# Microwave Staring Correlated Imaging Based on Unsteady Aerostat Platform
###### Abstract  
Microwave staring correlated imaging (MSCI), with the technical capability of high-resolution imaging on relatively stationary targets, is a promising approach for remote sensing. For the purpose of continuous observation of a fixed key area, a tethered floating aerostat is often used as the carrying platform for MSCI radar system; however, its non-cooperative random motion of the platform caused by winds and its unbalance will result in blurred imaging, and even in imaging failure. This paper presents a method that takes into account the instabilities of the platform, combined with an adaptive variable suspension (AVS) and a position and orientation system (POS), which can automatically control the antenna beam orientation to the target area and measure dynamically the position and attitude of the stochastic radiation radar array, respectively. By analyzing the motion feature of aerostat platform, the motion model of the radar array is established, then its real-time position vector and attitude angles of each antenna can be represented; meanwhile the selection matrix of beam coverage is introduced to indicate the dynamic illumination of the radar antenna beam in the overall imaging area. Due to the low-speed discrete POS data, a curve-fitting algorithm can be used to estimate its accurate position vector and attitude of each antenna at each high-speed sampling time during the imaging period. Finally, the MSCI model based on the unsteady aerostat platform is set up. In the simulations, the proposed scheme is validated such that under the influence of different unstable platform movements, a better imaging performance can be achieved compared with the conventional MSCI method. m 2019 21 June 2019 24 June 2019 201  
superior imaging performance without target relative motion, MSCI has attracted increasing attention and made progress in many aspects such as random radiation source optimization [9; 10; 11], imaging algorithm [12; 13; 14] and outfield imaging experiment [15]. At present, research on MSCI depends on the premise of an ideal stable imaging platform, i.e., the system platform of the MSCI radar is assumed to be stationary. However, it is not guaranteed in practical applications. To observe a fixed area, the MSCI radar needs to be raised to a certain height. A tethered aerostat is suitable to serve as the platform of MSCI radar with advantages of long-stay time in the air, wide coverage area and low cost [16; 17], but it cannot keep absolutely stationary in the air because of the non-cooperative motion caused by wind and unbalance. The platform instability will result in imaging system errors and the imaging performance will be seriously degraded when the random motion of platform becomes intense. The imaging system errors in MSCI have been investigated by many studies, since it generally exists in practice. For example, to compensate the gain-phase error in MSCI, [PERSON] et al. propose a sparse auto-calibration method, which is a cyclic iteration processing combined target reconstruction with gain-phase error estimation [18]. In reference [19], the MSCI with phase error is formulated as a Bayesian hierarchical prior modeling, and self-calibration variational message passing (SC-VMP) algorithm is proposed, which estimates the scattering coefficient and phase error iteratively by [PERSON] and [PERSON]'s method to improve the performance of MSCI with phase error. To estimate the gain-phase error and the synchronization error under high SNR, [PERSON] et al. add a reference receiver to the MSCI system to receive the direct wave signal and the gain-phase error and the synchronization error are estimated by the direct wave signals [20]. In reference [21], a method of strip-mode MSCI with self-calibration of gain-phase errors is proposed to solve the problem of MSCI with gain-phase errors in a large scene. Reference [22] considers the off-grid problem in MSCI and an algorithm based on variational sparse Bayesian learning (VSBL) is developed to solve the MSCI with off-grid problem.