Fangcang shelter hospitals – erected by installing medical equipment in large public venues – played an essential role during the COVID-19 pandemic in China. Their isolation, interior density and patients' mutual exposure deviate from normal living conditions, necessitating the study on the adaptation, social organisation and emotional response of patients. For this purpose, we conducted spatial analysis, semi-structured interviews with patients and medical workers and social media mining. We found: (1) Patients were deprived of former identities and equalised upon hospitalisation, which formed the basis of later self-organised hierarchical social relationships. (2) Intimate spatial structures expedited relationship construction among neighbouring patients and facilitated community building by expanding the influence that the more active patients exerted on the passive ones. (3) These social situations generally helped alleviate patients' anxiety. Our study reveals the social and emotional ramifications of such emergency spaces on people, thus providing insight for pandemic response and other global emergencies. It also responds to the theory of ‘the production of space' and elucidate the theory of ‘total institutions' from a new perspective.

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Space-time steering vector mismatch of targets usually happens in the space-time adaptive processing (STAP) technique, causing space-time beam distortion. Due to the effects of clutter spectrum broadening and jamming, performance of the conventional STAP method deteriorates dramatically. This paper proposes a novel robust STAP method to improve the performance of clutter suppression and moving target detection. The proposed method employs the alternating projection and clutter cancellation method, to reconstruct the clutter plus jamming plus noise covariance matrix (CJNCM) and re-estimate the target steering vector. Firstly, reconstruction of CJNCM consists of summing the eigenvectors of the projection operator, combining the sample covariance matrix and the integral covariance matrix (ICM) of specific region. Secondly, the re-estimated target steering vector is obtained by estimating the dominant eigenvector of the ICM of the Region of Interest (ROI), which contains the target signal purely because CJNCM is cancelled before estimation. Finally, the robust space-time adaptive filtering weight vector is calculated through the MVDR method with the reconstructed CJNCM and the re-estimated target steering vector. Simulation results indicate that the proposed algorithm shows robust performance against space-time steering vector mismatch, better output signal-to-noise ratio performance and better moving target detection performance than the traditional robust STAP method. INDEX TERMS Space-time adaptive processing, space-time steering vector mismatch, covariance matrix reconstruction, alternating projection, clutter cancellation.

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