459results about "Direction/deviation determination systems" patented technology

The invention belongs to the field of underwater acoustic array signal processing, and particularly relates to an underwater acoustic signal direction-of-arrival estimation method based on an improved Mama network, which comprises the following steps of: 1, establishing a hydrophone array model, independently sampling an information source signal, calculating a covariance matrix of the signal, separating a real part and an imaginary part of the covariance matrix, and calculating a direction-of-arrival estimation model; carrying out normalization processing on the covariance matrix after splicing processing, and taking the covariance matrix as a data set training model; step 2, establishing a lightweight module based on a Mama network, compressing a covariance matrix, adjusting dimensions, obtaining relevance of information, mixing signals of all time steps, and outputting a characteristic spectrogram of the signals; 3, processing the characteristic spectrogram by using a CBMA module, and outputting an estimated value of a corresponding direction of arrival by using a Sigmoid function after adjusting dimensions through a full connection layer; and 4, cascading the models, training by using a data set, adjusting parameters, and carrying out direction-of-arrival estimation on unknown signals by using the trained models. The operation complexity is reduced, and the method is more suitable for the underwater environment.

A method for locating a target object includes: determining a plurality of position-orientation-distance-angle values, each including: a position of a user equipment (UE) relative to a reference position; an orientation of the UE relative to a reference orientation; a distance corresponding to the position of the UE, the distance being between the position of the UE and the target object based on wireless ranging signal(s) between the UE and the target object; and an angle corresponding to the position and orientation of the UE, the angle being an AoA of the wireless ranging signal(s) at a first and second antenna of the UE, where a combination of the position and orientation of the UE in each of the position-orientation-distance-angle values is different; and calculating a position of the target object using the plurality of position-orientation-distance-angle values, where the position of the target object is relative to the reference position.

A communication apparatus for performing underwater visible light communication with a target communication apparatus includes a visible light communicator that transmits and receives a visible light signal including communication data, a sound wave communicator that receives a sound wave signal including information used to control establishment of visible light communication connection, the sound wave signal having been transmitted from the target communication apparatus or another communication apparatus, and a controller that performs control to establish the visible light communication connection with the target communication apparatus, based on the information included in the received sound wave signal.

A system for generating live sonar images is provided having a first and second sonar transducer assembly. The sonar transducer assemblies each have sonar transducer elements configured to transmit sonar beam(s) into an underwater environment to form respective coverage volumes. The sonar transducer assemblies each define a respective facing direction. The system includes bracket(s) having alignment feature(s). The bracket(s) are configured to mount the sonar transducer assemblies to a watercraft, and the alignment feature(s) are configured to position the sonar transducer assemblies so that the facing directions are different and relative to each other so as to create continuous coverage of the underwater environment. Continuous coverage has an overall coverage volume that is greater than either of the coverage volumes individually. Sonar return data from the sonar transducer elements is used to form a live sonar image representative of sonar returns received from the overall coverage volume.

The invention discloses a multi-node mutual positioning method and system in a complex environment, each node can be set as a master node or a slave node, and the nodes realize a mutual positioning function by transmitting and receiving triangular wave frequency modulation continuous wave signal waveforms; when working in a master node mode, the node is synchronized with working signals of other slave nodes through coarse synchronization and fine synchronization steps, then distance information of the slave nodes is obtained through round-trip flight time calculation, distance information of the slave nodes is obtained through Doppler frequency shift calculation, angle information of the slave nodes is estimated through a DOA method, and positioning of the slave nodes is completed; when the node works in the slave node mode, a to-be-synchronized signal sent by the master node is waited, the to-be-synchronized signal is synchronized with the signal of the master node through the steps of coarse synchronization and fine synchronization, and then the synchronized signal is sent to the master node so that the master node can locate the position of the slave node. According to the method, high-precision mutual positioning among multiple nodes is realized through a working principle of realizing the frequency modulation continuous wave radar among the nodes.

The invention discloses an underwater sound environment sensing method based on multi-array-element sparse channel estimation. An underwater sound receiving end receives signals transmitted through an underwater sound multipath channel through a multi-array-element array; performing Hilbert transform on the receiving signal of each array element to obtain an analysis signal, and calculating a cross-correlation function of the analysis signal and the transmitting signal; based on the cross-correlation function, sparse channel parameters, including path amplitude and time delay, of each array element are estimated by adopting an orthogonal matching pursuit algorithm combined with a constant false alarm detection dynamic threshold value; the method comprises the following steps: constructing an array response vector by using sparse channel parameters of a multi-array element array, and searching and estimating angles of arrival, including a direction angle and a pitch angle, of a multipath signal through a spatial spectrum peak value; and based on the arrival angles, amplitudes and time delays of the direct path and the reflection path, inverting an underwater environment structure through a ray acoustic theory, including a reflection point distance and a reflection surface normal vector, so as to realize three-dimensional perception of the underwater reflector. According to the invention, multipath resolution can be improved, and false alarm and missing detection can be effectively reduced.

A method, computer program product, and computing system for generating a plurality of acoustic relative transfer functions associated with a plurality of audio acquisition devices of an audio recording system deployed in an acoustic environment. Acoustic relative transfer functions of at least a pair of audio acquisition devices of the plurality of audio acquisition devices may be compared. Location information associated with an acoustic source within the acoustic environment may be determined based upon, at least in part, the comparison of the acoustic relative transfer functions of the at least a pair of audio acquisition devices of the plurality of audio acquisition devices.

The invention discloses a room geometry inference method based on positioning of a direct sound source and a first-order reflection sound source, which comprises the following steps of: 1) acquiring reverberation signals of a target room by using a microphone array, converting the reverberation signals into FOA signals, and calculating a time-frequency covariance matrix of the FOA signals, estimating the direction of arrival DOAs of the direct sound source s and the direction of arrival DOAs'of the n first-order reflection sound sources s' according to the time-frequency covariance matrix; 2) estimating the distance ds of the direct sound source s according to the DOAs, the DOAs'and the height dh of the microphone array, and determining the position Ps of the direct sound source s; 3) estimating the distance ds'of the first-order reflection sound source s' based on the FOA signal, the DOAs, the DOAs' and ds, and determining the position Ps' of the first-order reflection sound source s'; and 4) obtaining the geometric space structure of the target room according to the position Ps of the direct sound source s and the position Ps'of each first-order reflection sound source s'.

An autonomous underwater vehicle (1) has a housing (10) having a measurement system (13) with three acceleration or speed sensors on different axes from one another, and a processing unit configured to generate seismic data from the received seismic waves (S1) and to determine the direction (D2) of the acoustic waves (S2) transmitted by an acoustic transmitter (20) from a base (2) which are received by at least two sensors of the measurement system (13). The processing unit generates and transmits, to the navigation system (12), guidance data relating to the determined direction (D2) of the received acoustic waves (S2). The navigation system (12) is configured to control the propulsion and steering system (11) of the vehicle according to the guidance data in order to guide the movement of the vehicle towards the base.

The invention provides a DOA estimation and error correction method and device based on joint sparse recovery, and the method comprises the steps: receiving a target signal through a linear array, building an array signal model fusing a phase error, and constructing an optimization model based on multiple measurement vectors; and compensating the phase error in each step of iteration by using an iterative optimization solution algorithm to enable the DOA to gradually approach a true value, thereby obtaining estimation results of the spatial spectrum and the phase error. The method provided by the invention has the advantages that random phase errors caused by array element position errors, system frequency errors, sound wave propagation errors and the like in DOA estimation are corrected. Under the condition that phase errors exist, a signal model and an optimization problem are constructed, and the phase errors and a DOA estimation value are solved through an iterative algorithm, so that a high-precision and low-sidelobe DOA estimation result is obtained. The method is suitable for DOA estimation under the conditions of small snapshots, strong noise and random phase errors.

Acoustic signals from an acoustic event are captured via sensing nodes of sensor group(s) that comprise a group of sensing nodes at a location comprising spatial boundaries. Each of the sensing nodes comprise a sensor area. Each of the sensor group(s) is based on: range limits of each of the sensing nodes; shared sensing areas of the sensing nodes; and intersections between the sensor area for each of the sensing nodes and the spatial boundaries. Solutions(s) are generated by processing the acoustic signals. The solution(s) indicate the location or trajectory of the acoustic event. A strength of solution compliance value for at least one of the solution(s) is determined. A refined solution is generated employing: sensor contributions of sensing nodes; and the strength of solution compliance value with the spatial boundaries and at least one of the solution(s). A report is created comprising the location or trajectory of the acoustic event.

The invention provides a vector array sparse Bayesian learning direction of arrival estimation method. The method comprises the following steps: constructing a far-field vector sparse signal model; and establishing a noise covariance model under the vector sound field. And estimating a signal power hyper-parameter through a vector array sparse Bayesian learning process. And through a maximum likelihood estimation technology, noise power hyper-parameter estimation is realized. And finally, carrying out peak searching on the converged signal power hyper-parameter to obtain a direction of arrival estimation result. The method has the advantages that the vector array signal processing performance advantage is obtained, higher signal processing gain is obtained, and meanwhile the method has the capability of restraining the azimuth ambiguity problem; and by using the difference between the noise covariance matrix and the signal covariance matrix under the vector noise, the estimation precision of hyper-parameters such as the signal power and the noise power is remarkably improved. Compared with a traditional vector array DOA estimation method, the vector array DOA estimation method has a lower spectrum background and a sharper spatial spectrum peak, and the resolution and DOA estimation precision are remarkably superior to those of other methods.

The invention relates to the field of underwater sound signal processing, in particular to an underwater DOA (direction of arrival) estimation method based on graph nerves and a convolutional neural network, which comprises the following steps: 1, establishing a linear array, and enabling narrow-band signals to simultaneously reach an underwater sound array; 2, performing signal preprocessing to obtain a signal covariance matrix, and performing normalization processing; 3, extracting correlation between array elements and spatial features of array signals, and performing data supplementation on sparse linear array information; 4, forming a double-branch structure, enhancing the information aggregation capability, and extracting features from a space path and a time domain path; and 5, constructing an adjacent matrix, filling node features of damaged array elements, adopting a double-branch structure, extracting spatial features and time domain features, carrying out feature integration, and outputting a DOA estimation result. The spatial correlation between array elements is extracted and the array sparsity problem is processed by using the graph neural network, and the time domain features of the signals are extracted in combination with the convolutional neural network, so that more accurate and more robust DOA estimation can be realized under the conditions of low signal-to-noise ratio and array sparsity.

The invention discloses a co-prime array robust DOA estimation method based on a small number of auxiliary array elements. According to the scheme, three auxiliary array elements are added on an original co-prime array according to array element spacing, received data of a new co-prime array is decomposed into two sub-array data, and amplitude-phase error estimation is performed on the two sub-arrays by using amplitude-phase characteristics of each element in a covariance matrix; and the amplitude-phase errors of the sub-arrays are sorted and recombined to obtain the amplitude-phase error of the new co-prime array. The method comprises the following steps: firstly, receiving data, then compensating a covariance matrix error of the received data and performing Toeplitz reconstruction, relaxing a non-convex low-rank matrix reconstruction problem through atomic norm optimization, solving by utilizing a convex optimization CVX tool box to obtain a rank recovery matrix, and finally performing angle estimation by utilizing an orientation estimation method. Compared with a traditional method, the method has the advantages that errors of the array are effectively corrected, the number of auxiliary array elements is reduced, resources are effectively saved, and the method has important application value in subsurface buoys or mobile observation platforms for ocean observation.

Acoustic signals from an acoustic event are captured via sensing nodes of sensor group(s) that comprise a group of sensing nodes at a location comprising spatial boundaries. Each of the sensing nodes comprise a sensor area. Each of the sensor group(s) is based on: range limits of each of the sensing nodes; shared sensing areas of the sensing nodes; and intersections between the sensor area for each of the sensing nodes and the spatial boundaries. Solutions(s) are generated by processing the acoustic signals. The solution(s) indicate the location of the acoustic event. A strength of solution compliance value for at least one of the solution(s) is determined. A refined solution is generated employing: sensor contributions of sensing nodes; and the strength of solution compliance value with the spatial boundaries and at least one of the solution(s). A report is created comprising the location of the acoustic event.

A half-spectrum search DOA estimation method based on characteristic value gradient jump belongs to the field of array signal processing, and comprises the following steps: modeling a received signal to obtain an array output vector; calculating a received signal covariance matrix; introducing a complex conjugate covariance matrix and a scanning source to construct a new covariance matrix, and constructing a spatial spectrum function; constructing a characteristic value gradient discrimination function, and adaptively estimating a signal source number by using the characteristic value gradient discrimination function; scanning angles are traversed by using a half-spectrum search method, and a signal direction of arrival is verified and determined by combining an MVDR algorithm according to a sharp spectrum peak formed by a spatial spectrum function. According to the method, source number priori knowledge is not needed, the source number can be identified autonomously, the problem that estimation is inaccurate under complex conditions in traditional half-spectrum search is solved, and the method has good robustness under the scene of unknown source number. According to the method, the limitation that few traditional half-spectrum search angle estimation is inaccurate is overcome, and a new thought is provided for half-spectrum search DOA estimation under the condition that the number of information sources is unknown.

The invention discloses an indoor multi-sound-source positioning method based on DOA estimation and DOA association, and the method comprises the steps: judging single-source time-frequency points through the correlation of time delay inequality vectors of adjacent time-frequency points between microphones, and constructing a variable-size elastic single-source region; applying the FSSZ distribution condition of the sound source in a reference array to all arrays by using the high correlation characteristic of the FSSZ time-frequency distribution of the same sound source at different microphone arrays in a frame; performing middle DOA estimation on the FSSZs of all the arrays in the whole observation time period by using a circular integral cross spectrum method, and constructing a sound source position histogram in combination with FSSZ aggregation degree weighting; 2D positions of the plurality of sound sources are estimated from the sound source position histogram using 2D-CFAR. According to the invention, under the conditions of indoor reverberation and noise, position estimation can be carried out on a plurality of sound sources with an unknown number, the detection performance under the conditions of positioning precision and missed detection is obviously superior to that of an existing method, and the defects of the prior art are overcome.

For training a perception algorithm to detect an emergency vehicle, respective audio datasets are received from two microphones and respective spectrograms are generated. At least one interaural difference map is generated based on the spectrograms, audio source localization data is generated, which specifies a number of audio sources in respective grid cells of a spatial grid, by applying a CRNN to first input data containing the spectrograms and the least one interaural difference map. An image is received from a camera and output data comprising a bounding box for the emergency vehicle is predicted by applying at least one further ANN to second input data containing the image and the spectrograms. Network parameters are adapted depending on the output data and the audio source localization data.

Method for locating a sound source, particularly in a vehicle environment, comprising the following steps: - Providing (S1) at least two microphones, in particular vehicle microphones, which are attached to different vehicle components in order to obtain raw data; - Providing (S2) position data of the microphones; - transforming (S3) the raw data from a time domain to a frequency domain by means of a spectral transformation in order to obtain spectral data comprising amplitude data and phase data; - preprocessing (S4) the amplitude data and / or the phase data and / or data derived therefrom, using artificial intelligence, to obtain filtered data; - Calling (S5) a beamforming algorithm with the filtered data and the position data of the microphones to obtain directional data for the raw data.

The invention provides an MVDR beam forming method and device, and the method comprises the steps: obtaining an observation acoustic matrix formed after a plurality of acoustic sensors receive a plurality of target signal sources, and enabling the plurality of acoustic sensors to be arranged in an array manner; drawing a potential function numerical diagram based on the observation acoustic matrix to determine the number of target signal sources and the direction of arrival corresponding to each target signal source; calculating a covariance matrix based on the observation acoustic matrix; revising the covariance matrix based on the number of the target signal sources to generate an optimized covariance matrix; and obtaining a beam forming result based on the observation acoustic matrix, the optimization covariance matrix and the array response matrix. The method and the device are used for improving signal extraction quality and accuracy of MVDR beam forming in a multi-signal-source and noise interference environment.

The invention discloses a training method of a multi-sound-source direction-of-arrival estimation model, a multi-sound-source direction-of-arrival estimation method, equipment, a medium and a product, and relates to the technical field of signal processing and artificial intelligence crossing, and the training method comprises the steps: calculating the time-frequency characteristics and cross-correlation characteristics of original signals of a multi-channel array; respectively carrying out position coding on the time-frequency characteristics and the microphone position information; fusing the features into an input matrix, and inputting the input matrix into a backbone network of an improved Transform model; calculating the attention score of the input matrix and generating head output by using the first multi-head self-attention layer, inputting the head output into the second multi-head self-attention layer, calculating the attention score and generating head output, and inputting the output into a multi-task output module to obtain a direction estimation result. The multi-head attention mechanism is introduced, long-time and multi-band complex dependence is captured, the sound source distinguishing capacity is improved, multiple heads capture direction information from different view angles, and it is guaranteed that high accuracy can still be guaranteed in the complex environment.

A system can operate a speech-controlled device to perform user engagement detection (UED) processing to detect when speech represented in audio data is directed to the device. For example, the device may extract audio features from the audio data and process these audio features using a classifier to estimate an orientation of the user's head, which may be used as a proxy for user engagement. Thus, if the head orientation is within an engagement zone (which varies based on distance to the user), the device may determine that the user is engaged with the device and perform language processing on input speech. In contrast, if the head orientation is outside of the engagement zone, the device may determine that the user is not engaged and ignore the input speech. To enable additional functionality, the classifier may optionally output a coarse estimate of the head orientation along with the UED determination.

The invention discloses a method for detecting radiation characteristics of a to-be-detected target in a shallow sea waveguide environment, which comprises the following steps of: 1, extracting real normal wave modal parameters of a sound source radiation signal in the shallow sea waveguide environment through an orthogonal constraint modal search method according to data received by a vertical array; 2, according to the real normal wave modal parameters, through a normal wave expression in a waveguide environment, performing inversion to obtain a sound field of the whole shallow sea waveguide; and step 3, acquiring sound field information required in the sound field of the shallow sea waveguide, mapping the sound source information into the free space by using the reciprocity theorem, realizing sound field migration from the shallow sea waveguide to the free space, and further acquiring the radiation characteristics of the target to be measured in the free space. The method is used for obtaining the radiation characteristics of the to-be-measured target in the free space according to the data received by the vertical array.

Systems can include an acoustic sensor array configured to receive acoustic signals, an illuminator configured to emit electromagnetic radiation, an electromagnetic imaging tool configured to receive electromagnetic radiation, a distance measuring tool, and a processor. The processor can illuminate the target scene via the illuminator, receive electromagnetic image data from the electromagnetic imaging tool representative of the illuminated scene, receive acoustic data from the acoustic sensor array, and receive distance information from the distance measuring tool. The processor can be further configured to generate acoustic image data of the scene based on the received acoustic data and received distance information and generate a display image comprising combined acoustic image data and electromagnetic image data. The processor can determine depths of various acoustic signals within a scene and generate a representation of the scene the shows the determined depths, including floorplan and volumetric representations.

The application provides a method and an electronic device for guiding a user. The second electronic device can send a first ultrasonic signal, the first electronic device can determine first angle information with the second electronic device according to the first ultrasonic signal, the first electronic device outputs a first prompt signal to prompt the user to rotate the first electronic device. After the user rotates the first electronic device, the second electronic device can send a second ultrasonic signal again, and the first electronic device can determine second angle information with the second electronic device according to the second ultrasonic signal. The first electronic device can output a first guiding signal by using the first angle information, the second angle information and the rotation angle of the first electronic device, so as to guide the user to find the second electronic device. The user can find the second electronic device according to the first guiding signal, and the first electronic device and the second electronic device do not need to be installed with UWB chips, so that the cost can be reduced.

A mobile robot includes a microphone array with a set of microphones. The microphone array is at least partially disposed on the mobile robot. The mobile robot receives audio signals from the microphone array. Audio feature data of acoustic activity is extracted from the audio signals. Direction of arrival (DOA) data of the acoustic activity is generated based on the audio signals. A machine learning model is configured to generate audio event data using the audio feature data. The audio event data identifies at least one sound source of the audio feature data. A knowledge graph is queried using the audio event data to obtain entity data. The entity data has a predetermined relation with the audio event data. Semantic audio scene data is generated using the audio event data, the DOA data, and the entity data. The mobile robot performs an action based on the semantic audio scene data.

The invention provides a multi-sound-source arrival direction estimation method and device based on a frequency focusing spatial spectrum. The method comprises the following steps: firstly, generating microphone array data and constructing a training data set by using a preset simulation environment and a microphone array signal simulator; secondly, constructing a mask estimation network model through sound source masking training; then, sub-bandwidth division is carried out on the enhanced multi-sound-source signals in a frequency band splicing mode, and a weighted focusing covariance matrix is obtained; thirdly, broadband focusing spatial spectrum features are constructed through a sub-bandwidth frequency focusing covariance matrix obtained through beam forming, and a convolutional neural network is constructed based on the features; and finally, according to the mask estimation network model and the multi-sound-source DOA spatial spectrum estimation network model, decoding to obtain a multi-sound-source DOA spatial spectrum, and completing multi-sound-source DOA estimation. According to the invention, the intermediate representation based on the broadband frequency focusing spatial spectrum reduces the influence of nonlinear distortion of the neural network on DOA estimation; and the stability of algorithm performance and the fault tolerance of DOA estimation are improved.

Audio signal processing methods, systems, terminal devices, conference devices, teaching devices, intelligent vehicle-mounted devices, server device, and computer-readable storage media are provided. The method comprises: obtaining current audio signals acquired by a microphone array, the microphone array comprising at least two microphones; generating, according to phase difference information of the current audio signals acquired by the at least two microphones, current sound source spatial distribution information corresponding to the current audio signals; and according to the current sound source spatial distribution information, in combination with the conversion relationship between single speech and overlapping speech learned on the basis of historical audio signals, identifying whether the current audio signals are overlapping speech. Compared with single-channel audio, the audio signals acquired by the microphone array are used, and the sound source spatial distribution information is included, thus, the techniques of the present disclosure accurately identify whether the current audio signals are overlapping speech, thereby satisfying the detection requirement for a product level.

Some systems include an acoustic sensor array configured to receive acoustic signals, an electromagnetic imaging tool configured to receive electromagnetic radiation, a user interface, a display, and a processor. The processor can receive electromagnetic data from the electromagnetic imaging tool and acoustic data from the acoustic sensor array. The processor can generate acoustic image data of the scene based on the received acoustic data, generate a display image comprising combined acoustic image data and electromagnetic image data, and present the display image on the display. The processor can receive an annotation input from the user interface and update the display image based on the received annotation input. The processor can be configured to determine one or more acoustic parameters associated with the received acoustic signal and determine a criticality associated with the acoustic signal. A user can annotated the display image with determined criticality information or other determined information.