Pseudo-Zernike Based Multi-Pass Automatic Target Recognition From Multi-Channel SAR
This addresses the challenge of reliable target recognition in battlefield scenarios using radar data, though it appears incremental as it applies an existing mathematical tool (pseudo-Zernike moments) to a specific domain.
The paper tackled the problem of Automatic Target Recognition (ATTR) from multi-channel multi-pass SAR data by applying pseudo-Zernike moments to exploit diversities and invariant properties, achieving high confidence ATR with small computational complexity and data transfer requirements as demonstrated on real data.
The capability to exploit multiple sources of information is of fundamental importance in a battlefield scenario. Information obtained from different sources, and separated in space and time, provide the opportunity to exploit diversities in order to mitigate uncertainty. For the specific challenge of Automatic Target Recognition (ATR) from radar platforms, both channel (e.g. polarization) and spatial diversity can provide useful information for such a specific and critical task. In this paper the use of pseudo-Zernike moments applied to multi-channel multi-pass data is presented exploiting diversities and invariant properties leading to high confidence ATR, small computational complexity and data transfer requirements. The effectiveness of the proposed approach, in different configurations and data source availability is demonstrated using real data.