DOA estimation with anti-jitter based on DMA heterogeneous codebook cyclic Kalman filtering

The dynamic metasurface antenna (DMA) has become an innovative technology designed to estimate the direction of arrival (DOA) for airborne platforms. However, its performance can be significantly impacted by platform jitter. To address the issue of random angular jitter in airborne DOA estimation, an anti-jitter DOA estimation algorithm based on DMA heterogeneous codebook cyclic Kalman filtering was proposed. Firstly, a scheme for addressing the nonlinearity in received data due to random angular jitter was proposed. This method transformed jitter-induced errors into linear components, facilitating the subsequent filtering of jitter components. Secondly, a heterogeneous codebook cycling scheme was proposed to ensure compatibility between received data and the Kalman filtering algorithm. This involved constructing identical DMA codewords over extended time scales, enabling the Kalman filter to leverage accumulated temporal information for jitter error identification and filtration. Finally, the data processed by the Kalman filter was restored to sparse signals using the atomic norm method. Furthermore, spatial spectrum estimation was performed using the multiple signal classification (MUSIC) algorithm based on Hankel matrix decomposition. The simulation results confirm that, under the exact signal-to-noise ratio (SNR) conditions, the proposed method improves the estimation accuracy by 48% compared to the traditional approach of averaging multiple estimates. It refines the estimation results to approximate the ideal jitter-free state.