Hybrid MIMO Phased Array Radar With Center-Spanned Subarrays

Abstract

Hybrid Multiple-Input-Multiple-Output(MIMO) phased array radar, or Phased MIMO radar, is recognized for its capacity to provide trade-off between transmit coherent gain and waveform diversity gain. Such trade-off can be flexibly implemented by changing subarray partition schemes of the system, which in turn enhances system performance. In this paper, a center-spanned subarray configuration is proposed with detailed steps to develop: First, the center element of the whole transmit array is chosen. Next, the surrounding region of center element is determined as the center subarray for subsequent spanning. Finally, the subarray partition is implemented through spanning subarrays around the center subarray. Both theoretical derivations and simulation results reveal that the hybrid MIMO phased array radar with the proposed center-spanned subarray configuration has lower sidelobe beampattern and higher Signal to Interference Noise Ratio(SINR) than that of equally or unequally overlapped subarray partitions. INDEX TERMS Beampattern, center-spanned sub-arrays, phased MIMO radar, SINR.

Covariance matrix reconstruction and steering vector estimation for robust adaptive transmit/receive beamforming in full phased‐MIMO radar

“…In this section, the proposed FPMIMO radar is compared to the conventional HPMIMO with equal subarrays (HPMIMO‐ES) [4], the HPMIMO with unequal subarrays (HPMIMO‐US) [6], and the hybrid MIMO phased array radar with centre‐spanned subarrays (HMIMOPA‐CS) [8]. For the tested HPMIMO radars, receive antenna elements are placed closely with the transmit sensors to form a collocated radar, and the weight vectors are obtained by the MVDR beamformer.…”

“…Compare to the HPMIMO radar with equal subarrays, the HPMIMO radar with unequal partition can improve the output signal‐to‐interference‐plus‐noise ratio (SINR). In [8], a centre‐spanned subarray partition was proposed by developing the centre subarray and spanning elements from its two sides. In [9], the full phased‐MIMO (FPMIMO) radar was introduced with overlapping subarrays on both transmit and receive arrays.…”

“…Simulation results: In this section, simulation examples are performed to verify the effectiveness and robustness of the proposed scheme. Assume that the ULA is composed of N = 16 elements divided into M = 8 In this section, the proposed FPMIMO radar is compared to the conventional HPMIMO with equal subarrays (HPMIMO-ES) [4], the HPMIMO with unequal subarrays (HPMIMO-US) [6], and the hybrid MIMO phased array radar with centre-spanned subarrays (HMIMOPA-CS) [8]. For the tested HPMIMO radars, N receive antenna elements are placed closely with the transmit sensors to form a collocated radar, and the weight vectors are obtained by the MVDR beamformer.…”

An Optimal Steering Vector Generation Using Chaotic Binary Crow Search Algorithm for MIMO System

“…In general, beam forming uses multiple antennas to control the direction of a wave front by appropriately weighting the magnitude and phase of individual antenna signals in an array of multiple antennas. The applications of beam forming are found in numerous fields such as radar, seismology, sonar, and wireless communications [1,2]. The MIMO radar performs multiple transmissions and has receiving antennas that has received enduring attention from researchers due to its capacity of providing diversity to enhance system performance [3].…”

Coherence optimised subarray partition for hybrid MIMO phased array radar

“…The contributions of this work are thus: Current research on array partitions or subarray configurations is concentrated on the performance analysis of the proposed design, whose advantages are attributed to the trade‐off between two gains without an in‐depth discussion on the essential advantage brought by proposed design. Although theoretical derivations are provided in Hassanien and Vorobyov [6] and Li and Wang [15], those propositions focus on validating the improved performance of some particular metrics without depicting the connection between the array configuration and performance. Here, the connections among the trade‐off, cross‐correlations among different waveforms, cross‐correlations among steering vectors and the coherence of array manifold matrix are exhibited so that the effect of array partitioning can be better characterised and the DOF of array configuration can be exploited more flexibly to obtain improved system performance. After characterising the connection between trade‐off and the feature of array configuration, a method for optimising the coherence of an array manifold matrix is proposed, which is equivalent to finding multiple lines with an optimised minimum distance between them, which is equivalent to the Grassmannian line packing problem.…”