High-Precision Synchronous Sensing in Distributed Wireless Passive Surface Acoustic Wave Sensors

Surface acoustic wave (SAW) sensors enable wireless and passive measurement in harsh environments and are commonly deployed in a spatial distribution to improve performance. However, current sequential interrogation used in distributed sensing introduces errors due to asynchronous multi-signal acquisition and processing. Synchronous excitation and echo signal frequency acquisition on multiple SAW resonators (synchronous sensing) can avoid this error and improve sensing efficiency. Nevertheless, practical implementation of SAW synchronous sensing remains unavailable currently. Aiming at this challenge, this paper proposes a systematic synchronous frequency estimation technology and achieves high-precision synchronous SAW sensing. First, through derivation of the Cramér-Rao Lower Bound (CRLB) for sensor signals, we establish range guidelines for key parameters about synchronous sensing scheme (working frequency interval of SAW resonators, signal length). This step ensures optimal resource allocation (excitation bandwidth, sampling length) to enhance frequency estimation accuracy and measurement precision. Subsequently, a high-performance multi-frequency estimation algorithm is proposed. It combines the proposed adaptive step-size subspace iterative reconstruction algorithm with the improved weighted root-MUSIC algorithm. Monte Carlo experiments demonstrate our algorithm achieves the CRLB-approaching estimation (within 2dB of MSE), ensuring the feasibility of the CRLB-derived guidelines. Finally, high-precision synchronous differential SAW pressure sensing is achieved and our frequency estimation algorithm outperforms conventional algorithms facing the real sensor signals (with estimation variance of 0.18kHz2). Our work establishes a foundation for the practical application of SAW synchronous sensing technology and will advance the development of SAW distributed sensing arrays.