Search

In correlation-based processing, sparse arrays offer the capacity to resolve a greater number of uncorrelated sources than physical sensors due to the considerable breadth of their difference coarrays, originating from variations in the locations of elements. Consequently, there is significant interest in devising sparse arrays with sizable difference coarrays and expanding the analysis to encompass additional array characteristics like symmetry, resilience, and cost-effective engineering. We present a scalable and systematic methodology for designing large sparse arrays. Considering several attributes and factors, we can address Fractal arrays that were used for low-side lobe antenna array designing and have very low degrees of freedom; hence, sparsity is introduced to design a hole-free difference coarray which not only increases the number of degrees of freedom in fractal arrays but also aids in better beamforming applications and enhanced DoA results due to regularization in coarrays. We develop an innovative sparse fractal array to enhance the accuracy of DoA estimation for predicting a maximum number of uncorrelated sources with a minimum possible actual sensors. First, the 1D sparse fractal array is constructed and then it is extended to a 2D sparse fractal array for both azimuth and elevation angle estimation. Comprehensive robustness analysis is conducted on the proposed sparse fractal array, encompassing one-dimensional (1D) and two-dimensional (2D) configurations, in response to sensor failures. RMSE analysis shows that the proposed 1D and 2D arrays possess the minimum error when used for direction estimation. [ABSTRACT FROM AUTHOR]

In this study, a moving single-station direct position determination (DPD) algorithm based on virtual interpolated arrays is proposed. Existing moving single-station algorithms face challenges such as the incomplete utilization of sparse array apertures and insufficient consideration of mixed circular and non-circular signals. To address these issues, we propose an enhanced gridless DPD algorithm, suitable for multiple mixed circular and non-circular sources. Through constructing a non-zero unconjugated covariance matrix from the non-circular components of the mixed signals, the data dimensionality is expanded, and the gridless method is used to fill the voids in the coarray, significantly improving localization performance. Additionally, a unitary transformation method is applied to reduce computational complexity. This method transforms complex operations into real operations by applying unitary transformations to steering vectors and subspaces. Simulation results demonstrate that the proposed algorithm offers significant advantages in terms of array degrees of freedom and localization accuracy. [ABSTRACT FROM AUTHOR]

In this paper, the direction of arrival (DOA) estimation problem for the unfolded coprime linear array (UCLA) is researched. Existing common stacking subarray-based methods for the coprime array are invalid in the case of its subarrays, which have the same steering vectors of source angles. To solve the phase ambiguity problem, we reconstruct an improved unfolded coprime linear array (IUCLA) by rearranging the reference element of the prototype UCLA. Specifically, we design the multiple coprime inter pairs by introducing the third coprime integer, which can be pairwise with the other two integers. Then, the phase ambiguity problem can be solved via the multiple coprime property. Furthermore, we employ a spectral peak searching method that can exploit the whole aperture and full DOFs of the IUCLA to detect targets and achieve angle estimation. Meanwhile, the proposed method avoids extra processing in eliminating ambiguous angles, and reduces the computational complexity. Finally, the Cramer–Rao bound (CRB) and numerical simulations are provided to demonstrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Based on the principle of spatial power combination of sparse array time reversed signals, the relationship of distances between constructive interference points to cross angle of beams and viewing angles has been analyzed. Then the formulas of distances between adjacent constructive points at 360-degree views are derived. Considering that the distance between constructive interference points around target is frequently needed to be less than target's antenna size, The variation law of the view range with beam crossover angle has been simulated and analyzed with MATLAB when the above conditions are met at the same frequency and without errors. On this basis, array deployment recommendations are summed up. In addition, the effects of the received time window length and the difference of the distance between each array element and the target point on the synthesized power have been analyzed. Finally, the spatial power combination simulation experiments of the sparse array time-reversed signal have been carried out. The experimental results show that the methods and conclusions of this paper can provide theoretical basis and reference for the research and application of sparse array power combination technology in long-range wireless energy delivery, high-power microwave weapons, electronic warfare, etc. [ABSTRACT FROM AUTHOR]

Using sparse matrix for total focusing method (TFM) imaging can improve computational efficiency in non-destructive testing (NDT). In sparse matrix design, binary particle swarm optimisation (BPSO) and genetic algorithm (GA) often fall into local optimal solution, and the computational efficiency decreases significantly with the expansion of array scale. In this paper, a discrete war strategy optimisation (DWSO) is proposed to realise sparse array ultrasonic imaging. This method uses equidistant scatter mapping to real-number encode the array position and limit the search range. Then, the fitness function is constructed with low side lobe peak and narrow main lobe width to obtain the optimal array element distribution. Experiments on standard test block demonstrate the DWSO algorithm has a narrower main lobe width and lower side lobe peaks than BPSO and GA. The imaging time of the sparse array constructed by this algorithm is reduced by more than 65% compared with the full array. As array scale grows, the running time variation of the proposed method is reduced by 73.2% and 77.3% compared with GA and BPSO, which has good adaptability and provides theoretical support for real-time defect detection. [ABSTRACT FROM AUTHOR]

Enhancing imaging performance is one of long-term goals pursued by ultrasonic phased array in the field of nondestructive testing. However, the computational complexity will grow when the imaging methods are improved. In this work, delay-multiply-and-sum (DMAS) is combined with sparse array technique to achieve better imaging performance and maintain the almost same computational cost as sparse total focus method (TFM) needs. Comparison analysis is conducted on the beam pattern and imaging metrics, and the result indicates that sparse DMAS works better over sparse TFM at the same level of imaging time. [ABSTRACT FROM AUTHOR]

The paper considers the design of sparse arrays in the interference active environment to minimise the system complexity and achieve reduced hardware cost. For this, we propose a sparse array design methodology to attain maximum signal-to-interference plus noise ratio (MaxSINR) in the presence of interfering signals. We formulate the optimisation problem as a real-valued quadratically constrained quadratic program (QCQP) with the non-convex $\textstyle\ell_p$ ℓ p norm to promote sparsity, which is iteratively controlled in the proposed approach. We employ the semidefinite relaxation (SDR) technique and the principle of the majorization-minimisation (MM) algorithm to solve the non-convex QCQP problem. The efficacy of the proposed algorithm is demonstrated through the simulation results. The sparse array obtained through the proposed method performs well when compared with the reweighted $\textstyle\ell_1$ ℓ 1 algorithm. [ABSTRACT FROM AUTHOR]

To address the problem of the destruction of virtual array continuity and the degradation of degree of freedom due to missing data in two-dimensional sparse array under the conditions of sensor failures, a two-dimensional DOA estimation algorithm is proposed. Firstly, the virtual array is constructed based on the two-dimensional difference coarray, and then the covariance matrix data is recovered in the form of matrix completion by using decoupled atomic norm minimization to realize the virtual array interpolation. Finally, the SS-MUSIC algorithm is used for the two-dimensional DOA estimation of multiple sources. The proposed method compensates for the effects caused by the failure of physical sensors. It recovers the complete aperture characteristics of the original virtual array and maintains the degree of freedom of the virtual array, which ensures a higher-precision two-dimensional DOA estimation performance. Simulation results demonstrate that under the same number of physical elements and sensor failure, the proposed algorithm can effectively estimate more sources compared with the existing methods, and exhibits higher robustness under the conditions of a small number of snapshots and low signal-to-noise ratio. This approach maximally retains and utilizes the degree of freedom advantage of sparse arrays in the two-dimensional DOA estimation.

The concept of sparse array synthesis has attracted considerable attention due to its potential to reduce hardware costs in array design and enhance the efficiency of array aperture utilization. In contrast to conventional artificial neural networks (ANNs), a more efficient encoder–decoding ANN framework is proposed in this letter for designing sparse linear arrays. The decoder is pretrained with a randomly generated training set, enabling its ability to rapidly predictiction of far‐field patterns. This process considers the actual coupling effect among the antenna elements. The output of the encoder is used as the input of the decoder to synthesize the sparse arrays by providing the desired pattern to the encoder. During the training process, a novel lp ${l}_{p}$‐norm (0

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Aero Weaponry is the property of Aero Weaponry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we propose a tensor-based subspace algorithm for the two-dimensional direction of arrival (2D-DOA) estimation using a sparse array equipped with electromagnetic vector sensors (EMVS). Our approach capitalizes on the multidimensional characteristics of the collected data by arranging its covariance into a fourth-order tensor. Through the application of higher-order singular value decomposition, we improve signal subspace estimation compared to existing methods. To further enhance our algorithm, we integrate spatial rotation invariance techniques and vector cross-product methods. This combination enables automatic angle estimation without the need for pairing and without compromising aperture loss. Our proposed algorithm exhibits superior estimation performance, particularly in challenging scenarios characterized by low signal-to-noise ratios and limited snapshot availability. To validate the effectiveness and enhancements of our approach, we conduct numerical simulation experiments. [ABSTRACT FROM AUTHOR]

An optical phased array (OPA) with 2-D circular sparse array aperture has been proposed and demonstrated in the silicon integrated photonic platform. The sparse distribution of the antenna array can realize no grating lobes in 2-D full field of view (FOV). To achieve fast and accurate phase calibration for OPA, an improved rotating element electric field vector algorithm based on golden section search method (GSS-REV) has also been proposed and verified. The 32-element antenna sparse distribution of the proposed OPA is designed and fabricated. A far-field beam steering measurement across 20° × 20° range features the side lobe suppression ratio (SLSR) of larger than 4.81 dB and a full width at half-maximum (FWHM) of approximately 0.63° × 0.59°. The resolvable points are derived to be ∼1076. The OPA chip has also been demonstrated on range measurement with frequency-modulated continuous-wave (FMCW) system.

In this study, a moving single-station direct position determination (DPD) algorithm based on virtual interpolated arrays is proposed. Existing moving single-station algorithms face challenges such as the incomplete utilization of sparse array apertures and insufficient consideration of mixed circular and non-circular signals. To address these issues, we propose an enhanced gridless DPD algorithm, suitable for multiple mixed circular and non-circular sources. Through constructing a non-zero unconjugated covariance matrix from the non-circular components of the mixed signals, the data dimensionality is expanded, and the gridless method is used to fill the voids in the coarray, significantly improving localization performance. Additionally, a unitary transformation method is applied to reduce computational complexity. This method transforms complex operations into real operations by applying unitary transformations to steering vectors and subspaces. Simulation results demonstrate that the proposed algorithm offers significant advantages in terms of array degrees of freedom and localization accuracy.

In this paper, the direction of arrival (DOA) estimation problem for the unfolded coprime linear array (UCLA) is researched. Existing common stacking subarray-based methods for the coprime array are invalid in the case of its subarrays, which have the same steering vectors of source angles. To solve the phase ambiguity problem, we reconstruct an improved unfolded coprime linear array (IUCLA) by rearranging the reference element of the prototype UCLA. Specifically, we design the multiple coprime inter pairs by introducing the third coprime integer, which can be pairwise with the other two integers. Then, the phase ambiguity problem can be solved via the multiple coprime property. Furthermore, we employ a spectral peak searching method that can exploit the whole aperture and full DOFs of the IUCLA to detect targets and achieve angle estimation. Meanwhile, the proposed method avoids extra processing in eliminating ambiguous angles, and reduces the computational complexity. Finally, the Cramer–Rao bound (CRB) and numerical simulations are provided to demonstrate the effectiveness and superiority of the proposed method.

This article aims to improve the synthesis efficiency and radiation performance of large ultra-wideband (UWB) rotationally symmetric sparse circular planar arrays by using a modified differential evolution algorithm (MDEA). In the proposed MDEA, we adopt a new encoding mechanism in which an individual represents an element position expressed in polar coordinates. Importantly, such an encoding mechanism can facilitate the multiplication calculation for the array factor in the individual being updated while making it easier to meet the given minimum element spacing constraint. Moreover, to cater to the new encoding mechanism, some low-dimensional evolution operators are introduced to avoid the prematurity. In particular, the UWB rotationally symmetric sparse planar array synthesis problem is transformed into the sidelobe suppression problem of the array pattern at the highest frequency under the given array aperture, and the minimum spacing constraints is used to guarantee enough space to place physical UWB antenna elements. Two synthesis examples of UWB sparse planar arrays based on rotationally symmetric structures are presented. The results show that the peak sidelobe level (PSL) obtained by the proposed MDEA is significantly lower than the results obtained by some existing algorithms in an acceptable CPU time, which proves the effectiveness and superiority of the proposed MDEA. [ABSTRACT FROM AUTHOR]

Abstract Sparse arrays are able to generate more lags to extend the array aperture, which is a distinct advantage in mixed-field localization. To exploit these lags, existing algorithms in the known literature can be mainly divided into two types: the subspace-based algorithm and the sparsity-based algorithm. However, the former algorithm cannot fully utilize the time delay information provided by sparse array, and the second algorithm has basis mismatch problem. In this paper, an interpolation processing method based on atomic norm is proposed to solve the sparse array localization problem. The high-order cumulant matrix is reconstructed by the interpolation method to generate an augmented cumulant matrix without holes, which can make full use of all the lags. Then, the atomic norm minimization method is used to recover the sparse matrix after interpolation in a gridless way. The matrix after recovery enables gridless direction-of-arrival (DOA) estimation. After the interpolation reconstruction, more lags can be exploited, the degrees of freedom are further increased. The proposed algorithm can not only make full use of the array receiving information but also avoid the base mismatch problem, and the accuracy of DOA estimation is improved. Numerical simulations verify the superiority of the proposed algorithm compared with the existing algorithms.

Nowadays, sparse arrays have been a hotspot for research in the direction of arrival (DOA). In order to achieve a big value for degrees of freedom (DOFs) using spatial smoothing methods, researchers try to use multiple uniform linear arrays (ULAs) to construct sparse arrays. But, with the number of subarrays increasing, the complexity also increases. Hence, in this paper, a design method, named as the cross-coarray consecutive-connected (4C) criterion, and the sparse array using Q ULAs (SA-UQ) are proposed. We first analyze the virtual sensor distribution of SA-U2 and extend the conclusions to SA-UQ, which is the 4C criterion. Then, we give an algorithm to solve the displacement between subarrays under the given Q ULAs. At last, we consider a special case, SA-U3. Through the analysis of DOFs, SA-UQ can find underdetermined signals. Moreover, SA-U3 can obtain DOFs close to other sparse arrays using three ULAs. The simulation experiments prove the performance of SA-UQ. [ABSTRACT FROM AUTHOR]

An investigation on fractal-based sub-array (FBSA), of isotropic elements, that evolves from two Non-uniform Linear arrays, i.e. the minimum redundancy array and fractal Array, with a hole-free difference co-array shows competitive performance in direction of arrival estimation using (spatial smoothing-) MUSIC algorithm. Numerical experiments demonstrate that the algorithm can resolve 2° separation between sources. Considering mutual coupling, in terms of RMSE, the FBSA performs better than other competing configurations. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Multiband antenna arrays have the capability of effectively working in multiple frequency bands and thus significantly simplify the antenna system. To further reduce the system overhead, this paper discusses the joint design of antenna selection and adaptive beamforming for multiband antenna arrays, where the sidelobe level is also controlled so as to alleviate the effect of unknown sporadic interference. Based on the maximum signal-to-interference-plus-noise ratio (SINR) criterion and sidelobe level constraints, the proposed multiband sparse array design is formulated into a nonconvex constrained nonlinear optimization problem with an l 0 , 2 -mixed norm regularization. This problem ensures that the same antenna positions are selected at all operating frequencies while the beamformer weights of each frequency are optimized independently. By exploiting the semi-definite relaxation and the reweighted l 1 , ∞ -norm approximation, the problem is converted into a series of convex subproblems and is then effectively solved by the proposed iterative reweighted method. Numerical results show that the proposed multiband sparse array significantly reduces the sidelobe levels in all operating frequencies while maintaining the maximum SINR, so it provides superior performance of interference suppression. [ABSTRACT FROM AUTHOR]

Wideband beamformers have been widely studied in wireless communication, remote sensing and so on. Generally speaking, to improve the spatial filtering ability of beamformers, there usually needs more sensors, which implies increased computational complexity and hardware costs. Besides that, wideband beamformers are known to be exceedingly sensitive to sensor mismatches in practice. Nevertheless, there is still a gap in research on the design of robust sparse wideband beamformers. In this paper, a two-step design of this topic is proposed. Firstly, a robust design based on the worst-case performance optimization (WCPO) using circular-model (CM) sensor mismatches is reformulated to address shortcomings of constraint sensitivity. Secondly, inspired by the joint sparse technology in compressive sensing theory, we focus on the sparse design of wideband beamformer. The constraints for the response characteristics and robustness are set from first step, and an iterative algorithm based on reweighted ℓ 2 , 1 optimization is adopted to achieve maximum sparsity of the sensor array. The mainly advantages of the work are that the proposed design exhibits accordant performance in terms of response and robustness, but few sensors compared with the counterpart with uniform array. Moreover, we surprisingly find that the optimized sparse array is also applicable to other design based on WCPO criterion. Simulation results are provided to verify the superior of the proposed methods compared to the existing counterparts. [ABSTRACT FROM AUTHOR]

This paper describes a 79 GHz frequency modulated continuous wave (FMCW) radar system with a sparse array to achieve high angular resolution with fewer Rx channels. The system consisted of one Tx channel and eight Rx channels placed in a nonlinear sparse array. The 79 GHz Tx and Rx chips were designed using a 65 nm CMOS process, and the measured transmission power was 10.93 dBm, conversion gain of the Rx was 30.1 dB, and noise figure is ∼13 dB at a 1 MHz IF signal. The Rx antenna array consisted of a 1×8 array of azimuth directions and a microstrip patch antenna with a simulation gain of ∼16.5 dBi was designed for the Tx and Rx antennas. The chirp time of the system is 100 μs, and the data was given a gain of 20 dB on the baseband board with filtering and sampled at a rate of 5 MSPS with an eight-channel 12-bit ADC. The corresponding ADC data were acquired by the FPGA, and the target distance and angle information were obtained based on the FFT. Subsequently, the distance and angle estimation of the target and angle resolution of the system were verified through measurements. [ABSTRACT FROM AUTHOR]

To address the issue that existing direction-finding approaches perform poorly under impulse noise and do not work well in underdetermined scenarios, a novel sparse array direction-finding approach on the background of impulse noise is proposed in this work. The approach introduces an infinite norm Gaussian kernel to restrain the impulse noise and obtains accurate estimates via the maximum likelihood algorithm. Meanwhile, a novel quantum transient search optimization (QTSO) algorithm is designed to solve the corresponding cost function. In addition, we prove the convergence of QTSO and derive the Cramér–Rao bound of sparse array direction finding in the presence of impulse noise. Compared with some traditional direction-finding approaches, the proposed approach shows excellent performance through simulation results in different schemes, which can also be a general framework to address other complex direction-finding problems. [ABSTRACT FROM AUTHOR]

In order to solve the design problem of wideband wide-angle low sidelobed phased array antenna caused by factors such as large loss, manufacturing process, T/R component size, number of channels and location, a wideband type horn antenna unit in the frequency range of 80~110 GHz is designed in this study, and the antenna standing wave ratio is less than 1.8. Considering the space limitation of T/R components, a 64-element high-gain, low-sidelobe, wide-band and wide-angle scanning array antenna is designed by optimizing the array position, and a small low-profile air waveguide feeding network is designed for the array. The simulation results show that the designed 64 element sparse array has the advantages of wide bandwidth, wide angle, low sidelobes, high gain, easy fabrication and realization, and the gain is higher than 22.6 dB, the sidelobes level is lower than -6.2 dB, and the active VSWR of each element is less than 2 under the scanning range of ±40°. [ABSTRACT FROM AUTHOR]

This study presents a low profile wide band wide angle low sidelobe circular-polarized sparse phased array antenna for K band. The radiation part of the antenna unit consists of a circular patch with parasitic ring radiator and an L-shaped patch. Due to the feed port limitations of the T/R assembly, the antenna is miniaturized through the underlying microstrip cable connection metal through-hole, making the antenna easy to integrate with the internal T/R assembly. At the same time, the sequential rotation technique is applied in the antenna array to expand the axial ratio bandwidth. In addition, considering the constraints of the element layout introduced by T/R assembly, a multi-constrained subarray level optimization algorithm is designed. Only through the optimization of the subarray position, a 400-cell wide band, wide angle and low sidelobe sparse array is designed. Simulation results show that the sparse array achieves normal gain of 30.46 dB, beam width of less than 4.5° and sidelobes level of less than -13.13 dB within ±55° under the sparse ratio of 44.6%, which is suitable for K-band satellite communication phased array antenna. [ABSTRACT FROM AUTHOR]

Due to their ability to achieve higher DOA estimation accuracy and larger degrees of freedom (DOF) using a fixed number of antennas, sparse arrays, etc., nested and coprime arrays have attracted a lot of attention in relation to research into direction of arrival (DOA) estimation. However, the usage of the sparse array is based on the assumption that the signals are independent of each other, which is hard to guarantee in practice due to the complex propagation environment. To address the challenge of sparse arrays struggling to handle coherent wideband signals, we propose the following method. Firstly, we exploit the coherent signal subspace method (CSSM) to focus the wideband signals on the reference frequency and assist in the decorrelation process, which can be implemented without any pre-estimations. Then, we virtualize the covariance matrix of sparse array due to the decorrelation operation. Next, an enhanced spatial smoothing algorithm is applied to make full use of the information available in the data covariance matrix, as well as to improve the decorrelation effect, after which stage the multiple signal classification (MUSIC) algorithm is used to obtain DOA estimations. In the simulation, with reference to the root mean square error (RMSE) that varies in tandem with the signal-to-noise ratio (SNR), the algorithm achieves satisfactory results compared to other state-of-the-art algorithms, including sparse arrays using the traditional incoherent signal subspace method (ISSM), the coherent signal subspace method (CSSM), spatial smoothing algorithms, etc. Furthermore, the proposed method is also validated via real data tests, and the error value is only 0.2 degrees in real data tests, which is lower than those of the other methods in real data tests. [ABSTRACT FROM AUTHOR]

Multibeam planar arrays are investigated as shared apertures for dual functionality in millimeter-wave (mmWave) joint communication and sensing (JCAS), providing time division duplex communication and full-duplex sensing with steerable beams. The conventional uniform planar arrays (CUPA)s have limited angular resolution, whereas the sparse planar arrays (SPA)s are often very costly to implement. In order to have a low-cost aperture with high angular resolution, we propose to design a sparse tiled planar array (STPA) shared aperture. Our proposed solution is modular tiling and uniform at the subarray level but sparse at the aperture level. The modular tiling and sparse design of a planar array are non-convex optimization problems; however, we exploit the fact that the more irregularity of the antenna array geometry, the less the side lobe level (SLL). In a JCAS scenario, we compare the performance of STPA, CUPA. and SPA, regarding the spectral efficiency of a line-of-sight (LoS) included communication link, detection loss rate, and detection accuracy rate for sensing, and the blockage time in case of an overlapping communication and sensing beam. The SPA for comparison has the same size and beamwidth as STPA, but less average SLL and less modular design. The results show that the same spectral efficiency is achieved in the communication link for CUPA, SPA, and STPA. The effect of a smaller beamwidth of the STPA and SPA is reflected in the lower detection loss rate of them compared to that of the CUPA, but the side lobes of these sparse solutions result in errors in the association of the detected and true targets and hence a reduction in the detection accuracy. In such a multibeam solution for JCAS, it is critical to study blockage time, and we show that the STPA and SPA have a 40% shorter blockage time compared to the CUPA when a blocker moves across the LoS of the communication link. Therefore, STPA is a trade-off solution between CUPA and SPA, since it has uniformly distributed antennas within the subarrays as in CUPA, but a sparse solution in the whole aperture as in SPA, which guarantees the same beamwidth and sensing performances as a SPA. [ABSTRACT FROM AUTHOR]

Sparse arrays are able to generate more lags to extend the array aperture, which is a distinct advantage in mixed-field localization. To exploit these lags, existing algorithms in the known literature can be mainly divided into two types: the subspace-based algorithm and the sparsity-based algorithm. However, the former algorithm cannot fully utilize the time delay information provided by sparse array, and the second algorithm has basis mismatch problem. In this paper, an interpolation processing method based on atomic norm is proposed to solve the sparse array localization problem. The high-order cumulant matrix is reconstructed by the interpolation method to generate an augmented cumulant matrix without holes, which can make full use of all the lags. Then, the atomic norm minimization method is used to recover the sparse matrix after interpolation in a gridless way. The matrix after recovery enables gridless direction-of-arrival (DOA) estimation. After the interpolation reconstruction, more lags can be exploited, the degrees of freedom are further increased. The proposed algorithm can not only make full use of the array receiving information but also avoid the base mismatch problem, and the accuracy of DOA estimation is improved. Numerical simulations verify the superiority of the proposed algorithm compared with the existing algorithms. [ABSTRACT FROM AUTHOR]

Sparse arrays are of deep concern due to their ability to identify more sources than the number of sensors, among which the hole-free difference co-array (DCA) with large degrees of freedom (DOFs) is a topic worth discussing. In this paper, we propose a novel hole-free nested array with three sub-uniform line arrays (NA-TS). The one-dimensional (1D) and two-dimensional (2D) representations demonstrate the detailed configuration of NA-TS, which indicates that both nested array (NA) and improved nested array (INA) are special cases of NA-TS. We subsequently derive the closed-form expressions for the optimal configuration and the available number of DOFs, concluding that the DOFs of NA-TS is a function of the number of sensors and the number of the third sub-ULA. The NA-TS possesses more DOFs than several previously proposed hole-free nested arrays. Finally, the superior direction of arrival (DOA) estimation performance based on the NA-TS is supported by numerical examples. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we consider an integrated sensing and communications system assisted by a reconfigurable intelligent surface (RIS). A small number of sparsely placed active sensors are applied in the RIS to perform effective channels and, thereby, enable optimized beamforming for both communications and sensing objectives, namely, establishing reliable communication links with communication users (CUs) and effectively localize targets. The time-varying multipath channels between the RIS and the CU as well as the time-varying channel between the RIS and the targets are estimated by exploiting an interpolated Hermitian and Toeplitz covariance matrix followed by direction-of-arrival estimation using the MUSIC algorithm. Based on such results, we jointly optimize the transmit beamformer at the base station and the unit-modulus RIS passive beamformer. The RIS beamformer is optimized to maximize its minimum beam-pattern gain towards the desired sensing angles subject to the minimum signal-to-noise ratio requirement at the CU. Simulation results verify the effectiveness of the proposed approach, and the performance of different sparse array configurations is compared.

This study developed a novel S-band radar system for planar location applications. High-resolution range imaging and target angle estimation were achieved by using a stepped frequency continuous wave (SFCW) signal and single input multiple output (SIMO) architecture with a linear sparse array layout, respectively. An improved time-frequency method was utilized to link the independent range profile and angle spectrum results to obtain the plane positions of the targets. The radar hardware was composed of the antenna array with one transmit element and five receive elements, an RF transceiver, and a signal processing component. Under the proposed waveform parameters and signal processing scheme, a 16-ms process cycle, 0.3-m ranging error, and 0.4° angle estimation error for target positioning were achieved in field tests. These results demonstrate the effectiveness and advantages of the proposed radar system.

Colocated multiple-input multiple-output (MIMO) radar can transmit a group of distinct waveforms via its colocated transmit antennas and the waveform diversity leads to several advantages in contrast to conventional phased-array radar. The performance depends highly on the degrees available, and element spacing can be deemed as another source of degrees of freedom. In this paper, we study the joint waveform and element spacing optimization problem. A joint waveform and array optimization criterion is proposed to match the transmit beampattern, the suppression range, and the angular sidelobes, under the constraints of minimal element spacing and total array aperture. Meanwhile, the effect of receive beamforming on suppressing mutual correlation between returns from different spatial directions is also incorporated into the optimization criterion. The optimization problem is solved by the sequential quadratic programming algorithm. Numerical results indicate that with more degrees of freedom from array spacings, colocated MIMO radar achieves a better transmit beampattern matching performance and a lower sidelobe level, compared with a fixed half-wavelength spaced array, but the benefits from additional degrees of freedom from array spacing optimization have a limit. [ABSTRACT FROM AUTHOR]

Mutual coupling effect and degrees of freedom (DOFs) play a crucial role in the sparse array design. In this paper, we judiciously configure the dense subarray and sparse subarray of the nested coprime array with displaced subarrays (NCADiS) to obtain an improved NCADiS (INCADiS). Then, the dense subarray of the INCADiS is split into two parts and rearranged on the left and right sides of the sparse subarray of the INCADiS to structure an augmented nested coprime array with displaced subarrays (ANCADiS). We also derive the closed-form expressions for the element locations and the number of uniform DOFs (uDOFs) of the ANCADiS configurations with any number of elements. The proposed array configuration has the same uDOFs as the INCADiS and the prototype optimised NCADiS (PNCADiS), and at the same time achieves reduced mutual coupling. Some theoretical properties are proved, and numerical simulations are included to verify the superiority of the proposed configuration. [ABSTRACT FROM AUTHOR]

Currently, the covariance matrix (CM) or pseudo CM of the received signal is processed to improve the degrees of freedom (DOFs) and estimation accuracy. This is where the second-order cumulant (SOC) in the two-dimensional (2-D) direction-of-arrival (DOA) estimation algorithm based on sparse arrays comes into play. This paper proposes a 2-D DOA estimation approach for non-circular (NC) signals based on fourth-order cumulant (FOC), which fully exploits its inherent benefits in virtual array expansion and denoising white Gaussian noise in comparison with SOC. The proposed algorithm, on the other hand, discards the original 2-D spectral peak search theory and uses the properties of the NC signal and parallel sparse array (PSA) to place the 2-D angle information in Toeplitz matrices for estimation, greatly reducing the computational complexity of processing the FOC without compromising estimation accuracy. Simulation results demonstrate that the proposed algorithm outperforms the conventional algorithms in terms of estimation accuracy while utilizing fewer physical sensors and a smaller array aperture. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Unlike conventional phased array (PA), frequency diversity array (FDA) can perform the beampattern synthesis not only in an angle dimension but also in a range dimension by introducing an additional frequency offset (FO) across the array aperture, thus greatly enhancing the beamforming flexibility of an array antenna. Nevertheless, an FDA with uniform inter-element spacing that consists of a huge number of elements is required when a high resolution is needed, which results in a high cost. To substantially reduce the cost while almost maintaining the antenna resolution, it is important to conduct a sparse synthesis of FDA. Under these circumstances, this paper investigated the transmit–receive beamforming of a sparse-fda in range and angle dimensions. In particular, the joint transmit–receive signal formula was first derived and analyzed to resolve the inherent time-varying characteristics of FDA based on a cost-effective signal processing diagram. In the sequel, the GA-based low sidelobe level (SLL) transmit–receive beamforming of the sparse-fda was proposed to generate a focused main lobe in a range-angle space, where the array element positions were incorporated into the optimization problem. Numerical results showed that 50% of the elements can be saved for the two linear FDAs with sinusoidally and logarithmically varying frequency offsets, respectively termed as sin-FO linear-FDA and log-FO linear-FDA, with only a less than 1 dB increment in SLL. The resultant SLLs are below −9.6 dB, and −12.9 dB for these two linear FDAs, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a joint estimation method for source number and DOA based on an improved convolutional neural network for unknown source number and undetermined DOA estimation. By analyzing the signal model, the paper designs a convolutional neural network model based on the existence of a mapping relationship between the covariance matrix and both the source number and DOA estimation. The model, which discards the pooling layer to avoid data loss and introduces the dropout method to improve generalization, takes the signal covariance matrix as input and the two branches of source number estimation and DOA estimation as outputs, and achieves the unfixed number of DOA estimation by filling in invalid values. Simulation experiments and analysis of the results show that the algorithm can effectively achieve the joint estimation of source number and DOA. Under the conditions of high SNR and a large snapshot number, both the proposed algorithm and the traditional algorithm have high estimation accuracy, while under the conditions of low SNR and a small snapshot, the algorithm is better than the traditional algorithm, and under the underdetermined conditions, where the traditional algorithm often fails, the algorithm can still achieve the joint estimation. [ABSTRACT FROM AUTHOR]

Sparse-array synthesis can considerably reduce the number of sensor elements while optimizing the beam-pattern response performance. The sparsity of an array is related to the degrees of freedom of the array elements. A sparse-array method based on iterative convex optimization and a simulated-annealing expanded array is proposed in this paper. This method transforms the sparse-array problem into a minimum l1 norm problem and obtains the sparse array by solving the convex optimization problem using the primal-dual algorithm. Meanwhile, to improve the degree of freedom, array elements are expanded using stochastic perturbation. According to the simulated-annealing algorithm, the closed array elements are reopened with a specific probability, which is iteratively thinned and expanded. The results show that the proposed method can obtain an extremely sparse array, which is better than that obtained using the existing methods. [ABSTRACT FROM AUTHOR]

Ground-based synthetic aperture radars (GBSAR) are popular instruments widely used for the monitoring of infrastructures. One of the main problems of ground-based interferometric radars is the elevation ambiguity. Multiple-input multiple-output (MIMO) arrays could solve this problem. This work proposes a study on possible MIMO configurations to achieve elevation resolution in ground-based radar measurements. Specifically, two array configurations are compared: a random sparse array suitable for the compressive sensing technique, and a non-uniform array. The two solutions are compared by means of simulations and experimental tests. An ad hoc system has been developed to jointly test the two configurations, and results obtained in a controlled and real urban scenario are shown. It is found that both systems are able to solve elevation ambiguity. The non-uniform array seems to achieve good performance in a general scenario, while the CS processing can outperform the other only after optimization, depending on the specific scenario and application. [ABSTRACT FROM AUTHOR]

Abstract Frequency diverse array‐multiple input multiple output (FDA‐MIMO) radar not only has the advantage of waveform diversity but also can realise azimuth‐range correlation map, so it can realise high precision azimuth‐range joint estimation and has the ability to distinguish different range unit targets in the same azimuth. However, the existing research mainly adopts uniform linear array (ULA) model and mainly focusses on incoherent targets. At present, the elevation‐range joint estimation algorithm of metre wave FDA‐MIMO radar based on ULA has the problems of sharp decline in angle‐range measurement accuracy of ultra‐low altitude targets and large fluctuation of elevation‐range measurement accuracy with changing elevation. In order to solve the above difficult problems, starting from the perspective of reducing the influence of multipath effect by array structure, this paper innovatively combines sparse array with FDA‐MIMO radar, establishes the low elevation‐range joint estimation model of metre wave FDA‐MIMO radar based on sparse array, and puts forward the low elevation‐range joint estimation method suitable for this model, and it fills the gap of low elevation‐range measurement method based on sparse array metre wave FDA‐MIMO radar. Simulation experiments compare two typical sparse arrays with ULA of equal array elements, which verify the superiority of low elevation‐range measurement performance of monostatic metre wave FDA‐MIMO radar based on sparse array and the effectiveness of the proposed method.

A single-element antenna is usually unable to meet the communication requirements of the wireless system and the main solution is to use an array antenna. The desire to reduce the weight and cost of the array antenna gave rise to the sparse array. A linearly polarized sparse array antenna with ultra-wideband wide-angle scanning characteristics based on a genetic algorithm (GA) is developed in this paper. The antenna array consists of 40 units, which are arranged by a rectangular array with a side length of 300 mm. Compared to the conventional periodic array, the proposed sparse array has reduced about 190 antenna elements. The sparse array can achieve S11 < -10 dB in the ultrawideband of 2.7-12 GHz (120%). Experimental results for the proposed antenna exhibit an azimuth scanning angle of ±45°, and a pitching scanning angle of ±30°. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we consider an integrated sensing and communications system assisted by a reconfigurable intelligent surface (RIS). A small number of sparsely placed active sensors are applied in the RIS to perform effective channels and, thereby, enable optimized beamforming for both communications and sensing objectives, namely, establishing reliable communication links with communication users (CUs) and effectively localize targets. The time-varying multipath channels between the RIS and the CU as well as the time-varying channel between the RIS and the targets are estimated by exploiting an interpolated Hermitian and Toeplitz covariance matrix followed by direction-of-arrival estimation using the MUSIC algorithm. Based on such results, we jointly optimize the transmit beamformer at the base station and the unit-modulus RIS passive beamformer. The RIS beamformer is optimized to maximize its minimum beam-pattern gain towards the desired sensing angles subject to the minimum signal-to-noise ratio requirement at the CU. Simulation results verify the effectiveness of the proposed approach, and the performance of different sparse array configurations is compared. [ABSTRACT FROM AUTHOR]

To address the weakness that the difference co-array (DCA) only enhances the degrees of freedom (DOFs) to a limited extent, a new configuration called the generalized nested array via difference–sum co-array (GNA-DSCA) is proposed for direction of arrival (DOA) estimation. We consider both the temporal and spatial information of the array output to construct the DSCA model, based on which the DCA and sum co-array (SCA) of the GNA are systematically analyzed. The closed-form expression of the DOFs for the GNA-DSCA is derived under the determined dilation factors. The optimal results show that the GNA-DSCA has a more flexible configuration and more DOFs than the GNA-DCA. Moreover, the larger dilation factors yield significantly wider virtual aperture, which indicates that it is more attractive than the reported DSCA-based sparse arrays. Finally, a hole-filling strategy based on atomic norm minimization (ANM) is utilized to overcome the degradation of the estimation performance due to the non-uniform virtual array, thus achieving accurate DOA estimation. The simulation results verify the superiority of the proposed configuration in terms of virtual array properties and estimation performance. [ABSTRACT FROM AUTHOR]

The microwave interferometric radiometer (MIR) uses aperture synthesis technology to equate multiple small-aperture antennas into a large-aperture antenna to improve spatial resolution. At present, MIR antennas that operate at frequencies above the C-band mostly use horn antennas, waveguide slot antennas, etc., which have the disadvantages of a high profile and large mass. In this paper, a new type of miniaturized, low-profile, and lightweight K-band fan-beam microstrip grid antenna is designed for the airborne campaign of the K-band one-dimensional MIR of a Microwave Imager Combined Active and Passive (MICAP) onboard a Chinese Ocean Salinity Mission (COSM). With a limited size constraint (12.33 mm) on the antenna width, a fan-beam shape antenna pattern was achieved with a 5.34° 3-dB beamwidth in the narrow beam direction and up to a 55° 3-dB beamwidth in the fan-beam direction. A periodic structural unit is proposed in this paper to reduce the design complexity of Taylor weighting, achieving desirable performances on gain (19.1 dB) and sidelobe level (<−20 dB) in the H-plane. Four antenna elements were fabricated and arranged in a non-redundant sparse array. The performance of the four-element sparse array was evaluated with a simulation and real measurement in an anechoic chamber. The coupling between antenna elements was less than −25 dB, and the consistency of phase patterns was better than 3.4°. These results verify the feasibility of the proposed K-band microstrip grid antenna for airborne MIR applications. [ABSTRACT FROM AUTHOR]