96results about "Radio wave direction/deviation determination systems" patented technology

This invention relates to an angle estimation method in an integrated communication and sensing system, belonging to the fields of wireless communication and radar technology. The angle estimation method in an integrated communication and sensing system is characterized by the base station simultaneously transmitting data in the downlink time slot. M There are data streams, among which M ≥2, the M Each data stream uses a different beamforming vector; the base station is based on the... M Constructing beamforming vectors P There are 1 guiding matrix, where P ≥2; the above P Each of the guidance matrices and N r The post-processed data from each receiving antenna is used to perform an inner product operation, resulting in... P The angle corresponding to the largest modulus among the inner product results is selected as the estimated angle of the target. N r ≥1.

The application discloses a low signal-to-noise ratio DOA estimation method based on deep learning, comprising the following steps: S1, generating an array signal data set and calculating a covariance matrix; S2, converting the DOA estimation problem into a multi-label classification task, discretizing the angle range into multiple grid points and constructing a multi-label binary vector as a supervised signal; constructing a convolution classification network, the network comprising a multi-scale convolution module, multiple cascaded residual modules with attention mechanisms, a pooling module and an output layer; training the network using the data set; S3, inputting the preprocessed to-be-estimated signal into the trained network to obtain the target existence probability of each angle grid, and obtaining the DOA estimation value through peak value detection. The application strengthens key feature extraction through multi-scale convolution and attention mechanism, maintains high-precision estimation under the condition of low signal-to-noise ratio and low snapshot, and simultaneously realizes lightweight network structure and is suitable for edge deployment.

This application belongs to the technical field of staggered baseline arraying for interferometer direction finding systems, specifically relating to a method for staggered baseline arraying in an interferometer direction finding system, including: calculating the shortest length L of a long baseline. sL ; Calculate the basic line element length d0; Design the staggered baseline length as: d i =n i d0, i = 1, 2, ..., M; n M >…>n2>n1≥D / d0,n1,n2,…,n M Pairwise coprime; n M ≥L sL / d0; when i>1, n i -n i‑1 ≥D / d0; where d i The length of the i-th staggered baseline; M is the number of staggered baselines; n i is the coefficient of the i-th staggered baseline; D is the minimum spacing between line elements.

The present application relates to the field of communication radar, in particular to a kind of omnidirectional correlation interferometer real-time direction finding method and system based on spread spectrum signal;Construct omnidirectional antenna array;Utilize radio frequency channel time-sharing switching and receive the array signal of single array plane currently working, obtain baseband signal;A set of fixed base line is distributed to the uniform circular array in the array plane currently working;Based on the pseudo-random code matching the spread spectrum signal generated locally and received, each way of baseband signal is carried out correlation despreading, and the phase difference corresponding to each base line is obtained;The phase difference obtained is input into the phase difference sample library established in advance to carry out matching, calculates similarity function, and the angle stored in the phase difference sample library corresponding to the maximum value of similarity function is determined as the measurement direction angle;The measurement direction angle is carried out data processing, and the optimized measurement direction angle is obtained;The method of the present application has the advantages of full-space coverage, low cost, direction finding precision and tracking precision.

The application discloses a kind of robust direction of arrival estimation method based on ADMM-Net, by expanding ADMM algorithm into model-driven deep network ADMM-Net to improve DOA estimation accuracy, speed up DOA estimation speed and have robustness to array disturbance.Firstly, the sparse transformation of source and array received multi-shot data is carried out by space over-complete dictionary, and the DOA estimation is converted into compressed sensing sparse recovery problem;Then, ADMM algorithm is expanded, and model-driven deep network ADMM-Net with explainability is formed;ADMM-Net is used to reconstruct source power spectrum and carry out DOA estimation.The present application can learn the hyperparameters in iterative algorithm and array disturbance, solve the problem that existing compressed sensing DOA estimation method based on calculation speed is slow, and fails under the condition of array disturbance, realize fast, accurate, explainable and robust DOA estimation.

The application provides a few-channel switching direction finding method based on blind source separation, and belongs to the technical field of emergency search and rescue direction finding. The method comprises the following steps: constructing a corresponding original signal matrix of each array element switching, and performing pretreatment to obtain a corresponding standard signal matrix; performing blind source separation on each standard signal matrix to obtain a corresponding separation matrix and a separation signal matrix; constructing a corresponding array manifold matrix of each array element switching, and calculating all waveform correlation coefficients; using all waveform correlation coefficients, completing signal alignment of all array manifold matrices, and using a reference matrix to obtain a calibration array manifold matrix corresponding to each array element switching; and using all calibration array manifold matrices to perform direction of arrival estimation on each rescue target. The application can effectively estimate the noise of a few-channel direction finding system, and improve the direction finding precision, angle resolution capability and robustness of the few-channel direction finding system.

This disclosure relates to automotive radar using genetic apodization sparse array processing for direction-of-arrival estimation. A system and method for determining multiple MIMO virtual antenna arrays are provided. Each of the multiple MIMO virtual antenna arrays is associated with a subset of multiple transmit antennas and a subset of multiple receive antennas. Signals received by the multiple receive antennas are used to determine a signal spectrum for each of the multiple MIMO virtual antenna arrays. Each signal spectrum includes a signal amplitude value and has an associated angle value, wherein the main lobe position of the signal spectrum of the multiple MIMO virtual antenna arrays is at the same angle across all signal spectra, and the side lobe positions of the signal spectrum are not at the same angle across all signal spectra. A first combination of MIMO virtual antenna array signal spectra is determined by combining the minimum values ​​of the signal spectra at each angle value. The first combination of MIMO virtual antenna array signal spectra is used for object detection.

The application discloses a continuous DOA estimation method based on complex number Transformer and mask reconstruction and belongs to the technical field of array signal processing. The method comprises the following steps: constructing a data set, constructing a complex number Transformer network structure, pre-training the complex number Transformer network structure by adopting a self-supervised learning strategy, fine-tuning training the pre-trained complex number Transformer network structure based on the data set, obtaining a final complex number Transformer network structure, and running the final complex number Transformer network structure on test data with different signal-to-noise ratios, using RMSE as a performance evaluation index, and outputting a continuous DOA estimation result. By means of complex number Transformer and mask self-supervised learning, the application realizes high-precision and gridless continuous DOA estimation under low signal-to-noise ratio, and significantly improves estimation robustness and real-time performance.

Aspects of the present application relate to communication between the transmitter and the receiver regarding the association between channel paths and beams. One of the transmitter and the receiver may process parameters and measurements received from the other of the transmitter and the receiver. A result of the processing may be that given beams are associated with given channel paths. One of the transmitter and the receiver may use the information for beam switching / tracking / refinement and beam failure recovery.

The application discloses a CE-OFDM communication and sensing integrated anti-nonlinear DOA estimation method, belongs to the field of communication sensing, and comprises the following steps: generating a 0dB peak-to-average power ratio CE-OFDM signal at a transmitting end, so that the CE-OFDM signal can drive a power amplifier to work in a saturation region, thereby avoiding nonlinear distortion while ensuring high power amplifier efficiency. A uniform linear array is used to receive signals at a receiving end, the signals are decomposed into a frequency domain through discrete Fourier transform, narrowband MVDR spatial spectra are independently calculated for each subcarrier frequency point, wideband spatial spectra are obtained through non-coherent fusion, and finally high-precision DOA estimation is realized through peak value searching. The application fundamentally suppresses power amplifier nonlinear distortion by using the constant envelope characteristic of CE-OFDM, avoids eigenvalue decomposition and source number estimation by combining the IMVDR algorithm, supports frequency point level parallel calculation, and realizes high-precision and low-complexity DOA estimation while ensuring communication reliability.

The application discloses an adversarial sample detection method based on cross-model perception and local gradient difference, and comprises the following steps: constructing a multi-domain data sample set containing IQ data and signal characteristics through a pretreated array signal, so as to train a target direction of arrival estimation model and a plurality of auxiliary direction of arrival estimation models; constructing a clean sample and adversarial sample mixed data set by applying an attack to the target direction of arrival estimation model to generate an adversarial sample; calculating the confidence output of different models under corresponding characteristics and the local gradient information of the intermediate layer feature mapping corresponding to the prediction category confidence output by using the mixed data set, and fusing the two to construct a multi-feature fusion detection data set, which is used for training an adversarial sample detection network; and after the to-be-detected signal is fused by the same confidence and intermediate layer local gradient calculation and input into the detection network, whether the to-be-detected signal is an adversarial sample can be determined. The application realizes complementary enhancement of information by using the difference between the intermediate layer local gradient information and the confidence output of the adversarial sample among different models, and improves the performance of adversarial detection.

The application relates to a UAV array dynamic angle of arrival estimation method and system, which comprises the following steps: S1, acquiring array snapshot data after reference channel auxiliary synchronization and waveform compensation; S2, establishing a parameterized space-time coupling phase model in a short coherence processing interval; S3, based on the array snapshot data, obtaining a single-channel input sequence through beam forming, and estimating a radial parameter pair by using time domain atom matching filtering; S4, based on the radial parameter pair, performing common time phase compensation on the array snapshot data to obtain a compensated snapshot matrix; S5, performing two-dimensional correlation calculation by using a space-time steering atom to estimate an angle parameter pair; S6, constructing a complete space-time dictionary atom, and estimating a complex amplitude by using a least square criterion; and S7, performing successive interference cancellation to update a residual error. Compared with the prior art, the application has the advantages of keeping low signal-to-noise ratio coherence gain and significantly reducing calculation complexity.

This invention relates to the field of antenna technology and discloses a wideband high-precision DOA estimation method based on an optimal redundant linear array. The method includes: establishing a sampling model: constructing a sampling model based on uniform sampling samples at the Nyquist sampling frequency, the sampling model including a multi-set sampling model and a dual-channel coprime sampling model; processing the sampled data and then substituting the processed data into a formula for high-precision frequency estimation. This wideband high-precision DOA estimation method based on an optimal redundant linear array, using a coprime sampling model, significantly reduces the system's data processing volume and sampling hardware requirements. For coprime sampling conditions, a frequency estimation algorithm based on sparse Bayes is proposed, and the algorithm runs quickly, thus meeting the real-time requirements of the frequency measurement system. The coprime sampling model can firstly reduce the ADC's sampling rate, thus reducing the ADC's hardware cost, decreasing the amount of data in the receiving channel, and lowering the requirements for the chip's data processing capabilities.

According to an example embodiment, a client device is configured to receive a first positioning reference signal; identify an orientation of a polarisation of the first positioning reference signal based on the received first positioning reference signal; estimate an orientation of the client device based on at least the identified orientation of the polarisation of the first positioning reference signal; and provide the orientation estimate to a network node device, Devices, methods, and computer programs are disclosed.

The application provides a two-dimensional DOA estimation method for arbitrary array single-base MIMO radar based on data rearrangement, and relates to the technical field of array sensor direction finding. The rank structure of a coherent signal covariance matrix is recovered by rearranging a received data matrix through arbitrary array element positions of a transmitting array and a receiving array; S2: eigenvalue decomposition is performed on the signal covariance matrix to obtain a signal subspace; S3: reliable rough estimation is obtained through rotation invariance; S4: an integer ambiguity vector corresponding to spatial phase ambiguity caused by array element spacing greater than half a wavelength is solved; S5: the phase of an array flow pattern with phase ambiguity is compensated, and an accurate estimation value of the 2D-DOA of the target is calculated; the high-precision 2D-DOA estimation problem in a complex scene of a coherent signal source, arbitrary array geometry and phase ambiguity caused by part of array element spacing greater than half a wavelength is solved, unambiguous and high-precision angle parameter estimation is realized, and the estimation accuracy and algorithm reliability are improved.

A method of detecting vessels is described. A vessel is detected if a match exists between an object detection from each of two or more detectors 100, based on data obtained by the two or more detectors 100, which are selected from: an RF antenna 102; a light sensor 104; a radar detector 106; and an Automatic Identification System receiver 108. A method for detecting the presence of vessels at night is also described. The method comprises detecting a plurality of blobs within image data, applying an image mask to the image data to remove regions of the image data that are associated with having static objects and to retain the remaining portions of the image data, identifying one or more clusters of the detected plurality of blobs within the retained image data, and determining the presence of one or more vessels based on the one or more identified clusters.

The present invention concerns an amplitude goniometer comprising P receiver channels, P being greater than or equal to 2, each receiver channel being identified by an index p, each receiver channel comprising an antenna (A0,..., A5) coupled to a receiver chain followed by at least two digital receiver modules each comprising an analogue-to-digital conversion module associated with a respective sampling frequency, each sampling frequency not complying with the Shannon criterion and not being a multiple of another frequency, N being the number of frequencies, N being greater than or equal to 2, each frequency being referenced by an index n, the amplitude goniometry estimator working from the amplitudes of the signals originating from at least Q adjacent receiver channels of the P receiver channels, Q being at most equal to P, the sampling frequencies being associated with the analogue-to-digital conversion modules of the Q adjacent receiver channels.

Disclosed are techniques for wireless communication. In an aspect, a network entity determines a location of a target base station and a location of at least one reference device (710), determines an angle-of-arrival (AoA) measurement of one or more reference signals received by at least one antenna array of the target base station from the at least one reference device (720), determines an expected AoA between the at least one antenna array and the at least one reference device based on the location of the target base station and the location of the at least one reference device (730), and determines an orientation offset of the at least one antenna array based on a difference between the expected AoA and the AoA measurement (740).

This application discloses a method for determining the presence of angular misalignment in a vehicle-mounted radar, comprising: determining the horizontal and vertical angles of a target in a point cloud generated by the radar relative to the normal direction of the radar and obtaining the fast angle of the horizontal angle or the fast angle of the vertical angle; calculating the variance and standard deviation of all fast angles of the horizontal or vertical angles within a time-domain window ending at the current time; if the variance is less than a non-zero positive integer multiple of the standard deviation, then executing a second sub-process for the horizontal or vertical angle; the second sub-process includes: if the fast angle of the horizontal angle or the fast angle of the vertical angle exceeds a predetermined limit, then determining that the vehicle-mounted radar has angular misalignment and issuing an alarm to perform radar calibration.

Embodiments of the present application provide a UAV signal direction finding method, device and electronic equipment fusing time-frequency analysis and target detection in a complex electromagnetic environment, and relate to the field of UAV detection. The method comprises: collecting UAV signals of a target area using an antenna array and processing the UAV signals to obtain a time-frequency graph; using a target detection method to frame the time-frequency graph; and based on the framing result, using an MVDR algorithm to separate signals and find directions. The technical solution of the present application uses a time-frequency graph, uses frequency domain information of signals, can extract effective components from complex background interference, distinguish different UAV signals that are difficult to distinguish in the time domain and separate them, so as to obtain better direction finding results.

This invention discloses a robust beamforming method based on interference plus noise covariance matrix reconstruction. The method includes: calculating the DOA (Directivity of Analysis) by using a priori known reference sequence of the desired signal and the received signal to calculate the cross-correlation vector, effectively suppressing unrelated interference and noise; selecting the main lobe sector boundary containing the desired signal and numerically integrating the Capon spatial power spectrum of the received signal within the spatial angle interval where pure interference and noise are located to reconstruct a pure interference plus noise covariance matrix, eliminating the influence of the desired signal component; and calculating the optimal weighting vector based on the cross-correlation vector and the reconstructed interference plus noise covariance matrix. This method can sense the spatial interference distribution and form deeper nulls in the interference direction, significantly improving the output signal-to-interference-plus-noise ratio and beamforming robustness of the array system.

A method for jointly estimating the direction of arrival and polarization parameters of an electromagnetic wave signal, comprising the following steps: (1) establishing a uniform linear array with dual-fed orthogonal linear polarization units as array elements; (2) sampling the received signal to obtain the received signal matrix Z; (3) calculating and performing eigenvalue decomposition to calculate the variance; (4) generating a pseudo-noise matrix with zero mean and variance; (5) superimposing the two signals to obtain the matrix; (6) calculating and performing eigenvalue decomposition; (7) obtaining the parameter estimates according to the Root-MUSIC algorithm. The beneficial effect of this invention is to improve the parameter estimation accuracy of the array in a low signal-to-noise ratio environment. This invention can be applied to the field of parameter estimation technology in signal processing.

This invention discloses a centralized array partial amplitude and phase error self-correction angle estimation method, comprising: constructing a received signal model for a bistatic MIMO radar array where some array elements of the transmitting and receiving arrays have amplitude and phase errors; performing identity transformations on the steering vector matrices of the receiving and transmitting arrays respectively to obtain the rank-loss intermediate matrices of the receiving and transmitting arrays; obtaining the estimated value matrices of the steering vector matrices of the receiving and transmitting arrays by performing PARAFAC decomposition on the radar received signal model; performing eigenvalue decomposition on the covariance matrix of the estimated value matrices to obtain the noise subspace corresponding to the received signals of the receiving and transmitting arrays; obtaining the estimated values ​​of the amplitude and phase error vectors of the receiving and transmitting arrays using the relationship between eigenvectors and eigenvalues; constructing a spectral peak function using the estimated values ​​of the amplitude and phase error vectors of the receiving and transmitting arrays, obtaining the 2D-DOD estimate and the 2D-DOA estimate through two-dimensional space search, and performing target pairing based on the maximum likelihood function.

This invention discloses a lightweight deep neural network array direction finding method based on data redundancy removal, which solves the problem that the traditional RDNN direction finding method has a large number of deep neural network nodes and cannot achieve network lightweighting. The method performs redundancy removal operation on the upper triangular elements of the covariance matrix, simplifies the number of subsequent hidden layer nodes, reduces the computational complexity of the model, and ensures that the direction finding accuracy of the model is not lost, thus realizing high-precision direction finding of lightweight deep neural networks.

The application discloses a DOA estimation method and system based on a generative adversarial network. The method first acquires time-domain coherent signals received by a uniform linear array, performs preprocessing, and generates a coherent signal covariance matrix and a corresponding incoherent covariance matrix. Secondly, a covariance matrix reconstruction model is constructed, based on the coherent signal covariance matrix and the corresponding incoherent covariance matrix, a generator of the generative adversarial network is used to extract deep features from the preprocessed coherent signal covariance matrix, and the coherent signal covariance matrix is reconstructed. Then, the MUSIC algorithm is used to estimate and verify the DOA based on the reconstructed covariance matrix. The application improves the precision of coherent DOA estimation while not losing the utilization rate of the array aperture. The application has less parameter quantity, lower calculation complexity, faster covariance matrix reconstruction speed and stronger covariance matrix reconstruction capacity.

An apparatus (1) for determining a bearing to a radio beacon (2) comprises an arrangement of radio antennas (3) comprising a non-directional antenna system (2) and one or more directional antenna systems. The non-directional antenna system (2) has a receiving pattern that is omni-directional in a first plane. Each directional antenna system has a respective receiving pattern that is directional in a respective direction in the first plane. The apparatus (1) receives, at the arrangement of radio antennas (3), a direct-sequence spread-spectrum radio signal, transmitted by a radio beacon (2). It generates, for each antenna system, a respective digital signal from an electrical signal output by the antenna system in response to the received radio signal. For each of the one or more directional antenna systems, it performs a coherent despreading operation by intercorrelating the digital signal from the directional antenna system with at least a portion of the digital signal from the non-directional antenna system to determine a correlation value for the directional antenna system. It processes the one or more determined correlation values to determine a bearing to the radio beacon (2) relative to the arrangement of radio antennas (3).

The application provides a DOA estimation method based on a hybrid analog-digital array architecture design, which comprises the following steps: a hybrid analog-digital array is arranged at a receiving end architecture; the hybrid analog-digital array adopts a low-complexity HAD array architecture; the hybrid analog-digital array architecture is used to receive an incident signal and model; an initial value of an analog phase shifter network is designed; a difference virtual array of the low-complexity HAD array architecture is calculated; an initial angle estimation value is obtained from the difference virtual array receiving signal by using a classical direction finding algorithm; after the initial angle estimation value is obtained, the phase shifter network weight is redesigned, and then a difference virtual array output signal is obtained; and a DOA estimation result is obtained from the classical direction finding algorithm; the method can effectively avoid the problems of phase ambiguity and beam scanning, can reduce the cost and complexity, and can effectively approach the estimation performance of a full-digital array architecture.