Estimation Of Signal Parameters Via Rotational Invariance Techniques Research Articles

Time-Frequency Multi-Invariance ESPRIT for DOA Estimation

In this letter, a new ESPRIT-type algorithm is presented for estimating the direction of arrival (DOA). We reconstruct the received signal to form a time-frequency $(t{\hbox{-}}f)$ data model with a multi-invariance (MI) property, and then a $t{\hbox{-}}f$ MI Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) algorithm for DOA estimation is proposed. This algorithm constructs a spatial $t{\hbox{-}}f$ distribution matrix in terms of variable temporal and frequency points and combines multiple subarrays rearranged from a uniform linear array (ULA) to estimate far-field narrowband signals. Considering the model of multiple narrowband chirp sources, simulation results illustrate that $t{\hbox{-}}f$ MI-ESPRIT outperforms $t{\hbox{-}}f$ ESPRIT and MI-ESPRIT under different signal-to-noise ratio (SNR) and snapshot numbers.

Blind Direction-of-Arrival Estimation with Uniform Circular Array in Presence of Mutual Coupling

A blind direction-of-arrival (DOA) estimation algorithm based on the estimation of signal parameters via rotational invariance techniques (ESPRIT) is proposed for a uniform circular array (UCA) when strong electromagnetic mutual coupling is present. First, an updated UCA model with mutual coupling in a discrete Fourier transform (DFT) beam space is deduced, and the new manifold matrix is equal to the product of a centrosymmetric diagonal matrix and a Vandermonde-structure matrix. Then we carry out blind DOA estimation through a modified ESPRIT method, thus avoiding the need for spatial angular searching. In addition, two mutual coupling parameter estimation methods are presented after the DOAs have been estimated. Simulation results show that the new algorithm is reliable and effective especially for closely spaced signals.

Superresolved swept-wavelength interferometry using frequency estimation methods.

The high signal-to-noise ratios typical of swept-wavelength interferometry (SWI) enable distance measurements to be superresolved with 2σ uncertainties as low as 10-4-10-5 of Fourier transform-limited resolution. We compare three methods of superresolving SWI distance measurements: Local Linear Regression (LLR), Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT), and Nonlinear Least Squares (NLS). We find that the superresolution method limits both measurement precision and minimum superresolvable distance. Measurement uncertainty is determined by both the superresolution method and the SWI hardware, while SWI hardware alone limits the maximum superresolvable distance. For very short distances, between 2 and 20 times the SWI system's Fourier transform-limited resolution, NLS provides unbiased estimates with the least uncertainty. At longer distances, LLR provides the fastest unbiased estimates. LLR and NLS are more noise tolerant than ESPRIT and are found to operate close to the Cramér-Rao bound. With sufficient SNR, they provide 1σ measurement precision of 10-4 of the transform limit.

An Effective Pre-processing Technique for Robust ESPRIT-Based Single-Tone Frequency Estimation against an I/Q Mismatch

This letter proposes an effective pre-processing technique for robust estimation of signal parameters via rotational invariance techniques (ESPRIT)-based single-tone frequency estimation against an I/Q mismatch. For the implementation of a high-accuracy ranging/radar system, parametric-based algorithms such as MUSIC, ESPRIT and their variations have been adopted due to their superior estimation capability for single-tone frequency estimations. Since most ranging/radar system have the direct conversion architecture, I/Q imbalance problem occurs in the form of spurious frequencies of the frequency spectrum. For this reason, the proposed pre-processing technique is designed to make use of the Hilbert transform structure for received signals. The estimation performance of the proposed pre-processing technique in single-tone frequency is derived and compared with the results from a Monte-Carlo simulation. The root-mean-square error (RMSE) of the proposed method is compared with that of an ESPRIT-based method for various parameters. DOI: http://dx.doi.org/10.5755/j01.eee.21.6.13757

A HYBRID METHOD IN VEGETATION HEIGHT ESTIMATION USING POLINSAR IMAGES OF CAMPAIGN BIOSAR

Abstract. Recently, there have been plenty of researches on the retrieval of forest height by PolInSAR data. This paper aims at the evaluation of a hybrid method in vegetation height estimation based on L-band multi-polarized air-borne SAR images. The SAR data used in this paper were collected by the airborne E-SAR system. The objective of this research is firstly to describe each interferometry cross correlation as a sum of contributions corresponding to single bounce, double bounce and volume scattering processes. Then, an ESPIRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) algorithm is implemented, to determine the interferometric phase of each local scatterer (ground and canopy). Secondly, the canopy height is estimated by phase differencing method, according to the RVOG (Random Volume Over Ground) concept. The applied model-based decomposition method is unrivaled, as it is not limited to specific type of vegetation, unlike the previous decomposition techniques. In fact, the usage of generalized probability density function based on the nth power of a cosine-squared function, which is characterized by two parameters, makes this method useful for different vegetation types. Experimental results show the efficiency of the approach for vegetation height estimation in the test site.

Coupling Characterization of a Linear Dipole Array to Improve Direction-of-Arrival Estimation

The reciprocity theorem is applied to account for the electromagnetic coupling among antennas in a linear dipole array, in the presence of multiple incident plane waves. By utilizing the direction-dependent coupling information, the direction-of-arrivals of the incident waves can be estimated more accurately by using the conventional ESPRIT (estimation of signal parameter via rotational invariance technique). Different coupling compensation methods in the literatures are compared to validate this method, and multiple incident waves of both uncorrelated and coherent nature are also simulated.

Estimation of multipath parameters in wireless communications using multi-way compressive sensing

This paper addresses the problem of joint angle and delay estimation (JADE) in a multipath communication scenario. A low-complexity multi-way compressive sensing (MCS) estimation algorithm is proposed. The received data are firstly stacked up to a trilinear tensor model. To reduce the computational complexity, three random compression matrices are individually used to reduce each tensor to a much smaller one. JADE then is linked to a low-dimensional trilinear model. Our algorithm has an estimation performance very close to that of the parallel factor analysis (PARAFAC) algorithm and automatic pairing of the two parameter sets. Compared with other methods, such as multiple signal classification (MUSIC), the estimation of signal parameters via rotational invariance techniques (ESPRIT), the MCS algorithm requires neither eigenvalue decomposition of the received signal covariance matrix nor spectral peak searching. It also does not require the channel fading information, which means the proposed algorithm is blind and robust, therefore it has a higher working efficiency. Simulation results indicate the proposed algorithm have a bright future in wireless communications.

English

Source Direction of arrival (DOA) estimation plays an important role in array signal processing, and has a wide range of application. Array signal processing is an important branch in the field of signal processing. In recent years, it has developed dramatically. It can be applied in fields such as radar, communication, sonar, earthquake, exploration, astronomy and biomedicine. Over the past few years, all kinds of algorithms which can be used in DOA estimation have made great achievements, the most classic algorithms among which are Multiple Signal Classification (MUSIC) and Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT).In this paper, we will give an overview on performance of the DOA estimation based on MUSIC as well as ESPRIT (both TLS and Pro ESPRIT) algorithm on uniform linear array (ULA) and in the presence of white noise. We will describe what DOA estimation is, and give a mathematical model of DOA estimation for subspace based DOA methods such as MUSIC, TLS ESPRIT and Pro ESPRIT. Then estimate DOA based on the MUSIC, TLS ESPRIT as well as Pro ESPRIT algorithm, with simulations with MATLAB to simulate factors that affect accuracy and resolution of DOA estimation. The performance of these DOA algorithms for a set of input parameters such as number of snapshots, number of array elements, signal-to-noise ratio, angular separation, element spacing are investigated.

IJARCCE - Computer and Communication Engineering

The deployment of smart antennas provides greater capacity and performance benefits over standard antennas because they are used to customize antenna coverage patterns to match the traffic conditions in a wireless network. The two basic functions of any smart antenna are Direction of Arrival (DOA) Estimation and Adaptive Beam forming (ABF). Direction of Arrival algorithms work on the signals received at the antenna array elements and computes the angle of arrival of all incoming signals that include the desired user signal, interfering signals and multipath signals. Ultimately a beam is steered along the direction of the desired signal, while a null is generated along the direction of the interfering signal. The smart antenna sub system estimate the direction of arriving signals by employing DOA algorithms. The performance of smart antenna greatly depends on the effectiveness of DOA estimation algorithm. This paper analyzed the performance of MUSIC (Multiple Signal Classification) and ESPIRIT (Estimation of Signal Parameters via Rotational Invariance Technique) algorithms for DOA estimation. All the simulations are carried out using MATLAB.

Two-Dimensional DOA Estimation for Uniform Rectangular Array Using Reduced-Dimension Propagator Method

A novel algorithm is proposed for two-dimensional direction of arrival (2D-DOA) estimation with uniform rectangular array using reduced-dimension propagator method (RD-PM). The proposed algorithm requires no eigenvalue decomposition of the covariance matrix of the receive data and simplifies two-dimensional global searching in two-dimensional PM (2D-PM) to one-dimensional local searching. The complexity of the proposed algorithm is much lower than that of 2D-PM. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm and conventional PM algorithms, also very close to 2D-PM. The angle estimation error and Cramér-Rao bound (CRB) are derived in this paper. Furthermore, the proposed algorithm can achieve automatically paired 2D-DOA estimation. The simulation results verify the effectiveness of the algorithm.

Transmit and Receive Array Gain-Phase Error Estimation in Bistatic MIMO Radar

An Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT)-based method is presented to estimate the gain-phase errors of both transmit and receive arrays in bistatic multiple-input–multiple-output (MIMO) radar. Unlike the recent ESPRIT-like methods, the directions of unknown targets and uncalibrated array steering vectors are first estimated with ESPRIT-based algorithm, and the array gain-phase errors are calculated subsequently. Compared to the existing ESPRIT-like methods, the proposed method is much more computationally efficient and has better performance. Simulation results confirm the effectiveness of the proposed method.

Joint DFT-ESPRIT Estimation for TOA and DOA in Vehicle FMCW Radars

This letter proposes a joint discrete Fourier transform (DFT)—estimation of signal parameters via rotational invariance techniques (ESPRIT) estimator for time-of-arrival (TOA) and direction-of-arrival (DOA) in vehicle frequency-modulated continuous-wave (FMCW) radars. Since the vehicle FMCW radar should recognize vehicles in the side/rear area when the driver initiates a lane change, the estimation of the joint TOA/DOA between the radar and targets is an important issue for solving complicated location tasks. However, conventional joint estimation methods such as 2D-ESPRIT and 2D-multiple signal classification (MUSIC) cannot be adopted for real-time implementation due to their high computational loads. To satisfy the required accuracy specifications and reduce complexity compared with the conventional estimator, we propose a low-complexity joint TOA and DOA estimator that uses the combined DFT-ESPRIT algorithm for FMCW radars. The performance of the proposed estimation in multitarget environments was derived and compared with the Monte Carlo simulation results. The root-mean-square error (RMSE) of the proposed method was compared with that of 2D-ESPRIT with various parameters. To verify the performance of the proposed combination method, we implemented the FMCW radar and verified its performance in an anechoic chamber environment.

Two-Dimensional Direction of Arrival (DOA) Estimation for Rectangular Array via Compressive Sensing Trilinear Model

We investigate the topic of two-dimensional direction of arrival (2D-DOA) estimation for rectangular array. This paper links angle estimation problem to compressive sensing trilinear model and derives a compressive sensing trilinear model-based angle estimation algorithm which can obtain the paired 2D-DOA estimation. The proposed algorithm not only requires no spectral peak searching but also has better angle estimation performance than estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Furthermore, the proposed algorithm has close angle estimation performance to trilinear decomposition. The proposed algorithm can be regarded as a combination of trilinear model and compressive sensing theory, and it brings much lower computational complexity and much smaller demand for storage capacity. Numerical simulations present the effectiveness of our approach.

Frequency Domain Extended-MUSIC Algorithm for TOA Estimation in Indoor UWB Radio Impulse Channels

indoor localization systems have grown considerably, in order to obtain a high performance in dense multipath propagation channels. Many studies have been done in this area. Different methods have been proposed based on the use of Impulse Radio Ultra Wide band (IR-UWB) signals. These systems are usually based on time of arrival estimation using different algorithms. In this paper, a new time of arrival estimation algorithm is proposed for impulse radio UWB systems, which is based on the EXTENDED-MUSIC algorithm. Furthermore, Simulation results show these performances in multipath channels. The proposed method is compared with other high resolution algorithms such as Multiple SIGNAL Classification (MUSIC), estimation of signal parameters via rotational invariance technique (ESPRIT) and the MATRIX PENCIL method. Simulation will justify the high accuracy and resolution localization capabilities of the proposed algorithm. KeywordsRadio Ultra Wide band (IR-UWB), Time of arrival estimation (TOA), EXTENDED-MUSIC, ESPRIT, MATRIX PENCIL

Fast ESPRIT algorithms based on partial singular value decompositions

Let h(x) be a nonincreasing exponential sum of order M. For N given noisy sampled values hn=h(n)+en(n=0,…,N−1) with error terms en, all parameters of h(x) can be estimated by the known ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) method. The ESPRIT method is based on singular value decomposition (SVD) of the L-trajectory matrix (hℓ+m)ℓ,m=0L−1,N−L, where the window length L fulfills M≤L≤N−M+1. The computational cost of the ESPRIT algorithm is dominated by the cost of SVD. In the case L≈N2, the ESPRIT algorithm based on complete SVD costs about 218N3+M2(21N+913M) operations. Here we show that the ESPRIT algorithm based on partial SVD and fast Hankel matrix–vector multiplications has much lower cost. Especially for L≈N2, the ESPRIT algorithm based on partial Lanczos bidiagonalization with S steps requires only about 18SNlog2⁡N+S2(20N+30S)+M2(N+13M) operations, where M≤S≤N−L+1. Numerical experiments demonstrate the high performance of these fast ESPRIT algorithms for noisy sampled data with relatively large error terms.

A Direction of Arrival Estimation Method for Wideband LFM Signals Based on RWT and ESPRIT Algorithm

In this paper, we proposed a new algorithm to estimate the direction of arrival (DOA) for wideband linear frequency modulation (LFM) signals, using Radon-Wigner transform (RWT) and estimation of signal parameter via rotational invariance techniques (ESPRIT). To eliminate the cross-terms, we first utilize the RWT with its excellent time-frequency concentration performance. Then, through peak searching, the number of targets, the initial interference and the frequency modulation slope are estimated. On the this base, the array signals are reconstructed. Finally, we adopt the ESPRIT algorithm to estimate the DOA of the array signals. The simulation results show that the proposed algorithm can estimate the DOA of non-stationary signals with high precision.

Joint angle and Doppler frequency estimation of coherent targets in monostatic MIMO radar

This paper discusses the problem of joint direction of arrival (DOA) and Doppler frequency estimation of coherent targets in a monostatic multiple-input multiple-output radar. In the proposed algorithm, we perform a reduced dimension (RD) transformation on the received signal first and then use forward spatial smoothing (FSS) technique to decorrelate the coherence and obtain joint estimation of DOA and Doppler frequency by exploiting the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. The joint estimated parameters of the proposed RD-FSS-ESPRIT are automatically paired. Compared with the conventional FSS-ESPRIT algorithm, our RD-FSS-ESPRIT algorithm has much lower complexity and better estimation performance of both DOA and frequency. The variance of the estimation error and the Cramer–Rao Bound of the DOA and frequency estimation are derived. Simulation results show the effectiveness and improvement of our algorithm.

Time-Delay Estimation for Ground Penetrating Radar Using ESPRIT With Improved Spatial SmoothingTechnique

Estimating the time delays of buried target echoes is particularly important for the application of ground penetrating radar (GPR). Due to its smaller computational burden, the estimation of signal parameters via rotational invariance technique (ESPRIT) is preferred to process the buried target echoes in the frequency domain and to obtain the accurate super-resolution time delays. In this letter, we give an in-depth analysis of the essential preprocessing steps for the application of ESPRIT to practical GPR measurement data. In particular, an improved spatial smoothing method is adopted to construct the correlation matrix for the robustness of the time-delay estimation result. The effectiveness of the algorithm is verified by the synthetic data from a horizontally stratified medium model using the finite-difference time-domain method, which explicitly takes into account surface scattering for a more realistic scenario.

Unitary reduced‐dimensional estimation of signal parameters via rotational invariance techniques for angle estimation in monostatic multiple‐input–multiple‐output radar with rectangular arrays

In this study, the issue of two-dimensional direction of arrival estimation in monostatic multiple-input–multiple-output (MIMO) radar with rectangular arrays is studied and an algorithm based on estimation of signal parameters via rotational invariance techniques (ESPRIT) is proposed. Through the reduced-dimensional transformation and unitary transformation, the proposed algorithm has very low complexity because of the low-dimension and real-valued computations. Meanwhile, the angle estimation performance of the proposed algorithm is improved compared with conventional ESPRIT and Unitary ESPRIT. The real-valued rotational invariance is utilised to achieve automatically paired estimations of the azimuth and elevation angles in monostatic MIMO radar. Error analysis of the angle estimation and Cramer-Rao bound in MIMO radar are derived. Simulation results verify the improvement and usefulness of our algorithm.

Computationally efficient near-field source localization using third-order moments

In this paper, a third-order moment-based estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm is proposed for passive localization of near-field sources. By properly choosing sensor outputs of the symmetric uniform linear array, two special third-order moment matrices are constructed, in which the steering matrix is the function of electric angle γ, while the rotational factor is the function of electric angles γ and ϕ. With the singular value decomposition (SVD) operation, all direction-of-arrivals (DOAs) are estimated from a polynomial rooting version. After substituting the DOA information into the steering matrix, the rotational factor is determined via the total least squares (TLS) version, and the related range estimations are performed. Compared with the high-order ESPRIT method, the proposed algorithm requires a lower computational burden, and it avoids the parameter-match procedure. Computer simulations are carried out to demonstrate the performance of the proposed algorithm.