Optimal Subarray Research Articles

A Novel Multi-objective Synchronous Optimal Subarray Partition Method for Transmitting Array in Microwave Wireless Power Transmission

A Novel Multi-objective Synchronous Optimal Subarray Partition Method for Transmitting Array in Microwave Wireless Power Transmission

Transceiver Optimization for mmWave Line-of-Sight MIMO Systems Using Hybrid Arrays

The performance of millimeter wave (mmWave) line-of-sight multiple input multiple output (LOS MIMO) systems using hybrid arrays of planar subarrays was studied. We characterized the achievable maximum spatial multiplexing gain for such LOS MIMO systems by the measures of spectral efficiency and effective degree of freedom (EDoF). By proposing a joint plane-wave and spherical-wave-based general 3D channel model, we derived the optimal design parameters in the analog domain, i.e., the optimal subarray separation products, and analyzed their sensitivity on the system performance. We also gave analytical eigenvalue expressions of the equivalent LOS MIMO channel matrix, which are applicable to the case of a non-optimal design, as well as the upper and lower bounds of the EDoF for system performance evaluation. A piecewise uniform quantization codebook was further designed for quantizing phase shifter values in practical applications. The numerical and simulation results show that planar subarrays are superior to traditional arrays in terms of spectral efficiency and EDoF in Ricean fading channels because they are more robust to the change in the communication distance and the deviation from the optimal design. The use of hybrid arrays of planar subarrays effectively removes the limitation of mmWave LOS MIMO systems using traditional arrays, through which, the conventional Rayleigh distance criterion has to be satisfied to achieve the optimal performance.

A polarization-sensitive array reconfiguration technique resorting to antenna selection for navigation interference mitigation

Polarization-sensitive array (PSA) processing works well in navigation system applications for interference mitigation. However, the fixed configuration of conventional PSA increases the computational burden of subsequent adaptive array processing and can even be inefficient and redundant in some cases. Furthermore, since the radio frequency (RF) front end is more costly than the antenna, it is urgent to exploit a PSA reconfiguration method that can lower the cost of array design and achieve sufficiently good anti-interference performance when employing fewer RF front ends and antennas. In this paper, a PSA reconfiguration technique maximizing the signal to interference plus noise ratio (SINR) is presented to solve the problem. Combining with adaptive processing, we derive the formula between the SINR and the polarization-spatial correlation coefficient (PSCC), thereby building the mathematical model with minimizing PSCC as the objective function. Then, the lower bound of the optimal PSCC, gained by the Lagrange Dual Relaxation method, is utilized to guide the selection of the antenna number. Finally, we adopt an iterative correlation measurement (ICM) approach to confirm the antenna element position of the optimal subarray with the minimum PSCC. Numerical simulations illustrate the effectiveness and correctness of the proposed technique and derived theoretical results.

Optimal subarray selection strategy for long towed array based on target range

The larger the aperture of the towed array, the higher the array gain. However, when the aperture dimension is very large, the far field assumption may no longer hold. As such, simply using the plane wave beamforming method with the whole array to obtain the target signal will inevitably cause signal distortion. This article first proposes that in order to obtain less distorted signal, the coherence bandwidth of the beam-domain signal should be larger than the bandwidth of the source signal. Following, this paper analyzes the locating performance of the towed array. It is shown that the bearing estimation is more accurate than the range estimation. However, when the shape of the towed array is almost colinear, the range of the target can be approximately estimated. Hence, this paper puts forward a dynamic aperture choosing algorithm according to the target range, which dynamically selects the optimal subarray to perform appropriate beamforming method by trading-off between the array gain and the coherence bandwidth. The sea trial data verifies the feasibility of estimating the target range by long towed array when the tow ship moves slowly in a straight line. Simulation results show that this algorithm has better performance than previous methods when the targets are located at any range.

Cognitive radar antenna selection via deep learning

Direction-of-arrival (DoA) estimation of targets improves with the number of elements employed by a phased array radar antenna. Since larger arrays have high associated cost, area and computational load, there is a recent interest in thinning the antenna arrays without loss of far-field DoA accuracy. In this context, a cognitive radar may deploy a full array and then select an optimal subarray to transmit and receive the signals in response to changes in the target environment. Prior works have used optimisation and greedy search methods to pick the best subarrays cognitively. In this study, deep learning is leveraged to address the antenna selection problem. Specifically, they construct a convolutional neural network (CNN) as a multi-class classification framework, where each class designates a different subarray. The proposed network determines a new array every time data is received by the radar, thereby making antenna selection a cognitive operation. Their numerical experiments show that the proposed CNN structure provides 22% better classification performance than a support vector machine and the resulting subarrays yield 72% more accurate DoA estimates than random array selections.

Design of Clustered Planar Arrays for Microwave Wireless Power Transmission

The design of microwave wireless power transmission systems sometimes requires the synthesis of large phased arrays, and the objective is to obtain the maximum beam collection efficiency (BCE). To reduce the feeding network complexity as well as system costs, subarray partition is usually indispensable, which is especially essential for large arrays in the context of long-range and high-power transmission applications. Through minimizing the difference between the subarrayed BCE and the reference one, the subarray partition problem can be reduced to an excitation matching one. Toward this end, a clustering method named k-means algorithm is adopted to determine the optimal subarray configuration. To assess the effectiveness and efficiency of the proposed method, representative examples on different sizes and shapes of transmitting arrays and receiving regions are presented. Moreover, comparisons with the state-of-the-art technique are reported as well.

基于改进遗传算法的相控阵雷达最优子阵划分

基于改进遗传算法的相控阵雷达最优子阵划分

A Novel ESPRIT-Based Algorithm for DOA Estimation with Distributed Subarray Antenna

Distributed subarray antennas (DSAs), which are sparse arrays consisting of two or more far separated subarrays, have many advantages over uniform linear array, especially for direction of arrival (DOA) estimation. However, they are subject to manifold ambiguity, which has significant influence on DOA estimation. In order to solve the manifold ambiguity of uncorrelated sources for DSA, a novel method based on estimation of signal parameters via rotational invariance technique by utilizing optimal subarray partition is proposed in this paper. In the proposed method, an optimized reference estimation is obtained by the rotational invariance between the new subarrays with optimal partition of DSA. The high accuracy and unambiguous DOA estimations are then disambiguated easily according to the optimized reference estimation. In this way, the performance of disambiguated DOA estimation can be enhanced in cases of the low signal-to-noise ratio and the large spacing between the subarrays. Moreover, the ambiguity threshold effect of DOA estimation for DSA is analyzed by means of the maximum a posteriori estimator. Computer simulation results show good performance of the proposed method and the effectiveness of the ambiguity threshold for DSA.

Optimal Frequency Diverse Subarray Design With Cramér-Rao Lower Bound Minimization

This letter proposes an optimal subarray design strategy for frequency diverse array (FDA) radar target localization via Cramer-Rao lower bound (CRLB) minimization. To decouple the range and angle response of targets, we divide the FDA transmit array elements into multiple subarrays. For a given number of array elements and subarrays, the optimal array division and frequency increments design strategies are formulated as a constrained CRLB minimization problem. Then it is converted into an unconstrained optimization problem and further resolved by iteratively utilizing the Nelder–Mead algorithm. Numerical results show that, under the same condition, FDA radar localization performance is significantly improved by employing the CRLB minimization strategy.

Cluster Analysis for the Synthesis of Subarrayed Monopulse Antennas

The design of monopulse radar systems requires the synthesis of both a sum pattern and a difference pattern, which is quite flexible and hard to be optimized for subarrayed antennas. In this study, innovative approaches to synthesize the optimal sum and difference patterns independently for subarrayed monopulse arrays are presented. By exploiting the relationship of the subarray partition and optimal excitations, the synthesis problem is formulated as the clustering of a set induced by the optimal excitations. Thus, the optimal subarray partition is transformed to a clustering problem. A clustering method is presented to solve the array synthesis with uniform excitation at element level. Furthermore, an improved approach is proposed to synthesize low sidelobe patterns. When compared with several classical approaches, various numerical experiments validate the effectiveness and potentiality of the proposed methods.

Sidelobe reduction of the low profile multi-subarray antenna by genetic algorithm

Abstract In this paper, the sidelobe of the low profile multi-subarray antenna for satcom on-the-move (SOTM) is reduced by genetic algorithm (GA). The characteristics of the antenna are analyzed theoretically. It is indicated that the sidelobe level of the antenna is difficult to be reduced by amplitude tapering for its dispersed framework. In order to suppress the sidelobe level of the antenna in the whole elevation scan range, the subarray distances are optimized by GA. Two types of the antennas, the fixed-space and the active-space multi-subarray antennas, are analyzed respectively. Then the effects of amplitude perturbation on sidelobe cancellation are discussed under two conditions, i.e., the initial subarray distances and the optimal subarray distances. Simulation results prove the validity of the theoretical analysis, show that the perturbation of subarray distances can suppress the sidelobe level effectively, and the perturbation of amplitude is almost ineffective in sidelobe reduction. The conclusions can be used as the reference to the design of the multi-subarray antenna and other antennas whose array calibrations are discontinuous.

Adaptive processing at the subarray level

This paper describes a class of partially adaptive arrays with adaptive processing applied to the outputs of steered subarrays. The problem is to detect a signal or estimate its direction of arrival in the presence of jammers. The advantage of applying adaptive processing to subarrays is that it requires much less CPU time than the corresponding fully adaptive processing. The subarray processing equations for the two kinds of problem are described. In this paper, we compare partially adaptive processing performance with fully adaptive processing performance in the case of the following antenna and signal sources: • - array with regularly spaced sensors ; • - between 20 and 100 elements ; • - between 3 and 20 subarrays ; • - a single jammer ; • - desired signal from the antenna zenith. We suggest a method for determining the optimal subarray configurations in this case. An example is given to show that the performance of an antenna with five subarrays is comparable to that of a fully adaptive thirty-element array for eliminating a single jammer with a target at the zenith.