Angle Of Arrival Measurements Research Articles

Optimised solution for hybrid TDOA/AOA‐based geolocation using Nelder‐Mead simplex method

A hybrid geolocation model that combines advantages of time difference of arrival (TDOA) and angle of arrival (AOA) techniques is widely used to determine a mobile location. However, measurement noise in TDOA and AOA data restricts the estimation accuracy of a mobile location. Based on general geolocation equation using TDOA and AOA measurements, the authors derived a total noise-enhanced localisation (TNEL) model here. Unlike the general geolocation equation, the proposed TNEL model overcomes the accuracy limit even when geolocation equation contains environmental error. Moreover, the objective function and constraint derived from TNEL model can yield optimal solution that is closer to the real location of an emitter. In order to increase localisation measurement accuracy, the authors applied Nelder-Mead (NM) simplex algorithm-based optimisation technique to minimise the estimation error of the TNEL model. Simulation results confirmed the performance of authors’ proposed geolocation method.

Enhanced Authentication Based on Angle of Signal Arrivals

In the scope of vehicular communication networks, the nature of exchanged safety/warning messages renders itself highly location dependent, as it is usually for incident reporting. Vehicles are required to periodically exchange beacon messages that include speed, time, and GPS location information. Reported position information must be accurate and authentic. To mitigate the potential risk of location spoofing/falsifying attack, this paper presents Angle-of-Arrival (AoA) estimation as a physical layer authentication scheme for cross verification of the reported location information. We consider a multi-antenna road side unit under location spoofing attack. Within the considered vehicular communication settings, fundamental limits of AoA estimation are developed in terms of its Cramer–Rao Bound and existence of efficient estimator. The problem of deciding whether the received signal originated from the claimed GPS location is formulated as a two-sided hypotheses testing problem whose solution is given by Wald test statics . We give correct decision $P_D$ and false alarm $P_F$ probabilities in a closed form. To offset the gap between information theoretical studies and practical implementations of physical layer security architectures, theoretical results presented in this paper are further supported by detailed hardware implantation using software-defined radios and field experimental results. We show that the keystone for obtaining reliable AoA measurements is the accurate characterization of the array response vector. Our hardware measurements showed that, unlike idealized models, hardware imperfections leave the actual array response vector of non-structured form. To address this problem, we introduce the method for obtaining the actual array response vector empirically, which led to accurate and stable AoA measurements. Field experimental results shows the feasibility of the proposed AoA authentication scheme.

A description of the Elevation sensitive Oblique Incidence Sounder Experiment (ELOISE)

The Elevation sensitive Oblique Incidence Sounder Experiment (ELOISE) was an extensive experiment undertaken by the Defence Science and Technology (DST) Group that focused on collecting ionospheric sensor data from multiple overlapping ionospheric paths in the Australian region in order to improve understanding of the characteristics of ionospheric variability and its effect on HF radio propagation. The experiment ran from July to October 2015 and included a period of three weeks of increased sample density and three days of dedicated over-the-horizon (OTH) radar operations. It was anticipated that ELOISE would sample a wide range of environmental conditions and present an opportunity to characterise periods of “normal variability” and periods of “exceptional variability” in the ionosphere.This report is a general description of the aims of this experiment and the types of data collected. Particular interest focused on observing and measuring variability in ionospheric electron density gradients and their effect on oblique HF propagation. To this end, ELOISE established a pair of two dimensional HF receiver arrays to directly measure the oblique angle of arrival (AoA) on many overlapping oblique paths. ELOISE also established a dense sub-network of spatially separated quasi-vertical incidence soundings in the vicinity of Alice Springs in central Australia. This enabled a comparison of gradients observed in a dense network of vertical sounders with gradient effects observed in oblique propagation passing overhead. Several additional ionospheric observing systems were also used to give complementary pictures of the fine scale characteristics of ionospheric variability in the region. Plain Language SummaryThis paper is an overview of the 2015 Elevation sensitive Oblique Incidence Sounder Experiment (ELOISE), an experiment designed to observe and characterise mid-latitude ionospheric disturbances in the Australian region and understand their impact on high frequency (HF) signal propagation.ELOISE involved the simultaneous operation of a large collection of ionospheric sounders enabling ionospheric variability to be characterised on a finely sampled large scale.Particular efforts were made to provide direct high fidelity measurements of the angle of arrival (AoA) on many oblique HF propagation paths. These direct AoA measurements imply horizontal electron density gradients that can be compared to ionospheric gradients estimated from conventional models of the ionosphere derived from the spatial network of sounder sites.The size and scope of the experiment are detailed in this paper and some preliminary results are presented.

A Linear Estimator for Network Localization Using Integrated RSS and AOA Measurements

This letter addresses the problem of simultaneous localization of multiple targets in three-dimensional cooperative wireless sensor networks. To this end, integrated received signal strength and angle of arrival measurements are employed. By exploiting the convenient nature of spherical representation of the considered problem, the measurement models are linearized and a sub-optimal estimator is formulated. Unlike the maximum likelihood estimator, which is highly non-convex and difficult to tackle directly, the derived estimator is quadratic and has a closed-form solution. Its computational complexity is linear in the number of connections and its accuracy surpasses the accuracy of existing ones in all considered scenarios.

A Novel Localization Method Based on RSS-AOA Combined Measurements by Using Polarized Identity

This paper proposes a novel scheme to solve the target localization problem by using combined measurements of received signal strength and angle-of-arrival (AOA) hybrid measurements in three-dimensional (3D) wireless sensor networks (WSNs). The proposed scheme can work effectively in both cases of known and unknown target transmit power. In the known transmit power case, by using polarized identity, we transform the available AOA measurements into a norm form and establish new relationships between the AOA measurements and the unknown target location, a novel target localization method is then proposed by using the second-order cone programming relaxation technique. Furthermore, we demonstrate that by using mean inequality, the proposed method can be effectively extended to the unknown transmit power case. In this case, we propose a practical alternating estimation procedure which alternatively estimates the target location and transmit power. Computer simulations are carried out to demonstrate that comparing with other methods, the proposed method can achieve significant performance gains for different settings in 3D WSNs.

Linear closed-form estimator for sensor localisation using RSS and AOA measurements

Using the hybrid received signal strength (RSS) and angle of arrival (AOA) measurements, a position estimation model is proposed for sensor localisation in three-dimensional plane. Then the unconstraint linear least square (ULLS) and constraint linear least square (CLLS) estimators are designed to obtain the closed-form solutions to the positions of source nodes by considering the known transmit power. When the transmit power is unavailable, a global linear least square (GLLS) estimator is also put forward to estimate the positions of source nodes along with the transmit power. The simulations show that the computational complexity of the proposed linear estimators is greatly lower than that of the convex semidefinite programming (SDP) method. When the measurement noises are small, the linear ULLS, CLLS and GLLS estimators perform better than that of the SDP method. Due to the exploiting of constraint condition, the accuracy performance of the CLLS estimator can approach the Cramer-Rao lower bound (CRLB) of position estimation.

Angle-of-Arrival Measurement System Using Double RF Modulation Technique

A new technique for determining the angle of arrival (AoA) of an RF signal is presented. It is based on a cascaded modulator structure, where continuous wave light from an optical source is modulated by an incoming RF signal twice. The AoA of an RF signal can be determined by the system output RF signal power. The proposed structure is capable for measuring the AoA of both narrow and broadband RF signals. This overcomes the limitation in all previously reported photonics-based AoA measurement systems, which are applicable to either a single-frequency RF signal or a broadband RF signal. It also does not involve electrical components and high extinction ratio modulators, which are required in reported structures. Experimental results are presented that show an AoA measurement range of 0° to over 65° with errors of less than 1.9° for a microwave signal at 2.65 and 12.62 GHz. Results also show that the system is capable to measure the AoA of a pseudorandom binary sequence signal at a microwave frequency.

AOA Measurement based Localization Using RLS Algorithm under NLOS Environment

In localization problem, the estimation accuracy of a target location is one of the most significant issues. The global positioning system (GPS) that is the most widely used localization method can be disrupted by some disturbances under non-line-of-sight (NLOS) condition such as indoor environment and downtown. In this paper, we suggest a localization algorithm using the angle of arrival (AOA) measurements. AOA measurement based localization is one of the most efficient geolocation methods that use a wireless active signal. Moreover, AOA method has an advantage that only two base stations are required to estimate the target location. In all kinds of localization methods using wireless signal, the NLOS and measurement noise are the significant problems that decrease an estimation accuracy of a target location. In this paper, we suggest a Kalman filter based hypothesis test and a recursive least square scheme to overcome NLOS and measurement noise problem, respectively. Using Kalman filter based hypothesis test, the measurement data set from each base station can be identified whether it contains the NLOS noise or not. Also, recursive least square (RLS) scheme can obtain the precise location of a target with rapid calculation speed when additional measurement data is received from auxiliary base stations. Simulation result confirms the high estimation accuracy and computational speed of our proposed scheme.

Node localization with AoA assistance in multi-hop underwater sensor networks

This paper proposes a novel node localization method in sparse underwater wireless sensor networks (UWSNs) where the locations of only a small number of anchor nodes are available. The proposed method estimates the Euclidean distances from anchor nodes to multi-hop sensor nodes with the help of angle of arrival (AoA) measurements in the local coordinate systems of the routing nodes. A new distance estimation method is proposed for sensor nodes with greater-than-two-hops to an anchor node by accurately estimating the rotation matrix between the routing nodes. By forwarding distances hop-by-hop through the network, the sensor nodes are able to obtain distance estimates to more than four or five anchor nodes. Then the location of the sensor node is solved by a weighted Least Squares method. This paper develops a practical table of weights for different number of hops and AoA estimation errors. Simulation results show that the proposed localization method outperforms the existing multi-hop localization algorithms, such as DV-hop, DV-distance, Euclidean, and Cosine-law methods, in terms of distance estimation and location estimation, even if the AoA measurement error is large. Meanwhile, the proposed method maintains almost the same high localization coverage as the existing methods.

On Target Localization Using Combined RSS and AoA Measurements.

This work revises existing solutions for a problem of target localization in wireless sensor networks (WSNs), utilizing integrated measurements, namely received signal strength (RSS) and angle of arrival (AoA). The problem of RSS/AoA-based target localization became very popular in the research community recently, owing to its great applicability potential and relatively low implementation cost. Therefore, here, a comprehensive study of the state-of-the-art (SoA) solutions and their detailed analysis is presented. The beginning of this work starts by considering the SoA approaches based on convex relaxation techniques (more computationally complex in general), and it goes through other (less computationally complex) approaches, as well, such as the ones based on the generalized trust region sub-problems framework and linear least squares. Furthermore, a detailed analysis of the computational complexity of each solution is reviewed. Furthermore, an extensive set of simulation results is presented. Finally, the main conclusions are summarized, and a set of future aspects and trends that might be interesting for future research in this area is identified.

Relative Self-Calibration of Wireless Acoustic Sensor Networks Using Dual Positioning Mobile Beacon

Sensor nodes deployed for event localization are required to be location aware. When event of interest is acoustic, node calibration (involving localization and orientation) during deployment phase become significant for accurate event localization during normal operation. This paper proposes a novel approach for node calibration in an acoustic surveillance network. Each node is equipped with three microphones and an RF transceiver. Nodes are calibrated for their relative positions and orientations by transmitting acoustic and RF signals, simultaneously, from beacon nodes at unknown locations. The acoustic signal, received by microphone array, is used for angle of arrival measurement. While an RF signal is transmitted for time difference of arrival measurement, which is used to quantify distance between beacon and sensor nodes. Proposed approach is attractive as it does not require expensive on board GPS module and compass. The effectiveness of proposed scheme is demonstrated using experimental setup comprising five sensor nodes and one beacon node. Maximum likelihood estimation is employed to determine network configuration under noisy measurements. Simulations are performed to observe the effect of network parameters, such as node density on node position estimation accuracy.

Unified Near-Field and Far-Field Localization for AOA and Hybrid AOA-TDOA Positionings

Point positioning of a signal source is feasible if it is not far from the sensors and direction of arrival (DOA) localization is only applicable if it is distant. Point positioning and DOA localization employ different estimation models and prior knowledge about the source range is often not available to decide which model is appropriate. This paper introduces the modified polar representation to unify the localization of a source using angle of arrival (AOA) regardless if it is near or far. From the Gaussian AOA measurements, we utilize the hybrid Bhattacharyya-Barankin (HBB) bound to illustrate it is not possible to obtain the Cartesian coordinates of a distant source when applying the near-field model, and derive the DOA bias of a not so distant source when using the far-field model. An iterative maximum likelihood estimator (MLE) is next derived under the modified polar representation with a single model, where the HBB bound confirms the stable behavior of the estimator regardless it is near or far. The algorithm yields a position if the source is close and a DOA if it is distant. A preliminary solution to initialize the MLE using semidefinite relaxation is also proposed. The HBB bound, the analysis and the algorithm are extended for hybrid AOA-TDOA localization.

Efficient 3-D Positioning Using Time-Delay and AOA Measurements in MIMO Radar Systems

This letter investigates the problem of 3-D target localization in multiple-input multiple-output radars with distributed antennas, using hybrid time-delay and angle of arrival measurements. We propose a closed-form positioning method based on weighted least squares estimation. The proposed estimator is shown theoretically to achieve the Cramer-Rao lower bound under mild noise conditions. Numerical simulations also verify the theoretical developments.

Enabling Phased Array Signal Processing for Mobile WiFi Devices

Modern mobile devices are equipped with multiple antennas, which brings various wireless sensing applications such as accurate localization, contactless human detection, and wireless human-device interaction. A key enabler for these applications is phased array signal processing, especially Angle of Arrival (AoA) estimation. However, accurate AoA estimation on commodity devices is non-trivial due to limited number of antennas and uncertain phase offsets. Previous works either rely on elaborate calibration or involve contrived human interactions. In this paper, we aim to enable practical AoA measurements on commodity off-the-shelf (COTS) mobile devices. The key insight is to involve users’ natural rotation to formulate a virtual spatial-temporal antenna array and conduce a relative incident signal of measurements at two orientations. Then by taking the differential phase, it is feasible to remove the phase offsets and derive the accurate AoA of the equivalent incoming signal, while the rotation angle can also be captured by built-in inertial sensors. On this basis, we propose Differential MUSIC ( D-MUSIC ), a relative form of the standard MUSIC algorithm that eliminates the unknown phase offsets and achieves accurate AoA estimation on COTS mobile devices with only one rotation. We further extend D-MUSIC to 3-D space, integrate extra measurements during rotations for higher estimation accuracy, and fortify it in multipath-rich scenarios. We prototype D-MUSIC on commodity WiFi infrastructure and evaluate it in typical indoor environments. Experimental results demonstrate a superior performance with average AoA estimation errors of 13 ${}^{\circ}$ with only three measurements and 5 ${}^{\circ}$ with at most 10 measurements. Requiring no modifications or calibration, D-MUSIC is envisioned as a promising scheme for practical AoA estimation on COTS mobile devices.

Target Tracking with Sensor Navigation Using Coupled RSS and AoA Measurements.

This work addresses the problem of tracking a signal-emitting mobile target in wireless sensor networks (WSNs) with navigated mobile sensors. The sensors are properly equipped to acquire received signal strength (RSS) and angle of arrival (AoA) measurements from the received signal, while the target transmit power is assumed not known. We start by showing how to linearize the highly non-linear measurement model. Then, by employing a Bayesian approach, we combine the linearized observation model with prior knowledge extracted from the state transition model. Based on the maximum a posteriori (MAP) principle and the Kalman filtering (KF) framework, we propose new MAP and KF algorithms, respectively. We also propose a simple and efficient mobile sensor navigation procedure, which allows us to further enhance the estimation accuracy of our algorithms with a reduced number of sensors. Model flaws, which result in imperfect knowledge about the path loss exponent (PLE) and the true mobile sensors’ locations, are taken into consideration. We have carried out an extensive simulation study, and our results confirm the superiority of the proposed algorithms, as well as the effectiveness of the proposed navigation routine.

Indoor Multipath Assisted Angle of Arrival Localization.

Indoor radio frequency positioning systems enable a broad range of location aware applications. However, the localization accuracy is often impaired by Non-Line-Of-Sight (NLOS) connections and indoor multipath effects. An interesting evolution in widely deployed communication systems is the transition to multi-antenna devices with beamforming capabilities. These properties form an opportunity for localization methods based on Angle of Arrival (AoA) estimation. This work investigates how multipath propagation can be exploited to enhance the accuracy of AoA localization systems. The presented multipath assisted method resembles a fingerprinting approach, matching an AoA measurement vector to a set of reference vectors. However, reference data is not generated by labor intensive site surveying. Instead, a ray tracer is used, relying on a-priori known floor plan information. The resulting algorithm requires only one fixed receiving antenna array to determine the position of a mobile transmitter in a room. The approach is experimentally evaluated in LOS and NLOS conditions, providing insights in the accuracy and robustness. The measurements are performed in various indoor environments with different hardware configurations. This leads to the conclusion that the proposed system yields a considerable accuracy improvement over common narrowband AoA positioning methods, as well as a reduction of setup efforts in comparison to conventional fingerprinting systems.

Bayesian methodology for target tracking using combined RSS and AoA measurements

This work addresses the target tracking problem based on received signal strength (RSS) and angle of arrival (AoA) measurements. The Bayesian methodology, which integrates the information given by observations with prior knowledge extracted from target motion model in order to enhance the estimation accuracy was employed. First, by converting the considered highly non-linear measurement model into a linear one, i.e., a novel linearization technique of the measurement model is proposed. The derived model is then merged with the prior knowledge, and a novel maximum a posteriori (MAP) estimator whose solution is given in closed-form is proposed. It is also shown that the Kalman filter (KF) can be directly applied on top of the linearized observation model, which results in a proposal of a novel KF algorithm. Furthermore, to the best of authors’ knowledge, this paper premierly presents the application of the extended KF (EKF) and the unscented KF (UKF) to the considered tracking problem, by applying first-order linearization technique to the original non-linear model, and by applying the unscented transformation to carefully selected sample points, respectively. Finally, importance weights are computed for a large number of randomly selected sample points to render a well-known particle filter (PF) solution. Simulation results show that the proposed algorithms perform better than a naive one which uses only information from observations. They also confirm the effectiveness of the proposed linearization technique in comparison with the existing one, reducing the estimation error for about 25%.

Optical angle‐of‐arrival (AoA) sensor for the motion analysis of knee joints

The most widely used clinical technique for estimating bone movements in knee joints with acceptable accuracy utilises implantation of heavy metallic beads into the bones. These small metallic objects become visible as highly contrasting features in captured radiographs, thus facilitating post kinematic assessment of bones in human joints with pinpoint accuracy. Although, this approach provides necessary precision for clinical diagnosis, pre- and post-surgery planning for restoring joint function and locomotion, however, this approach also incurs high degree of invasiveness. In this study, a novel optical sensor is proposed for use in non-invasive motion measurement of knee joints in three-dimensional (3D) space. The approach is based on 3D angle-of-arrival (AoA) estimation using image processing procedures on the two-dimensional images of sensor's aperture shadows. The novelty of the sensor in terms of design, operation and its potential applications for a wide range of possibilities including precise AoA estimation for unconstrained 3D motion analysis of knee joints is described. Finally, error analysis of the sensor's AoA measurement uncertainty is provided for different incident angles of optical beams, which demonstrates the potential of the sensor not only for high-precision motion analysis of human joints; however, for other motion and position-aware systems.

A New Close Form Location Algorithm with AOA and TDOA for Mobile User

A new closed form location algorithm for mobile user is proposed in this paper. In wireless communication systems, the time difference of arrival (TDOA) measurements can be obtained by computing the correlation function of the forward link pilot signals, the angle of arrival (AOA) measurements can be obtained by calculating the spatial spectrum for the reverse link pilot signal. With the measurements TDOA and AOA, the position of a mobile station (MS) can be determined. Firstly, the nonlinear TDOA and AOA measurement equations without noise are transformed to linear equations for the positions of MS. Secondly, the linear errors are calculated by the noisy measurement, and then a weighted least square method is used to solve the linear equations. The Cramer Rao bound (CRB) is analyzed in this paper. Finally, computer simulation is given. Numerical results demonstrate that the proposed method can attain to CRB at moderate measurement noise.

Positioning Using Terrestrial Multipath Signals and Inertial Sensors

This paper extends an algorithm that exploits multipath propagation for position estimation of mobile receivers named Channel-SLAM. Channel-SLAM treats multipath components (MPCs) as signals from virtual transmitters (VTs) and estimates the positions of the VTs simultaneously with the mobile receiver positions. For Channel-SLAM it is essential to obtain angle of arrival (AoA) measurements for each MPC in order to estimate the VT positions. In this paper, we propose a novel Channel-SLAM implementation based on particle filtering which fuses heading information of an inertial measurement unit (IMU) to omit AoA measurements and to improve the position accuracy. Interpreting all MPCs as signals originated from VTs, Channel-SLAM enables positioning also in non-line-of-sight situations. Furthermore, we propose a method to dynamically adapt the number of particles which significantly reduces the computational complexity. A posterior Cramér-Rao lower bound for Channel-SLAM is derived which incorporates the heading information of the inertial measurement unit (IMU). We evaluate the proposed algorithm based on measurements with a single fixed transmitter and a moving pedestrian carrying the receiver and the IMU. The evaluations show that accurate position estimation is possible without the knowledge of the physical transmitter position by exploiting MPCs and the heading information of an IMU.