Robust Least Squares Constant Modulus Beamforming with Sparse Constraint

Calculation of beamforming for the HF radar with the two-element crossed-loop/monopole antenna array

“…[18,19] Digital beam formation (DBF) is a signal processing technology which can form beam digitally. [20][21][22][23] The signal in the direction which the beam pointed through the DBF algorithm can become stronger than the original signal. [22] Since the DBF result of the two-element crossed-loop/monopole array can provide narrower beam width than the DBF result of the single crossed-loop/ monopole antenna, [24] the two-element crossed-loop/monopole array is extremely important for the wave measurement.…”

A robust adaptive beamforming method based on iterative optimization algorithm

“…[1] Therefore, many approaches have been proposed to overcome the mismatch problem and improve the robustness of the adaptive beamformer. [2][3][4][5][6] In [2], Zhuang proposes a robust beamforming method which assumes the true steering vector belonging to a known subspace which can be used to design a robust array beamformer mitigating the problem of poor signal-subspace estimation. Nai proposes a framework based on the use of a set of beam-pattern shaping constraints.…”

“…[3] In [4], Elnashar additionally enriches the proposed robust adaptive beamformer by imposing a cooperative quadratic constraint on the weight vector norm to overcome noise enhancement at low SNR. In [5], Li proposed a robust least squares constant modulus beamforming with sparse constraint, which is more efficient than these conventional linearly constrained least squares constant modulus algorithm in the presence of mismatches between the actual and presumed steering vectors. A adaptive beamforming technique based on neural networks (NNs) was proposed.…”

“…[7] Recently, some robust beamforming approaches using as little as possible prior information have been proposed. [1,5] One common ground of these approaches is that they all use an integration matrix related to the array steering vector within some special angular sectors. This implies that in these approaches, the array antenna is assumed without any calibration error.…”

A Robust Direct Data Domain Least Squares Beamforming With Sparse Constraint

“…To mitigate the disadvantage of the high sidelobe level, Xing and Cai [20] set a sidelobe constraint to restrain the sidelobe level within a preset threshold, and the resulting problem is a convex optimization problem, which can be solved by CVX [21]. Recently, sparse constraint on beam pattern has been used in beamforming algorithms [22][23][24] to gain performance improvement. Some modified sparse constraints are used in [25,26] to enhance the performance of sidelobe suppression.…”