Array sensors failure can significantly deteriorate the performance of direction of arrival (DOA) estimation. To address this problem, an algorithm via covariance matrix reconstruction is proposed. Firstly, we devise a diagnosis method to detect the failure sensors. Based on the robustness of the array, the sensor failure scenarios are divided into redundant sensor failures and non-redundant sensor failures. Then, the corresponding DOA estimation method is adopted for two failure scenarios. For redundant sensor failures, the virtual sensors of the difference coarray can be used to occupy the positions of the failed physical sensors by utilizing the array redundancy. For non-redundant sensor failures, the virtual sensors in the difference coarray will have holes. Employing the matrix completion theory, we use trace norm instead of the rank norm for convex relaxation to recover the matrix, thereby realizing the filling of the virtual sensor holes in the difference coarray and restoring the DOFs. Compared with the sparsity-based methods, the proposed method can eliminate the effect of the off-grid. Finally, the subspace method is employed for DOA estimation. Theoretical analysis and simulation results show that the proposed methods can alleviate the effect of array sensor failure and improve the estimation performance.

Most of the current direction of arrival estimation (DOA) methods are proposed based on the unbiased knowledge of the array manifold, which may not be guaranteed in some practical applications, since the clock drifting and sensor position uncertainties always exist. To match the actual array reception condition, a DOA estimation method that uses partly calibrated nested array is presented. The proposed method completes the array gain-phase errors estimation by exploiting the continuous multiplication operator and simple algebraic operation, and then constructs a sparse vector model via vectorization operation on array covariance matrix in sparse representation framework, which owes consecutive degrees of freedom. Finally, the influence of the finite number of samples is considered, and the DOAs are successively estimated by applying sparse total squares (STLS) algorithm based on the estimated result of gain-phase errors. The proposed method not only performs independent of gain-phase errors, but also can provide improved resolution and estimation accuracy, by depending on the high DOFs provided by nested array and anti-disturbance characteristics of STLS algorithm. Simulation results validate the effectiveness of the proposed method.

An algorithm for estimation of direction of arrival (DOA) and range of near field source based on the spatial differential technique is proposed in this paper. The algorithm firstly utilizes the feature that the stationary noise covariance matrix is symmetrical about the main diagonal and constructs the spatial difference matrix only containing the target signal location information. Then, it proves the distribution characteristics of the matrix eigenvalues and selects the noise subspace reasonably. Finally, the DOA and range estimations for near-field sources can be obtained through the spectral searching. The algorithm can effectively suppresses the unknown stationary noise and avoid the pseudo peak problems for the application of the spatial differential method when used to solve the source localization. Computer simulations confirm the satisfactory performance of the proposed algorithm.

According to the ionospheric backscatter echoes characteristics, a method of DOA (Direction Of Arrival) estimation based on Capon beamforming, diagonal loading and windowing technology is proposed. The arrival angle of backscatter echoes is obtained by using the "L" type short wave two-dimensional receiving array. The frequency-group-elevation ionogram is obtained. The accuracy of the backscatter system is verified by the DOA estimation of direct wave and quasi-vertical signals.

Aiming at the problem that the number of commercial Wi-Fi access points (AP) antennas restricts the high-precision positioning based on angle of arrival (AoA), this paper proposes an indoor real-time positioning algorithm based on Wi-Fi signal. An AoA estimation algorithm using Wi-Fi is proposed, which can quickly estimate the AoA of the line of sight (LoS) path with few antennas and snapshots to ensure real-time positioning. The IEEE 802.11 Saleh-Valenzuela (S-V) channel model analyses the influence of multipath signals on the energy spectrum peaks of direct signals. In order to improve the positioning accuracy, a multi-AP joint positioning algorithm based on antenna selection is proposed. A real-time positioning demonstration system is built to verify the effectiveness of the system. The experiment results show that the proposed algorithm can achieve 67% 1.2m positioning accuracy and the positioning delay is less than 0.5 seconds.

Frequency diverse array radar has the problem of ambiguity in angle and range localization. Based on the same ranging principle as early multi-frequency continuous wave radar, a model for the multi-frequency continuous wave (MFCW) radar is proposed, which overcomes the ambiguity of the angle-range beampattern of the traditional frequency diverse array (FDA).The signal models of the MFCW radar under four operating modes are deduced according to the number of transmitting and receiving elements, and the reason for the decoupled angle-range localization is analyzed compared with the traditional FDA. A joint angle-range localization method based on iterative interpolation Fourier coefficients (IIFC) is proposed for the single-input multiple-output (SIMO) mode. The performance of the algorithm is analyzed theoretically. Simulation results show the effectiveness of the proposed scheme and the algorithm.

Multipath signals can be used to realize localization since they are abundant and contain geometry information of indoor environments. Based on this, this paper proposes a multipath-assisted target localization algorithm. Firstly, the fitness function about the target and scatterer locations is constructed with Time of Flight (TOF) differences. Then, the locations of the target and scatterers are searched jointly by Particle Swarm Optimization (PSO) and Angle of Arrivals (AOAs) that determines searching ranges. Secondly, the estimated locations of scatterers and TOF differences are used to estimate the target location. Finally, all target locations are clustered by using Affinity Propagation Clustering (APC), and a clustering criterion is proposed to eliminate big localization errors. The simulation results show that the proposed algorithm can achieve high localization accuracy with a single base station.