194 results about "Sparse array" patented technology

The invention provides a preparation technology of a three-dimensional camera shooting sonar sparse sensing array, and the method comprises the following steps: forming a patterned electrode on a PVDF or P (VDF-TrFE) piezoelectric film through vacuum adsorption and silk-screen printing according to sparse array element coordinates, and arranging a virtual transition array element at the periphery; performing laser etching on the edge contour and the exhaust hole of the piezoelectric film; designing a bonding positioning auxiliary tool, combining an ultrathin double-sided adhesive tape and a single-sided mask adhesive tape, and utilizing conductive silver paste to realize accurate electric connection between the array element and the PCB bonding pad; designing a polytetrafluoroethylene-metal flattening module, and laminating the array in a vacuum environment for curing and bonding; the matching layer is made of a low-impedance sound transmission material, glue filling and vacuum defoaming are performed from the back side in batches, and the sparse sensing array is prepared in combination with a polyimide film and a metal super-flat plate. The preparation problem of the large-aperture high-frequency imaging sonar on the sensing array is solved, and the array element position accuracy, the array element consistency and the imaging resolution can be improved.

The invention discloses a large-scale phased array multi-channel partition beam control method and device based on an FPGA and a storage medium. The method comprises the steps that a control instruction of an antenna control unit is acquired; analyzing the control instruction which is verified to be correct, judging an instruction type according to an analysis result, when the instruction type is a beam switching instruction, determining a beam control parameter in the instruction, and calculating a base code of the phased-array antenna according to the beam control parameter; the array elements of the phased-array antenna are divided into a plurality of areas, according to the base codes and the position coordinates of the array elements, the wave control codes of the array elements are calculated in parallel by adopting a multi-channel partitioning method, and the wave control codes are normalized and then written into a beam forming chip in parallel; and after the operation is completed, a corresponding response instruction is sent to the antenna control unit. The method has the advantages of being high in beam switching speed, small in resource consumption and good in universality, an array can form a sparse array in the mode of sending a control instruction, and therefore side lobes are restrained, and power consumption is reduced.

Exemplary embodiments provide systems and methods for portable medical ultrasound imaging. Preferred embodiments utilize a tablet touchscreen display operative to control imaging and display operations without the need for using traditional keyboards or controls. Certain embodiments provide ultrasound imaging system in which the scan head includes a beamformer circuit that performs far field sub array beamforming or includes a sparse array selecting circuit that actuates selected elements. Exemplary embodiments also provide an ultrasound engine circuit board including one or more multi-chip modules, and a portable medical ultrasound imaging system including an ultrasound engine circuit board with one or more multi-chip modules. Exemplary embodiments also provide methods for using a hierarchical two-stage or three-stage beamforming system, three dimensional ultrasound images which can be generated in real-time.

The invention provides a sparse array direction-of-arrival estimation method based on a local and global self-supervised network, and the method comprises the steps: 1, carrying out the signal processing: constructing any sparse array receiving signal, and carrying out the difference set virtual array extension, and obtaining the difference set virtual array measurement data; constructing a neural network training data set according to the measurement data; 2, establishing a local and global feature extraction network; step 3, establishing an ANM (Atomic norm Minimization) self-supervised network; and step 4, performing meshless signal recovery and DOA (direction of arrival) estimation, realizing label-free self-supervision data recovery, and finally performing DOA estimation by using the recovered meshless data. The new scheme provided by the invention has meshless recovery performance, solves the problem of base mismatching of a traditional neural network method, and has more accurate recovery and estimation performance.

The invention discloses a signal acquisition and processing method and system based on a multifunctional radar, and relates to the technical field of signal acquisition and processing, and the method comprises the steps: employing a quantum genetic algorithm to optimize radar transmission waveform parameters, including center frequency, bandwidth and frequency modulation slope, and generating a nonlinear frequency modulation waveform through FPGA hardware; a non-uniform sparse array is adopted to receive a target echo signal, time domain random interval sampling and frequency domain pseudo-random frequency point selection are synchronously implemented, and time-space-frequency three-dimensional compressed sensing observation data are formed; performing trilinear tensor joint sparse reconstruction on time-space-frequency three-dimensional compressed sensing observation data, decomposing a polarization scattering matrix eigenvalue from a reconstructed signal, and calculating a coupling characteristic quantity of eigenvalue entropy and micro-Doppler frequency; inputting the coupling characteristic quantity into a deep reinforcement learning model, and dynamically outputting a constant false alarm detection threshold, a moving target display filter order and a resource allocation weight; and detecting a threshold based on a constant false alarm rate.

The invention belongs to the technical field of antennas, discloses a double-frequency common-caliber high-isolation phased-array antenna in a sparse array distribution mode, and mainly solves the problems that an existing double-frequency common-caliber array antenna sparse distribution optimization algorithm is large in calculation amount and low in design efficiency. According to the invention, through the thirteen-sided Einstein pattern, aperiodic tiling of an infinite plane can be realized through a certain splicing rule. A Ka-band high-frequency antenna and a K-band low-frequency antenna are placed at the gravity center of the Einstein pattern, and the high-frequency antenna and the low-frequency antenna are distributed in a staggered mode in a translation mode. The antenna unit adopts a cross symmetric array antenna, and high isolation of the dual-frequency antenna unit is realized by loading a coaxial filter at the bottom of the antenna unit. The rear end of each antenna unit is connected with a T / R assembly, and array beam scanning is realized by controlling the amplitude and phase of an input signal. The method is low in arrangement cost and high in design efficiency, and can be used for radar, communication, navigation and information confrontation.

The invention relates to the technical field of array antennas, and discloses a common-caliber layout-based ultra-wideband array antenna, which comprises a low-frequency unit and a high-frequency unit which are arranged based on a common caliber, and is characterized in that the working frequency of the low-frequency unit covers an fmin-f0 frequency band, and the working frequency of the high-frequency unit covers an f0-fmax frequency band. The low-frequency unit is a broadband dual-polarized antenna, a uniform tight coupling array distributed at equal intervals is formed by adopting a tight coupling principle, and the unit interval is calculated based on a grating lobe suppression condition at the frequency f0. The high-frequency unit is a miniaturized broadband dual-polarized antenna and adopts a sparse array form; the initial unit spacing and the unit number of the sparse array are calculated based on the grating lobe suppression condition at the highest frequency f2 of the f1-f2 frequency band with the side lobe requirement between the f0-fmax frequency bands. The ultra-wideband array antenna gives consideration to full-band array performance, can provide sufficient space for active circuit arrangement, and effectively reduces power consumption and cost.

The invention discloses a K-band-based close-range end-to-end sparse MIMO FMCW radar system. The method comprises the following steps: performing range-fast Fourier transform on an intermediate frequency signal acquired by a sparse MIMO FMCW millimeter wave radar system to obtain a frequency domain signal; after strong leakage interference targets in the frequency domain signals are filtered through a frequency domain leakage filtering technology and intermediate frequency signals of interested targets are reserved, speed-fast Fourier transform is carried out to obtain a distance-Doppler graph, and distance and speed information of the interested targets is extracted; performing sparse array traditional beam forming on the obtained target signal to estimate angle information of the target, and matching angle and distance information by using a digital beam forming technology to obtain the distance, the speed and the angle of each interested target so as to form a spatial point cloud; and finally, for multi-frame acquisition of the spatial point clouds formed by the above steps, carrying out angle averaging on the point clouds at the same distance to smooth the trajectory, and forming the motion point cloud trajectory of the target. The method is suitable for synchronous and asynchronous systems, solves the existing multipath leakage problem, and is high in robustness.

This invention discloses a deep learning-based channel estimation method for asymmetric all-digital communication systems, comprising: designing an uplink receiving array based on a nested sparse array topology to recover the received signal of the sparse array into a virtual array signal of full dimension; decomposing the obtained virtual signal into a grid part and an off-grid part based on Taylor formula, and designing a two-step-angle estimation net (Ts-AEnet) to estimate the signal angle of arrival; solving a least-squares problem to estimate the signal path gain and reconstructing the downlink channel. This invention proposes an uplink receiving array based on a nested sparse array topology in asymmetric all-digital communication systems, minimizing information loss caused by missing antennas, and proposes a deep learning-based Ts-AEnet angle estimation network, which reduces the complexity of channel estimation while ensuring the system's estimation accuracy.

The invention discloses a super-resolution communication perception integration method and system based on sparse MIMO, and belongs to the field of wireless communication and signal processing. Signals emitted by all users are received through a sparse array, and signal processing is carried out to estimate the two-dimensional angle of arrival of the users; pairing the two-dimensional angles by using a permutation matrix; a beam forming matrix is constructed by matching the estimated angle, and channel gain is estimated by using a small amount of pilot frequency, so that super-resolution two-dimensional sensing and efficient channel estimation of users in the system are realized. According to the method, the characteristic of large array aperture of the sparse array is combined, the problem of low positioning precision of dense distribution of multiple users is solved under the condition of not increasing hardware loss, a large amount of pilot frequency resources are saved, and the channel estimation precision is further improved.

The invention discloses a method for designing and manufacturing a flexible ultrasonic transducer of a sparse array, and relates to the technical field of transducer manufacturing, a sparse model taking array element number-imaging quality-cost as a target is constructed, and a genetic algorithm is utilized to carry out non-uniform array element arrangement optimization; positive and negative electrodes are manufactured by adopting a microelectronic printing technology, an S-shaped negative electrode loop is designed to reduce crosstalk, PDMS is spin-coated in a layered manner to realize light and thin packaging, and acoustic impedance matching and bendability are improved in combination with a flexible backing material; the method comprises the following steps: simulating a wearing scene, identifying an ultrasonic signal distortion rate, calculating a signal-to-noise ratio and a resolution ratio in real time based on pulse echo data, adjusting an array element driving phase through feedback, and carrying out dynamic calibration optimization. According to the flexible ultrasonic transducer, the imaging performance and reliability are remarkably improved while the manufacturing cost is reduced through sparse, multi-objective optimization and dynamic calibration technologies, and the applicability of the flexible ultrasonic transducer in the fields of wearable medical treatment, industrial detection and the like is expanded.

The invention relates to the technical field of ultrasound, in particular to an ultrasonic phased array sparse arraying method. Comprising the following steps: firstly, constructing a planar rectangular ultrasonic array model and determining basic parameters; setting a fitness function by taking a minimum maximum sidelobe level (MSLL) as an optimization criterion; secondly, introducing a tent chaotic mapping-good point set mixing method to obtain a classical solution population; a quantum bit coding strategy is introduced in the coding stage, individuals in the population are expressed as quantum bits, a standard NOA strategy is executed on the classical solution population to update the population, then the update quantity is synchronized to the quantum bits, and finally a new generation of classical solution population is generated through quantum rotation and collapse operation and the fitness is evaluated; integrating a covariance matrix adaptive evolution strategy (CMA-ES) in the optimization iteration process, and dynamically adjusting the shape, direction and step length of search distribution; and finally updating the global optimum. According to the method, the array element distribution of the sparse array is optimized through the multi-strategy fusion ENOA algorithm, the peak side lobe level of the array can be remarkably reduced, the anti-interference capability and precision of target detection are improved, and meanwhile, the hardware cost of the array is reduced.

The invention discloses an array antenna forming directional diagram synthesis method based on beam space mapping and dimensionality reduction optimization. According to the method, the traditional high-dimensional excitation optimization problem is converted into low-dimensional coefficient optimization by constructing the beam space basis matrix, so that the calculation efficiency of large-scale array antenna shaping directional diagram synthesis is remarkably improved. The specific implementation process comprises the following steps: firstly, establishing a steering vector parameterization model in arbitrary array arrangement, and then constructing a beam space basis matrix containing beams of a forming region and a null region to realize dimension reduction mapping; performing intelligent optimization in a low-dimensional space by adopting an alternating projection algorithm, and synchronously realizing accurate shaping of a main lobe, side lobe level suppression and multi-null control; and finally, obtaining realizable array excitation distribution through beam space inverse mapping reconstruction. Compared with a traditional method, the method can remarkably reduce the dimensionality of the optimization variable, is suitable for any geometric arrangement form such as a conformal array and a sparse array, and can be widely applied to beam optimization design in the fields of 5G communication, phased array radar and the like.

The invention relates to a sparse array antenna, a construction method and device and electronic equipment. Wherein the sparse array antenna comprises a sparse sub-array and a uniform sub-array, and the sparse sub-array and the uniform sub-array are located at a group of diagonal positions of the same rectangle; the sparse sub-array comprises a first antenna array, a second antenna array, a third antenna array and a fourth antenna array, and a first straight line where the first antenna array and the second antenna array are located is perpendicular to a second straight line where the third antenna array and the fourth antenna array are located; the uniform sub-array comprises a fifth antenna array and a sixth antenna array, and a third straight line where the fifth antenna array is located is perpendicular to a fourth straight line where the sixth antenna array is located. The array structure of the sparse array antenna provided by the embodiment of the invention has a closed-form solution, so that unique deterministic structures of various scales can be realized; moreover, the structure enables the number of FODC virtual array holes of the sparse array antenna to be reduced, and effectively improves the degree of freedom.

The present invention discloses a MIMO radar sparse array optimization method based on a sleep monitoring scenario. The sparsity of the array elements is determined in combination with the actual sleep scenario, and the two-dimensional beam angle is constrained. The full angle scan is achieved by changing the beam pointing within the constrained angle, and the highest sidelobe level value of the directional pattern under all angle pointing is obtained. The multi-objective particle swarm iterative model is used to solve the optimal array element spacing, and the excitation coefficient is solved in combination with the convex optimization algorithm, and finally the array element position and excitation coefficient matrix are obtained. Compared with the uniform array, this method reduces the number of array elements, reduces the hardware implementation cost, weight and pressure, and improves the angular resolution; compared with the traditional sparse array algorithm, this method can determine the sparsity of the array elements in combination with the actual sleep scenario, and solves the problem that when the beam pointing changes in the traditional sparse array, the directional pattern will have high sidelobes and large grating lobes, ensuring that the directional pattern under all angle pointing within the constraint range has a low sidelobe level.

The present invention discloses a joint multi-beam angle radar high-resolution imaging method based on the least squares method. By establishing a complete signal model, this method incorporates the sidelobe and grating lobe signals of the transmitted beam into the imaging process, avoiding treating them as interference but rather as an effective supplement to the reflected information of the target direction. The present invention proposes a joint estimation method based on the least squares method, which significantly reduces sidelobe artifacts, improves angular resolution, and alleviates the interference problem of grating lobes in sparse arrays by performing overall optimization processing on the echo signals of multiple transmitted beam angles. This method can effectively improve the imaging resolution of millimeter-wave radar in complex scenarios and has broad practical application potential.

The present application relates to the field of structural damage online monitoring, in particular to a near-field acoustic emission source positioning method based on orthogonal matching pursuit under a sparse array, comprising the following steps: step 1, acoustic emission signal acquisition and preprocessing; step 2, dimension reduction process of the near-field acoustic emission source; step 3, near-field acoustic emission source positioning based on orthogonal matching pursuit; and step 4, near-field acoustic emission source positioning. The present application provides a crack tip positioning method without judging the sensor wave arrival time, which can effectively improve the positioning accuracy.

The application discloses a non-uniform noise background-based sparse array gridless parameter estimation method, and the implementation steps are as follows: a sparse array structure is arranged at a receiving end; a sparse array receiving signal in a non-uniform noise background is modeled; a covariance matrix of the array receiving signal is calculated; receiving data of a virtual differential common array is calculated through vectorization and de-redundancy; a distribution to which a fitting error is subjected is derived, and a parameter estimation optimization model based on atomic norm minimization and avoiding a complex regularization parameter determination process is constructed; a semi-positive definite programming form of a primal problem and a dual problem is derived; and a wave direction estimation result is obtained through a MUSIC algorithm or polynomial root seeking. In addition, on the basis of fully utilizing a virtual differential common array of the sparse array to provide extended degrees of freedom and all information, the application restrains the influence of non-uniform noise by constraining the sparsity of data atoms to be recovered and selecting a regularization parameter from a chi-square distribution table according to a distribution to which a fitting error between the data to be recovered and actual receiving data not affected by noise is subjected, overcomes the grid mismatch problem caused by parameter domain discretization, avoids a complex process of determining the regularization parameter, and can obtain a higher-precision source direction estimation result.

This document describes techniques and systems for radar systems with an angle determination process for sparse uniform arrays. The described radar systems include a processor and an antenna that can receive electromagnetic energy reflected by an object in a surrounding environment. The antenna includes a one-dimensional (ID) or two-dimensional (2D) sparse array. The processor can use the received electromagnetic energy to determine a signal subspace associated with the object that includes an invariance equation. Using an estimated solution of the invariance equation, the processor determines a solution of the invariance equation. The solution of the invariance equation is used to determine an angular phase associated with the object. The processor can then use the angular phase to determine an angle associated with the object. In this way, the described angle determination process enables a radar system with a sparse array to efficiently determine an angle associated with an object without blind spots.

The invention discloses a V-shaped co-prime array two-dimensional direction finding method and system based on array transformation. According to the method, firstly, array improvement is carried out on the basis of a traditional co-prime array; secondly, receiving a signal through an array antenna of a sparse array structure after array improvement, and obtaining a noise subspace through covariance matrix solving and eigenvalue decomposition; and finally, constructing a MUSIC spectral function, searching to obtain a joint pitch angle and an azimuth angle based on the noise subspace, and finally calculating a DOA estimated value of the information source. The array structures VCACS and VCACSS provided by the invention have a larger degree of freedom and fewer array elements to carry out two-dimensional parameter estimation, so that the azimuth angle and the elevation angle do not influence each other when the azimuth angle and the elevation angle are estimated respectively, and the estimation result is more accurate.

This invention discloses a sparse array optimization method and related equipment based on amplitude mapping. The method includes: acquiring array information and preset sparse constraints of an antenna array; initializing an amplitude weight matrix based on the array information and sparse constraints; mapping the amplitude weight matrix to a sparse binary layout; quantizing the sidelobe level based on the sparse binary layout; if the sidelobe level meets preset requirements, the corresponding sparse binary layout is used as a sparse array; otherwise, the amplitude weight matrix is ​​updated through an optimization algorithm, and the step of mapping the amplitude weight matrix to a sparse binary layout is returned to be executed until the sidelobe level meets the preset requirements. This invention transforms the discrete, massive cell position combination search problem into a continuous, relatively low-dimensional amplitude weight optimization problem, which can greatly compress the solution space and thus efficiently complete the optimization of large-scale arrays. It can be widely applied in the field of data processing technology.

The invention discloses a four-stage reconstruction-based sparse double-station MIMO radar parameter estimation method, which mainly solves the problems of limitation of only passive receiving signals and insufficient degree of freedom of a uniform array in the existing sparse array research, and comprises the following implementation steps of: modeling a sparse double-station MIMO radar signal; a first step of sparse double-station MIMO radar covariance reconstruction; carrying out secondary-step sparse double-station MIMO radar covariance reconstruction; 3, reconstructing a sparse double-station MIMO radar covariance; reconstructing a tail step sparse double-station MIMO radar covariance; and realizing two-dimensional direction of arrival and two-dimensional direction of departure estimation based on the reconstructed covariance corresponding to the uniform double-station MIMO radar. According to the method, the four-stage covariance matrix reconstruction technology is effectively utilized, an efficient solution is provided for joint estimation of the two-dimensional direction of arrival and the two-dimensional direction of departure of the sparse double-station MIMO radar, and the method can be used for active target detection and positioning under the underdetermined condition.

The invention discloses a broadband two-dimensional sparse array layout optimization method based on compressed sensing and subarray optimization, which comprises the following steps of: firstly, optimizing broadband two-dimensional uniform array directional diagram representation based on a two-dimensional subarray optimization method, constructing an optimization model of a sparse two-dimensional array based on L0-norm, setting a two-dimensional subarray constraint condition meeting a beam forming requirement, and constructing a sparse two-dimensional array layout optimization model based on L0-norm; and generating a two-dimensional array coefficient by using the optimization model, carrying out sparsity check on the two-dimensional array coefficient, and finally determining an optimal coefficient according to the generated two-dimensional sparse array to obtain a finally determined two-dimensional sparse array. According to the scheme of the invention, the broadband two-dimensional sparse array arrangement optimization method based on compressed sensing and subarray optimization is provided, and a two-dimensional subarray optimization algorithm and an L0-norm compressed sensing algorithm are combined. According to the method, the array element number of the large-scale two-dimensional array can be efficiently compressed, the beam width, the main-to-auxiliary ratio and the broadband response characteristic of the main lobe of the two-dimensional array can be restrained at the same time, and hardware overhead can be remarkably saved.

The present invention provides a shortwave signal direction-finding method and device for an irregular sparse array, belonging to the technical field of shortwave communication direction-finding. Addressing the direction-finding ambiguity problem in irregular sparse arrays, the present invention employs an array arrangement of antenna elements with different horizontal and vertical positions, simultaneously eliminating angular ambiguity in both azimuth and elevation. A dual-polarized antenna is used to receive incoming signals for direction-finding, fully utilizing the two polarization components of the shortwave skywave signal to improve direction-finding capability. Peak correlation between the current phase difference vector and expected phase difference vectors in different azimuths is used to determine the direction-finding azimuth angle range, reducing the computational complexity of DOA estimation. Azimuth consistency judgment and DOA spectrum peak size comparison methods are used to fuse the direction-finding results to obtain a correct direction-finding result. This method can effectively resolve the direction-finding ambiguity problem in irregular sparse arrays, improve the direction-finding capability for incoming signals with composite polarization patterns, and achieve both accuracy and real-time performance.

The present application relates to the technical field of array signal processing and adaptive beamforming, and particularly relates to a sparse array selection and robust beamforming method under angle perturbation conditions.S1, a candidate array element set is obtained, and a signal model and a common angle perturbation model are established;S2, a common angle perturbation scene sample is generated, and a scene average interference plus noise covariance matrix is calculated based on the scene sample;S3, a scene average sidelobe penalty matrix is constructed in the sidelobe angle domain;S4, a composite robust matrix is constructed, and robust MVDR weight values are solved;S5, a performance index is calculated, and quantile results are counted;S6, a greedy array selection is performed based on the quantile index, and an array selection result is output.The present application improves the low tail reliability of output SINR under perturbation conditions, reduces the probability of accidental failure, suppresses the sidelobe peak outlier risk, improves the sidelobe controllability of endfire / edge angles, realizes the adjustable trade-off of "robust reliability-sidelobe risk", and avoids combination exhaustion.

The application discloses a two-dimensional sparse array single-shot DOA estimation method based on a deep unfolding convolutional network, and belongs to the technical field of cross array signal processing and deep learning. First, a two-dimensional uniform planar array is constructed, and sparse observation data are constructed. Then, an isomorphic tensor mapping mode of separating real parts and imaginary parts is adopted to decompose the sparse observation signals and splice them along the channel dimension, so that a three-dimensional input tensor containing real part channels and imaginary part channels is constructed to maintain the two-dimensional spatial topological structure of the array. Then, the overall structure of the deep unfolding network is unfolded, and a sliding window rule with a predetermined size is used to rearrange the two-dimensional array data into a block Hankel matrix. Finally, network training is performed to realize DOA estimation. The application maintains the two-dimensional spatial structure, improves the reconstruction accuracy, reduces the computational complexity, improves the robustness under a low signal-to-noise ratio condition, realizes high-precision DOA estimation under a single-shot condition, and has good engineering application value.

The present application belongs to the technical field of signal processing, and particularly relates to a sparse array angle estimation method based on Bayesian learning and a device thereof, which comprises the following steps: vectorizing the corresponding received signals received by each array element in a sparse array to obtain output signals in vector form, outputting the output signals in vector form by the sparse array to obtain a two-dimensional output signal model, reducing the dimension of the two-dimensional output signal model to obtain a one-dimensional output signal model; extracting a measurement matrix and a two-dimensional spatial spectrum matrix to be recovered from the obtained one-dimensional output signal model; using a Bayesian learning method to construct an optimization function according to the obtained one-dimensional output signal model and the measurement matrix when the two-dimensional spatial spectrum matrix to be recovered satisfies the RIP characteristic; solving the constructed optimization function to obtain a two-dimensional space-time spectrum, accumulating the obtained two-dimensional space-time spectrum in time to obtain a spatial spectrum of a target, and performing constant false alarm detection on the spatial spectrum of the target to obtain an azimuth estimation of the target.

The invention relates to a target DOA estimation method and system for constructing a differential array based on an MIMO radar sparse array. The method comprises the following steps: subtracting any two array element intervals in an original MIMO sparse array to obtain an array element interval array; forming a differential array; for a non-newly-added first array element interval, if the first array element interval is the same as a certain array element interval in the original MIMO sparse array layout, putting a complex signal corresponding to the array element interval to the position of an array element corresponding to the first array element interval to serve as a first complex signal of a differential array; for the newly added second array element spacing, if only one group exists, obtaining a second complex signal based on two array element spacings in the spacing difference value corresponding to the group; if multiple groups of second complex signals exist, averaging the respective module values and phases of the multiple groups of corresponding second complex signals to obtain third complex signals; a complex signal of the differential array is obtained; and performing DOA estimation to obtain a target angle. According to the invention, the DOA angle measurement precision and accuracy are improved.

The invention provides a mobile sparse array optimization method for low signal-to-noise ratio DOA (direction of arrival) estimation, which is suitable for direction of arrival estimation under the condition of low signal-to-noise ratio. According to the method, array receiving signal data models of the sparse array on a horizontal motion platform and a vertical motion platform are respectively established aiming at virtual array element expansion and degree-of-freedom improvement characteristics formed in the motion process of the sparse array, and a corresponding direction of arrival estimation Ziv-Zakai boundary expression is deduced. Compared with a method using a Cramer-Rao boundary as an array configuration optimization performance index, the Ziv-Zakai boundary can more accurately represent a parameter estimation error lower bound in a low signal-to-noise ratio region, and a theoretical performance lower bound is provided for optimization design of horizontal and vertical motion sparse arrays in the low signal-to-noise ratio region. And ZZB is used as a target function for configuration optimization of the mobile sparse array, so that the sparse array with higher DOA estimation precision in a low signal-to-noise ratio scene is designed.

The invention provides a sparse array underground target size non-contact measurement method based on zebra optimization, and belongs to the technical field of underground target non-contact size measurement. The problem that an existing method cannot effectively suppress the sidelobe level is solved. The method comprises the following steps: constructing a measurement signal pattern optimization model of a sparse array; zebra algorithm parameters are initialized, and parameter configuration of each individual in the population is initialized randomly; updating parameter configuration of individuals in the population according to the zebra foraging behavior; according to the anti-predation behavior of the zebra, when a preset triggering condition is met, performing jump updating on parameter configuration of individuals in the population; the fitness of the current population is calculated, if a termination condition is met, optimal array parameter configuration is output, otherwise, the position of the predator is updated, and then the updating step is returned to continue iteration; and utilizing the optimal array parameter configuration to drive a sparse array to detect the underground target, and calculating the size of the underground target based on an echo signal obtained after detection. The method is mainly used in the nondestructive testing field.