104 results about "Thinned array" patented technology

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 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 provides a distributed millimeter wave radar network-oriented DOA estimation method, a medium and equipment, and belongs to the technical field of radar signal processing. According to the method, after a large-aperture MIMO radar system is formed based on a plurality of small millimeter wave radars, a two-dimensional single-snapshot sparse array signal model is constructed by using a steering vector of a two-dimensional MIMO equivalent virtual plane array, and data interpolation and extrapolation processing are performed on aperture missing existing in a sparse array by constructing a block Hankel matrix and applying a matrix completion technology. On the basis, a fast two-dimensional angle estimation algorithm based on phase coherence factor weighting is adopted, and efficient joint estimation of the azimuth angle and the pitch angle of the target is achieved. The method can be flexibly applied to different types of distributed millimeter wave radar networks, effectively overcomes the interference effect of DOA estimation sidelobes and grating lobes in the prior art, and improves the signal-to-noise ratio level of array response.

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 relates to the technical field of millimeter wave near-field imaging, in particular to a near-field imaging method for a sparse MIMO array cylindrical scanning SAR system, and the method comprises the steps: dividing a complete comprehensive array of the SAR system into a plurality of sub-arrays, selecting local echo data corresponding to the plurality of sub-arrays from complete echo data, and calculating the local echo data of each first-stage sub-array in (u, v, v); according to the method, a uniform sampling grid with a uniform sampling rate in an (x, y, z) coordinate system is obtained, the uniform sampling grid is converted into a first-level non-uniform sampling grid in the (x, y, z) coordinate system, a first-level sub-image is reconstructed in the first-level non-uniform sampling grid based on local echo data, and a pairwise coherent superposition process is iteratively executed; and obtaining a three-dimensional imaging result of the target linear sparse MIMO array cylindrical scanning SAR system. According to the invention, the calculation complexity of imaging can be effectively reduced, and accurate and efficient near-field three-dimensional imaging of an arbitrary linear sparse MIMO array cylindrical scanning SAR system is realized.

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 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.

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 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 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 relates to a method for improving angular accuracy of a sparse array antenna of a millimeter wave radar. The method comprises the following steps: obtaining a real receiving channel of transmitting antennas of a millimeter wave radar antenna array comprising three transmitting antennas and four receiving antennas; determining virtual channel cavity positions, and calculating distances between 16 virtual channel positions and the first receiving channel; virtual expansion signals are calculated, and 16 virtual expansion signals are respectively inserted into virtual channel cavity positions to obtain a new uniform antenna array; performing FFT (Fast Fourier Transform) processing on each uniform array signal base output by the new antenna array to obtain angle data of a target; according to the method, the hole signals of the sparse array are complemented through virtual extension, the equivalent uniform linear array is generated, the problems that in the prior art, the cost of a uniform linear array is high, and under-sampling is caused by holes existing in the sparse array are solved, meanwhile, spectrum sidelobes are effectively restrained, and the angle measurement precision is remarkably improved.

The invention relates to the field of arrays, in particular to a V-shaped sparse array based on four-level uniform linear arrays and a DOA estimation method, the V-shaped sparse array comprises two four-level uniform linear arrays, the included angle of the two four-level uniform linear arrays is omega, and the intersection point of the two four-level uniform linear arrays is an original point; and each four-stage uniform linear array comprises four sub-arrays. The DOA estimation method is aimed at the V-shaped sparse array. For the V-shaped sparse array, the invention also provides a DOA estimation method. According to the method, redundancy in the virtual array element generation process is reduced, the array aperture is expanded, virtual array element full coverage from 0 to the maximum array element position can be achieved, and the incoming wave direction can be estimated more accurately.

A method for designing a sparse MIMO virtual antenna array is disclosed. The method includes: determining the size of an equivalent uniform linear array (ULA) based on a target number of sparse elements of the sparse MIMO virtual antenna array; setting a first optimization target as the maximum signal level of one or more side lobes of the ULA when the beam is directed towards a boresight; setting a second optimization target as the maximum signal level of one or more side lobes of the ULA when the beam is directed towards the edge of the field of view; determining a set of separation distances between antenna array elements of the sparse MIMO virtual antenna array, and simultaneously minimizing the first and second optimization targets simultaneously, or minimizing the sum of the first and second optimization targets; and constructing a sparse MIMO virtual antenna array based on the determined set of separation distances.

The invention belongs to the technical field of direction of arrival, and relates to a sparse array coherent signal source DOA estimation method based on Toeplitz matrix reconstruction. Comprising the following steps: S1, assuming that a coherent signal source is incident to a sparse array, further constructing a sparse array observation signal model, and further solving a theoretical covariance matrix of the sparse array observation signal model; s2, performing solution operation on the theoretical covariance matrix in the S1; s2, on the basis of the S2, further constructing a Hermitian Toeplitz matrix through virtual array interpolation, and further realizing DOA estimation based on the virtual array interpolation; and S3, obtaining a virtual ULA based on the sparse array of the S1 by combining the S2, performing solution operation on the virtual ULA, and then constructing a Toeplitz matrix based on physical array interpolation so as to realize DOA estimation based on virtual array interpolation. According to the algorithm provided by the invention, the coherent signal source can be accurately estimated under the condition that the degree of freedom and the array aperture are not lost. Simulation experiment results show that the proposed algorithm is superior to the existing algorithm in the aspect of estimating the coherent source.

The invention belongs to the field of array signal processing, and particularly relates to a mobile sparse array optimal configuration method based on a backward greedy pruning strategy. According to the method, on the basis of a direction of arrival estimation performance evaluation objective function Ziv-Zakai boundary, the characteristics of monotonicity and marginal income decreasing about the number of sensor position sets are deduced. And generating an initial uniform array position set meeting aperture constraints under the condition of given sensor number constraints, adopting a backward greedy pruning strategy, gradually eliminating array element positions which contribute the minimum to performance indexes of the Ziv-Zakai boundary in an iteration process until the preset sensor number constraints are met, and outputting an optimal configuration result of the mobile sparse array. According to the method, the calculation complexity of array configuration optimization is effectively reduced while the array estimation performance is ensured, and the method is suitable for mobile sparse array design under the condition of low signal-to-noise ratio.

The application discloses a DOA estimation method based on a multi-frequency nested MIMO array, and is based on a traditional nested MIMO array and a low-rank matrix completion theory. First, a set of mutually orthogonal multi-frequency detection waves are sent by a transmitting array of the nested MIMO array, are reflected by a target, are received by a receiving array of the nested MIMO array, are subjected to matched filtering, and then a plurality of groups of received signals are generated. A summation cooperative array with uniformly distributed holes is constructed. Signal reconstruction and low-rank matrix completion operations are performed on the summation cooperative array signals, and finally, DOA estimation is performed. Compared with a traditional DOA estimation method based on a sparse array, the DOA estimation method can improve the maximum estimable incident target number and resolution of the array, and reduce complexity, cost and size. Compared with other DOA estimation methods based on low-rank matrix completion and sparse arrays, the application optimizes the array structure for low-rank matrix completion, and improves the array aperture expansion efficiency.

The invention relates to the technical field of array antennas, and provides a sparse array configuration method based on an improved particle swarm algorithm, and the method comprises the steps: building a sparse array population set which comprises a plurality of sparse array populations, and each sparse array population is composed of a plurality of particle array elements; and obtaining a fitness function corresponding to each sparse array population. A target sparse array configuration is obtained based on an improved particle swarm algorithm of a symbiotic strategy, the position parameter sets corresponding to the multiple sparse array populations and the fitness function, the symbiotic strategy is used for updating the position parameter sets, and therefore the particle swarm algorithm is improved through the symbiotic strategy; in the subsequent updating process of the positions of the particle array elements of the sparse array population, the mutual influence relation between the sparse array populations can be considered, local optimum is avoided, and the optimization effect of the antenna sparse array configuration is improved.

The invention discloses a fast deconvolution sound source localization method based on array sparsity and broadband comprehensive processing, which comprises the following steps of: selecting microphones to form a sparse array, and converting a regular focusing plane into an irregular plane to obtain grid coordinates; calculating a sparse array cross-spectrum matrix based on coordinates, obtaining a function beam through eigenvalue decomposition and power exponent operation, and outputting the function beam; constructing a frequency domain deconvolution model according to the convolution relationship between the frequency domain and sound source distribution and the convolution relationship between the frequency domain and a rising power point spread function, and solving a sound source intensity distribution matrix by using fast Fourier transform and iterative optimization; estimating a noise floor-to-signal-to-noise ratio matrix, traversing frequency points, taking the maximum signal-to-noise ratio, and fusing the frequency points into a broadband imaging matrix; a time smoothing factor is introduced to carry out weighted fusion on the inter-frame imaging matrix, and positioning is completed; according to the method, low-consumption, high-precision and strong-robustness sound source localization is realized through array rarefaction and fast deconvolution load reduction, precision protection by means of function beam forming and Gaussian filtering, multi-frequency point fusion adaptive imaging, inter-frame smooth flicker suppression and adaptation to complex scenes.

A DOA estimation method of a delayed co-prime sparse linear array is suitable for signal processing of DOA estimation of radar, sonar and wireless communication, and comprises the following steps: measuring a signal source by using the delayed co-prime sparse array to obtain a signal sample; constructing two uniform subarray segmentation schemes with different delays, and segmenting the signal sample to obtain data; analyzing the segmented data by using a unitary ESPRIT algorithm to obtain a winding phase; and constructing a congruence equation set for each pair of wound phases, and solving the congruence equation set by using a robust Chinese remainder theorem algorithm to obtain a DOA estimated value.

The invention relates to the technical field of radar three-dimensional imaging, in particular to a TDM-MIMO radar signal processing method based on an improved sparse array and related equipment. The TDM-MIMO radar signal processing method based on the improved sparse array comprises the following steps: acquiring first echo data of a moving target based on a first MIMO array; exchanging the positions of a transmitting channel and a receiving channel in the first MIMO array to obtain a second MIMO array, and determining an equivalent reference channel from the second MIMO array; determining a delay increase rate based on the equivalent reference channel; wherein the time delay increase rate represents the increase rate of the echo time delay generated by the moving target due to displacement in the process of switching the adjacent communication channels; and correcting the first echo data based on the delay increase rate.

The invention particularly relates to a sparse array DOA (direction of arrival) estimation method based on an optimized array manifold dictionary, and the method comprises the steps: firstly carrying out the layout optimization of an initial uniform area array through combining a genetic algorithm, constructing an eight-element sparse array structure, and maintaining a directional diagram which is close to a full array while greatly reducing the number of array elements; performing energy screening on multichannel observation signals acquired by the sparse array, and extracting a dominant frequency band to improve an effective signal-to-noise ratio; and an optimized array manifold dictionary is constructed based on a phase compensation mechanism and an amplitude weighting mechanism, and the internal correlation of the dictionary is reduced by minimizing off-diagonal elements of a dictionary Gramer matrix. And on the basis, sparse reconstruction is carried out on the optimized dictionary by using an orthogonal matching pursuit algorithm, so that a direction-of-arrival estimation result of the sound source signal is obtained. According to the scheme, inherent direction finding fuzziness and grating lobes of the sparse array are effectively suppressed, the direction estimation precision is improved, and high reliability and high robustness are still kept under the condition that the number of array elements is remarkably reduced.

The application discloses a V-shaped coprime array two-dimensional direction finding method and system based on array transformation, which first improves the array on the basis of a traditional shape coprime array. Secondly, the array antenna of the array improved shape sparse array structure receives signals, and through solving a covariance matrix and eigenvalue decomposition, a noise subspace is obtained. Finally, a MUSIC spectrum function is constructed, joint elevation and azimuth are searched and obtained based on the noise subspace, and finally, the DOA estimation value of the signal source is calculated. The array structure VCACS and VCACSS proposed in the application have greater degrees of freedom and fewer array elements for two-dimensional parameter estimation, so that the azimuth and the elevation are not affected when the azimuth and the elevation are estimated respectively, and the estimation result is more accurate.

A method for designing a sparse MIMO virtual antenna array is disclosed. The method includes determining a size of an equivalent uniform linear array (ULA) based on a target number of sparse elements of the sparse MIMO virtual antenna array, setting a first optimization target to a maximum signal level of one or more sidelobes of the ULA when a beam is pointed to a boresight, setting a second optimization target to a maximum signal level of the one or more sidelobes of the ULA when the beam is pointed to an edge of a field of view, determining a set of spacing distances between antenna array elements of the sparse MIMO virtual antenna array that simultaneously minimizes the first optimization target and the second optimization target or minimizes a sum of the first optimization target and the second optimization target, and constructing the sparse MIMO virtual antenna array based on the determined set of spacing distances.