795 results about "Sonar" patented technology

Disclosed in the present invention is a bridge scour curved surface morphological feature reconstruction method based on three-dimensional sonar point clouds, comprising the following steps: collecting point cloud data of a three-dimensional morphology of an underwater riverbed terrain near a bridge foundation, and acquiring original point cloud data of a complete morphology of an underwater riverbed; performing denoising and ball pivoting reconstruction preprocessing on the collected original point cloud data to complete preliminary reconstruction of point clouds; on the basis of the point cloud data having undergone preliminary reconstruction, performing point cloud binarization-based scour morphology recognition on the basis of a computer vision principle, calculating scour pit element classification values of all the point clouds, and recognizing scour pit point clouds; on the basis of the recognized scour pit point clouds, using a K-means clustering algorithm for clustering, and calculating the maximum depth of each pier scour pit; and on the basis of the obtained scour pit element classification values of all the point clouds, providing a curved surface reconstruction method based on scour recognition to perform high-precision curved surface reconstruction on the scour pits. The present invention can achieve high-precision scour pit morphology reconstruction.

The invention discloses a robot target identification method and system based on multi-source data driving, and relates to the technical field of intelligent robot environment awareness, and the method comprises the steps: inputting a dynamic environment vector into an LSTM network, and outputting the real-time fusion weight of vision, sonar and laser radar through an activation function; performing weighted fusion on vision, sonar and laser radar based on real-time fusion weights, constructing a causal graph neural network, separating false correlation features through anti-fact intervention samples, and outputting causal feature vectors; and inputting the causal feature vector into a pre-trained lightweight target recognition model for target classification, and when a new scene is detected, triggering knowledge base retrieval and comparative distillation to generate a recognition confidence coefficient. According to the method, the causality and correlation in the multi-modal sensor data are effectively distinguished by constructing the causality graph neural network and introducing an anti-fact intervention mechanism, so that the recognition accuracy and generalization ability of the model in a complex dynamic environment are improved.

The invention relates to a sonar fault intelligent diagnosis method and system based on a knowledge graph and a large language model. The sonar fault intelligent diagnosis method comprises the following steps: receiving an initial fault description text input by a user through a natural language interaction interface; carrying out integrity analysis on the initial fault description text, and judging whether the initial fault description text contains all preset key information dimensions or not; if the judgment result of the integrity analysis is no, starting an interactive guide mechanism until a complete fault description text is obtained; based on the complete fault description text, performing reasoning in a pre-trained large language model configured with a sonar fault knowledge graph; and based on the conclusion of the joint reasoning, generating and outputting a structured fault diagnosis report. According to the invention, by introducing a fault description integrity evaluation and interaction guide mechanism, the problem that the system diagnosis accuracy is reduced due to fuzzy or incomplete fault description input by a user in the prior art is solved, and the accuracy and stability of overall diagnosis are improved.

The invention relates to the technical field of underwater static target object detection and processing, in particular to an unmanned cross-domain positioning and acoustic fingerprint processing method and system for an underwater static target. Comprising the following steps: performing wide-area scanning on a task sea area, and scheduling an AUV and unmanned aerial vehicle cluster to a target area after discovering a suspicious target; the unmanned aerial vehicle cluster receives an acoustic signal of the AUV and transmits the acoustic signal to the cooperative resolving center in combination with self-positioning data so as to provide accurate coordinate guidance for the AUV; the AUV sails to a target area, parallel processing map construction and accurate positioning, target identification and dual-mode fingerprint generation are carried out, and a target task package is packaged; the ROV receives and analyzes the task packet, autonomously plans a path and sails to a target area, a sonar is started to collect data and generate real-time feature fingerprints, and the real-time feature fingerprints are matched with fingerprints in the task packet; and after matching succeeds, a specific job task is autonomously executed. According to the invention, a complete automatic solution is provided for scenes such as deep and far sea detection, emergency salvage, pipeline maintenance and the like.

A mounting system for a front view sonar transducer. The mounting system has a mounting assembly a mounting bracket affixed to a boat and to a mounting clamp. The mounting clamp has a clamping portion configured to clamp around an outside perimeter of a support tube. The mounting system further includes a waterproof motor housing positioned above the mounting bracket, the motor housing secured to a flange on the support tube. A tubular shaft extends through the waterproof motor housing and support tube, the tubular shaft having an upper portion extending upwardly from the waterproof motor housing and a lower portion configured to receive a front view sonar transducer. The tubular shaft directionally controlled by a motor in the motor housing. A trolling motor shaft extending through the tubular shaft with a motor for directionally controlling the trolling motor in the motor housing. The mounting system further includes one or more foot pedal assemblies configured to operate one or both of the electric motors within the waterproof motor housing.

The invention relates to the technical field of underwater target detection, in particular to a multi-modal underwater target detection method and system based on layered feature alignment. The method comprises the following steps: acquiring an underwater sonar image and an optical image; sonar and optical features are extracted through a double-flow backbone network; background noise is suppressed and key features are enhanced through a local enhancement module; decomposing the features into low-frequency and high-frequency components through a hierarchical alignment module, and establishing a cross-modal feature corresponding relation by using deformable convolution; multi-modal fusion features are generated through a fusion module; and target detection is carried out through the detection head. According to the method, the problem of feature misalignment caused by the difference between the underwater sonar and the optical image due to the imaging principle is solved, adaptive cross-modal feature alignment and efficient fusion are realized, and the precision and robustness of underwater target detection are remarkably improved.

The invention discloses a communication-detection integrated sonar signal design method and device based on multi-pulse composite modulation, and the method comprises the steps: dividing a total frame period into a detection sub-frame and a communication sub-frame, enabling the detection sub-frame to achieve the high-resolution distance measurement through employing a bidirectional linear frequency modulation (BILFM) signal, a communication sub-frame adopts a multi-frequency multi-phase composite modulation (MTPM) signal to realize high-bit-rate data transmission; after band filtering, orthogonal demodulation, low-pass filtering and down-sampling preprocessing are carried out on detection sub-frame echoes, distance and speed information is extracted by adopting fast-time low-sampling two-dimensional fast Fourier transform (2D-FFT), unambiguous Doppler frequency is recovered by utilizing the Chinese remainder theorem (CRT), and accurate speed measurement of long-distance and high-speed targets is realized; a communication subframe receiving signal is subjected to IQ demodulation after down-conversion, Costas loop carrier synchronization and matched filtering, and multi-frequency multi-phase coded data are extracted. According to the method, the time-frequency collaborative design of detection and communication is realized, the distance resolution and the speed measurement range are considered, and the signal bandwidth utilization rate and the underwater information transmission reliability are remarkably improved while the system complexity is kept low. The device comprises a processor and a memory, and an executable program for implementing the method is stored in the memory. The technology can be widely applied to the fields of unmanned underwater vehicles, ocean monitoring, underwater sensing networks and the like.

The invention discloses an intelligent water depth measurement method for waterway maintenance of a water transportation project, and particularly relates to the technical field of water conservancy projects. Historical sounding data, meteorological and hydrological parameters, real-time sonar echo data and unmanned surveying ship path information of the target channel area are acquired; a multi-source sounding data fusion model is constructed, and heterogeneous data fusion is realized by adopting wavelet domain feature extraction, remote sensing delay compensation and a graph attention mechanism; constructing a water depth evolution characteristic spectrum based on multi-scale spatial domain characteristic extraction; modeling a water depth propagation relationship between the sounding units by using a graph neural network; projecting the evolution graph to a section grid, and outputting a continuous water depth prediction value in combination with residual error correction and a rolling prediction strategy; identifying a water depth deviation by comparing with a designed navigation depth, and constructing a risk map layer; the method has the advantages of high prediction precision, strong adaptability, good response real-time performance and the like, and can be widely applied to scenes of channel siltation monitoring, scouring early warning, shipping maintenance management and the like.

The invention discloses a deep-sea mining area sediment plume dynamic monitoring system and a multi-module fusion analysis method thereof, and belongs to the technical field of deep-sea environment monitoring and ocean engineering. The system comprises a fixed monitoring unit and a movable monitoring unit, the fixed monitoring unit is composed of an in-situ optical and three-dimensional sonar observation base station, a sediment capturer array and a multi-parameter sensor array and is arranged in a mining area; and the mobile unit comprises an AUV (Autonomous Underwater Vehicle) carrying a multispectral laser radar and an ROV (Remote Operated Vehicle) carrying a binocular vision system and a micro sampler. According to the analysis method, a plume motion field is extracted through multi-source data fusion, dark channel defogging and a pyramid optical flow method, a physical-data dual-drive model is constructed to simulate the plume diffusion and settlement process, a three-dimensional dynamic thermodynamic diagram is generated in real time, and an intelligent early warning threshold value is set based on the diffusion radius and the settlement flux. According to the invention, plume multi-scale, high-precision, real-time and dynamic monitoring and evaluation are realized, and deep-sea mining operation optimization and environment compliance supervision are effectively supported.

The invention relates to the technical field of underwater detection, and discloses a forward-looking sonar underwater detection system based on multi-beam forming. The system comprises seven modules: a sonar data acquisition module obtains a multi-beam sonar original data sequence; a historical sonar database retrieval module retrieves a matched historical detection record set according to the region identifier; the heterogeneous sonar record distinguishing module classifies the historical sets and determines a plurality of distinguishing sets; a sonar feature extraction module traverses and distinguishes the set calculation to obtain a sonar feature set value and an associated bandwidth; the multi-scale sonar analysis module processes a network layer receptive field by taking a feature association bandwidth as a beam, and analyzes original data to obtain a multi-scale detection feature set; a cross-scale sonar fusion module performs interactive fusion on the multi-scale features to generate a target interactive fusion sonar feature set; and the underwater target area evaluation module outputs a thermodynamic map containing potential target area probability distribution based on fusion feature evaluation, and adapts to complex underwater environment detection.

The invention provides a motion compensation method and device for passive sonar detection. The compensation method comprises the following steps: acquiring underwater acoustic signals acquired by a passive sonar in real time within a preset period; performing segmentation processing and Fourier transform on the underwater acoustic signal to obtain an underwater acoustic frequency domain signal segment; constructing a parameterized search matrix of each water audio frequency domain signal segment, and determining a motion compensation parameter of each water audio frequency domain signal segment through similar energy spectrum search; based on the motion compensation parameter, performing phase compensation on the water audio frequency domain signal segment to obtain a compensation frequency domain signal segment; and performing inverse discrete Fourier transform on the compensation frequency domain signal segment to obtain a compensation time domain signal segment so as to determine a long-time coherent integration result corresponding to the underwater acoustic signal. Through the method, the gain of passive sonar detection processing is improved under the condition of low signal-to-noise ratio, and the accuracy and stability of passive sonar detection are improved.

The invention discloses a hydropower station data operation and maintenance method, which comprises the following steps of: arranging a sensor network on hydraulic facilities and electromechanical gold junction equipment, acquiring data, preprocessing the data, registering in space and time scales, converting data signals into acoustic, optical and electric signals, and transmitting the acoustic, optical and electric signals to a server; the method comprises the following steps: performing signal fusion of a three-dimensional sonar signal and an RGB image based on an attention concentration mechanism, performing deep analysis on data, extracting a sensor data mode of hydraulic facilities and electromechanical junction equipment under normal working condition operation, establishing a fault diagnosis model, performing real-time diagnosis on the hydraulic facilities and the electromechanical junction equipment, and constructing a response decision knowledge base. Establishing a hydropower optimization scheduling model, and displaying the information of the hydropower station; the system integrates the Internet of Things, big data, artificial intelligence, cloud computing, BIM three-dimensional modeling and digital twinning technologies, realizes real-time monitoring, intelligent diagnosis and optimal scheduling of equipment and facilities of the hydropower station, and improves the overall digital intelligent operation management level of the hydropower station.

The invention discloses an underwater acoustic target classification and identification method based on multi-domain feature deep fusion and multi-platform sonar data learning. The method comprises the following steps: firstly, preprocessing target tracking beam data collected by a multi-platform sonar, and constructing a time domain waveform sample set and a time-frequency spectrogram sample set; secondly, constructing an underwater acoustic target classification and recognition network model which comprises a primary feature extraction parallel double-branch network module and a feature deep fusion and classification recognition module, the primary feature extraction parallel double-branch network module extracts time domain and time frequency features respectively, and the feature deep fusion and classification recognition module realizes feature deep fusion and classification through a bidirectional cross attention mechanism; training the model by adopting a multi-stage training strategy, wherein the training comprises pre-training, special training, end-to-end training and fine tuning and incremental training; finally, unknown target data is preprocessed and then input into the trained model for forward reasoning, and a classification recognition result is output according to the majority voting principle. According to the method, target multi-domain information can be fully mined, multi-source data are effectively utilized, and the model generalization ability and the recognition precision are improved.

The invention relates to the technical field of bridge pier scour monitoring, discloses an air-water-shore cooperative overwater float type bridge pier scour monitoring system, and belongs to the technical field of bridge safety monitoring. According to the system, an integrated framework is formed by a buoy platform, a shore-based cooperative monitoring station and a cloud monitoring platform. The buoy platform is fixed to a pier through a mooring lead, the underwater sonar module of the buoy platform can scan the terrain of a riverbed at multiple angles, and the overwater module integrates high-precision positioning and video monitoring. The shore-based station is provided with a laser scanner and a high-definition camera to monitor the overwater structure and the macroscopic environment of the pier. The cloud platform serves as a control center, converges the multi-source data of the buoys and the shore stations through a communication network, carries out time sequence correlation and fusion analysis, and dynamically evaluates the scour pit development and the bridge pier structure state. According to the invention, the limitation of a single monitoring mode is broken through, collaborative acquisition and intelligent fusion of air, water and shore data are realized, the comprehensiveness, accuracy and early warning capability of monitoring are remarkably improved, and reliable guarantee is provided for safe operation of bridges.

In various examples, associating object detections for sensor data processing for autonomous and semi-autonomous systems and applications is described herein. Systems and methods described herein may group object detections (e.g., echoes, etc.) that are detected using multiple sensors (e.g., ultrasonic sensors, sonar sensors, etc.) and then use the groupings to process the object detections to perform one or more tasks, such as object or feature detection. In some examples, the object detections are grouped using one or more configurations, such as an order associated with analyzing sensor data generated using the sensors and / or threshold distances associated with determining that object detections are associated with the same object. Additionally, a respective group may be generated for one or more (e.g., each) detected object such that the locations of the object surrounding the machine may be determined.

The invention discloses a large-scale underwater vehicle three-dimensional dynamic obstacle avoidance method based on multiple single-beam sonar arrays, and the method comprises the steps: S1, collecting three-dimensional space detection data according to the arrangement of a space array composed of a center sonar and four peripheral sonars, and generating three-dimensional environment perception information; s2, based on the three-dimensional environment perception information, fusing data of an inertial navigation system, a Doppler velocimeter and a depth sensor, and generating real-time attitude and obstacle distribution information of the underwater vehicle; s3, on the basis of the real-time attitude of the underwater vehicle and the obstacle distribution information, risk level assessment and obstacle avoidance path planning are executed, and an obstacle avoidance control instruction is generated; and S4, based on the obstacle avoidance control instruction, controlling a power system of the underwater vehicle to adjust the course, the pitch angle and the speed so as to realize three-dimensional dynamic obstacle avoidance. The problem that a traditional single-beam sonar is insufficient in stereoscopic perception capability is solved, and the defect that the traditional single-beam sonar cannot effectively detect obstacles in the pitch angle direction (such as a submarine abrupt slope and a suspended rock frame) is overcome.

The invention discloses a distributed file backup method for a sonar database system, which belongs to the technical field of sonar data processing and comprises the following steps: acquiring file data containing underwater acoustic signal data from the sonar database system; fragmenting the file data to generate data blocks and dynamically adjusting the sizes of the data blocks; generating a unique identifier for the data block based on the content hash value and the metadata; selecting distributed storage nodes based on availability, storage capacity, network delay and geographical distribution factors; the transmission data block uses an encryption protocol; updating the storage position and state of the backup metadata record; periodically verifying the integrity and availability of the backup data; executing a data recovery process to recover the original file data; the dynamically adjusted backup policy is based on real-time operation requirements and data importance levels. According to the method, through distributed storage and dynamic optimization, the backup efficiency, the data security and the system reliability are improved, and the method is suitable for sonar data management in an underwater detection system.

The invention discloses a method for detecting, positioning and classifying underwater gas leakage through multiple beams based on deep learning, and relates to the field of underwater acoustic detection and deep learning. And carrying out azimuth correction on the multi-beam data with attitude offset to obtain multi-beam data without attitude offset. And through operations of seabed detection, median filtering, morphological expansion and the like, a high-quality multi-beam sonar image is generated. And constructing a data set for training an instance segmentation model and a flux classification model. And performing gas plume detection and segmentation on the preprocessed sonar image, and outputting mask information. And extracting a target contour based on a mask, determining plume coordinates through minimum circumscribed rotating rectangle fitting, and calculating the position of a seabed leakage point in combination with a conversion factor. And selecting complete plume data near the position right below the sonar equipment, and classifying leakage flux levels based on plume morphological characteristics by adopting a flux classification model. Multi-beam sonar and a deep learning algorithm are integrated, and detection, positioning and classification of underwater gas leakage are realized.

The invention discloses a modal fusion method of camera-sonar equipment under the condition of unknown external parameters, and relates to the field of underwater multi-sensor data fusion. The method comprises the following steps: firstly, acquiring synchronous underwater optical images and sonar images, and converting sonar polar coordinate imaging data into a three-dimensional point cloud under a sonar coordinate system through a geometric model; then, under the condition that camera-sonar external parameters do not need to be calibrated in advance, a spatial transformation matrix between a camera coordinate system and a sonar coordinate system is automatically estimated and calibrated through the corresponding relation between images and target features in point clouds, and alignment fusion of two kinds of modal data is achieved. According to the method, optical and acoustic information can be effectively fused in a severe water quality environment, the sensing limitation of a single sensor is made up, the robustness and accuracy of underwater dam crack and other target detection are improved, and the method has the advantages of no need of manual calibration and high adaptability.

The invention relates to the field of artificial intelligence, and discloses a multi-base cooperative detection method based on an improved marine predator algorithm. The method comprises the following steps: adopting a Tent chaotic sequence to guide initialization so as to improve the global exploration capability; dynamically dividing a three-stage search process according to the effective dimension, and adaptively prolonging the global exploration time; constructing Levy disturbance vector enhanced directivity search of chaotic modulation; designing a composite fitness function containing key area weighting and spacing penalty; and an FADs effect and an elite solution library linkage mechanism are introduced to break through convergence stagnation. The system comprises a parameter input module, an environment loading module, a probability calculation module, a chaos generation module, a stage control module, a disturbance modulation module, a fitness evaluation module and the like. The convergence speed and the key sea area detection efficiency of the multi-base sonar layout scheme are improved, and experiments show that the convergence speed is improved, and the key area detection probability is improved.

The invention relates to the technical field of sonar buoy detection, in particular to a multi-channel positive and negative pressure airtightness detection system which comprises an equipment cabinet, a high-pressure pump, a vacuum pump, a computer and a detected cabin. The equipment cabinet is provided with two ventilation air paths which are respectively connected with the high-pressure pump and the vacuum pump, the balance tank and the detected cabins are inflated or deflated by controlling valves in the air paths, and a differential pressure sensor is used for detecting the pressure difference value of the balance tank and the detected cabins to judge whether the balance tank and the detected cabins leak air or not. According to the invention, by outputting multiple detection channels, multiple detected cabins can be detected at the same time and independent detection results can be given, so that the detection efficiency is improved; the airtightness detection system is simple in composition structure, does not need additional high-pressure-resistant sealing tanks and tools, adopts the differential pressure type sensor to improve the test precision and accuracy, and is convenient to operate, safe and reliable.

The invention discloses a sonar target motion identification method based on a low-frame-rate bright spot structure sequence. The sonar target motion identification method comprises the steps of 1, roughly intercepting a bright spot structure; 2, accumulating a bright spot structure, sonar platform information and tracking information; step 3, motion compensation of the sonar platform; 4, accurately intercepting a bright spot structure; and 5, searching the optimal radial speed. According to the method, target radial motion parameters are extracted by means of distance steep motion of a sonar target among multiple frames of echoes, and a speed estimation loss function based on bright spot structure global optimal estimation is innovatively designed for the problems that the time interval between adjacent echoes of a low-frame-rate sonar target is long and the bright spot structure fluctuates greatly. According to the method, the global similarity between input period bright spot structures is fully utilized, the distance steep motion measurement precision is improved, the radial speed of the sonar target can be given at the same time, and the motion identification precision and stability of the sonar target are improved.

The invention discloses a marine environment spring layer characteristic diagnosis data processing method, and aims at complex dynamic characteristics of a land frame edge sea spring layer to realize accurate diagnosis through multi-source data cooperation and an intelligent algorithm. The method comprises the following steps: acquiring and preprocessing field observation, remote sensing, sonar, terrain and tidal ocean current data; extracting spatio-temporal characteristics based on a CNN-LSTM hybrid network, and generating a dynamic spring layer threshold; fusing multi-source data by adopting an improved D-S evidence theory, and constructing a three-dimensional spring layer characteristic field; predicting an internal wave in combination with an attention mechanism model, and optimizing a preorder model by using sonar data. The method breaks through the limitation of a fixed threshold value, realizes spring layer dynamic identification, three-dimensional reconstruction and internal wave prediction, improves the diagnosis precision of a complex sea area, and provides key technical support for underwater detection, engineering safety and environmental research.

The invention discloses a power transmission pole foundation pit scanning sonar detection device, system and method based on an unmanned aerial vehicle, and the method comprises the steps: receiving a detection task instruction inputted by terminal equipment, carrying out the initialization and path planning according to the received detection task instruction, and carrying out the calibration; setting an automatic program; generating a defect identification result and an execution signal; performing visualization processing on the acquisition process and the execution process to generate a detection report and a defect distribution diagram; and carrying out quality analysis and classification processing. In the implementation process of the technical scheme, the detection task instruction is input through the terminal equipment, accurate positioning and automatic detection of the power transmission pole foundation pit are achieved, the execution signal is generated according to the defect recognition result, the unmanned aerial vehicle automatically executes the corresponding defect processing operation, and therefore the efficiency and accuracy of foundation pit detection are improved; and meanwhile, the collected data is visually displayed through the visual processing module, and a visual detection report and a defect distribution diagram are generated.

The invention discloses a sonar monitoring system suitable for wind power pile scouring, and relates to the technical field of wind power pile sonar monitoring, and the system comprises a sonar data collection module which generates a standardized sonar monitoring data set; the scouring state analysis module is used for analyzing the scouring depth, range and rate of the seabed around the wind power pile and generating a scouring state evaluation result; the risk assessment module is used for constructing a risk assessment model and acquiring the scouring risk level of the wind power pile; the monitoring updating module is used for generating a dynamic monitoring report and an early warning prompt; and the data traceability module generates a data traceability log. The underwater sonar data is collected in real time, noise reduction and format conversion are carried out, the scouring condition of the sea area around the wind power pile is mastered in real time, scouring risk assessment is carried out in combination with the seabed scouring depth, range and rate and in combination with the wind power pile foundation and hydrological data, a comprehensive data tracing function is provided, operation and maintenance personnel are helped to visually know the scouring state, and the operation and maintenance efficiency is improved. And wind power pile maintenance and safety management are optimized.

The invention discloses an underwater three-dimensional reconstruction and sonar pose joint optimization method based on a multidirectional sonar, and belongs to the technical field of image reconstruction, and the method comprises the steps: obtaining and preprocessing multi-modal sensor data, and converting coordinates into a world coordinate system; dead reckoning is carried out through the IMU and the DVL to obtain a rough pose; constructing and optimizing an implicit occupancy probability model; screening effective frame pairs; calculating point cloud loss; a total loss function is constructed, the pose and the model parameters are jointly optimized, and a reconstruction result and an optimization track are output; according to the underwater three-dimensional reconstruction and sonar pose joint optimization method based on the multidirectional sonar, the reconstruction problem caused by lack of features and unstable sensor data in a turbid water area is effectively solved.

Systems and Methods for Labelling Sonar Data A computer-implemented method is provided of automatically labelling passive sonar hydrophone data for use as a supervised learning dataset, comprising: for a selected passive sonar hydrophone: obtaining actual received sound data throughout a detection time-period, obtaining a hydrophone location, and determining a maximum detection radius around the hydrophone location; retrieving timestamped vessel data associated with vessels within the maximum detection radius during the detection time-period, the timestamped vessel data comprising for each vessel: vessel location information and vessel identification information; determining, for a particular vessel identification information, an estimated received sound metric for each vessel throughout the detection time-period, based on the timestamped vessel data; labelling the actual received sound data, the labels including the particular vessel identification information and being associated with times during the detection time-period where the estimated received sound metric is above a threshold.

The invention discloses an unmanned ship self-adaptive safety redundancy system for an inland river ultra-shallow water environment and a control method. The system is composed of four-stage closed-loop redundancy of sensing, decision making, execution and laws and regulations. A 0.1 m-stage shallow water three-dimensional map is constructed in real time by a dual-millimeter-wave radar and a high-frequency single-beam sounding sonar; the main and standby controllers process the quay wall effect, backflow and grounding nonlinear disturbance on line through a BLF-RBFNN fixed time convergence algorithm, and the upper bound of convergence time is 4.2 s and is irrelevant to the initial state; the variable-configuration double-pump jet-air cushion hybrid propulsion device adjusts the draft on line according to the water depth of 0.12-0.45 m, and is matched with the self-righting honeycomb composite ship body to automatically stop leakage and return within 60 seconds when the ship body is damaged; and when the ship-end double control fails, the ship-shore cloud collaborative digital twinborn body completes regulation-level minimum risk takeover within 10 seconds. Experiments prove that the path error is less than or equal to 0.08 m and the fault degradation success rate is 100% in a 0.15 m water tank and 0.35 m Yangtze River channel, and zero-grounding, zero-overturning and zero-regulation illegal sailing of the ultra-shallow water unmanned ship is realized.

The invention relates to an active sonar echo signal phase jump compensation method and device based on a synchronous serial number, and belongs to the technical field of underwater acoustic signal processing. The method comprises the following steps: adding a synchronous serial number into a wet end signal to form an expansion matrix; transmitting the extended signal to a signal processor and windowing the extended signal; traversing a synchronous serial number in the windowing signal, and detecting the continuity of the synchronous serial number to judge whether packet loss exists or not; if packet loss exists, positioning a packet loss position and performing zero padding operation; and finally, carrying out fast Fourier transform on the zero-filled signal to obtain a spectral analysis result. According to the method, accurate identification and phase compensation of the packet loss position are achieved through the synchronous serial number, the method is suitable for underwater acoustic single-frequency signals, and when the packet loss rate does not exceed 2.5%, spectrum sidelobes can be effectively restrained, and the peak value of the spectrum sidelobes is reduced to be within 10% of the packet loss state. The method has the advantages of being clear in principle, easy and convenient to operate and high in calculation efficiency, is suitable for real-time signal processing, and remarkably improves the reliability and efficiency of target detection.

An aerial vehicle configured for operating within indoor or outdoor spaces is equipped with acoustic sensors for detecting reflections of sound, or echoes, from objects. Distances and bearings to such objects may be calculated based on such echoes. The echoes may be reflections of sound actively emitted by the aerial vehicle, such as by a speaker, or sound radiating from operating components aboard the aerial vehicle, such as rotating motors or propellers. The echoes may be captured by multiple sensors such as microphones provided around the aerial vehicle and used to calculate distances or bearings to the objects, such as by trilateration, triangulation, or in any other manner. Such distances or bearings may also be utilized along with distances or bearings determined from cameras, range sensors, or other systems, and used to generate a navigation map of the space, or compared to a navigation map generated for that space.