Kegen Yu

UWB Location and tracking for wireless embedded networks

In this paper, we investigate the performance of different position estimation methods which make use of time-of-arrival (TOA) of ultra wideband (UWB) signals for low cost/low complexity UWB systems. We first propose a simple and robust two-stage, non-coherent TOA estimation approach. We then explore positioning algorithms utilizing both noniterative and iterative techniques. A review of positioning in distributed networks is also performed and a positioning algorithm is proposed for node location in multi-hop distributed networks. Furthermore, we consider smoothing techniques to improve accuracy when tracking moving objects and we propose the use of sinc functions to smooth the estimate of the mobile position in order to achieve both good accuracy and low complexity. The system modelled and investigated corresponds to an actual test environment in a ski field where skiers are tracked.

Statistical NLOS Identification Based on AOA, TOA, and Signal Strength

Nonline-of-sight (NLOS) propagation is one of the challenges in radio positioning. Significant attention has been drawn to the mitigation of the NLOS effect in recent years. This paper focuses on the identification of NLOS conditions by employing the statistical decision theory. A Neyman-Pearson (NP) test method is first derived for scenarios where either 1-D or 2-D angular measurements are provided. A time-of-arrival (TOA) based method is then developed under idealized conditions to provide a performance reference. In the presence of both TOA and received signal strength (RSS) measurements, a joint identification method is derived to efficiently exploit the TOA and RSS measurements. Analytical expressions of the probability of detection (POD) and the probability of false alarm (PFA) are derived for all the scenarios considered. Two theorems and one corollary regarding the line-of-sight (LOS) conditions based on the angle of arrival (AOA) are also presented, and the proofs are provided. Simulation results demonstrate that the proposed methods perform well, and the joint TOA- and RSS-based method considerably outperforms the TOA-based methods. The proposed methods are robust to the model errors, as demonstrated through simulations. It is also shown that the analytical results agree well with the simulated ones.

Improved Positioning Algorithms for Nonline-of-Sight Environments

Nonline-of-sight (NLOS) conditions pose a major challenge to radio positioning. In this paper, a constrained-optimization-based location algorithm is proposed to jointly estimate the unknown location and bias by using the sequential quadratic programming (SQP) algorithm. This method does not rely on any prior statistics information, and simulation results show that the proposed method considerably outperforms existing methods. To reduce the complexity of the SQP-based algorithm, we further propose a Taylor-series expansion-based linear quadratic programming (TS-LQP) algorithm. It is demonstrated that the computational complexity of the TS-LQP algorithm is only a fraction of that of the SQP algorithm, whereas the accuracy loss is limited. Also, maximum-likelihood (ML) algorithms that are suited for different NLOS error statistics are developed under several circumstances when there are different levels of a priori information. The analytical performance of the ML estimation (MLE) is investigated. Moreover, analytical expressions to approximate the variance of the MLE with and without model parameter mismatches are derived. Simulation results show that the approximate variance can be used as a better accuracy measure than the Cramer-Rao lower bound (CRLB).

GDOP Analysis for Positioning System Design

Geometric dilution of precision (GDOP) has been widely used as an accuracy metric for navigation and tracking systems. Since high accuracy in a positioning system requires both accurate measurement of the range and a good geometric relationship between the mobile device and the measuring points, the analysis of GDOP is an essential feature in determining the performance of a positioning system. In this paper, we perform GDOP analysis to obtain concise analytical expressions for a number of scenarios, which are generally applicable to geometries where the mobile device is surrounded by base stations. Comparison of the analytical results with simulations using the typical geometries of indoor positioning systems shows good agreement, except when the mobile position is close to a base station. This effect is a consequence of the ranging errors being a significant proportion of the range in short-range tracking systems and discontinuities in GDOP at the base station. The results provide useful information for the design and testing of tracking systems, as well as for the determination of the geometric deployment of base stations for good GDOP in the coverage area.

3-d localization error analysis in wireless networks

In the paper we investigate 3-D positioning by making use of distance/range and angle-of-arrival (AOA) measurements. Two estimation methods (linear least squares (LS) estimator and optimization) are developed for node positioning in wireless networks. We derive the Cramer-Rao lower bound (CRLB) for positioning with both range and AOA measurements in a 3-D environment, which is not seen in the literature. Also we derive compact approximate expressions of the variances of the LS algorithms. In the literature, positioning accuracy is usually studied by assuming perfect anchor location information. To evaluate positioning accuracy under realistic conditions, we also analyze the impact of anchor position error. Numerical results demonstrate that the derived analytical results have a good match with the simulated results.

Distributed Inter-Network Interference Coordination for Wireless Body Area Networks

In this paper we consider the inter-network interference problem in Wireless Body Area Networks (WBANs). We propose a distributed inter-network interference aware power control algorithm motivated by game theory. A power control game is formulated considering both interference between nearby networks and energy efficiency of WBANs. We derive a distributed power control algorithm called ProActive Power Update (PAPU), which can efficiently find the Nash Equilibrium representing the best tradeoff between energy and network utility. A realistic power control procedure is proposed assuming limited cooperation between WBANs. We compare our algorithm with the ADP algorithm where users are punished for interfering with others and we show that our solution can utilize energy much more efficiently by only sacrificing a small amount of network utility. In addition, we show that by adjusting the energy price, PAPU provides a methodology for application scenarios where WBANs have different energy constraints and quality of service requirements.

UWB positioning for wireless embedded networks

The paper reports on the development of a low-power and low-cost device for low data rate communications with tracking and positioning capabilities. We investigate the performance of a range of position estimation methods making use of the estimate of the time-of-arrival (TOA) of the ultra wideband (UWB) signal at a set of receivers/sensors. The performance evaluation is performed in terms of the root-mean-square (RMS) error of the position coordinates estimation and the failure rate. We first discuss TOA estimation. A two-stage acquisition method is employed to speed up synchronization so to obtain the TOA estimate. We then study several optimization-based estimation methods including the Gauss-Newton type methods and the quasi-Newton method. Those methods are quite practical due to the fact that they do not require the knowledge of the characteristics or the TOA estimation error. Performance of the methods is examined through simulation. Furthermore, velocity of the moving devices and/or position estimate filtering can be exploited to improve the accuracy of the position estimation.

NLOS Error Mitigation for Mobile Location Estimation in Wireless Networks

Most radio positioning methods are based on the measurements of distance between different wireless nodes. Owing to the existence of non-line-of-sight (NLOS) radio propagation, unfortunately, not all the measured distances are reliable. One way to tackle the problem of positioning is therefore to take two-steps: (i) identifying the NLOS measurements; (ii) smart signal processing of the mixed LOS and NLOS measurements. This paper is focused on the second issue. Under the assumption that the NLOS measurements have been identified, we first propose a simple method to suppress the effect of the NLOS error. Simulation results demonstrate that the proposed method achieves similar or better accuracy than several other known methods and the computational complexity is reduced considerably. We also present an optimal location estimator under the assumption of Gaussian distributed measurement noise and Rayleigh distributed NLOS error. Although it is difficult to achieve the optimal performance in practice due to modeling uncertainties, the optimal estimator provides a performance benchmark.

2-D Unitary ESPRIT Based Joint AOA and AOD Estimation for MIMO System

This paper presents a subspace-based algorithm for the simultaneous estimation of angle of arrivals (AOAs) and angle of departures (AODs) from an estimated spatial signature at the receive antenna array. The algorithm exploits a 2-D unitary ESPRIT-like technique to separate and estimate the phase shifts due to the AOAs and AODs with automatic pairing of the two parameter sets. The Cramer-Rao lower bound (CRLB) on the AOAs and AODs estimates is provided. The performance of the algorithm is illustrated based on the computer simulation. It shows that the proposed algorithm is an efficient estimator which is able to achieve the CRLB

Geometry and Motion-Based Positioning Algorithms for Mobile Tracking in NLOS Environments

This paper presents positioning algorithms for cellular network-based vehicle tracking in severe non-line-of-sight (NLOS) propagation scenarios. The aim of the algorithms is to enhance positional accuracy of network-based positioning systems when the GPS receiver does not perform well due to the complex propagation environment. A one-step position estimation method and another two-step method are proposed and developed. Constrained optimization is utilized to minimize the cost function which takes account of the NLOS error so that the NLOS effect is significantly reduced. Vehicle velocity and heading direction measurements are exploited in the algorithm development, which may be obtained using a speedometer and a heading sensor, respectively. The developed algorithms are practical so that they are suitable for implementation in practice for vehicle applications. It is observed through simulation that in severe NLOS propagation scenarios, the proposed positioning methods outperform the existing cellular network-based positioning algorithms significantly. Further, when the distance measurement error is modeled as the sum of an exponential bias variable and a Gaussian noise variable, the exact expressions of the CRLB are derived to benchmark the performance of the positioning algorithms.