Zhixin Zhao, Yanghang Gong, Huilin Zhou, Yulong Cao
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引用次数: 0
摘要
尽管现有的一些稀疏表示(SR)方法对于无源双向静态雷达(PBR)中的目标检测具有很强的鲁棒性,但它们仍然面临着计算复杂度高和对极低信号杂波比(SCR)目标检测性能差的挑战。因此,本文研究了一种平均有效子载波(AES)域稀疏表示方法。首先,提出了基于 AES 的 SR 模型来解决计算复杂度高的问题,该模型是利用正交频分复用(OFDM)与循环前缀(CP)信号在每个有效子载波域的稀疏性建立的。然后,考虑到探测极低 SCR 目标的难度,通过基于 SR 的优化模型实现杂波消除。提出了两种 AES-S 算法,即基于 AES-S 的时域杂波消除算法(AES-S-T)和基于 AES-S 的子载波域杂波消除算法(AES-S-C),并进一步降低了计算复杂度。最后,大量的仿真和实验结果表明,所提出的算法在 PBR 检测场景中具有良好的检测性能和较低的计算复杂度。
Average effective subcarrier-domain sparse representation approach for target information estimation in CP-OFDM-based passive bistatic radar
Although some existing sparse representation (SR) methods are robust for target detection in passive bistatic radar (PBR), they still face the challenges of high computational complexity and poor detection performance for extremely low-signal-to-clutter ratio (SCR) target. So, an average effective subcarrier (AES)-domain sparse representation approach is investigated in this paper. Firstly, the AES-based SR model is proposed to solve the problem of high computational complexity, which is established by utilizing the sparseness of the orthogonal frequency-division multiplexing (OFDM) with cyclic prefix (CP) signals in each effective subcarrier domain. Then, considering the difficulty of detecting extremely low-SCR targets, clutter cancellation is implemented by the SR-based optimization model. Two AES-S algorithms, namely AES-S-based clutter cancellation in the time domain (AES-S-T) and AES-S-based clutter cancellation in the subcarrier domain (AES-S-C), are proposed, and the computational complexity is further reduced. Finally, extensive simulation and experimental results illustrate that the proposed algorithms have good detection performance and low computational complexity in PBR detection scene.
期刊介绍:
The aim of the EURASIP Journal on Advances in Signal Processing is to highlight the theoretical and practical aspects of signal processing in new and emerging technologies. The journal is directed as much at the practicing engineer as at the academic researcher. Authors of articles with novel contributions to the theory and/or practice of signal processing are welcome to submit their articles for consideration.