ISAR Simulation with Stretch Processing Implementation

In the field of radar signal processing, ISAR (Inverse Synthetic Aperture Radar) imaging technology has gained widespread application due to its capability to achieve high-resolution imaging of moving targets. Among various techniques, stretch processing serves as an efficient wideband signal processing method extensively employed in ISAR imaging systems.

Stretch processing is fundamentally a dechirping technique primarily designed to address challenges in receiving and processing wideband linear frequency modulated (LFM) signals. This technique effectively compresses signal bandwidth by mixing echo signals with reference signals, thereby reducing sampling rate requirements and computational complexity for subsequent processing stages. In code implementation, this typically involves generating a reference LFM signal that matches the transmitted waveform and performing complex multiplication with the received echoes.

In multi-point target ISAR simulation, the implementation of stretch processing algorithm encompasses several critical steps: First, constructing an appropriate target scattering point model to accurately simulate electromagnetic characteristics of multiple scatterers. This can be implemented through coordinate arrays and radar cross-section (RCS) values for each scattering point. Second, designing proper LFM signal parameters including bandwidth, pulse width, and center frequency. The core algorithm implementation involves generating reference signals with matching parameters and performing dechirping operations through complex conjugate multiplication. Key functions would include signal generation routines, mixing operations, and fast Fourier transform (FFT) implementations for frequency domain analysis. Finally, imaging algorithms convert processed signals into recognizable 2D images through range-Doppler processing techniques.

Notably, the simulation process must account for various practical factors affecting imaging quality, including motion compensation for target movement and noise interference suppression. A well-designed simulation program should allow flexible parameter adjustment and clearly demonstrate imaging results under different conditions, providing reliable verification means for algorithm research and system design. This can be achieved through modular coding with configurable parameters for signal characteristics, target motion models, and processing chain components.

Such simulations not only facilitate understanding of fundamental ISAR imaging principles but also establish important foundations for subsequent algorithm optimization and practical system implementation. By adjusting target characteristics, signal parameters, and processing algorithms, researchers can thoroughly investigate how various factors influence final imaging quality through systematic parameter sweeps and performance metrics evaluation.