Abstract
Smart environment sensing and other applications play a more and more important role along with the rapid growth of device-free sensing-based services, and extracting parameters contained in channel state information (CSI) accurately is the basis of these applications. However, antenna arrays in wireless devices are all planar arrays whose antenna spacing does not meet the spatial sampling theorem while the existing parameter estimation methods are almost based on the array satisfying the spatial sampling theorem. In this paper, we propose a parameter estimation algorithm to estimate the signal parameters of angle of arrival (AoA), time of flight (ToF), and Doppler frequency shift (DFS) based on the service antenna array, which does not satisfy the spatial sampling theorem. Firstly, the service antenna array is mapped to a virtual linear array and the array manifold of the virtual linear array is calculated. Secondly, the virtual linear array is applied to estimate the multi-dimensional parameters of the signal. Finally, by calculating the geometric relationship between the service antenna and the virtual linear array, the parameters of the signal incident on the service antenna can be obtained. Therefore, the service antenna can not only use the communication channel for information communication, but also sense the surrounding environment and provide related remote sensing and other wireless sensing application services. Simulation results show that the proposed parameter estimation algorithm can accurately estimate the signal parameters when the array antenna spacing does not meet the spatial sampling theorem. Compared with TWPalo, the proposed algorithm can estimate AoA within 3∘, while the error of ToF and DFS parameter estimation is within 1 ns and 1 m/s.
Highlights
Chen et al proposed channel state information (CSI) forward/backward smoothing algorithm to improve the performance of angle of arrival (AoA) estimation, which effectively alleviates the decline of AoA estimation accuracy under low signal-noise ratio (SNR) and can obtain higher localization accuracy [21]
4 Conclusion In this paper, a multi-dimensional joint parameter estimation algorithm based on the AoA, time of flight (ToF), and Doppler frequency shift (DFS) of service antenna array is proposed
The algorithm maps the antenna in the service antenna array which does not meet the spatial sampling theorem from the incident direction of the signal to the virtual linear array, and estimates the signal parameters through the virtual linear array
Summary
With the gradual popularization of wireless sensor technology, smart environment sensing-based applications [1,2,3,4] have gradually become an indispensable part of people’s daily life, and the wireless sensing technology [5,6,7,8,9] has been widely used in the places where people gather, such as shopping malls and airports.Extracting parameters of information contained in wireless signal accurately is the basis of human detection, behavior recognition, and other applications [10,11,12,13,14,15]. Xu et al proposed a 3D joint parameter estimation algorithm, which uses the time difference of arrival (TDoA) and AoA of three receivers to locate the target in non-lineof-sight (NLOS) scene [24]. By improving the expectation maximization (EM) algorithm, the space alternating generalized expectation maximization (SAGE) algorithm proposed in widar2.0 [25] can estimate AoA, ToF, DFS, and signal amplitude, and its parameter estimation accuracy can reach Cramer-Rao lower bound (CRLB) in theory. Whether AoA is estimated alone or AoA, ToF, and other parameters are estimated jointly, the receiving antenna array must satisfy the spatial sampling theorem; otherwise, there will be more false peaks in the estimation results, which will lead to misjudgment. The existing parameter estimation algorithms cannot accurately estimate the parameters of the wireless signal received by the service antenna array, which does not meet the spatial sampling theorem
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