Adaptive Dual-Windowing Strategies for Multi-Target Detection in OFDM ISAC

TL;DR

This paper introduces an adaptive dual-windowing approach for multi-target detection in OFDM ISAC systems, balancing resolution and sidelobe suppression to improve detection accuracy and reduce complexity.

Contribution

It proposes a novel dual-window periodogram algorithm with a decision mechanism that adaptively switches between resolution and suppression optimized windows.

Findings

Achieves high detection performance at high SNR

Reduces computational complexity compared to fixed strategies

Effectively balances resolution and sidelobe suppression

Abstract

In Orthogonal Frequency Division Multiplexing (OFDM) Integrated Sensing and Communication (ISAC) systems, a key challenge is balancing sidelobe attenuation and resolution for multi-target detection scenarios. While windowing functions are typically used to manage this trade-off, state-of-the-art methods rely on a single, fixed window followed by a predefined detection strategy (e.g., Binary Successive Target Cancellation (BSTC) (low complexity) or Coherent Successive Target Cancellation (CSTC) (high performance)). This paper proposes a novel dual-window periodogram-based algorithm that leverages two complementary windows: one optimized for resolution and the other for sidelobe suppression. Then, a low-complexity detection algorithm (e.g., BSTC) is applied to both, and a decision mechanism compares the outputs. When results align, the resolution-optimized estimates are directly used; otherwise, high performance algorithm (e.g., CSTC) is triggered to resolve ambiguities. This adaptive approach dynamically balances the detection performance and the complexity, addressing limitations in existing fixed strategies. The Numerical results confirm that the proposed method achieves high performance while reducing the complexity, especially at a high Signal to Noise Ratio (SNR).