Robust adaptive beamforming with null-pattern constraints

Zhuang, Jie; Ni, Lihua; Guo, Jianhua; Tan, Tianhan

doi:10.1016/j.sigpro.2019.107420

Cited by 7 publications

(5 citation statements)

References 25 publications

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“…In Figure 10, we present the output SINR versus transmit SNR for our method and other approaches. Our method achieves the highest output SINR, surpassing NCCB [53], MVDR [16], ADMM [51], SDR [50], and ASM-ADMM [28] by 64 dB, 48 dB, 48 dB, 18 dB, and 21.3 dB, respectively. Those results demonstrate that our AMO-NI has higher signal quality and availability of communication in FDA-MIMO radar systems.…”

Section: Results Of Sinrmentioning

confidence: 93%

“…Half-Power Width Angle (Degree) ↓ Range (km) ↓ NCCB [53] 6.0 14.2 MVDR [16] 10.0 35.0 ADMM [51] 4.0 9.4 SDR [50] 87.0 16.9 ASM-ADMM [28] 5.7 10.8 AMO-NI (Ours) 3.6 8.9…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, we compare our method with some other representative optimization approaches in terms of complexity and running time as shown in Table 2. The compared optimization methods include NCCB [53], MVDR [16], ADMM [51], SDR [50], and ASM-ADMM [28]. It can be observed that (1) in a single iteration, the complexity of the proposed method is O(M 3 K 3 + N 2 ), which is lower than other methods.…”

Section: The Convergence Evaluation and Complexity Analysismentioning

confidence: 99%

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Efficiently Refining Beampattern in FDA-MIMO Radar via Alternating Manifold Optimization for Maximizing Signal-to-Interference-Noise Ratio

Geng,

Li,

Dong

et al. 2024

Remote Sensing

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Joint transceiver beamforming is a fundamental and crucial research task in the field of signal processing. Despite extensive efforts made in recent years, the joint transceiver beamforming of frequency diverse array (FDA)-based multiple-input and multiple-output (MIMO) radar has received relatively less attention and is confronted with some tricky challenges, such as range–angle decoupling and the interaction between multiple performance metrics. In this paper, we initially derive the generalized ambiguity function of the FDA-MIMO radar to explore the intrinsic correlation between its waveform design and resolution. Following that, the joint beamforming optimization is formulated as a nonconvex bivariate quadratic programming problem (NBQP) with the aim of maximizing the Signal-to-Interference-Noise Ratio (SINR) of the FDA-MIMO radar system. Building upon this, we introduce an innovative alternating manifold optimization with nested iteration (AMO-NI) algorithm to address the NBQP. By incorporating manifold optimization into iterative updates of transmit waveform and receiving filter, the AMO-NI algorithm considers the interdependencies among the optimization variables. The algorithm efficiently and expeditiously finds global optimum solutions within a finite number of iterations. Compared with other methods, our approach yields a superior beampattern and higher SINR.

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Section: Results Of Sinrmentioning

confidence: 93%

“…Half-Power Width Angle (Degree) ↓ Range (km) ↓ NCCB [53] 6.0 14.2 MVDR [16] 10.0 35.0 ADMM [51] 4.0 9.4 SDR [50] 87.0 16.9 ASM-ADMM [28] 5.7 10.8 AMO-NI (Ours) 3.6 8.9…”

Section: Methodsmentioning

confidence: 99%

Section: The Convergence Evaluation and Complexity Analysismentioning

confidence: 99%

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Efficiently Refining Beampattern in FDA-MIMO Radar via Alternating Manifold Optimization for Maximizing Signal-to-Interference-Noise Ratio

Geng,

Li,

Dong

et al. 2024

Remote Sensing

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“…According to (21) and a e ≤ ε e , we have max a e |≤ε e ω H (a(θ) + a e (θ)) ≤ ω H a(θ) + ε e ω (22) where the equality holds only if a e (θ) = ε e e jϕ ω/ ω with ϕ = angle ω H a(θ) . The lefthand side of (21) has two terms, one for the ideal pattern function and the other for beam robustness.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

“…Ref. [22] proved that the ripple control over the steering vector uncertainty set can be transformed into a norm constraint for the weight vector, and a doubly constrained robust Capon beamformer (DCRCB) [23] proved to be the preferred choice for applications requiring high SINRs by combining a constant-norm constraint with a spherical uncertainty set constraint. Inspired by these works, Jiang described the uncertainty set as a rhombus to represent the l ∞ -norm constraint and devise a computationally acceptable strategy for efficiently solving largescale l ∞ -beamforming problems via ADMM [24].…”

Section: Introductionmentioning

confidence: 99%

Robust Low-Sidelobe Transmit Beamforming under Peak-to-Average-Power Ratio Constraint

Cai

Chu

Ding

et al. 2023

Sensors

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Transmit beamforming (TBF) provides the capability of focusing illuminating power in the desired directions while reducing the emitting power in undesired directions. It is significantly important in low-altitude and slow-speed small (LSS) radar, which usually suffers from heavy clutter and rapidly changing interference on the near-ground side. Due to nonideal factors such as an inaccurate target direction and array gain-phase error, the robustness of TBF is also necessary to consider in practical applications. In this paper, we provide a robust TBF method that enables sidelobe control in preset regions and possesses high transmit efficiency in virtue of the peak-to-average-power ratio (PAPR) constraint on transmit weights. To achieve robustness, a norm upper bound is introduced to limit the fluctuation of transmit weights, and the steering vector mismatch is also considered by using a spherical uncertainty set surrounding the nominal steering vector. As the proposed robust TBF is nonconvex because of the nonconvexity of both the objective function and constraints, we translate it into a series of convex subproblems via several kinds of convex relaxation schemes. In particular, based on the special structure of the objective function and constraints, the translation of the nonconvex problem into a tractable SOCP problem is realized by using the combination of the triangle inequality and Cauchy–Schwartz inequality. Numerical results demonstrate the improvement in the efficiency and robustness of the proposed TBF method in comparison with traditional TBF methods.

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