Information-Theoretic Approach to Joint Design of Waveform and Receiver Filter With Desired Cross-Correlation Properties for Imaging Radar

• Published in: IEEE transactions on geoscience and remote sensing Vol. 62; pp. 1 - 12
• Main Authors: Zhang, Jiawei, Xu, Huaping, Liu, Wei, Li, Chunsheng, Chen, Yifan
• Format: Journal Article
• Language: English
• Published: New York IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Approximation; Constant modulus; constrained optimization; Constraints; Convergence; Correlation analysis; Cross correlation; Data processing; Image filters; Image quality; Imaging; Imaging radar; Information theory; Iterative methods; Linear programming; Maximization; Objective function; Optimization; Polynomials; Radar; Radar imaging; receive filter; Scattering; Sidelobes; transmit waveform; Waveforms
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2024.3425717

An imaging radar is expected to provide high-quality images for interesting targets. To this end, an information-theoretic approach is used in this article to jointly optimize waveform and receive filter with desired cross-correlation properties. First, the problem formulation is achieved by maximizing the mutual information (MI), subject to constant modulus, high resolution, and low peak sidelobe ratio (PSLR) constraints. Second, to solve the resultant problem with a fractional quadratic objective function and various nonconvex constraints, four customized iterative loops are performed to transform the problem into a series of solvable subproblems, via minorization-maximization (MM), alternate direction penalty method (ADPM), and feasible point pursuit successive convex (FPP-SCA) approximation. Convergence of every iterative loop is proved, resulting in guaranteed convergence of the whole procedure with polynomial-time complexity. Finally, numerical examples are presented to demonstrate that the proposed method can construct a unimodular waveform and filter with better information acquisition ability and more desirable cross-correlation function.