High-resolution Parameter Estimation Algorithms Research Articles

An Improved High-Resolution Parameter Estimation Algorithm Incorporating Beamforming Techniques

When the conventional Space-Alternating Generalized Expectation-maximization (SAGE) algorithm is used to estimate the incident angle of the signal received by an antenna array with fixed aperture, the accuracy and resolvability of angle estimation are affected and limited by the aperture of the antenna array. Therefore, in order to improve the angle estimation accuracy and resolvability beyond the limitation, we propose a beamforming (BF)-incorporated SAGE (BFI-SAGE) algorithm, which is a SAGE-based improved high-resolution parameter estimation (HRPE) algorithm, and it steers the orientation of beam utilizing a predefined BF weighting matrix. A non-uniform scanning mode is determined to achieve higher angle estimation accuracy in the angle range of interest, and the analysis of the angle estimation performance is performed for the cases of uniform and non-uniform scanning modes, respectively. The resolvability performance of the algorithm is also discussed.

Modeling Multi-Frequency Characteristics for Classroom and Hall Scenarios at 2-4, 9-11 and 27-29 GHz Bands

This paper investigates the wideband channel characteristics obtained in a classroom and a hall indoor scenarios at 2-4, 9–11 and 27-29GHz. A virtual uniform circular array (UCA) based channel sounding system was utilized to capture wideband spatial channel characteristics. The propagation parameters of multipath components (MPCs) were estimated by using a high resolution parameter estimation (HRPE) algorithm. The estimated MPCs are further grouped into clusters via a novel clustering identification algorithm based on the KPowerMeans algorithm. The comparison of the composite and cluster-level characteristics at multiple frequency bands in both scenarios is investigated. Moreover, the impact of the indoor room environment, i.e. dimension and furniture, on the propagation channel is also analyzed. The statistics of channel parameters at multiple frequency bands extracted constitute a stochastic clustered spatial channel model.

An Improved ESPRIT-Like Algorithm for Coherent Signals DOA Estimation

ESPRIT-like algorithm and its variants are widely used in preprocessing schemes that provide high performance of high-resolution parameter estimation algorithms for coherent signals. However, in the presence of certain phase differences between coherent signals and signal angles, this method suffers serious performance degradation and even fails. In this letter, a modified scheme based on Forward and Backward Partial Toeplitz Matrices Reconstruction named as FB-PTMR is proposed that exploits half rows of the sample covariance matrix (SCM) to reconstruct the data matrix to overcome this weakness. The new approach provides better performance on estimation and probability of resolution compared to the ESPRIT-like method by exploiting a small part of information of the sample covariance matrix. The simulation results verify the effectiveness of our proposed method.

A Complexity-Efficient High Resolution Propagation Parameter Estimation Algorithm for Ultra-Wideband Large-Scale Uniform Circular Array

Millimeter wave (mm-wave) communication with large-scale antenna array configuration is seen as the key enabler of the next generation communication systems. Accurate knowledge of the mm-wave propagation channels is fundamental and essential. In this contribution, a novel complexity-efficient high resolution parameter estimation (HRPE) algorithm is proposed for the mm-wave channel with large-scale uniform circular array (UCA) applied. The proposed algorithm is able to obtain the high-resolution estimation results of the spherical channel propagation parameters. The prior channel information in the delay domain, i.e., the delay trajectories of individual propagation paths observed across the array elements, is exploited, by combining the high-resolution estimation principle and the phase mode excitation technique. Fast initializations, effective interference cancellations, and reduced searching spaces achieved by the proposed schemes significantly decrease the algorithm complexity. Furthermore, the channel spatial non-stationarity in path gain across the array elements is considered for the first time in the literature for propagation parameter estimation, which is beneficial to obtain more realistic results as well as to decrease the complexity. A mm-wave measurement campaign at the frequency band of 28-30GHz using a large-scale UCA is exploited to demonstrate and validate the proposed HRPE algorithm.

Experimental Characterization of Millimeter-Wave Indoor Propagation Channels at 28 GHz

The increasing requirement for the mobile data traffic accelerates the research of millimeter-wave (mm-wave) for future wireless systems. Accurate characterization of the mm-wave propagation channel is fundamental and essential for the system design and performance evaluation. In this paper, we conducted measurement campaigns in various indoor scenarios, including classroom, office, and hall scenarios, at the frequency bands of 27–29 GHz. The spatial channel characteristics were recorded by using a large-scale uniform circular array. A high-resolution parameter estimation algorithm was applied to estimate the mm-wave spherical propagation parameters, i.e., the azimuth angle, elevation angle, delay, source distance, and complex amplitude of multipath components. With the same measurement system, the channel parameters including decay factor, delay spread, angular spread, and line of sight power ratio are investigated thoroughly in individual indoor scenarios and compared in different indoor scenarios. Furthermore, the impact of the furniture richness level and indoor geometry on the propagation parameters are also investigated.

High-Resolution Parameter Estimation for Time-Varying Double Directional V2V Channel

This paper describes a high-resolution parameter estimation algorithm applied to the vehicle-to-vehicle (V2V) multi-dimensional channel measurements. The algorithm, which is based on a maximum likelihood estimator, jointly estimates parameters of multipath components, such as time-of-arrival, direction of departure, direction of arrival, and Doppler shift. It serves to provide a comprehensive understanding of the V2V propagation channel. We propose computationally attractive methods to initialize parameters in the global search and evaluate key components in the local optimization. We also apply the algorithm to actual V2V channel measurement data. The results suggest an overall good performance of the estimator, where 80% of the snapshots have a diffuse power ratio less than 20% and the dominant specular paths match well with the environment and dynamics of the measurement.

Performance Analysis of Multi-Dimensional ESPRIT-Type Algorithms for Arbitrary and Strictly Non-Circular Sources With Spatial Smoothing

Spatial smoothing is a widely used preprocessing scheme to improve the performance of high-resolution parameter estimation algorithms in case of coherent signals or if only a small number of snapshots is available. In this paper, we present a first-order performance analysis of the spatially smoothed versions of R-D Standard ESPRIT and R-D Unitary ESPRIT for sources with arbitrary signal constellations as well as R-D NC Standard ESPRIT and R-D NC Unitary ESPRIT for strictly second-order (SO) non-circular (NC) sources. The derived expressions are asymptotic in the effective signal-to-noise ratio (SNR), i.e., the approximations become exact for either high SNRs or a large sample size. Moreover, no assumptions on the noise statistics are required apart from a zero-mean and finite SO moments. We show that both R-D NC ESPRIT-type algorithms with spatial smoothing perform asymptotically identical in the high effective SNR regime. Generally, the performance of spatial smoothing based algorithms depends on the number of subarrays, which is a design parameter and needs to be chosen beforehand. In order to gain more insights into the optimal choice of the number of subarrays, we simplify the derived analytical R-D mean square error (MSE) expressions for the special case of a single source. The obtained MSE expression explicitly depends on the number of subarrays in each dimension, which allows us to analytically find the optimal number of subarrays for spatial smoothing. Based on this result, we additionally derive the maximum asymptotic gain from spatial smoothing and explicitly compute the asymptotic efficiency for this special case. All the analytical results are verified by simulations.

Deterministic Cramér-Rao Bound for Strictly Non-Circular Sources and Analytical Analysis of the Achievable Gains

Recently, several high-resolution parameter estimation algorithms have been developed to exploit the structure of strictly second-order (SO) non-circular (NC) signals. They achieve a higher estimation accuracy and can resolve up to twice as many signal sources compared to the traditional methods for arbitrary signals. In this paper, as a benchmark for these NC methods, we derive the closed-form deterministic R-D NC Cramer-Rao bound (NC CRB) for the multi-dimensional parameter estimation of strictly non-circular (rectilinear) signal sources. Assuming a separable centro-symmetric R-D array, we show that in some special cases, the deterministic R-D NC CRB reduces to the existing deterministic R-D CRB for arbitrary signals. This suggests that no gain from strictly non-circular sources (NC gain) can be achieved in these cases. For more general scenarios, finding an analytical expression of the NC gain for an arbitrary number of sources is very challenging. Thus, in this paper, we simplify the derived NC CRB and the existing CRB for the special case of two closely-spaced strictly non-circular sources captured by a uniform linear array (ULA). Subsequently, we use these simplified CRB expressions to analytically compute the maximum achievable asymptotic NC gain for the considered two source case. The resulting expression only depends on the various physical parameters and we find the conditions that provide the largest NC gain for two sources. Our analysis is supported by extensive simulation results.

Parameter estimation using the sliding-correlator’s output for wideband propagation channels

In this contribution, a high-resolution parameter estimation algorithm is derived based on the Space-Alternating Generalized Expectation-maximization (SAGE) principle for extracting multipath parameters from the output of sliding correlator (SC). The SC allows calculating channel impulse responses with a sampling rate less than that required by Nyquist criterion, and hence is widely used in real-time wideband (e.g., >500 MHz) channel sounding for the fifth generation wireless communication scenarios. However, since the sounding signal needs to be sent repetitively, the SC-based solution is unacceptable for time-variant channel measurements. The algorithm proposed here estimates multipath parameters by using a parametric model of both low- and high-frequency components of the SC’s output. The latter was considered as distortions and discarded in the conventional SC-based channel sounding. The new algorithm allows estimating path parameters with less repetitions of transmitting the sounding signal and still exhibits higher estimation accuracy than the conventional method. Simulations are conducted and illustrate the root mean square estimation errors and the resolution capability of the proposed algorithm with respect to the bandwidth and the length of the SC’s output. These studies pave the way for measuring time-variant wideband propagation channels using SC-based solutions.

R-dimensional esprit-type algorithms for strictly second-order non-circular sources and their performance analysis

High-resolution parameter estimation algorithms designed to exploit the prior knowledge about incident signals from strictly second-order (SO) noncircular (NC) sources allow for a lower estimation error and can resolve twice as many sources. In this paper, we derive the R \mathchar"702DD NC Standard ESPRIT and the R \mathchar"702DD NC Unitary ESPRIT algorithms that provide a significantly better performance compared to their original versions for arbitrary source signals. They are applicable to shift-invariant R \mathchar"702DD antenna arrays and do not require a centro-symmetric array structure. Moreover, we present a first-order asymptotic performance analysis of the proposed algorithms, which is based on the error in the signal subspace estimate arising from the noise perturbation. The derived expressions for the resulting parameter estimation error are explicit in the noise realizations and asymptotic in the effective signal-to-noise ratio (SNR), i.e., the results become exact for either high SNRs or a large sample size. We also provide mean squared error (MSE) expressions, where only the assumptions of a zero mean and finite SO moments of the noise are required, but no assumptions about its statistics are necessary. As a main result, we analytically prove that the asymptotic performance of both R \mathchar"702DD NC ESPRIT-type algorithms is identical in the high-effective SNR regime. Finally, a case study shows that no improvement from strictly non-circular sources can be achieved in the special case of a single source.

Audio Modeling Based on Delayed Sinusoids

In this work, we present an evolution of the Damped and Delayed Sinusoidal (DDS) model introduced within the framework of the general signal modeling. This model, named the Partial Damped and Delayed Sinusoidal (PDDS) model, takes into account a single time delay parameter for a set (sum) of damped sinusoids. The proposed modification is more consistent with the transient audio modeling problem. The validity of the approach is shown by its comparison with the well-known Exponentially Damped Sinusoids (EDS) approach. Finally, the performances of the three models based high-resolution parameter estimation algorithms are compared on synthetic fast time-varying signals, and on two typical audio transients.