Accurate 2-D AOA Estimation and Ambiguity Resolution for a Single Source under Fixed Uniform Circular Arrays

This paper presents an ultra wideband (UWB) channel sounding scheme with a technique for time of arrival (TOA) and angle of arrival (AOA) estimation using transformation between time and frequency domain signals. Since the measured UWB power spectrum can be transformed into a time domain signal, both frequency and time domain signals are available for the channel estimation. We propose the TOA estimation using a peak search over time domain signal, and the AOA estimation using MUSIC and AOA histograms. The AOA histogram is counted for AOA samples of discrete frequencies over the UWB bandwidth. Comparing both AOA histograms before and after TOA subtraction, we can determine pairing the elements of TOA vector and those of AOA vector. The anechoic chamber tests indicated that both TOAs and AOAs were precisely resolved for two-path model, even if either TOAs or AOAs were too close to resolve.

An angle of arrival (AOA) estimator is presented. Many applications require accurate AOA estimates such as wireless positioning and signal enhancement using space-processing techniques. The proposed AOA estimator depends on the Cholesky decomposition of the received signal autocorrelation matrix. The resultant decomposed matrices are used to modify the crosscorrelation matrix of the received signals at the antenna array doublets. The proposed method is named the Cholesky-decomposition-based-AOA (CDBA) estimator. In comparison with the TLS-ESPRIT algorithm which utilizes the eigenvalue decomposition (EVD) of the received signal autocorrelation and crosscorrelation matrices, the CDBA method has better performance than the TLS-ESPRIT algorithm especially in low signal-to-noise-ratio (SNR) cases. Simulations for the proposed CDBA method are shown to assess its performance.

Although massive multiple-input multiple-output (MIMO) can enhance the overall system performance significantly, it could suffer from high cost and power consumption issues due to using a large number of radio frequency (RF) chains. Two different approaches are commonly exploited to overcome these issues. The first approach is using hybrid beamforming, which consists of analog and digital beamforming, to reduce the total number of RF chains. The second approach is adopting low-resolution analog-todigital converters (ADCs) for each RF chain. For both approaches, channel estimation becomes a difficult task. This paper addresses the problem of channel angle of arrival (AoA) estimation in massive MIMO using both hybrid beamforming and one-bit magnitudeaided (OMA) ADCs. An iterative algorithm is developed to estimate the channel AoA, and the appropriate threshold per iteration is analyzed. Numerical results show that the proposed technique can achieve sufficient AoA estimation performance with practical values of the signal-to-noise ratio (SNR).

A new type of hybrid antenna array consisting of analogue subarrays followed by a digital beamformer is proposed for practical implementation of long range high data rate millimetre wave communications systems. An adaptive algorithm, referred to as the differential beam search (DBS), is proposed for the angle of arrival (AoA) estimation to control the phase shifters in the analogue subarrays and to perform digital beamforming. This algorithm does not need the knowledge of a reference signal and effectively solves the phase ambiguity problem in AoA estimation inherent to the practical subarray configuration. The performance of the proposed DBS algorithms is demonstrated by simulations.

This paper proposes conditioning angle of arrival (AOA) algorithms for pseudo-spectrum fingerprint acquisition based on line of sight (LOS) and non-LOS detection schema for optimizing indoor localization. The proposed approach merges two AOA based methods being that of the MUltiple Signal Classsification (MUSIC) algorithm and virtual MUSIC algorithm into a conditional based localization approach with a uniform circular array (UCA). The paper begins by demonstrating the environmental dependencies of the two AOA approaches based on the Rician <tex>$K$</tex>-factor metric. The <tex>$K$</tex>-factor is then exploited as an algorithm selection metric to arrive at improved localization performance in a realistic indoor environment.

The Global Positioning System (GPS) is one of the core technologies for the Location Based Service (LBS), and has various applications from the leisure business to the military. LBS requires accurate location information of an object, which GPS estimates using at least four satellites. In order to enhance the GPS signal quality, angle-of-arrival (AOA) information is required, but these are difficult to estimate because the system operates with a low signal-to-noise ratio (SNR) and in the presence of high-power interference. In this paper, we explore an extension of a recently proposed GPS AOA estimation technique based on a modified despreader for multiple GPS signals. Since the extended estimator does not require a singular value decomposition (SVD), unlike conventional AOA estimation algorithms such as Multiple Signal Classification (MUSIC), it has a relatively low computational complexity. Also, this technique does not need to choose the GPS AOAs from all estimated AOAs because those of the interference signals are not estimated. We demonstrate the performance of the GPS AOA estimation algorithm for multiple satellites via a computer simulation example.

When conventional phase shifter based arrays are used in millimeter-wave systems, the angle of departure (AoD) and angle of arrival (AoA) estimates are obtained through high-overhead exhaustive beam sweeping (EBS). Recently, true-time-delay arrays re-emerged as a promising architecture for fast angle estimation. In this work, we develop an algorithm for joint AoD and AoA estimation using only one Orthogonal Frequency Division Multiplexing (OFDM) symbol and frequency-dependent beams that can be synthesized by fully digital and true-time-delay arrays. We compare the developed algorithm with wideband single-carrier based EBS in terms of the misalignment probability and required training overhead. Numerical simulations in millimeter-wave channels reveal the advantages of the proposed algorithm.

This paper proposes a new precoding approach that utilizes two-dimension (2D) Angle of Arrival (AOA) based precoding (beamforming) approach for multi-cell massive Multiple Input Multiple Output (m-MIMO) systems. The proposed approach enhances the performance of m-MIMO systems by overcoming pilot contamination that is caused by corrupted Channel State Information. It exploits the spatial properties of the propagated signals to distinguish between various users in the multi-cell m-MIMO system and selects the partial space for beamforming based on AOA information to suppress inter-cell and intra-cell interference. We mathematically analyze the impact of pilot contamination in the uplink training and determine the closed-form approximation of uplink Minimum Mean Square Error (MMSE) estimator. By using MMSE estimator as well as 2D AOA and channel estimation, a corresponding MMSE precoding which minimizes the effect of pilot contamination and improves the downlink achievable rate of the system is proposed. Furthermore, a comparison is made of the resolution between the various 2D AOA estimations for an m-MIMO and the optimum values closest bound for the proposed precoding was chosen to reduce pilot contamination. The performance of the proposed MMSE precoding based on 2D AOA estimation methods: 2D Unitary Estimation of Signal Parameters via Rotational Invariance Techniques (2D UESPRIT), 2D Fourier Domain Line Search MUSIC (2D FDLSM), and 2D Propagator Method (2D PM)) were evaluated and compared with pilot contaminated system and deterministic MMSE precoding in terms of their respective achievable rate. Simulation results reveal that the achievable sum rate gains of 2D UESPRIT, 2D FDLSM, and 2D PM based precoding which achieved 97.6%, 92.4%, 86.3% of the desired deterministic performance, respectively, compared to pilot contaminated system, which achieved only 90% even when in the best case of having only one contaminated user out of all users within the activated cell. Findings from our research indicate the feasibility of utilizing the AOA based beamforming to reduce pilot contamination effect that is inherent in a larger order m-MIMO system for the Fifth Generation (5G) wireless transmissions.

The conventional approaches to estimate the angle of arrival (AOA) have the limitations of hardware complexity, and where algorithms are used to optimize the estimation, there is the associated computational cost. This work presents a simplified approach in which the sum and difference of the received signals using a hybrid rat race (HRR) coupler forms the basis for the estimation of AOA. A 2 × 1 array designed to operate at 10 GHz is fed with quarter wave transformer impedance matching technique and is integrated with HRR coupler. The integrated array is evaluated for AOA estimate in both the azimuth and elevation planes. The transmit antenna foot print of 0° to ± 90° is considered. Result of the study indicates that the designed HRR coupler works optimally, exhibiting minimal reflection loss at each port, reasonable isolation between ports and acceptable phase difference at the sum (Σ) and difference (Δ) ports. The array exhibits a resolution capacity of ± 55° in the azimuth plane and ± 48° in the elevation plane, with an RMS error of less than 5° in both the planes. Beyond the estimated AOA, the array exhibits very high values of RMS error. Typically, in the transmit antenna footprint of ± 80°, the RMS error is as high as 42.5°. An important observation from the study is that the RMS error is independent of the position of the radiators in space, geometry of the array elements, and mis-orientation in the planes of the array. For a specific transmit antenna footprint of + 15° in the azimuth plane, the gain is optimal compared to that in the elevation plane. The proposed design is easy to implement, without the complexity of scanning or phased array. In addition, the absence of algorithm of any type eliminates the computation cost issue that is common in estimators based on adaptive beam forming and smart antenna.

Device-free localization and tracking (DFT) is a novel technique which can estimate the location of a target without equipping it with any devices. Existing DFT systems mainly rely on machine learning techniques with a labor-intensive training. Recently some training-free DFT systems are designed by deriving the angle-of-arrival (AOA) using the dedicated hardware. However, limited by the hardware imperfection, realizing a training-free DFT system using the commodity Wi-Fi is still a challenging task to solve. To address this issue, we develop a novel DFT system which can track the target motion via both target velocity and AOA estimations simultaneously. First, the motion-induced phase shifts are refined from extremely noisy channel state information (CSI) measurements to detect the Doppler shift based on the signal superposition analysis. Then, according to the phased-array signal processing, we realize joint Doppler velocity and AOA estimation of the target path under the compressive sensing framework. It formulates joint Doppler velocity and AOA estimation as a two-dimensional sparse reconstruction problem, which can achieve a high accuracy to further estimate the target velocity and track the target motion at a decimeter level. We implement the proposed DFT system on commercial Wi-Fi devices and validate its performance with extensive evaluations in three indoor scenarios.

This work proposes an angle of arrival (AoA) estimator for analog beam refinement in cellular systems operating in millimeter wave (mmWave) bands. Differently from previous works, the proposed method exploits the polarization domain and the concept of virtual arrays to estimate the AoA through a cross-correlation function. The proposed AoA estimator requires only a polarization-multiplexed measurement to estimate the AoA at the receiver. To cope with polarization leakage issues, this work proposes a channel equalization in the polarization domain conditioned on the estimation of the cross-channel gain. Simulation results indicate that the beam refinement procedure with the proposed AoA estimator has similar performance to the classical beam sweeping, but using less time-multiplexed measurements.

Emerging applications require the location information of clients to enable human-environment interactions or personalized services. With an increasing number of antennas equipped in today's wireless devices, recent research has shown possibility of sub-meter level localization based only on the angle of arrival (AoA) of WiFi sig- nals. While most existing work provides promising median accu- racy, their tail performance however is usually far worse. We ob- serve from measurements that the root cause is due to unequal AoA estimation reliability. In some critical areas, a small variation in the channel state information of signals could introduce an extremely large AoA estimation error. With this observation, we propose UAT (Unequal Angle Tracking), a confidence-aware AoA-based localiza- tion system. We show that unequal reliability of AoA measures can be mathematically quantified, allowing a system to weigh the de- cisions of different APs according to their confidence. Our testbed evaluation shows that UAT's confidence-aware design provides reli- able decimeter level localization for around 90% of locations. UAT is especially effective for risky areas and can reduce their localiza- tion errors by 27.5%, as compared to reliability-oblivious designs.

In an ultra-wideband (UWB) system, the two-dimensional (2D) multiple signal classification (MUSIC) algorithms based on high-precision 2D spectral peak search can jointly estimate the time of arrival (TOA) and angle of arrival (AOA). However, the computational complexity of 2D-MUSIC is very high, and the corresponding data model is only based on the dual antennas. To solve these problems, a low-complexity algorithm for joint AOA and TOA estimation of the multipath ultra-wideband signal is proposed. Firstly, the dual antenna sensing data model is extended to the antenna array case. Then, based on the array-sensing data model, the proposed algorithm transforms the 2D spectral peak search of 2D-MUSIC into a secondary optimization problem to extract the estimation of AOA via only 1D search. Finally, the acquired AOA estimations are brought back, and the TOA estimations are also obtained through a 1D search. Moreover, in the case of an unknown transmitted signal waveform, the proposed method can still distinguish the main path signal based on the time difference of arrival of different paths, which shows wider applications. The simulation results show that the proposed algorithm outperforms the Root-MUSIC algorithm and the estimation of signal parameters using the rotational invariance techniques (ESPRIT) algorithm, and keeps the same estimation accuracy but with greatly reduced computational complexity compared to the 2D-MUSIC algorithm.

When the femtocell base station has perfect channel state information, it can adopt beamforming to avoid interference. However, the perfect channel assumption may not be practicable due to the channel-state information delay, channel estimator error, and bandwidth wastage while reporting channel parameters. In this paper, we proposed an angle of arrival (AOA) based beamforming structure for orthogonal frequency division multiple access (OFDMA)-based Hetnet systems. The proposed scheme has interference resistant capability without the needs in the estimation of the channel parameters and the calculation of beamforming weight vector for each subcarrier. It can also reduce extra burden and delay generated from communication link between the femtocell and the central entity for using beamforming technology. The AOA estimates are also utilize to modify the spatial grouping method appears in [1].

In this paper, we study precise localization using Angle of Arrival (AOA) estimations by smart-antenna equipped beacons in Ad-Hoc networks. The node to be localized sends a signal to its surrounding beacons. The beacons estimate the signal directions with high resolution AOA methods and feed them back to the node for position calculation. In other words, position calculation is not required at beacons. When AOA estimates from three or more beacons are received, ambiguity occurs. Three resolution methods, namely (a) simple averaging (b) Minimax and (c) Precision-weighted averaging are proposed and compared. As estimation bias is heavily dependent on antenna orientations the center-facing approach is found to give better performance in a square field.