4237 results about "Millimetre wave" patented technology

The invention discloses a multi-mode pet health monitoring and motion artifact elimination method and system based on a millimeter wave radar, and relates to the technical field of pet health monitoring, and the method comprises the following steps: S001, building a unified time baseline and an energy fingerprint auditing surface, constructing an energy distribution model from a chest to a tail, and taking the model as a reference for artifact evolution, identifying a signal spectrum coupling trend in a time-frequency domain; and S002, based on the energy distribution model, performing causal playback on continuous time sequence signals acquired by the millimeter-wave radar, extracting a pseudo-motion energy nucleus caused by tail or limb movement, and calibrating a phase anchor point and a space observation area of a respiratory signal. According to the method, multi-source physiological data are fused, pure respiratory signals are extracted through energy distribution modeling, causal playback, phase anchor point calibration and three-dimensional resampling, risk assessment is achieved based on physiological credibility tensor, and the accuracy and anti-interference capacity of health monitoring are improved by combining phase conjugate traction and a space-time regulation strategy.

The invention discloses a robot obstacle avoidance method and system based on millimeter wave radar sparse point cloud, and relates to the technical field of obstacle avoidance recognition. A robot obstacle avoidance system based on millimeter wave radar sparse point cloud comprises a point cloud acquisition module, a negative obstacle identification module, a weak obstacle identification module, a point cluster identification module, a risk map module, a tentative verification module and an obstacle avoidance decision module. According to the invention, suspected obstacle point clusters are extracted based on a reflection intensity threshold and a spatial proximity relation in an enhanced point cloud, a theoretical parallax model of a real static obstacle is constructed under the constraint of a robot motion trajectory, and Doppler velocity distribution of each frame is combined with a static obstacle Doppler physical law for comparison. And classifying the point clusters which do not meet the multi-view geometric consistency or Doppler physical law, and distinguishing multipath false point clusters from dynamic point clusters.

The invention discloses a building facility detection data intelligent analysis and report system and method, and relates to the technical field of building structure health monitoring, and the method comprises the steps: achieving the high-precision data capture in a strong light dust raising environment through a laser radar and a dual-spectrum camera of a multi-mode anti-interference collection module; a millimeter wave radar array and an IMU inertial unit of the high-risk area strengthening module penetrate through severe working conditions to monitor key structure displacement; the credibility management engine is provided with an NFC timestamp chip and a double-chain block chain, and data judicial-level credibility and operation traceability are ensured; through humidity response gel packaging and Peltier semiconductor refrigeration of the self-maintenance sensing network, node environment self-adaption and drift suppression are achieved; and the multi-stage analysis center fuses edge-cloud computing power based on federal learning, generates a dynamic risk map and automatically outputs a compliance report. Accurate and timely reference is provided for operation and maintenance decisions, and the safety management level of building facilities is effectively improved.

The invention relates to a robot path planning method based on multi-sensor fusion and free space topology composition, and belongs to the technical field of robot autonomous navigation. The method comprises the following steps: firstly, fusing laser radar and millimeter wave radar data, constructing multi-modal point cloud data, extracting a three-dimensional obstacle boundary by combining depth and normal vector mutation features, and generating a three-dimensional obstacle map and a free region tree; according to the constructed space model, factors such as energy consumption, dynamic obstacles, path smoothness and the like are integrated, target selection weights are dynamically regulated and controlled, and self-adaptive screening of intermediate navigation targets is achieved. And based on the intermediate navigation target, generating a safe trajectory satisfying dynamic constraints by adopting geometric-dynamic dual-mode fusion modeling, and enhancing the feasibility and robustness of the trajectory through trajectory envelope optimization and pre-execution fault-tolerant control. The method can be widely applied to autonomous navigation systems such as mobile robots and unmanned vehicles, and has good environment adaptability and path execution stability.

The invention provides a reservoir bank slope deformation body instability volume prediction method, which comprises the following steps: respectively acquiring earth surface displacement and rock mass internal deformation data through a millimeter wave radar and a tilt angle sensor, and after processing through an adaptive noise decomposition algorithm, identifying a key deformation area and generating a data set. And performing space-time alignment on the data by using the engineering coordinate system and the topological relation to generate a fusion matrix. And reconstructing a potential slip crack surface geometric model in combination with slip crack surface features of historical cases, and calculating instability volume probability distribution by adopting Monte Carlo simulation. And finally, inputting the multi-dimensional data into the space-time prediction model, and outputting an instability volume prediction result with probability distribution. According to the invention, the accuracy and reliability of the prediction result can be improved, and scientific basis and technical support are provided for safety monitoring and disaster early warning of the reservoir bank slope.

The invention discloses a target detection tracking method and device based on radar visual fusion perception, and a medium, and the method employs visual detection as a leading part to establish a radar visual fusion tracking module, and solves a problem that the detection precision, tracking stability and environment robustness are difficult to give consideration to the existing single-mode perception in a complex traffic environment at the same time. Through unified multi-modal fusion of distance and speed information of visual detection, visual tracking and millimeter wave radar, stable, continuous and reliable target identification and track output of targets such as pedestrians and vehicles under a low-computing-power platform are realized.

The invention provides a falling detection method and system based on millimeter wave radar fusion human body postures, and relates to the technical field of falling detection, and the method comprises the steps: collecting human body echo signals, and constructing three-dimensional point cloud data through distance, angle and speed estimation; the point cloud is processed to generate a dynamic sequence, and skeleton features and key point coordinates are extracted in combination with a graph convolutional network. A skeleton connection relation is constructed based on the key points, attitude features are calculated, key change features are extracted through a self-attention mechanism, and the key change features are fused with a point cloud sequence to construct multi-modal features. And the double-branch state recognition network processes the fusion features, and when abnormality is detected, further analysis is carried out through residual attention and space-time diagram convolution, and finally, the falling state is recognized and the risk level is evaluated.

The invention discloses a model concept drift detection and adaptive learning method and system, and the method comprises the steps: obtaining a millimeter wave image collected by a millimeter wave sensor, and carrying out the prediction of the millimeter wave image through a pre-constructed neural network model; performing concept drift detection on the neural network model by taking the millimeter wave image and the corresponding model prediction result as data samples; the method specifically comprises the steps of calculating feature distribution of a data sample, and judging whether the feature distribution drifts or not; marking the data sample, calculating a model performance index of the marked data sample, and judging whether the model performance index drifts or not; and when drift triggering is detected, online incremental learning and model adaptive updating are carried out on the neural network model. Through the model concept drift detection and adaptive learning method, the generalization ability and robustness of the AI model can be greatly improved, so that the detection accuracy is remarkably improved, and the misjudgment rate is reduced.

According to the BIM-based construction process monitoring and early warning system provided by the invention, the data acquisition dimension and precision are remarkably improved through multi-source sensing fusion of millimeter-wave radar, multispectral imaging and voiceprint recognition; feature vector voxel units carrying material characteristics and process constraints are adopted, so that risk early warning has space-time relevance and process interpretability; through a composite risk calculation model containing environmental interference correction and time-varying gradient, the problem that a traditional threshold value method is poor in adaptability to complex working conditions is solved; the holographic early warning mechanism realizes upgrading from sound-light alarm to touch sense-three-dimensional projection cooperative interaction; the double-circulation self-optimization system not only guarantees real-time control, but also realizes block chain evidence storage of empirical data, and finally forms a construction monitoring closed-loop system with space-time perception, intelligent decision, accurate early warning and sustainable evolution capabilities.

Extended Reality (XR) applications may require high throughput and very low latency. New Radio (NR) Wireless receiver / transmitter units (WTRUs) running XR applications (XR-WTRUs) may be confronted with link failures when these applications are used in dense urban or indoor environments because of these devices operating in the millimeter band. Methods and devices are provided therefore for performing cell selection and beam management proactively to avoid link failure due to objects being in line of sight between the XR-WTRU and a network node with which it has established a connection. XR-WTRUs may therefore perform visual sensing that may enable visualizing current and future radio environment and using this information to prevent link failures by handing the XR-WTRU over to an alternative network node in line of sight of the XR-WTRU before the XR-WTRU is no longer in line of sight of the network node with which it has established a connection.

The invention discloses a millimeter-wave radar sparse point cloud-based human body posture recognition method, which belongs to the technical field of human body posture recognition, and comprises the following steps of: acquiring three-dimensional point cloud data of human body actions through a millimeter-wave radar, and preprocessing the three-dimensional point cloud data; and training a lightweight neural network model by using the preprocessed point cloud data, wherein the model comprises an edge convolution module and a grouping sparse Transform encoder module. The edge convolution module extracts spatial geometric features and detects dynamics, static postures are directly classified, dynamic postures capture a time sequence dependency relationship through a grouping sparse Transform module, attention calculation is only carried out on frames with feature changes exceeding a threshold value, and mean pooling aggregation is carried out on other frames. And finally, classifying the human body postures based on the spatial geometric features and the time sequence dependency relationship to obtain a classification result. The device is simple in structure, accurate in recognition and suitable for efficient deployment of edge equipment.

A computing device for monitoring a sleep stage of a user in a contactless manner includes a processor, a radar unit, and a memory. The processor executes instructions from memory to cause the computing device to perform a series of signal processing methodologies on the waveforms received from the radar unit to accurately determine the sleep stage of the user in a contactless manner.

The invention relates to the technical field of computer vision and multi-sensor data fusion, in particular to a multi-sensor image fusion obstacle real-time detection and tracking system, which comprises the following steps of: firstly, fusing data of a camera, a millimeter wave radar and a laser radar, and extracting and fusing multi-modal features; performing multi-target tracking based on a recurrent neural network: generating a target state through space-time modeling, associating a target with a historical track by using an attention mechanism, and maintaining track consistency; and the system performs semantic classification and interaction analysis on the obstacle, predicts the movement track of the obstacle, realizes deep semantic understanding, and finally outputs the identity label and the complete historical track of the obstacle.

The invention discloses an AI-based illumination control system self-adaptive dimming method, which comprises the following steps of: acquiring a two-dimensional coordinate, a motion track curve and voice communication data of a personnel position, and identifying social interaction scene categories of different time slices; determining a social interaction strength score according to the voice communication data, and predicting the most adaptive spectrum configuration data in the current social interaction scene in combination with the social influence factor matrix and the social interaction scene type; according to a historical dimming log and an energy consumption metering curve, identifying a short-term high-frequency dimming segment, and adjusting a real-time response strategy and a disturbance classification processing mechanism of the spectrum configuration data; and adjusting the illumination power distribution values of the high-frequency use area and the low-frequency use area according to the occupation raster data generated by the millimeter-wave radar and the infrared array in combination with the lamp area mapping relation and the power budget constraint. Through multi-source data fusion and a self-adaptive regulation and control mechanism, unification of real-time performance, pertinence and energy-saving performance of spectrum regulation is realized, and the dynamic response capability and the user perception experience of the intelligent illumination system are improved.

The present application belongs to the technical field of wireless communications and radar systems. Disclosed are a dual-band fusion and multiplexing frequency source, chips, a radio-frequency transceiver, a system, and a device. The system comprises: a full-function millimeter-wave CMOS 4+1 phased array chip, which is connected to an antenna end, and is used for realizing signal processing and frequency conversion between a millimeter-wave signal and a C-band intermediate-frequency signal, performing phase control and gain adjustment on the millimeter-wave signal, and providing an internal frequency source and a calibration function; a C-band front end, which is connected to the antenna end, and is used for receiving or transmitting a C-band signal; a C-band / intermediate-frequency transceiver multiplexing chip, which is used for performing frequency conversion between a C-band or intermediate-frequency signal and a baseband signal in a multiplexing manner, and performing signal processing and gain control on the C-band intermediate-frequency signal; a dual-band fusion and multiplexing frequency source, which is used for providing a local oscillator signal; and an all-digital control logic circuit, which is used for providing a control instruction. The present application improves the efficiency and performance of dual-band joint communications.

The invention belongs to the field of computer vision, and particularly relates to a computer vision target detection method and system based on multi-modal fusion. Aiming at the problems that in the prior art, single-mode detection is easily influenced by shielding and illumination variation, and view angle difference coordination is difficult and the calculation efficiency is low in multi-sensor fusion, the scheme provides a multi-mode detection framework fusing vision, point cloud and millimeter wave signals. According to the method, the limitation of a traditional detection method in dynamic shielding, low illumination and dense scenes is effectively overcome, the target detection precision and robustness are remarkably improved, and the real-time high-precision detection requirements in the fields of automatic driving, intelligent security and protection and the like are met.

The invention relates to an unmanned aerial vehicle autonomous path navigation system based on multi-modal fusion and adaptive optimization and an algorithm thereof. The system comprises a multi-modal sensor module, a data fusion processing module, an adaptive algorithm processing module and a flight control module. The method comprises the following steps: step 1, data acquisition; step 2, data fusion; step 3, algorithm processing; and 4, controlling the flight of the unmanned aerial vehicle. The method has the advantages that multiple sensors such as a visual camera, a laser radar and a millimeter wave radar are integrated through deep fusion of multi-modal sensors, a fusion algorithm based on deep learning and a Transform architecture is adopted, weights are automatically distributed through an attention mechanism, deep data fusion is achieved, and the accuracy and accuracy of data fusion are improved. Compared with a single sensor or a simple data fusion mode, the problem that the single sensor is easily interfered by the environment is effectively solved, and the obstacle recognition accuracy and the environment modeling precision are greatly improved under the complex environments of low illumination, strong smoke, electromagnetic interference and the like.

The invention discloses a sparse interaction beam prediction method fusing perception and communication. The method comprises the following steps: acquiring and preprocessing multi-modal sensing data; feature extraction is carried out on the preprocessed multi-modal sensing data; introducing a learnable potential vector by adopting a Perceeiver-Transform structure, and carrying out cross attention interaction on the learnable potential vector and the spliced sequence, so as to obtain a multi-modal fusion feature; and mapping the multi-modal fusion features to a millimeter wave beam space through a beam domain projection mechanism, generating probability distribution of the beam space, and outputting a predicted optimal beam according to the probability distribution. According to the method, the learnable potential vectors are adopted, sparse interaction and efficient fusion of the multi-modal fusion features are achieved, the sensing features are mapped to the millimeter wave beam space through a beam domain projection mechanism, the calculation amount is reduced, the robustness and prediction precision are improved, and the method is suitable for large-scale popularization and application. And the method is suitable for millimeter wave communication systems in low-altitude unmanned aerial vehicles, Internet of Vehicles and high-dynamic environments.

The invention relates to a cooperative control method and system for realizing self-organizing protection of a multi-unmanned aerial vehicle cluster, and the method comprises the steps: M1, enabling the unmanned aerial vehicle cluster to execute a task, obtaining the data information of the remaining power, RSSI signal intensity and IMU attitude of each unmanned aerial vehicle in real time based on an airborne built-in sensor, and acquiring data information of distances and relative speeds of obstacles around the unmanned aerial vehicles in real time based on an airborne external millimeter-wave radar, constructing a dynamic threshold function stop, and if the RSSI signal strength of each unmanned aerial vehicle is greater than the stop, outputting data information of the residual electric quantity and IMU attitude of each unmanned aerial vehicle. According to the method, misjudgment (such as transient packet loss due to electromagnetic shielding) caused by temporary signal interference is effectively avoided, the misjudgment rate is reduced by 40%, unnecessary return or cluster recombination is reduced, continuous task execution is guaranteed, it is ensured that leader handover is completed quickly (at the millisecond level) when a host fails, and the situation that a cluster falls into disorder due to single-point failure is avoided.

The invention provides a target frame detection optimization method based on a millimeter wave radar, a medium and electronic equipment. The method comprises the following steps: acquiring an initial target frame set generated based on the millimeter wave radar; calculating an overlapping area ratio of any two target frames in the initial target frame set; and determining an output target frame according to the overlapping area proportion and updating the initial target frame set. The method effectively solves the problem of frame size estimation deviation caused by point cloud sparsity in a traditional method, improves the precision and reliability of target detection, achieves the adaptive processing of a complex road scene, effectively reduces the false detection rate, improves the accuracy and stability of a detection result, and improves the detection efficiency. And the perception performance of the automatic driving system can be effectively improved.

The invention discloses an AI scene conversion method and system based on visitor behaviors. According to the method, visitor behavior data are collected in real time through a multi-mode sensor network, and a UWB positioning base station, a millimeter wave radar module, a high-definition camera, a microphone array and a pressure sensor matrix are included. The system adopts an edge computing architecture, is equipped with an NVIDIAJetsonAGXXavier processor and a 512-core VoltaGPU (Graphics Processing Unit), and operates three core AI models to realize intelligent analysis. The LSTM residence time prediction model adopts a three-layer bidirectional architecture, each layer is configured with 256 hidden units, and visitor behavior sequences within 30 minutes are analyzed to predict the residence time. The Transform interaction intention recognition model comprises six layers of encoders and eight multi-head attention modules, and visual, voice and behavior three-mode characteristics are fused to judge visitor interaction requirements. According to the technical scheme, self-adaptive adjustment of the exhibition hall environment is achieved, the visitor satisfaction degree is improved by 28%, the operation cost is reduced by 15%, and important technical support is provided for intelligent transformation of modern exhibition places.

The invention discloses a Ka-band four-tilt-beam magnetoelectric dipole antenna, and belongs to the technical field of millimeter wave radio frequency front-end devices. Based on a magnetoelectric dipole composite radiation structure, four centrosymmetric radiation units are integrated on a low-loss substrate. The antenna comprises a probe patch of a coaxial gamma-shaped feed probe, a radiation patch, a parasitic patch, an upper dielectric substrate, a lower dielectric substrate, a middle curing layer, a shorter metal through hole of the coaxial gamma-shaped feed probe, a longer metal through hole of the coaxial gamma-shaped feed probe, a metal through hole c of the radiation patch, a metal through hole d of the parasitic patch and a metal ground backboard, a probe patch, a radiation patch and a parasitic patch of the coaxial gamma-shaped feed probe are located on the upper surface of the upper-layer dielectric substrate, and a metal ground backboard is located on the lower surface of the lower-layer dielectric substrate; the antenna has ultra-wide impedance bandwidth, has the characteristics of low cost, miniaturization, low cross polarization and back lobe suppression, and is superior to a traditional inclined beam antenna and an antenna array scheme.

The invention discloses a visibility inversion method based on millimeter wave cloud radar and laser radar data fusion, and relates to the technical field of meteorology, and the method comprises the steps: guiding a millimeter wave cloud radar and a laser radar to carry out data correction and equivalent relation construction through employing a fog drop particle size distribution function measured by a fog drop spectrometer in a sample scene in advance; and then applying the correction coefficient and the equivalent function relationship to a target scene to perform multi-source data correction and fusion on the millimeter wave cloud radar and the laser radar and invert visibility. The method can fuse complementary advantages of the laser radar and the millimeter wave cloud radar; the introduction of the fogdrop particle size distribution function effectively avoids the distribution hypothesis of fogdrop shapes in the traditional model, can weaken the seasonal drift of the traditional empirical coefficient, significantly improves the accuracy and reliability of visibility inversion, and achieves the high-precision visibility monitoring under various weather conditions from clear sky to dense fog.

The invention provides a multi-parameter collaborative cardiovascular health monitoring system and method, and the system comprises a millimeter wave radar collection module which is used for collecting a multi-dimensional physiological signal of a target user; the third-stage signal processing module is used for carrying out noise reduction processing on the multi-dimensional physiological signals and carrying out layered feature extraction on the multi-dimensional physiological signals subjected to noise reduction to obtain multi-dimensional physiological feature parameters; and the fourth-order crowd adaptation AI evaluation module is used for carrying out crowd segmentation on the target user to obtain segmented crowds, dynamically adjusting the weight ratio of each feature parameter in the multi-dimensional physiological feature parameters according to the segmented crowds to obtain dynamic adjustment feature parameters, and carrying out blood pressure calculation, angiosclerosis calculation and vascular risk prediction according to the dynamic adjustment feature parameters. And obtaining a blood vessel health monitoring result. According to the embodiment of the invention, through software optimization and measurement technology expansion, the cardiovascular health monitoring accuracy and the application range are further improved.

The invention belongs to the technical field of wireless sensing and artificial intelligence, and discloses a high-robustness non-contact accurate electrocardiogram monitoring method based on a millimeter wave radar. Firstly, the distance and angle of a potential target are obtained through distance fast Fourier transform and digital beam forming technologies, and static background removal and thoracic cavity position detection are achieved in combination with mean filtering and a two-dimensional constant false alarm rate algorithm. And then a continuous phase is extracted by using a differential cross multiplication method, a two-step heartbeat-related phase extraction scheme is designed, body micro-motion and breathing interference are removed by adopting B-spline fitting and differential operation respectively, and a stable heartbeat-related phase signal is obtained. A heart rate-guided adaptive wavelet decomposition method is designed to obtain multiband features, and time-frequency joint features are extracted through a double-branch attention mechanism and a gating fusion part. Finally, the time-frequency joint features are input into an electrocardiosignal time domain reconstruction module based on a TransUNet architecture, high-quality reconstruction of electrocardiosignals is achieved, and the method has the advantages of being non-contact, continuous and convenient.

The invention relates to the field of intelligent traffic control, discloses an intelligent control method and system for a bidirectional driving wheel type sightseeing train, and aims to solve the problems of low efficiency, poor safety, weak adaptability and the like caused by dependence on a fixed track, path solidification and control lag of a traditional sightseeing train. According to the method, a dynamic environment map is constructed by deploying a front-back symmetric multi-mode sensor array and fusing laser radar, vision, millimeter wave radar and inertial data; an improved mixed A star algorithm is adopted to plan a global collision-free trajectory, high-precision tracking is realized in combination with model prediction control, bidirectional autonomous operation, multi-vehicle intelligent cooperation and full-scene adaptive control under a trackless condition are realized, and the traffic efficiency, safety and intelligent level of a scenic spot are remarkably improved.

The invention relates to the technical field of communication, and discloses a network communication chip signal interference suppression processing method. The method comprises the following steps: acquiring a local oscillator phase jitter sequence in real time through an on-chip phase noise monitoring unit; estimating Doppler frequency shift and channel coherence time based on the sequence; dynamically adjusting the closed-loop bandwidth of the phase-locked loop; and generating a phase compensation signal, and performing time-varying channel minimum mean square error equalization in combination with a received baseband signal to eliminate inter-symbol interference. The system integrates a phase noise monitoring function module, a channel parameter estimation function module, a phase-locked loop bandwidth regulation function module, a phase compensation generation function module and an interference joint elimination function module. By means of dynamic bandwidth reconstruction and high-precision phase compensation, the bit error rate under high-speed movement is remarkably reduced, the 5G millimeter wave communication standard requirement is met, and the method has the advantages of being achieved on a whole chip, low in delay and high in robustness.

The invention belongs to the technical field of intelligent wireless sensing and radar signal processing, and particularly relates to a millimeter wave radar gesture recognition feature processing method, which is particularly suitable for scenes with environment interference (such as walking of others and static clutter), and specifically comprises the following steps: S1, preliminary filtering by a self-adaptive filter; s2, carrying out improved I CEEMDAN decomposition; s3, I MF component classification and processing; s4, signal reconstruction; the experimental result shows that the method provides a robust and efficient solution for gesture recognition, and can be widely applied to the fields of man-machine interaction, virtual reality, intelligent equipment control and the like.

The invention relates to the technical field of night monitoring, and discloses a night dynamic target tracking and image restoration method, which comprises the following steps of: establishing a heat-motion correlation model of a target through a space alignment algorithm in combination with heat source contour and intensity information acquired by a thermal imaging sensor and motion parameters detected by a millimeter wave radar; outputting an initial position and a motion state; inputting a target initial state and radar point cloud data based on an LSTM network, predicting a future N-frame trajectory, and dynamically correcting a prediction result by using real-time radar data; inputting the position information of the occlusion area and the trajectory prediction value into a GAN network to generate a complemented image, fusing the complemented image with the original heat source image, and recovering the complete contour of the target; and superposing and rendering the complemented image and the trajectory prediction into an AR picture, and when the target distance triggers a preset threshold value, realizing multi-channel safety early warning through vibration feedback, AR highlight warning and voice prompt. According to the invention, high-precision tracking and safety early warning of the night dynamic target can be improved.

The invention discloses an all-weather sound source positioning system and method based on multi-sensor fusion, and the method comprises the steps: firstly, obtaining acoustic sensing data, millimeter wave radar data and infrared thermal imaging data, and enabling each data to have a collection timestamp; then, cross-modal time alignment processing is carried out on the multi-source data to map the multi-source data to a unified time reference, and multi-modal fusion features under a unified time axis are obtained; for each time point in the time-aligned multi-modal fusion features, according to the confidence coefficient of the data of each sensor, adaptive weighted fusion is carried out on the data of different modals, and fused common feature representation is generated; and finally, time sequence modeling and joint reasoning are carried out based on the common feature representations of a plurality of continuous time points, and continuous position information of the sound source in the three-dimensional space is regressed. According to the method, high-precision positioning of three-dimensional positions of a plurality of sound sources is realized, so that the accuracy and the stability of sound source positioning are improved in a complex environment and an all-weather condition.