164results about "Direction/deviation determination systems" patented technology

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.

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.

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.

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.

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.

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.

The invention discloses an automatic classroom director method and electronic equipment. Audio data and video data of a current classroom scene are acquired; based on the audio data, sound source direction positioning is carried out through a microphone array, and the current sound source direction is determined; face key points are extracted according to the video data, lip movement recognition is carried out according to the face key points, and a lip movement recognition result is obtained; extracting human body key points according to the video data, and performing action recognition according to the human body key points to obtain a target action recognition result; based on the sound source direction, the lip movement recognition result and the target action recognition result, a current shooting picture of the camera device is controlled, and the current shooting picture of the camera device is a director picture; and outputting and displaying the director picture. According to the application, audio and video multi-mode information is fused, high-precision identification and natural and accurate automatic picture switching of the speaker and the interaction object in the classroom scene are realized, and the automation level of director and the overall classroom recording and broadcasting effect are improved.

Approaches presented herein provide for identification of sound from a sound source relative to an array of microphones of a potentially unknown configuration using, in part, differences in the audio signals received by the microphones. In at least one embodiment, audio signals are captured using an array of microphones and audio features are extracted from those signals. The audio features can be processed using a first neural network to generate a feature vector representing a spatial location of an audio source with respect to the plurality of microphones, where the spatial location is inferred based on audio differences and independent of an availability of information indicating a physical configuration of the plurality of microphones. The feature vector can be provided to a task-specific model to perform at least one audio-related task based in part on the spatial location.

The invention belongs to the field of signal processing, and relates to a dynamic direction of arrival estimation method based on a fusion neural network and a beam forming technology. The method comprises the following steps: S1, receiving an observation signal, and extracting complementary features from three dimensions of a time-frequency domain, a spatial domain and a perception domain by using a multi-modal feature extraction method to form a basis of multi-modal feature representation; s2, based on the multi-modal features extracted in the S1, performing adaptive fusion among the features by adopting a gated cross attention mechanism; and S3, realizing DOA estimation in a complex scene through a synergistic effect of neural network parameter learning and beam forming theory constraint by adopting a multi-stage optimization framework fusing physical prior and data driving based on the features subjected to adaptive fusion in the S2. According to the method, the DOA of the signal can be efficiently, accurately and dynamically estimated, the signal receiving quality is improved, and a new thought with both theoretical preciseness and engineering practicability is provided for underwater acoustic monitoring.

The application relates to a real-valued super-resolution direction estimation method of a high-order acoustic field sensor array, which uses a received signal covariance matrix to approximately calculate an estimated value of noise power, avoids the iterative operation process of noise power in the prior art, improves the calculation efficiency, and reduces the floating-point operation amount. By constructing an augmented matrix, the array receiving data matrix and the array manifold matrix with a multi-dimensional structure of an element are made into Hermitian matrices, so that the unitary transformation processing of the related parameters of the high-order acoustic field sensor array is realized. On the basis of obtaining the array receiving data matrix and the array manifold matrix in the real number domain, the array receiving data matrix and the array manifold matrix are applied to a sparse approximate minimum variance method with a variable exponential factor, and the completely real-valued sparse approximate minimum variance direction estimation with a variable exponential factor is realized.

This invention relates to the field of deep learning technology and discloses a direction-of-arrival (DOA) estimation method, system, device, and medium. The method includes: acquiring multi-source signals in a target environment; converting the multi-source signals into a GCC-PHAT matrix, and then converting the GCC-PHAT matrix into a GCC-PHAT matrix image, the GCC-PHAT matrix image containing time delay information of the signals acquired by each microphone; converting the multi-source signals into a covariance matrix, and then converting the covariance matrix into a covariance matrix image, the covariance matrix image containing spatial distribution information of the signals acquired by each microphone; fusing the GCC-PHAT matrix image and the covariance matrix image and inputting them into a Vision Mamba network to extract the time delay features and spatial distribution features of the multi-source signals, and estimating the DOA of the multi-source signals based on the time delay features and spatial distribution features, thereby improving the accuracy of the DOA estimation.

The invention discloses a high-gain spatial directivity construction method of a single-vector hydrophone, and relates to the technical field of underwater acoustic signal processing. Acquiring a sound pressure signal and a three-dimensional vibration velocity signal of the target area by using a three-dimensional vector hydrophone; equally dividing the orientation guide angle of the three-dimensional vector hydrophone to obtain a plurality of guide directions; for each guide direction, extracting a horizontal vibration velocity component in the three-dimensional vibration velocity signal, and obtaining two paths of dipole combined vibration velocity signals in each guide direction according to the horizontal vibration velocity component; performing synthetic convolution operation according to the sound pressure signal and the two paths of dipole combined vibration velocity signals in each guide direction to obtain a plurality of synthetic convolution combined signals in each guide direction; and according to the response of all the synthetic convolution combination signals in each guiding direction, obtaining the corresponding synthetic convolution combination directivity. According to the invention, high-gain high-order combination directivity is constructed, and the stability of the detection precision of an underwater weak target is improved.

The invention provides an anti-pseudo peak frequency difference azimuth estimation method using sparse Bayesian learning, and relates to the technical field of underwater acoustic array signal processing. The method specifically comprises the steps of collecting signals, selecting processing frequency points, constructing a dictionary matrix and a joint spectrum matrix, quantifying the stability of signal intensity changing along with a reference high-frequency frequency point, optimizing the joint spectrum matrix according to the stability to eliminate false peaks, and obtaining an orientation estimation result. According to the technical scheme, false peaks can be effectively removed, the sidelobe height in the azimuth spectrum can be reduced, and the azimuth estimation accuracy of the target can be improved.

A device for determining a firing position from which a projectile has been fired at a target object is proposed, comprising:at least one sensor unit for detecting an impact of the projectile in the target object,at least one acoustic sensor unit for detecting a muzzle blast when the projectile is fired, anda determination unit which is arranged to determine the firing position on the basis of the detected muzzle blast and the detected impact.

To provide a display device, a program, and a display method with which it is made possible to display much more information at a time than before, even when displaying detection results by a plurality of detection methods.SOLUTION: Provided is a display device for displaying detection results of non-steady sounds included in sound waves on the basis of the output of a wave receiver array for receiving sound waves. The display device comprises: an arithmetic unit including a plurality of output units for calculating a detection value which is the detection result of a non-steady sound on the basis of the output of the wave receiver array, calculating a grayscale value on the basis of the detection value by using a detection method, and outputting the grayscale value and a data point having the colors set by the detection method; and a display unit for displaying a data point deriving by adding the colors of the plurality of data points outputted by the plurality of output units and the grayscale value together, as the detection result of the non-steady sound calculated by the plurality of output units. The plurality of output units calculate the detection value which is the detection result of a non-steady sound by using a detection method that detects a non-steady sound on the basis of respectively different physical amounts.SELECTED DRAWING: Figure 5

The application discloses a low-noise target detection and azimuth estimation method and a storage medium. Firstly, a low-frequency part is divided into a plurality of subbands, high-resolution beam forming is respectively performed on the subbands, and then the beam forming results are counted. If the number of the beam forming results in a peak value gathering angle range reaches a threshold, it is determined that there is a target, and the detection of the low-noise target is realized. The beam forming results in the peak value gathering angle range are weighted and summed, and the peak position of the processing result is the target azimuth, so that the azimuth estimation of the low-noise target is realized. Compared with the traditional low-noise target detection method through a line spectrum, the method does not need to detect the line spectrum, and realizes the detection and azimuth estimation of the low-noise target without a stable line spectrum or with an unobvious line spectrum.

The application relates to a MUSIC direction-finding method based on an underwater information system cross array, and belongs to the technical field of underwater acoustic direction finding. In view of the problems of low signal-to-noise ratio and low direction-finding precision of underwater signal direction finding, the application provides a MUSIC direction-finding method based on an underwater information system cross array. The method is based on a MUSIC algorithm, frequency extraction and spatial spectrum accumulation, a cross array is used to perform direction-finding processing on a signal, the relative position relationship between the signal and the cross array is obtained, a suitable linear array in the cross array is selected to perform direction-finding processing on the signal, and the direction finding of an underwater target acoustic signal under low signal-to-noise ratio can be effectively realized.

A kind of fixed beam type vector underwater acoustic sensing structure and its preparation method, wherein micro column transforms underwater acoustic pressure gradient into mechanical force, drives mass suspended in frame to deflect;Piezoresistive sensing unit includes two groups of piezoresistors arranged on left beam and right beam, which are arranged in different directions and respond to bending stress dominant state and shear stress dominant state respectively. First and second Wheatstone bridges are electrically connected with two groups of piezoresistors. After receiving underwater acoustic signal, micro column drives mass to deflect, which causes left and right beams to generate corresponding stress distribution. Two groups of piezoresistors change resistance value according to bending or shear stress change, and output X and Y direction vector underwater acoustic signals through corresponding bridges. In the same fixed beam, bending stress and shear stress are used simultaneously to realize two-way vector detection. The structure is simple and has high integration, two channels have "8" directional characteristic and no inter-axis crosstalk, which is easy to form array and can be used for underwater detection, communication and ocean environment monitoring.

The application discloses a multi-sound source direction-finding positioning method based on a vector microphone in a strong reverberation environment, which comprises the following steps: converting mixed voice signals collected by the vector microphone from a waveform domain to a time-frequency domain to obtain a spectrogram of an observation signal; removing virtual source single time-frequency points by using a frequency smoothing method and iteratively generating low-reverberation self-term source time-frequency point sets; constructing a steering vector dictionary; finding a steering vector most likely to appear in any time-frequency point in the self-term source time-frequency point sets from the steering vector dictionary, and then calculating the STFT coefficient value of the video point corresponding to the source according to the steering vector, determining whether the time-frequency point is a single source time-frequency point not interfered by reverberation according to the STFT coefficient value, and generating a single source time-frequency point set not interfered by reverberation; estimating the number of sources by using a smoothing histogram method; and obtaining multi-source direction-finding results by using MUSIC. The application can accurately extract single source time-frequency points not interfered by reverberation in the time-frequency domain, and then realize accurate multi-source positioning.

This invention belongs to the field of signal processing, and particularly relates to direction-of-arrival (DOA) estimation for electromagnetic and sonar signals. Specifically, it is a subspace-based DOA estimation method based on a distributed algorithm, which can be used for passive localization and target detection. The method includes the following steps: S1, establishing a distributed array model at the receiver; S2, calculating the signal subspace of the received signal; S3, calculating the weight w(θ); S4, calculating the spatial spectrum and performing a spatial domain search to find the DOA value. The proposed method achieves distributed solution of the algorithm, thus fundamentally avoiding the shortcomings of centralized algorithms. Simultaneously, it maintains good estimation performance, similar to that of centralized algorithms, and outperforms distributed MUSIC methods at low signal-to-noise ratios.

A noise camera comprises a plurality of audio detectors such as microphones, which are spatially disposed with respect to a tracking camera. The plurality of audio detectors may be arranged to detect a location of a noise source in a field of view of the tracking camera, thereby allowing identification of a source of noise such as a vehicle emitting noise exceeding a predetermined threshold such as a legal limit. The noise camera may comprise one or more detection cameras for identifying a noise source by techniques such as number plate recognition for the example of a vehicle. A location of a noise source in the field of view of the tracking camera is determined from a time difference of arrival of the noise from the noise source detected by a plurality of the audio detectors. The time difference of arrival indicating an angle of the noise from the tracking camera may be mapped into video captured with the field of view of the tracking camera as one or more pixel location values. Documentary evidence generated from the noise camera can include the tracking video, noise source pixel location values comprising, for each of one or more tracking images of the tracking video, an indication of a location of a pixel of a dominant noise source in the tracking video. The documentary evidence may be processed to identify a path of the dominant noise source in the tracking video from the noise source pixel location values. From the path, a detection time at which the path of the dominant noise source in the tracking video is closest to a detection point in a field of view of a detection camera is determined. The noise source such as a vehicle is identified from images at the detection time.

A directional acoustic sensor includes: a support including a first support portion and a second support portion that are separated from each other and face each other; a plurality of first resonators extending in a length direction thereof from the first support portion of the support; and a plurality of second resonators extending in the length direction thereof from the second support portion of the support and facing the plurality of first resonators, wherein each first resonator of the plurality of first resonators has a first end, wherein each second resonator of the plurality of second resonators has a second end, and wherein, in a first resonator arrangement of a region where the plurality of first resonators and the plurality of second resonators face each other, the first ends of the plurality of first resonators and the second ends of the plurality of second resonators form an intersecting structure.

The application discloses a kind of based on multi-array sparse channel estimation underwater acoustic environment perception method, underwater acoustic receiving end is received through multi-array array signal that is propagated through underwater acoustic multipath channel;The received signal of each array element is subjected to Hilbert transform, and analytic signal is obtained, and the cross-correlation function of analytic signal and transmitting signal is calculated;Based on cross-correlation function, using the orthogonal matching pursuit algorithm of combining constant false alarm detection dynamic threshold, the sparse channel parameters of each array element are estimated, including path amplitude and time delay;Utilize the sparse channel parameters of multi-array array, construct array response vector, estimate the angle of arrival of multipath signal, including direction angle and pitch angle by space spectrum peak value search;Based on the angle of arrival, amplitude and time delay of direct path and reflected path, the underwater environment structure is inverted by ray acoustics theory, including reflection point distance and reflection surface normal vector, realize the three-dimensional perception of underwater reflector.The application can improve multipath resolution, effectively reduce false alarm and miss detection.

The application belongs to the field of underwater acoustic signal processing and analysis, and discloses a wideband sound source DOA estimation method based on the multipath characteristics of a shallow sea channel. The method first combines a wideband pulse signal with a frequency domain sound pressure wideband, and then performs Fourier transform on the combined signal to perform time-frequency analysis. Then, the signal is subjected to Warping transformation to separate and extract each order signal of the normal mode wave, and the time-domain signal is obtained. The obtained time-domain signal is then passed through a vector hydrophone to obtain a complex sound intensity vector corresponding to the signal in the frequency domain. Finally, the obtained complex sound intensity vector is subjected to DOA estimation to obtain the average value of the direction angle. Compared with the traditional method, the DOA estimation method effectively suppresses the interference caused by the multipath characteristics and improves the accuracy of DOA estimation.

The invention discloses a wireless power supply network communication system, a communication method and a performance analysis method, and relates to the technical field of network communication, the system comprises a hybrid access point, a sensing intelligent reflecting surface and K single-antenna users, the hybrid access point is equipped with M antennas, the M antennas form a uniform linear array, and the K single-antenna users are connected with the sensing intelligent reflecting surface. The intelligent reflecting surface is provided with Na active reflecting units and N passive reflecting units, the active reflecting units are used for estimating the signal arrival direction, the passive reflecting units are used for transmitting wireless information and energy, the single-antenna user is used for sending data signals to the hybrid access point in an uplink mode, and the hybrid access point is used for sending energy signals to the user in a downlink mode. And the sensing intelligent reflecting surface is used for adjusting the amplitude and the phase of the passive reflecting unit by using the sensed signal arrival angle. According to the invention, the pilot frequency overhead of the system is effectively reduced, and efficient decoupling with the hybrid access point is realized.

A method, computer program product, and computing system for receiving a signal from each microphone of a plurality of microphones, thereby defining a plurality of signals. One or more inter-microphone gain-based augmentations can be performed on the plurality of signals, thereby defining one or more inter-microphone gain-augmented signals. Performing one or more inter-microphone gain-based augmentations on the plurality of signals can include applying a gain level from a plurality of gain levels to the signal from each microphone. Applying a gain level from a plurality of gain levels to the signal from each microphone can include applying a gain level from a predefined range of gain levels to the signal from each microphone. Applying a gain level from a plurality of gain levels to the signal from each microphone can include applying a random gain level from a predefined range of gain levels to the signal from each microphone.

Provided is a sound direction detection sensor capable of detecting a direction from which sound is coming by using a multi-resonator array. The disclosed sound direction detection sensor includes two resonator arrays, each including a plurality of resonators having different resonance frequencies. The two resonator arrays have different directivities. Each resonator array serves as an audio sensor, and the sound direction detection sensor detects a direction from which sound is incident, regardless of a distance between audio sensors.

The invention discloses a Bernoulli filter-based single target detection and DOA tracking method and system, and relates to the field of target tracking. The method solves the problems that a traditional tracking algorithm after detection cannot jointly detect the target when the underwater target repeatedly enters under the conditions of low signal-to-noise ratio and small snapshot, and the tracking effect is unstable. The method comprises the steps of constructing a Bernoulli random finite set model, defining a single target state as a dynamic description target state or a dynamic description target state, predicting the target existence probability by using a Bernoulli Markov process, and generating a prediction state distribution particle set composed of new particles and survival particles through particle filtering. A hydrophone array signal is decomposed into a noise subspace observation model and a signal subspace observation model, a generalized likelihood function after exponential weighting is used for updating a target posteriori existence probability and a particle weight, and an equal-weight particle set is generated through resampling. And judging whether the target exists or not according to the single-target posterior existence probability, thereby realizing single-target DOA joint detection and tracking.

Disclosed is a method for the acoustic detection of objects, especially of drones, and a system for the implementation of the method. The sound from the surroundings is processed in a first beamforming strip 2 and in a second beamforming strip 3 and in a SODAR 4, the data from the first beamforming strip 2 and the second beamforming strip 3 is processed by means of an I2S bus, whereas the SODAR 4, as an analog sound originating from a loudspeaker acting as a microphone, is subjected to conversion into a digital signal in an analog-to-digital converter and is transmitted to a microcontroller 1, where in the microcontroller 1 the data is processed by a beamforming algorithm to obtain 18 signals from the first beamforming strip 2 and from the second beamforming strip 3 and one SODAR signal, and the microcontroller 1 preferably controls the sending of short signals into the surroundings in order to receive echoes from the first beamforming strip 2 and from the second beamforming strip 3 and the SODAR 4 and calculates the device-to-drone distance based on the time of sending and reflection of the echoes, additional data including wind direction and speed, temperature, and humidity are obtained from a meteorological station 5 and, based on correction tables, the data originating from the first beamforming strip 2 and from the second beamforming strip 3 and the SODAR 4 are recalculated, whereas an IMU and GPS sensor 6 is intended to provide information about the directionality and position of the system, an LTE modem 8 by means of which data is sent to a central computing computer 10, and a power supply system 7, further the data is sent to the central computing computer 10 and a server 11 where it is processed for all modules located in the surveyed region and, based on triangulation and / or machine learning models, the exact location of the drone or other object is calculated, in turn, the data from the central computing computer 10 is sent to the user via a website and is visualized.