Joint Angle And Delay Estimation Research Articles

Efficient Joint DOA and TOA Estimation for Indoor Positioning With 5G Picocell Base Stations

The ubiquity, large bandwidth, and spatial diversity of the fifth generation (5G) cellular signal render it a promising candidate for accurate positioning in indoor environments where the global navigation satellite system (GNSS) signal is absent. In this paper, a joint angle and delay estimation (JADE) scheme is designed for 5G picocell base stations (gNBs) which addresses two crucial issues to make it both effective and efficient in realistic indoor environments. Firstly, the direction-dependence of the array modeling error for picocell gNB as well as its impact on JADE is revealed. This error is mitigated by fitting the array response measurements to a vector-valued function and pre-calibrating the ideal steering-vector with the fitted function. Secondly, based on the deployment reality that 5G picocell gNBs only have a small-scale antenna array but have a large signal bandwidth, the proposed scheme decouples the estimation of time-of-arrival (TOA) and direction-of-arrival (DOA) to reduce the huge complexity induced by two-dimensional joint processing. It employs the iterative-adaptive-approach to resolve multipath signals in the TOA domain, followed by a conventional beamformer to retrieve the desired line-of-sight DOA. By further exploiting a dimension-reducing pre-processing module and accelerating spectrum computing by fast Fourier transforms, an efficient implementation is achieved for real-time JADE. Numerical simulations demonstrate the superiority of the proposed method in terms of DOA estimation accuracy. Field tests show that a triangulation positioning error of 0.44 m is achieved for 90% cases using only DOAs estimated at two separated receiving points.

Joint Estimation of Multipath Angles and Delays for Millimeter-Wave Cylindrical Arrays With Hybrid Front-Ends

Accurate channel parameter estimation is challenging for wideband millimeter-wave (mmWave) large-scale hybrid arrays, due to beam squint and much fewer radio frequency (RF) chains than antennas. This article presents a novel joint angle and delay estimation (JADE) approach for wideband mmWave fully-connected hybrid uniform cylindrical arrays. We first design a new hybrid beamformer to reduce the dimension of received signals on the horizontal plane by exploiting the convergence of the Bessel function, and to reduce the active beams in the vertical direction through preselection. The important recurrence relationship of the received signals needed for subspace-based angle and delay estimation is preserved, even with substantially fewer RF chains than antennas. Then, linear interpolation is generalized to reconstruct the received signals of the hybrid beamformer, so that the signals can be coherently combined across the whole band to suppress the beam squint. As a result, efficient subspace-based algorithm algorithms can be developed to estimate the angles and delays of multipath components. The estimated delays and angles are further matched and correctly associated with different paths in the presence of non-negligible noises, by putting forth perturbation operations. Simulations show that the proposed approach can approach the Cramér-Rao lower bound (CRLB) of the estimation with a significantly lower computational complexity than existing techniques.

Wireless channel parameter estimation algorithms: Recent advances and future challenges

Temporal and three-dimensional (3D) spatial information is important for the characterization of wireless channels. In this paper, the commonly used array signal processing (ASP) methods to estimate channel parameters are summarized. Firstly, algorithms that can be used to estimate azimuth angle of arrival (AAoA) and elevation AoA (EAoA) are introduced. They include multiple signal classification (MUSIC), estimation of signal parameter via rotational invariance techniques (ESPRIT), and Unitary ESPRIT algorithms. Secondly, algorithms that can be used to jointly estimate delay, AAoA, and EAoA are given. They include joint angle and delay estimation (JADE) MUSIC, JADE ESPRIT, shift-invariance (SI) JADE, and space-alternating generalized expectation-maximization (SAGE) algorithms. We also propose an improved SI-JADE algorithm to further reduce computation complexity by incorporating with the Unitary ESPRIT algorithm. Performance of the above algorithms to extract only spatial information and to jointly extract temporal and spatial information is compared in both synthetic and 60 GHz real channel environments. Simulation results show that with the inclusion of delay estimation, the joint temporal and spatial estimation algorithms can provide better resolution than algorithms estimating only angles. Measurement data processing results show that MUSIC algorithm can provide comparable results with SAGE algorithm in estimating AAoA and EAoA. SI-JADE and the improved SI-JADE algorithms are also applicable to process 60 GHz channel measurement data. However, MUSIC, SI-JADE, and the improved SI-JADE algorithms can greatly reduce computational burden compared with SAGE algorithm. At last, some future directions are pointed out.

Data preprocessing for a vehicle-based localization system used in road traffic applications

Abstract. This paper presents a fixed-point implementation of the preprocessing using a field programmable gate array (FPGA), which is required for a multipath joint angle and delay estimation (JADE) used in road traffic applications. This paper lays the foundation for many model-based parameter estimation methods. Here, a simulation of a vehicle-based localization system application for protecting vulnerable road users, which were equipped with appropriate transponders, is considered. For such safety critical applications, the robustness and real-time capability of the localization is particularly important. Additionally, a motivation to use a fixed-point implementation for the data preprocessing is a limited computing power of the head unit of a vehicle. This study aims to process the raw data provided by the localization system used in this paper. The data preprocessing applied includes a wideband calibration of the physical localization system, separation of relevant information from the received sampled signal, and preparation of the incoming data via further processing. Further, a channel matrix estimation was implemented to complete the data preprocessing, which contains information on channel parameters, e.g., the positions of the objects to be located. In the presented case of a vehicle-based localization system application we assume an urban environment, in which multipath propagation occurs. Since most methods for localization are based on uncorrelated signals, this fact must be addressed. Hence, a decorrelation of incoming data stream in terms of a further localization is required. This decorrelation was accomplished by considering several snapshots in different time slots. As a final aspect of the use of fixed-point arithmetic, quantization errors are considered. In addition, the resources and runtime of the presented implementation are discussed; these factors are strongly linked to a practical implementation.

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.

Joint Space-Time Parameter Estimation for Underwater Communication Channels with Velocity Vector Sensor Arrays

In this paper, the problem of joint space-time parameter estimation for underwater wireless communication channels in a multipath environment is addressed. We consider the receive antenna array to be configured with multiple vector sensors, each of which consists of a pair of orthogonal velocity sensors. A quadrilinear model for the channel is formulated, and a quadrilinear decomposition method developed for joint angle and delay estimation (JADE). In addition, a computationally simple subspace-based algorithm is proposed for the problem under consideration. The basic idea behind this algorithm is to use the angle information embedded in the velocity vector sensors to start two polynomial rooting procedures for the angle and delay in succession. Simulation results are finally presented to verify the efficacy of the proposed algorithms.

PARALIND-based blind joint angle and delay estimation for multipath signals with uniform linear array

A novel joint angle and delay estimation (JADE) algorithm for multipath signals, based on the PARAllel profiles with LINear Dependencies (PARALIND) model, is proposed. Capitalizing on the structure property of Vandermonde matrices, PARALIND model is proved to be unique. Angle and delay of multiple rays of sources can be estimated by PARALIND decomposition and an ESPRIT-like shift-invariance technique. Simulation results show that the proposed algorithm outperforms the traditional JADE algorithm. It can automatically distinguish the estimated parameters between sources, and still be available when the number of rays is larger than the number of receiving antennae.

A Space-Time Signal Decomposition Algorithm for Downlink MIMO DS-CDMA Receivers

We propose a dimension reduction algorithm for the receiver of the downlink of direct-sequence code-division multiple access (DS-CDMA) systems in which both the transmitters and the receivers employ antenna arrays of multiple elements. To estimate the high order channel parameters, we develop a layered architecture using dimension-reduced parameter estimation algorithms to estimate the frequency-selective multipath channels. In the proposed architecture, to exploit the space-time geometric characteristics of multipath channels, spatial beamformers and constrained (or unconstrained) temporal filters are adopted for clustered-multipath grouping and path isolation. In conjunction with the multiple access interference (MAI) suppression techniques, the proposed architecture jointly estimates the direction of arrivals, propagation delays, and fading amplitudes of the downlink fading multipaths. With the outputs of the proposed architecture, the signals of interest can then be naturally detected by using path-wise maximum ratio combining. Compared to the traditional techniques, such as the Joint-Angle-and-Delay-Estimation (JADE) algorithm for DOA-delay joint estimation and the space-time minimum mean square error (ST-MMSE) algorithm for signal detection, computer simulations show that the proposed algorithm substantially mitigate the computational complexity at the expense of only slight performance degradation.

Numerically Efficient Implementation of JADE ML Algorithm

Numerical implementation of JADE (Joint Angle and Delay Estimation) ML (Maximum Likelihood) requires multidimensional nonlinear optimization which is computationally intensive and is highly dependent on initial estimates. If the number of the incident signal is L, we have to perform 2L-dimensional search which is computationally intensive for large L. To alleviate the high computational load associated with multi-dimensional search, we replace multi-dimensional search with successive two-dimensional search. Performance of the proposed scheme is evaluated using numerical results.

Angle and delay estimation of space-time channels for TD-CDMA systems

In mobile communications the antenna arrays make it possible to estimate the path delay, angle of arrival (or angle), and amplitude for the multipath propagation channel. This paper considers the problem of joint angle and delay estimation (JADE) in a time-division code-division multiple access system. The angle/delay estimation is made from multiple slots by exploiting the angle/delay invariance of the channel regardless of the fast faded amplitude variation (i.e., angle/delay is assumed quasistatic). JADE can be approximated by methods that estimate the frequencies of the two-dimensional complex sinusoids where the faded amplitudes act as sources. The shift invariance method (JADE-ESPRIT) is chosen for its capacity to pair delay and angle for each path and each user. The consistency of JADE-ESPRIT for a large enough number of slots guarantees the feasibility of the method for spatially correlated noise. The performance of the JADE method for channel estimation is evaluated analytically and numerically in propagation environments with increasing complexity.

Joint angle and delay estimation (JADE) for multipath signals arriving at an antenna array

In wireless communications, mobiles emit signals that arrive at a base station via multiple paths. Estimating each path's angle-of-arrival (AOA) and propagation delay is necessary for several applications, such as mobile localization for emergency services. We propose a novel subspace approach to estimate the AOA and delays of multipath signals from digitally modulated sources arriving at an antenna array. Our method uses a collection of estimates of a space-time vector channel. The Cramer-Rao bound (CRB) and simulations are provided.