Jiang Yinghai, Liu Feng
College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China.
Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Nankai University, Tianjin 300350, China.
Entropy (Basel). 2024 Jun 26;26(7):544. doi: 10.3390/e26070544.
As one of the most widely used spread spectrum techniques, the frequency-hopping spread spectrum (FHSS) has been widely adopted in both civilian and military secure communications. In this technique, the carrier frequency of the signal hops pseudo-randomly over a large range, compared to the baseband. To capture an FHSS signal, conventional non-cooperative receivers without knowledge of the carrier have to operate at a high sampling rate covering the entire FHSS hopping range, according to the Nyquist sampling theorem. In this paper, we propose an adaptive compressed method for joint carrier and direction of arrival (DOA) estimations of FHSS signals, enabling subsequent non-cooperative processing. The compressed measurement kernels (i.e., non-zero entries in the sensing matrix) have been adaptively designed based on the posterior knowledge of the signal and task-specific information optimization. Moreover, a deep neural network has been designed to ensure the efficiency of the measurement kernel design process. Finally, the signal carrier and DOA are estimated based on the measurement data. Through simulations, the performance of the adaptively designed measurement kernels is proved to be improved over the random measurement kernels. In addition, the proposed method is shown to outperform the compressed methods in the literature.
作为应用最广泛的扩频技术之一，跳频扩频（FHSS）已在民用和军事安全通信中得到广泛应用。在该技术中，与基带相比，信号的载波频率在很大范围内伪随机跳变。为了捕获FHSS信号，根据奈奎斯特采样定理，对于不了解载波的传统非协作接收机，必须以覆盖整个FHSS跳变范围的高采样率进行操作。在本文中，我们提出了一种用于FHSS信号载波和到达方向（DOA）联合估计的自适应压缩方法，以实现后续的非协作处理。压缩测量核（即传感矩阵中的非零项）已基于信号的后验知识和特定任务信息优化进行了自适应设计。此外，还设计了一个深度神经网络以确保测量核设计过程的效率。最后，基于测量数据估计信号载波和DOA。通过仿真证明，自适应设计的测量核的性能优于随机测量核。此外，所提方法在性能上优于文献中的压缩方法。
