Xiangzhi Li

Joint direction of arrival (DOA) and time delay (TD) estimation of wideband signals such as WIFI and mobile communication signals has to face the challenges aroused by multipath effect in indoor environments. New methods that can deal with coherent wideband signals with high estimation accuracy are necessary. To achieve localization when signals are coherent, this paper proposes a new joint estimation method which fully utilizes the subcarriers of wideband signals. This method could greatly extend the array aperture and accomplish the joint estimation through UCA-ESPRIT and one-dimensional (1-D) search. Simulation experiments are conducted to show the validity and efficiency of this method.

Joint time delay and direction of arrival estimation based on uniform linear arrays in Orthogonal Frequency Division Multiplexing (OFDM) systems has to face the problems posed by coherent multipath environments and high computational complexity. In this paper, a novel fast method is proposed to achieve a joint direction-of-arrival (DOA) and time-dealy (TD) estimation of multipath OFDM signals by fully using space-frequency characteristics. Firstly, we construct an extended virtual array by combining the array structure and frequency-domain information. Then, we calculate the extended channel frequency response matrix and adopt smoothing processing to eliminate the multipath effect. Next, we get the result of DOA estimation by using a closed-form solution, which costs little complexity and can achieve fast estimation. Finally, we conduct a one-dimensional spectral search using the obtained DOA values to estimate time delays. Simulation results show that our proposed methods have excellent performance even under low SNR conditions in different multipath environments. Furthermore, methods proposed in this paper have much less computational complexity and better estimation performance compared with the multidimensional spectral peak search methods.

Joint time of arrival and angle of arrival estimation based on a uniform circle array (UCA) involves problems including high computational complexity in the multi-dimensional spectral search method and lack of applicability in coherent multipath environments. In this paper, we introduce a fast estimation algorithm based on the orthogonal frequency division multiplexing system that uses space-frequency characteristics to reconstruct the virtual array and extend the array aperture. First, we combine the array structure with the signal frequency features to construct the extended virtual array. We then calculate the channel frequency response covariance matrix via frequency-domain smoothing preprocessing and determine a closed-form solution of angle of arrival using UCA estimating signal parameters via rotational invariance techniques. Finally, using the estimated angle values, we perform a one-dimensional spectral search to determine the time of arrival values. The simulation results indicate that the proposed method provides accurate estimation under the low signal-to-noise ratio conditions in coherent, independent or mixed multipath environments, and provides better performance than the multi-dimensional spectral peak search method, in terms of both computational complexity and estimation accuracy.

In estimating the two-dimensional (2D) direction-of-arrival (DOA) using a coprime planar array, the main issues are the high complexity of spectral peak search and the limited degree of freedom imposed by the number of sensors. In this paper, we present an algorithm based on the matrix completion theory in coprime planar array that reduces the computational complexity and obtains a high degree of freedom. The algorithm first analyzes the covariance matrix of received signals to estimate the covariance matrix of a virtual uniform rectangular array, which has the same aperture as the coprime planar array. Matrix completion theory is then applied to estimate the missing elements of the virtual array covariance matrix. Finally, a closed-form DOA solution is obtained using the unitary estimation signal parameters via rotational invariance techniques (Unitary-ESPRIT). Simulation results show that the proposed algorithm has a high degree of freedom, enabling the estimation of more signal DOAs than the number of sensors. The proposed algorithm has reduced computational complexity because the spectral peak search is replaced by Unitary-ESPRIT, but attains similarly high levels accuracy to those of the 2D multiple signal classification algorithm.