + aeromagnetic response," achieving core functions with a minimum number of necessary sensors. Specifically, it utilizes a distributed sonar array for active scanning to quickly detect anomalies, followed by a small airborne magnetometer for low-altitude acquisition of high-precision magnetic anomalies. Accurate alignment of acoustic and magnetic data is achieved through GPS timestamp calibration and regional magnetic field compensation, avoiding invalid and redundant signal inputs and simplifying the data link. In contrast, most existing devices employ simultaneous acquisition of acoustic, magnetic, and electrical multi-physics fields, resulting in a wide variety of sensors, large size, and fixed deployment. They passively rely on the target's own signals, making active detection and real-time tracking difficult. Furthermore, the multi-modal fusion algorithms used in existing devices are relatively complex and lack effective signal orientation and registration mechanisms, leading to data redundancy, limited positioning accuracy, and overall low efficiency. The detection system of this application reduces the number of sensors while ensuring complete functionality, achieving smaller data volume, a simpler processing link, and higher detection efficiency. Compared to passive fixed solutions, this application possesses active detection and real-time tracking capabilities, and is more adaptable to small targets and weak signals, better meeting the demands of low cost, high efficiency, and high precision in civilian environments.

[0105] The detection system in this application adopts a sea-air integrated mode of distributed sonar array and airborne magnetic anomaly detection. The sonar is responsible for wide-area active patrol and can quickly lock onto suspected target areas, while the magnetic anomaly platform can flexibly intervene, providing high-precision positioning and continuous tracking. GPS time synchronization and magnetic field compensation ensure the spatial consistency of data fusion. In contrast, most existing solutions rely on a single aircraft platform carrying multiple sensors. The detection range is mainly limited by the aircraft's operating radius and endurance. In complex environments, acoustics is limited by shallow water reverberation, and magnetic anomalies are not sensitive to weak magnetic targets, resulting in insufficient overall detection coverage and tracking stability. At the same time, the lack of efficient data registration methods can easily lead to inconsistencies between multi-source results. Unlike existing technologies limited by the operating range of a single platform, this application achieves a wider coverage and more efficient cross-platform collaboration. It can quickly detect small targets at long distances while maintaining high-precision positioning and continuous tracking, significantly improving overall detection efficiency and adaptability.

[0106] Figure 6 This diagram illustrates a hierarchical system architecture of an underwater target localization system using acoustic-magnetic joint detection, as described in an embodiment of this application. The entire system structure can include a data sensing and execution layer, a data transmission layer, a core processing layer, and an application interaction layer. The data sensing and execution layer is responsible for collecting data and executing commands in the physical world. The sensing end includes a submersible sonar system, a buoy sonar detector cluster, and an aeromagnetic detection system for collecting underwater acoustic data and atmospheric magnetic field data. The execution end receives commands from the upper layers, such as routine submersible mission commands and aeromagnetic tracking mission commands. The data transmission layer transmits uplink and downlink data via a high-speed communication system and may also include a database for storing and managing various types of data. The core processing layer is responsible for processing data, identifying anomalies, and scheduling resources. The core processing layer runs a processing scheduling algorithm to coordinate the entire system. The sonar data anomaly identification module and the aeromagnetic data anomaly identification module work in parallel, identifying potential targets from data from different sources. The application interaction layer presents the processing results to the user in an intuitive form, ultimately producing anomaly location reports and tracking and positioning maps, providing decision-makers with clear underwater situational awareness.

[0107] Based on the aforementioned system architecture, firstly, sonar equipment is deployed on the sea surface, and an airborne magnetic detection device is used to maneuver and acquire marine magnetic data for calculating magnetic compensation coefficients. After initial data acquisition, the system enters a data preprocessing phase, where the raw sonar and aeromagnetic data are cleaned, synchronized, and compensated to provide standardized input for subsequent analysis. Subsequently, the system enters a routine patrol phase, with sonar conducting wide-area monitoring and the airborne magnetic detection device acquiring data synchronously according to planned routes; both are capable of detecting anomalous targets. Once an anomaly is detected, relevant information is reported via 5G or satellite links, triggering resource scheduling and directing the airborne magnetic detection aircraft to quickly approach the target area for refined detection and positioning. Combining aeromagnetic measurements and compensation calculations, the system can accurately determine the location of the anomalous target and further conduct trajectory tracking to estimate its direction of travel and historical path. Finally, visualization results, such as a GIS map (Geographic Information System Map) or trajectory report, are generated and transmitted to the scheduling phase through a feedback mechanism for dynamically adjusting patrol and response strategies. This process combines the wide-area coverage of sonar with the precise response of aeromagnetic radar to achieve sub-meter level target positioning accuracy, improving overall detection efficiency by more than 30%, and is suitable for civilian scenarios such as marine resource development, environmental protection, and emergency search and rescue.

[0108] It should be noted that the underwater target positioning system for acoustic-magnetic joint detection provided in this application embodiment and the underwater target positioning method for acoustic-magnetic joint detection provided in this application embodiment are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned underwater target positioning method for acoustic-magnetic joint detection, and the repeated parts will not be described again.

[0109] Corresponding to the underwater target localization method using combined acoustic and magnetic detection provided in the above embodiments, based on the same technical concept, this application also provides an electronic device for executing the above method. Figure 7 To illustrate the structure of an electronic device according to various embodiments of this application, as shown in the following diagrams... Figure 7 As shown. Electronic device 500 can vary considerably due to differences in configuration or performance, and may include one or more processors 510 and memory 520. Memory 520 may store one or more application programs or data. Memory 520 may be temporary or persistent storage. The application programs stored in memory 520 may include one or more modules (not shown in the figure), each module may include a series of computer-executable instructions for the electronic device. Furthermore, processor 510 may be configured to communicate with memory 520 and execute the series of computer-executable instructions stored in memory 520 on the electronic device.

[0110] This application also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, which, when executed by a computer, implement the steps of the underwater target localization method using acoustic-magnetic joint detection as described above.

[0111] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0112] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0113] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0115] In a typical configuration, an electronic device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0116] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0117] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0118] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0119] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0120] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for locating underwater targets using a combination of acoustic and magnetic detection, characterized in that, The method includes the following steps: Acquire sonar and magnetic field data of the target water area, wherein the sonar data is obtained through multiple sonar nodes and the magnetic field data is obtained through multiple aircraft; The sonar data and magnetic field data are preprocessed to generate the sonar response parameters and magnetic compensation coefficients of the target water area, respectively. The target water area is monitored using the multiple sonar nodes, and a warning signal for a candidate target is determined based on the sonar response parameters. The warning signal includes location information. Based on the warning signal, a response aircraft is determined from the plurality of aircraft. The response aircraft collects the current magnetic field data of the candidate target based on the location information, processes the current magnetic field data based on the magnetic compensation coefficient, and determines the underwater target based on the processing result.

2. The method according to claim 1, characterized in that, The acquisition of sonar and magnetic field data of the target water area, wherein the sonar data is obtained through multiple sonar nodes and the magnetic field data is obtained through multiple aircraft, includes the following steps: Multiple sonar nodes are set up on the target water surface, including a static node group and / or a dynamic node group; The static node group includes sonar nodes fixed on buoys, the buoys forming a grid array, and the spacing between adjacent nodes is determined based on the area and depth of the target water area and a grid optimization algorithm. The dynamic node group includes sonar nodes set on the underwater vehicle, which performs maneuvering cruise within the grid-like array formed by the static node group.

3. The method according to claim 1, characterized in that, The step of determining a response aircraft from among the plurality of aircraft based on the warning signal, collecting current magnetic field data of the candidate target through the response aircraft based on the location information, processing the current magnetic field data based on the magnetic compensation coefficient, and determining the underwater target based on the processing result includes the following steps: Based on the location information and its environmental parameters, the multiple aircraft and their status information, and using genetic algorithms and path planning algorithms, the multiple aircraft are evaluated for response, and the aircraft with the best response evaluation result is determined as the responding aircraft. The responding aircraft is controlled to move to the corresponding low-altitude area according to the position information, and the current magnetic field data of the candidate target is collected by the magnetic detection device on the responding aircraft. The current magnetic field data is compensated according to the magnetic compensation coefficient. If the current magnetic field data after compensation is abnormal, the candidate target is taken as the underwater target.

4. The method according to claim 1, characterized in that, The method further includes the following steps: Obtain the initial position coordinates of the underwater target; The aircraft is controlled to perform measurements in the low-altitude region corresponding to the initial position coordinates to obtain magnetic field gradient data at continuous time points. The current direction of motion of the underwater target is calculated based on the magnetic field gradient data; Based on the aquatic environment information obtained from an external database and the calculated current movement direction of the underwater target, the movement trajectory of the underwater target is predicted and iteratively updated using a filtering algorithm.

5. An underwater target positioning system using combined acoustic and magnetic detection, characterized in that, The system includes the following: The deployment module is used to acquire sonar data and magnetic field data of the target water area. The sonar data is obtained through multiple sonar nodes, and the magnetic field data is obtained through multiple aircraft. The data preprocessing module is used to preprocess the sonar data and magnetic field data to generate the sonar response parameters and magnetic compensation coefficients of the target water area, respectively. A sonar patrol module is used to monitor the target water area using the multiple sonar nodes and determine the early warning signal of the candidate target based on the sonar response parameters. The early warning signal includes location information. as well as The airborne magnetic patrol and positioning module is used to determine a responding aircraft from the plurality of aircraft based on the warning signal, and to collect the current magnetic field data of the candidate target through the responding aircraft based on the location information, process the current magnetic field data based on the magnetic compensation coefficient, and determine the underwater target based on the processing result.

6. The system according to claim 5, characterized in that, The deployment module is also used to set up multiple sonar nodes on the target water surface, wherein the multiple sonar nodes include a static node group and / or a dynamic node group; The static node group includes sonar nodes fixed on buoys, the buoys forming a grid array, and the spacing between adjacent nodes is determined based on the area and depth of the target water area and a grid optimization algorithm. The dynamic node group includes sonar nodes set on the underwater vehicle, which performs maneuvering cruise within the grid-like array formed by the static node group.

7. The system according to claim 5, characterized in that, The system also includes: The resource scheduling module is used to evaluate the responses of the multiple aircraft based on the location information and its environmental parameters, the multiple aircraft and their status information, and based on genetic algorithms and path planning algorithms, and to determine the aircraft with the best response evaluation results as the responding aircraft.

8. The system according to claim 5, characterized in that, The system also includes: The target localization and tracking module is used to acquire the initial position coordinates of the underwater target, control the aircraft to perform measurements in the low-altitude region corresponding to the initial position coordinates, obtain magnetic field gradient data at continuous time points, calculate the current motion direction of the underwater target based on the magnetic field gradient data, and predict and iteratively update the movement trajectory of the underwater target based on the aquatic environment information obtained from an external database and the calculated current motion direction of the underwater target, and based on a filtering algorithm.

9. An electronic device, characterized in that, It includes a processor, a memory, a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method as described in any one of claims 1 to 4.

10. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions that, when executed by a computer, implement the steps of the method as described in any one of claims 1 to 4.

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