Recently, the joint estimation for time delay (TD) and direction of arrival (DOA) has suffered from the high complexity of processing multi-dimensional signal models and the ineffectiveness of correlated/coherent signals. In order to improve this situation, a joint estimation method using orthogonal frequency division multiplexing (OFDM) and a uniform planar array composed of reconfigurable intelligent surface (RIS) is proposed. First, the time-domain coding function of the RIS is combined with the multi-carrier characteristic of the OFDM signal to construct the coded channel frequency response in tensor form. Then, the coded channel frequency response covariance matrix is decomposed by CANDECOMP/PARAFAC (CPD) to separate the signal subspaces of TD and DOA. Finally, we perform a one-dimensional (1D) spectral search for TD values and a two-dimensional (2D) spectral search for DOA values. Compared to previous efforts, this algorithm not only enhances the adaptability of coherent signals, but also greatly decreases the complexity. Simulation results indicate the robustness and effectiveness for the proposed algorithm in independent, coherent, and mixed multipath environments and low signal-to-noise ratio (SNR) conditions.

In order to solve the problems of high complexity and absence of adaptability with the joint estimation for time delay (TD) and angle of arrival (AOA) in coherent multipath environments, a fast estimation algorithm based on orthogonal frequency division multiplexing (OFDM) technique is proposed. First, we combine OFDM signal characteristics with array features to obtain extended arrays. Then, we obtain the channel frequency domain response covariance matrices for TD and AOA estimation separately by smoothing preprocessing in the spatial and frequency domains, respectively. Finally, we estimate the TD values by a one‐dimensional (1‐D) spectral peak search, as well as determine the closed‐form solution for AOA by Unitary‐ESPRIT. In comparison with previous work, the proposed algorithm not only improves the adaptability in correlated or mixed multipath environments but also significantly reduces the complexity. The simulation results show the effectiveness and robustness of the proposed joint estimation algorithm.

Nowadays, the joint estimation of time delay (TD) and angle of arrival (AOA) using conventional vector structure suffers from the considerable complexity of multidimensional spectrum search. Therefore, a fast estimation method using orthogonal frequency division multiplexing (OFDM) technology and uniform planar array (UPA) is proposed in this paper, which adopts low-complexity tensor-based operations and spatial-frequency features to reconfigure the channel frequency response. To begin with, the array response is integrated with the OFDM signal characteristics to build an extended array in tensor form. Afterwards, we process the covariance matrix of the tensor structure by CANDECOMP/PARAFAC decomposition (CPD) to separate the respective signal subspaces of TD and AOA estimates. Finally, we conduct a one-dimensional (1-D) spectrum search to locate the TD estimates and a two-dimensional (2-D) spectrum search to locate the AOA estimates. The simulated performance demonstrates that the proposed algorithm offers precise estimates at low signal-to-noise ratios in a multipath environment and outperforms traditional vector-based algorithms with respect to computational complexity.