Adaptive Array Theory with Main Beam Shaping Algorithm for Arbitrary Linear Arrays

Based on adaptive array theory, a main beam shaping algorithm for arbitrary linear antenna arrays is proposed. This method iteratively derives an optimal set of weighting coefficients through numerical optimization techniques (such as gradient descent or convex optimization) to minimize the difference between the weighted shaped radiation pattern and the desired pattern within the main beam region. The algorithm implementation typically involves calculating the array steering vectors, constructing a cost function that quantifies pattern differences, and solving for optimal weights using matrix operations. Furthermore, the algorithm incorporates signal interference and noise considerations through covariance matrix estimation to enhance system performance. Key functions include adaptive nulling for interference suppression and signal-to-noise ratio optimization. Additionally, by increasing the number of antenna elements and optimizing array geometry through spacing and configuration adjustments, the directivity and anti-jamming capability of the antenna array can be further improved. Computational aspects involve array factor calculations and pattern synthesis algorithms. Therefore, this algorithm can not only be applied to antenna design in communication systems but also plays significant roles in radar systems, radio spectrum sensing, and other electromagnetic applications where pattern control is critical.