Direction Of Arrival Parameters Research Articles

Near-field multiple source localization using joint diagonalization

A new near-field multiple source localization algorithm for joint range and direction-of-arrival (DOA) estimation is proposed. Via joint diagonalization of a set of matrices, the array manifold matrix is estimated and the automatically paired DOA and range parameters are obtained subsequently. The proposed algorithm does not require any search over the parameter space or any additional 2-D parameter paring procedure or high-order statistics (HOS) computation compared with the existing near-field source localization methods. Simulation results validate that the root mean square errors (RMSEs) of the estimated DOA and range parameters are close to the Cramer–Rao bounds (CRBs).

Stochastic Maximum Likelihood Estimation Under Misspecified Numbersof Signals

The maximum likelihood (ML) approach for estimating direction of arrival (DOA) plays an important role in array processing. Its consistency and efficiency have been well established in the literature. A common assumption is that the number of signals is known. In many applications, this information is not available and needs to be estimated. However, the estimated number of signals does not always coincide with the true number of signals. Thus, it is crucial to know whether the ML estimator provides any relevant information about DOA parameters under a misspecified number of signals. In a previous study by this author [ML estimation under misspecified number of signals, presented at the Thirty-Ninth Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November 2005], he focused on the deterministic signal model and showed that the ML estimator under a misspecified number of signals converges to a well defined limit. Under mild conditions, the ML estimator converges to the true parameters. In the current correspondence, we extend those results to the stochastic signal model and validate our analysis by simulations.

Subspace-based method for joint range and DOA estimation of multiple near-field sources

A subspace-based method for joint range and DOA (direction-of-arrival) estimation of multiple near-field sources is presented. The proposed method uses fourth-order cumulants, and the range and DOA parameters are directly given by the eigenvectors and eigenvalues of a constructed matrix. Compared with some available methods, the 2D parameters are automatically paired and the loss of array aperture is also reduced. Performance evaluation via computer simulations is included to demonstrate the effectiveness of the proposed algorithm.

Cramer-Rao bounds of DOA estimates for BPSK and QPSK Modulated signals

This paper focuses on the stochastic Cramer-Rao bound (CRB) of direction of arrival (DOA) estimates for binary phase-shift keying (BPSK) and quaternary phase-shift keying (QPSK) modulated signals corrupted by additive circular complex Gaussian noise. Explicit expressions of the CRB for the DOA parameter alone in the case of a single signal waveform are given. These CRBs are compared, on the one hand, with those obtained with different a priori knowledge and, on the other hand, with CRBs under the noncircular and circular complex Gaussian distribution and with different deterministic CRBs. It is shown in particular that the CRBs under the noncircular [respectively, circular] complex Gaussian distribution are tight upper bounds on the CRBs under the BPSK [respectively, QPSK] distribution at very low and very high signal-to-noise ratios (SNRs) only. Finally, these results and comparisons are extended to the case of two independent BPSK or QPSK distributed sources where an explicit expression of the CRB for the DOA parameters alone is given for large SNR.

Gaussian Cramer-Rao bound for direction estimation of noncircular signals in unknown noise fields

This paper focuses on the stochastic Cramer-Rao bound (CRB) on direction of arrival (DOA) estimation accuracy for noncircular Gaussian sources in the general case of an arbitrary unknown Gaussian noise field parameterized by a vector of unknowns. Explicit closed-form expressions of the stochastic CRB for DOA parameters alone are obtained directly from the Slepian-Bangs formula for general noncircular complex Gaussian distributions. As a special case, the CRB under the nonuniform white noise assumption is derived. Our expressions can be viewed as extensions of the well-known results by Stoica and Nehorai, Ottersten et al., Weiss and Friedlander, Pesavento and Gershman, and Gershman et al. Some properties of these CRBs are proved and finally, these bounds are numerically compared with the conventional CRBs under the circular complex Gaussian distribution for different unknown noise field models.

DOA estimation under unknown mutual coupling and multipath

We propose a new method for direction of arrival (DOA) estimation in the presence of multipath propagation and mutual coupling for a frequency hopping (FH) system. With the use of pilot symbols and assuming perfect time-frequency synchronization for a linear array, we take mutual coupling and multipath propagation into account, and derive a maximum likelihood (ML) estimator for both the mutual coupling matrix and DOA estimation. We then formulate an iterative alternating minimization (AM) algorithm for finding the mutual coupling and DOA parameters in an alternate manner. Simulation results illustrating the performance of the algorithm and comparison with the Cramer-Rao bound (CRB) are also presented.

Wideband array signal processing using MCMC methods

This paper proposes a novel wideband structure for array signal processing. A new interpolation model is formed where the observations are linear functions of the source amplitudes but nonlinear in the direction of arrival (DOA) parameters. The interpolation model also applies to the narrowband case. The proposed method lends itself well to a Bayesian approach for jointly estimating the model order and the DOAs through a reversible jump Markov chain Monte Carlo procedure. The source amplitudes are estimated through a maximum a posteriori (MAP) process. Advantages of the proposed method include joint detection of model order and estimation of the DOA parameters, the fact that reliable performance can be obtained using significantly fewer observations than previous wideband methods, and that only real arithmetic is required. The DOA estimation performance of the proposed method is compared with the theoretical Crame/spl acute/r-Rao lower bound for this problem. Simulation results demonstrate the effectiveness and robustness of the method.

Tracking of Multiple Moving Sources Using Recursive EM Algorithm

We deal with recursive direction-of-arrival (DOA) estimation of multiple moving sources. Based on the recursive EM algorithm, we develop two recursive procedures to estimate the time-varying DOA parameter for narrowband signals. The first procedure requires no prior knowledge about the source movement. The second procedure assumes that the motion of moving sources is described by a linear polynomial model. The proposed recursion updates the polynomial coefficients when a new data arrives. The suggested approaches have two major advantages: simple implementation and easy extension to wideband signals. Numerical experiments show that both procedures provide excellent results in a slowly changing environment. When the DOA parameter changes fast or two source directions cross with each other, the procedure designed for a linear polynomial model has a better performance than the general procedure. Compared to the beamforming technique based on the same parameterization, our approach is computationally favorable and has a wider range of applications.

Stochastic CramÉr–Rao Bound for Noncircular Signals with Application to DOA Estimation

After providing an extension of the Slepian-Bangs formula for general noncircular complex Gaussian distributions, this paper focuses on the stochastic Crame/spl acute/r-Rao bound (CRB) on direction-of-arrival (DOA) estimation accuracy for noncircular sources. We derive an explicit expression of the CRB for DOA parameters alone in the case of noncircular complex Gaussian sources by two different methods. One of them consists of computing the asymptotic covariance matrix of the maximum likelihood (ML) estimator, and the other is obtained directly from our extended Slepian-Bangs formula. Some properties of this CRB are proved, and finally, it is numerically compared with the CRBs under circular complex Gaussian and complex discrete distributions of sources.

Canonical space-time processing for wireless communications

A canonical space-time characterization of mobile wireless channels is introduced in terms of a fixed basis that is independent of the true channel parameters The basis captures the essential degrees of freedom in the received signal using discrete multipath delays, Doppler shifts, and directions of arrival (DOA). The canonical representation provides a robust representation of the propagation dynamics and eliminates the need for estimating delay, Doppler and DOA parameters of different multipaths, Furthermore, it furnishes a natural framework for designing low-complexity space-time receivers. Single-user receivers based on the canonical channel representation are developed and analyzed, It is demonstrated that the resulting canonical space-time receivers deliver near-optimal performance at substantially reduced complexity compared to existing designs.

Direction-of-arrival estimation using signal subspace modeling

A computationally viable algorithm for estimating the direction-of-arrival (DOA) of multiple wavefields that are incident on an array of sensors is developed. The geometry of the array is unrestricted and the incident wavefields may be generated by mixtures of incoherent and coherent emitting sources. In the approach taken, the sensor signals are modeled as a noise-contaminated linear combination of steering vectors which are functionally dependent on a set of DOA parameters. These parameters are to be chosen so that this sensor signal model is most compatible with empirically measured data (i.e., snapshot data). An iterative procedure is developed for selecting the most data compatible set of DOA parameters in both the snapshot domain and the array covariance matrix domain. Critical to the success of such iterative solution procedures is the generation of quality DOA parameters to initialize the algorithm. A sequential beamforming method for this initialization is presented. Numerical examples to illustrate the effectiveness of the proposed algorithmic approach are given. >

On the sensitivity of the ESPRIT algorithm to non-identical subarrays

ESPRIT (estimation of signal parameters via rotational invariance techniques) is a recently introduced algorithm for narrowband direction-of-arrival (DOA) estimation. Its principal advantage is that the DOA parameter estimates are obtained directly, without knowledge (and hence storage) of the array manifold and without computation or search of some spectral measure. This advantage is achieved by constraining the sensor array to be composed of two identical, translationally invariant subarrays. In this paper, we analyse the sensitivity of ESPRIT to the assumption that the subarrays are identical. The analysis is applicable to a wide variety of array errors, including non-identical angle-dependent and angle-independent gain and phase perturbations, errors in the locations of the subarray elements, and mutual coupling effects. A representative simulation example will be presented to validate the analysis and compare the performance degradation of ESPRIT with that of the MUSIC algorithm.