Implementation and Analysis of Robust System-Informed Waveform Design

Due to rapid advances in high-speed analog-to-digital conversion and software-defined architectures, modern radar systems increasingly shift signal generation and conditioning into the digital domain. These architectures enable high-fidelity signal capture and provide substantial flexibility in waveform synthesis and signal processing that was previously impractical in analog implementations. Despite these advances, however, achievable radar performance remains fundamentally constrained by the physical transmit hardware through which the signal is ultimately realized. Nonlinear amplification, finite bandwidth, and memory effects introduce distortion that creates a significant gap between idealized waveform design and the waveform that is physically radiated.

To address this limitation, this work proposes a system-aware radar waveform design framework that couples data-driven system identification with deterministic optimization to generate waveforms tailored to the underlying transmit hardware. A complex baseband memory polynomial model is developed to characterize nonlinear transmit-chain behavior using loopback measurements, where $\ell_1$-regularized LASSO estimation is employed to improve robustness against ill-conditioning and feature redundancy. Under this architecture, a generalized integrated sidelobe level (GISL) objective is reformulated using logarithmic scalarization to produce a numerically stable and Pareto-tunable optimization criterion capable of balancing output energy and sidelobe suppression. Additionally, efficient vectorized gradient expressions are derived using Wirtinger calculus and implemented using gradient-based descent and the limited-memory BFGS algorithm for practical high-dimensional waveform synthesis.

To validate the framework, a comprehensive hardware-in-the-loop testbench was developed supporting direct model identification and experimental evaluation of optimized waveform performance. Simulation and experimental results demonstrate that continuous-phase FM waveforms exhibit strong inherent robustness to nonlinear distortion, while phase-coded waveforms with large instantaneous phase discontinuities show significantly greater sensitivity to transmit-chain impairments. Across both waveform classes, the proposed framework achieves substantial improvements in output power efficiency and pulse compression performance relative to system-agnostic waveform design. These results demonstrate that transmitter constraints must be treated as fundamental design variables rather than secondary effects and establish system-aware optimization as a practical framework for next-generation radar waveform synthesis.