ISAR imaging with separable sparse representation model
It is well-known that the sparse representation (SR) based ISAR imaging schemes suffer from high cost on memory and computation complexity, because the two-dimensional imaging observation procedure is jointly represented by a large-scale basis matrix. To overcome this difficulty, a novel separable SR-based ISAR imaging algorithm is developed in this paper. By introducing a separable observation model, the large-scale basis is replaced by two small-scale bases, which brings significant reduction on memory and complexity. To efficiently solve the imaging problem, the range profile is engaged as an auxiliary variable, and the sparsity on both the scene and the range profile is exploited, by which the scene can be reconstructed in matrix operation directly instead of the memory consuming vector operations. Compared with the traditional SR-based imaging scheme, the memory cost for storing the basis matrix can be dropped from MN×PQ to MP+QN, and the computation complexity is reduced from O(P3 Q3) to O(P3+Q3), where M, P and N, Q stand for the numbers of samples and grids in range and cross-range directions. The superior performance of the proposed algorithm is proved by simulated data and real data.