Robust Direction Of Arrival Estimation Research Articles

Robust DOA Estimation in the Presence of Miscalibrated Sensors

In this letter, we propose a robust direction-of-arrival (DOA) estimation algorithm in the context of sparse reconstruction, where some array sensors are miscalibrated. In this case, conventional DOA estimation algorithms suffer from degraded performance or even failed operations. In the proposed approach, the miscalibrated sensor observations are treated as outliers, and a weighting factor is adaptively optimized and applied to each sensor in order to effectively mitigate the effect of the outliers. An algorithm based on the maximum correntropy criterion is then developed to yield robust DOA estimation. The simulation results are presented to verify the effectiveness and superiority of the proposed approach compared with conventional DOA estimation algorithms.

Robust DOA Estimation via Sparse Signal Reconstruction With Impulsive Noise

In this letter, direction of arrival (DOA) estimation is considered for an impulsive noise case. The original complex-valued measurement for DOA problem is expressed as an augmented real-valued measurement using the real and imaginary parts. To deal with the impulsive noises, a function approximation technique is used. For the real-valued model, a robust DOA estimation algorithm is proposed based on sparse signal reconstruction. Simulation is given to demonstrate the validity and superiority of our method.

Robust DoA estimation in case of multipath environment for a sense and avoid airborne radar

This paper deals with the sense and avoid problem for an helicopter. The objective of such a system is to early detect collision targets (typically high voltage wires). The Direction of Arrival (DoA) of the target is then a crucial information. In severe multipath environments (flight over a river, for instance) classical DoA estimation schemes dramatically degrade. In this paper, we make use of a method based on the Maximum Likelihood (ML) principle that can resolve two highly correlated and close targets. The major drawback of ML algorithms, namely the computational burden, is removed using an approximation for closely space targets. The contribution of this paper is twofold. We first extend the approximated ML DoA estimation to the case of Non Uniform Linear Antennas (NULA) and we complete the procedure by a detection scheme. Second, we attest the validity of this new processing on real radar data. Hence, we show that the proposed procedure is able to detect a high voltage wire, over a river, at ranges up to 1 km, where a capon beamformer cannot.

Underdetermined High-Resolution DOA Estimation: A <formula formulatype="inline"><tex Notation="TeX">$2\rho$</tex> </formula>th-Order Source-Signal/Noise Subspace Constrained Optimization

For estimating the direction of arrival (DOA)s of non-stationary source signals such as speech and audio, a constrained optimization problem (COP) that exploits the spatial diversity provided by an array of sensors is formulated in terms of a noise-eliminated local 2ρth-order cumulant matrix. The COP solution provides a weight vector to the look direction such that it is constrained to the 2ρth-order source-signal subspace when the look direction is in alignment with the true DOA; otherwise, it is constrained to the 2ρth-order noise subspace. This weight vector is incorporated into the spatial spectrum to determine the degree of orthogonality between itself and either the 2ρth-order source-signal subspace when the number of sources is unknown, or the 2ρth-order noise subspace when the number of sources is known. For a uniform linear array (ULA) of M sensors, the spatial spectrum for known number of sources can theoretically be shown to identify up to 2ρ(M-1) sources. Realizing the difficulty in identifying stationarity in the received sensor signals, the estimate of the noise-eliminated local 2ρth-order cumulant matrix is marginalized over various possible stationary segmentations, for a more robust DOA estimation. In this paper, we focus on the use of local second and fourth order cumulants ( ρ = 1, 2), and the proposed algorithms when ρ = 1 outperformed the KR subspace-based algorithms and also the 4-MUSIC for globally non-stationary, non-Gaussian synthetic data and also for speech/audio in various adverse environments. We verified that the identifiability for ρ = 2 is improved by two-folds compared to that for ρ = 1 with an ULA.

A high resolution robust localization approach of high speed target based on vector sonar

Based on high speed moving target robust high-resolution direction of arrival (DOA) estimation problem under low signal-to-noise ratio and a small number of snapshots in the underwater acoustic vector signal processing framework, a novel spatial spectrum model combined with compressive sensing method is proposed. By studying the acoustic vector sensor structure, a generalized temporal filtering method based on sound pressure and particle velocity combined treatment is presented. According to the matrix spatial prefiltering theory, a new spatial filter with stopband constraint and mean square error min-max principle in passband is proposed which is used as vector sonar spatial prefiltering algorithm. Based on the methods above, a novel time-space domain jointly filtering high-resolution DOA estimation algorithm based on compressive sensing is proposed. The mathematical model, physical interpretation, and specific implement are explained in detail. Theoretical analysis and computer simulation results show that the new method has a lower dual-target distinguishing threshold and a higher estimation accuracy in solving the vector sonar high speed moving target robust DOA estimation problem under a small number of snapshots (single snapshot) condition. The higher robustness and better results of the proposed method are verified in the lake experiment.

Robust DOA estimation and target docking for mobile robots

Direction of arrival (DOA) guided automated target acquisition and docking system is proposed for mobile robots employing the dual-directional antenna system. The dual-directional antenna estimates the DOA of the signal of interest using the ratio of the signal strengths between two adjacent antennas. In practice, DOA estimation poses a significant technical challenge, since the RF signal is easily distorted by the environmental conditions. Therefore, the robot often loses its way in an electromagnetically disturbed environment. To cope with this problem, a robust DOA estimation algorithm is developed based on Kalman filtering. This algorithm allows the robot to reduce the potential error in the estimated DOA, and adjust the robot’s heading to the target transponder without needing to know the positions of current and previous measurements in a global coordinate system. The simulation and experiment results clearly demonstrate that the mobile robot equipped with the developed system is able to dock to a target transponder in an indoor environment partially occupied by obstacles.

Compensation for the mutual coupling effect in uniform circular arrays for 2D DOA estimations employing the maximum likelihood technique

An effective compensation method for the mutual coupling effect in uniform circular arrays (UCAs) employed for two-dimensional (2D) direction-of-arrival (DOA) estimations is introduced. A new 2D DOA searching algorithm using the maximum likelihood technique optimized by the emperor selective genetic algorithm (ML-EMSGA) is introduced for use with UCAs. This method circumvents the difficulty of dealing with coherent signals in 2D DOA estimations. ML-EMSGA is less computationally demanding than the maximum likelihood method (MLM) and statistically more efficient. Our study shows that ML-EMSGA can be effectively combined with the proposed compensation method, which is based on the introduction of a new mutual impedance, to give very accurate and robust 2D DOA estimation results. The structure of mutual impedance matrix for UCAs under the compensation method is fully explained. The theory of the ML-EMSGA for the UCAs is formulated. Computer simulation examples on several synthetic scenarios are presented to demonstrate the effectiveness of the mutual coupling compensation method and the superior performance of the ML-EMSGA for UCAs.

Robust DoA estimation by extension of the SAGE algorithm: algorithm description and trails results

By extending the space-alternating generalised expectation-maximisation (SAGE) parameter estimation algorithm to include a novel cost variant process, the robustness of direction of arrival (DoA) estimation from the SAGE algorithm is shown to improve. A novel extension to the standard SAGE algorithm that enhances the parameter estimation process is first introduced. Field trials that have been conducted using an adaptive antenna to verify the performance of this algorithm are presented. Results show that the extended SAGE algorithm improves DoA performance compared to standard SAGE by 5°, when operating in an urban environment.

Robust DOA estimation and array calibration in the presence of mutual coupling for uniform linear array

The presence of unknown mutual coupling between array elements is known to significantly degrade the performance of most high-resolution direction of arrival (DOA) estimation algorithms. In this paper, a robust subspace-based DOA estimation and array auto-calibration algorithm is proposed for uniformly linear array (ULA), when the array mutual coupling is present. Based on a banded symmetric Toeplitz matrix model for the mutual coupling of ULA, the algorithm provides an accurate and high-resolution DOA estimate without any knowledge of the array mutual couplings. Moreover, a favorable estimate of mutual coupling matrix can also be achieved simultaneously for array auto-calibration. The algorithm is realized just via one-dimensional search or polynomial rooting, with no multidimensional nonlinear search or convergence burden involved. The problem of parameter ambiguity, statistically consistence and efficiency of the new estimator are also analyzed. Monte-Carlo simulation results are also provided to demonstrate the effectiveness and behavior of the proposed algorithm.

Robust direction of arrival estimation for CDMA signals in impulsive noise

Direction of arrival estimation schemes based on multiuser detection for CDMA signals are investigated. The paper reports the performance improvements achieved by the linear decorrelator and the nonlinear M-regression decorrelator over the conventional matched filter scheme combined with the subspace-based MUSIC algorithm. The near-far resistant properties of the decorrelators show improved bearing estimation performance in the presence of strong interference. In particular, the minimax M-regression decorrelator combined with the MUSIC algorithm achieves an excellent performance trade-off by performing satisfactorily over the entire range of noise distributions, which, on the other hand, is unattainable by the ROC-MUSIC scheme.