The design of irregular subarrayed planar phased arrays composed of modular tiles is a research area of increasing interest. In this article, a heuristic iterative convex relaxation programming (H-ICRP) framework is proposed. By formulating the tiling of irregular subarray as a binary programming problem, this framework is suitable for arbitrary array aperture and subarray structure. To achieve exact aperture cover, the nonconvex binary constraint is relaxed to a convex approximation and solved through multiple iterations. Furthermore, for better performance of the scanning radiation pattern, the multitask Bayesian compressive sensing (MT-BCS) solver is adopted to find a heuristic initial iteration point instead of randomizing, such that the robustness and effectiveness can be improved remarkably. Good performance and potentialities of the proposed method are verified by comparing with existing approaches in various numerical examples.

Subarray design technique is widely employed in the design of planar phased arrays. However, it's hard to obtain full coverage and good radiation performance with the given shape of subarrays and array aperture. In this letter, the subarray design problem is formulated as an iterative convex relaxation model and solved through some steps subtly. Besides, the scanning sidelobe level (SSL) is adopted to retrieve the optimal configuration among the found solutions. Numerical experiments are carried out to assess the performance of the proposed method as well as the inherent superiority in adaptive beamforming.

The four-channel monopulse (FCM) technique enables a phased array radar (PAR) to track the angular location of two targets with reduced complexity and thus has been extensively investigated in the fields of radar multitarget tracking. However, the FCM technique has limited practical use because it was originally designed for a four-channel monopulse radar and is suitable only for a PAR configured with a rectangular planar array. In addition, the FCM technique is unable to resolve two targets when they have identical azimuthal or elevation angles due to angular ambiguity. In this paper, we propose a subarray-based FCM (SFCM) method to achieve an efficient, unambiguous, and fast two-target resolution, which is applicable to a PAR with a regular shape, i.e., circular arrays, elliptical arrays, and regular-octagonal arrays. Specifically, the proposed SFCM method consists of two stages: coarse estimation and precise estimation. Coarse estimation is performed by an inscribed rectangular array to initially estimate the angles of the two targets, and then precise estimation is achieved by a closed-form solution. Moreover, the proposed SFCM method can avoid the angular ambiguity issue via coordinate rotation. Furthermore, we prove that the optimal rotation angle is 45° for minimizing the estimation error. In the performance analysis, we evaluate the effectiveness, computational complexity and Doppler effect for the SFCM method. Numerical results demonstrate that the proposed method outperforms its counterpart FCM method in accuracy, applicability, and robustness.

Frequency diverse array (FDA) can offer new degrees-of-freedom, but the target localization of basic FDA is fundamentally limited by the range-angle-coupled and rangeperiodic peaks of beampattern. In this paper, we propose a localization system with the combination of subarray-based FDA and full-band FDA (FB-FDA), as the transmitter and the receiver respectively. The transmit and receive beampatterns are both decoupled in range and angle domains. Target rangeangle localization is achieved by two steps. Firstly, using the outputs of subarrays, we solve the range ambiguity and obtain a coarse location. Secondly, the monopulse processing of rangeangle localization is developed for the proposed system by taking the coarse location as the receiving beam pointing. The localization performance is also analyzed in Cramér-Rao lower bound (CRLB), computational complexity and output signal to interference-plus-noise ratio (SINR). Several simulation results demonstrate that unambiguous target range-angle localization can be achieved with a low computational cost by the proposed method.

In this letter, a general framework of cognitive resource allocation for target tracking in radar networks is proposed. Firstly, an allocation strategy evaluation metric is established in this framework, i.e., the predicted conditional Cramer-Rao lower bound (PC-CRLB), for evaluating the future response of each candidate allocation strategy. Then, the optimal resource allocation strategy can be found by solving a constrained optimization problem. To specify the framework implementation details, a dwell time allocation problem is considered. For this given problem, the analytical expressions of PC-CRLB and its derived scalar strategy evaluation metric are derived, enabling us to convert the dwell time allocation problem to a second-order cone program (SOCP). Numerical results demonstrate the effectiveness of the proposed framework.

Four-channel monopulse (FCM) technique is integral to contemporary radar systems for pinpointing and tracking multiple targets. However, the practical utility of FCM is significantly hindered by the issue of estimation failure, which arises from the specific relative phases and positions of the targets. In this paper, we introduce a polarimetric four-channel monopulse (PFCM) approach designed to deliver efficient, robust, and unambiguous resolution of two targets. Generally, PFCM adaptively modulates the null-polarization for each target. We begin by dissecting the limitations of the traditional method, utilizing the closed-form solution of FCM. Subsequently, we devise a novel closed-form solution that harnesses polarimetric adaptive nulling (PAN) technology to estimate the angles of the two targets under two distinct scenarios: the ideal condition and angular ambiguity. Under ideal conditions, estimation is conducted directly using the received signals. Incorporating polarization information mitigates the estimation results’ sensitivity to relative phase. To counteract the challenges of PAN failure in angular ambiguity situations, the proposed PFCM adeptly resolves the two targets by reconstructing the four-channel signal, indicating its applicability across various relative target positions. Furthermore, we present a comprehensive performance analysis focusing on three critical aspects. As a result, two closely spaced targets can be resolved robustly and unambiguously with minimal complexity. Numerical simulations and experimental results are substantiated to validate the effectiveness of the proposed method.