Estimation Of Signal Parameters Via Rotational Invariance Techniques Algorithm Research Articles

Direction Finding With Partly Calibrated Uniform Linear Arrays

A new method for direction finding with partly calibrated uniform linear arrays (ULAs) is presented. It is based on the conventional estimation of signal parameters via rotational invariance techniques (ESPRIT) by modeling the imperfections of the ULAs as gain and phase uncertainties. For a fully calibrated array, it reduces to the conventional ESPRIT algorithm. Moreover, the direction-of-arrivals (DOAs), unknown gains, and phases of the uncalibrated sensors can be estimated in closed form without performing a spectral search. Hence, it is computationally very attractive. The Cramer-Rao bounds (CRBs) of the partly calibrated ULAs are also given. Simulation results show that the root mean squared error (RMSE) performance of the proposed algorithm is better than the conventional methods when the number of uncalibrated sensors is large. It also achieves satisfactory performance even at low signal-to-noise ratios (SNRs).

Multi-invariance ESPRIT-like algorithms for coherent DOA estimation

Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) algorithm can estimate Direction-Of-Arrival (DOA) of coherent signal, but its performance can not reach full satisfaction. We reconstruct the received signal to form data model with multi-invariance property, and multi-invariance ESPRIT algorithm for coherent DOA estimation is proposed in this paper. The proposed algorithm can resolve the DOAs of coherent signals and performs better in DOA estimation than that of ESPRIT-like algorithm. Meanwhile, it identifies more DOAs than ESPRIT-like algorithm. The simulation results demonstrate its validity.

Multi-Invariance ESPRIT-Based Blind DOA Estimation for MC-CDMA With an Antenna Array

In this paper, we address the problem of direction-of-arrival (DOA) estimation for a multicarrier code-division multiple-access (MC-CDMA) system with an antenna array. We reconstruct the received signal to form a data model with a multi-invariance property, and then, a multi-invariance estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm for DOA estimation is proposed. This algorithm has improved DOA estimation performance and identified more DOAs compared with an ESPRIT algorithm. Moreover, our algorithm enables DOA estimation of a large number of impinging waves. Simulation results illustrate the performance of this algorithm.

A fast algorithm for joint two-dimensional direction of arrival and frequency estimation via hierarchical space–time decomposition

In this paper, we present a fast algorithm for joint estimation of the two-dimensional (2-D) directions of arrival (DOAs) and frequencies of the incoming signals in wireless communications using a hierarchical space–time decomposition (HSTD) technique. Based on the HSTD, the proposed algorithm makes use of a sequence of one-dimensional (1-D) Unitary estimation of signal parameters via rotational invariance technique (ESPRIT) algorithms to estimate these parameters alternatively in a hierarchical tree structure. Also, in between every other 1-D Unitary ESPRIT algorithm, a temporal filtering process or a spatial beamforming process is invoked to partition the incoming signals into finer groups stage by stage to enhance the estimation accuracy as well as to alleviate the contaminated noise. Furthermore, with such a tree-structured estimation scheme, the pairing of these parameters is automatically determined without extra computational overhead. Simulation results show that the new algorithm provides satisfactory performance but with drastically reduced computations compared with previous works.

Spatial correlation and capacity measurements for wideband MIMO channels in indoor office environment

This paper describes a broadband multiple input, multiple output (MIMO) channel characterization platform and capacity measurement results in indoor office environment. The MIMO testbed has been designed for broadband MIMO channel sounding, capacity measurements and for characterizing the directional-multipaths of the radio propagation channel. The MIMO channel data have been collected in 5 GHz band inside a modern office environment. Capacity results from these experiments are discussed for different propagation conditions, including non line-of-sight (NLOS) and LOS propagations with various spacings between array elements. Spatial correlations are analyzed from the measured data and a frequency selective MIMO channel model based on the correlation statistics is validated. Post-processing of the measured data with a sequential ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) algorithm is utilized to extract the directions of departures and arrivals of multipath components at the transmitting and receiving arrays, respectively. A method of obtaining broadband MIMO capacity, indirectly, from a single directional measurement of MIMO propagation channel is also proposed and verified from the measurement results.

Sinusoidal Modeling and Adaptive Channel Prediction in Mobile OFDM Systems

We propose a wireless fading channel prediction algorithm for a pilot-symbol aided orthogonal frequency division multiplexing (OFDM) system. Assuming a doubly selective (time and frequency varying) ray-based physical channel model and equispaced pilot subcarriers in time and frequency, this algorithm performs channel model parameter acquisition using a 2-Step 1-D ESPRIT (estimation of signal parameters via rotational invariance techniques) as a first stage, and channel prediction via model extrapolation as a second stage. Since the channel model parameter acquisition has cubic complexity, we also propose a linear complexity channel parameter tracking algorithm based on an improved adaptive ESPRIT algorithm to continuously adapt to the time-varying channel model parameters. We derive the Cramer-Rao lower bound (CRLB) and asymptotic CRLB (ACRLB) for the mean squared error (MSE) in OFDM channel prediction. We show that our proposed OFDM channel prediction algorithm has better MSE performance while maintaining similar computational complexity than previous methods in sparse multipath fading channels characterized by specular scattering, which is most suitable for outdoor mobile macrocell scenarios. Thus, our method can be seen as a complement to the existing schemes that are more suitable for dense multipath channels with diffuse scattering, which is typical of urban pico-cell and indoor wireless scenarios. We provide simulation results based on the IEEE 802.16e mobile broadband wireless access standard to corroborate our claims.

Frequency-Azimuth-Elevation Determination with a Single Acoustic Vector-Sensor Involved in a Reflecting Boundary

The temporally regularized multiresolution ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) algorithm is proposed to exploit the second-order information present in the superposition of incidence of direct and reflected monochromatic acoustic waves for uni-vector-sensor direction finding in the presence of a reflecting boundary. The data are organized such that the phase shifts for frequency estimation of each signal corresponding to two distinct sampling rates are separately embedded in the modulus and argument of the ESPRIT eigenvalue, whereas the direction cosines for azimuth-elevation direction-of-arrival (DOA) estimation dwell in the associated ESPRIT eigenvector, and thus the matching procedure for aligning the output of multidimensional and multi-invariant data manipulation is avoided. The temporal multiresolution herein realized is iteration free and needs merely one lower-dimensional eigendecomposition. The simplified version of the presented method offers a similar performance while requiring only two time-delayed data sets and thus has a comparable computational complexity with the traditional single resolution ESPRIT. This ESPRIT-type method is also blind to the reflection coefficient which generally depends on the boundary nature, medium property, and signal parameters still to be estimated, viz. DOAs and signal frequencies. Simulation results are included to validate the proposed algorithm.

Closed-form multiple invariance ESPRIT

The multi-least-squares fitting and regularization formulations of the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) problem are herein presented to provide a new framework for developing the closed-form multiple invariance ESPRIT (MI-ESPRIT) algorithm. The resulting two closed-form MI-ESPRIT algorithms incorporate all invariances in arrays composed of several (more than one) equally translated identical subarrays, and maintain the computational advantage of the standard single invariance ESPRIT.

Application of ESPRIT in broad beam HF ground wave radar sea surface current mapping

HF surface wave radar system OSMAR2000 is a broad-beam sea-state detecting radar. ESPRIT (Estimation of Signal Parameters via Rotational Invariance Technique) algorithm is proposed to apply in DOA (direction of arrival) determination of sea echoes. The algorithm of ESPRIT is briefly introduced first. Then discussions are made on the technique for application in the OSMAR2000 framework. Numerical simulation results are presented to demonstrate the feasibility of radial current mapping based on this method. The algorithm manifests significant performance and computational advantages compared with that of MUSIC. Data acquired by OSMAR2000 are processed to give radial current map and the synthesized vector currents are compared with the in-situ measurement with traditional means. The results show the validity of ESPRIT application in DOA determination for broad-beam radar.

Two-Dimensional ESPRIT With Tracking for Radar Imaging and Feature Extraction

Estimation of signal parameters via rotational invariance techniques (ESPRIT) appears to be the best of the known spectral-analysis methods. It has the highest resolution, has no spectral splatter and is robust in the presence of noise. It answers immediately and explicitly the question "What frequencies, real or complex, are present and what are their amplitudes?" Fourier methods (and other high-resolution methods), answer the less direct question "What amplitudes, applied to a set of regularly-spaced real frequencies, best represent the data?" They then present the problem of interpreting the amplitudes. These attributes of ESPRIT, in the two-dimensional (2-D) version described here, make it a natural for radar signal processing, where it answers the need for high-resolution imaging, free of sidelobes in range and range rate, and for high-fidelity feature extraction. The paper starts with the mathematical data model, describes a "resampling" procedure to fit the data better to the model, summarizes the 2-D ESPRIT algorithm itself, and presents examples of its performance. The paper covers the details of this least-squares version of ESPRIT, including an enhancement that allows the scatterers to be tracked individually. The algorithm properly distinguishes between scatterers having one coordinate in common, and it automatically pairs correctly the range and range-rate of each scatterer.

ESPRIT Direction-of-Arrival Estimation in the Presence of Spatially Correlated Noise

An algorithm is presented for ESPRIT (estimation of signal parameters via rotational invariance techniques) direction of arrival estimation when the expected value of the covariance of the measurement noise is different from a constant multiple of an identity matrix. The algorithm is based on a modification of the generalized singular value decomposition (GSVD) of two data matrices and requires only unitary transformations. The modification results in a significant simplification of the GSVD-based ESPRIT algorithm and produces more accurate solutions than prewhitening.

Two-dimensional angle and polarization estimation using the ESPRIT algorithm

It is shown how the ESPRIT (estimation of signal parameters via rotational invariance techniques) algorithm may be used with a square array of crossed dipoles to estimate both the two-dimensional arrival angles and the polarization of incoming narrowband signals. The ESPRIT algorithm exploits the invariance properties of such an array so that both angle and polarization estimates may be computed. Some typical examples showing the use of this approach are presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Improving ESPRIT via beamforming

Beamforming techniques are presented that improve the performance of the ESPRIT (estimation of signal parameters via rotational invariance techniques) algorithm for solving the narrowband multiple source location problem with uniform linear arrays. Using ESPRIT with these beamforming approaches maintains the computational advantage of the original ESPRIT while further exploiting the special structure of uniform linear arrays. Simulation results show that the use of ESPRIT with beamforming yields significantly improved performance compared with the original ESPRIT.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Angle and polarization estimation using ESPRIT with a polarization sensitive array

It is shown how estimation of signal parameters via rotational invariance techniques (ESPRIT) may be used to estimate both the arrival directions and the polarizations of incoming plane waves with a uniform linear array of crossed dipoles. The ESPRIT algorithm exploits the invariance properties of such an array so that both angle and polarization estimates may be computed. Some typical examples showing the use of this method are presented. >

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.