Sparse Phased Array Optimization for Airship-Borne Weather Radar

‍ ‍Typhoons are persistent tropical cyclones that often bring gales， rainstorms， and other disastrous weather. On average， approximately seven typhoons occur every year in China， which has severe implications for the densely populated southeast coast and some inland areas. Meteorological radar observation can detect and predict typhoons and other disastrous weather； however， existing meteorological radars are mostly ground-based phased array radars with long-distance coverage but low spatial resolution. They cannot observe the internal structure of typhoons clearly， resulting in inaccurate predictions of typhoon intensity and travel path. The National Natural Science Foundation （NSF） of China’s Major Research Instrument Development Project （referred to as the “Big Instrument” project） undertaken by Beihang University proposed to reduce the losses caused by typhoon disasters by using near-space airships equipped with meteorological radars to track and detect typhoons， providing long-term and detailed observation data on the internal structure of typhoons. Unlike ground-based weather radars， the unique platform limitations of near-space airships constrain the cost parameters of meteorological radars， such as weight， power consumption， and size. However， achieving high-performance typhoon detection urgently requires large aperture arrays. Therefore， the technical difficulty of airship-borne typhoon detection radar lies in achieving the highest performance at the lowest cost. Supported by the NSF “Big Instrument” project， this study proposes a sparse phased array meteorological radar that can achieve high-resolution detection while reducing system overhead. The main contributions of this study are summarized as follows： First， the time series of meteorological echoes received by a single antenna was simulated and then extended to simulate the echo data of multi-antenna phased array meteorological radars， with the accuracy verified via the Doppler analysis. Second， we considered the sparse phased array via antenna selection from a uniform counterpart， decreasing the number of elements to reduce the hardware cost while maintaining high-resolution meteorological detection. Finally， the array configuration and beamforming weights were jointly optimized. The weight coefficients are entailed to satisfy the constant modulus constraint to maximize the radar transmission power efficiency， and the sidelobe level is further reduced to improve the anti-interference performance of the meteorological radar. The simulation and experimental results demonstrated that phased array radars could achieve accurate and efficient meteorological detection， and the introduction of sparse arrays reduced the hardware cost while maintaining a high resolution. Specifically， using the same number of array elements， the optimized sparse array exhibited superior performance to its uniform counterpart in estimating meteorological echo reflectivity， Doppler frequency， and spectral width.