Radio-based localization is an active research topic with a range of applications. In this paper, we focus on localization of a radio receiver equipped with an inertial measurement unit. The localization is performed while simultaneously constructing a map of the small-scale fading pattern in the local radio environment. The map in our case is a ray-trace-based multipath channel model. This solution is enabled by sensor fusion of information from the channel estimation data and the inertial sensors, and it does not assume knowledge of, e.g., transmitter locations. The sensor data are fused in a recursive state space model that combines the kinematic motion model with the ray-based radio channel model, and the state vector is estimated using a particle filter. The choice of the particle filter is justified by the multimodal characteristics of the posterior likelihood distributions that follow from the nonlinearities of the problem. The work is assuming a single receiver antenna, but the approach can be transferred to multiple-antenna systems. We study the performance of the approach under realistic assumptions, based on the performance of today's low-cost inertial sensors and radio systems, including accelerometer and gyroscope noise and radio receiver frequency error and noise. Simulations show significant improvement in long-term positioning performance, evaluated against dead reckoning. The work is concluded with experiments that serve as the proof of concept for the proposed technique, using no extra equipment compared to what can be found in a modern cellular phone.

Using antenna arrays for direction of arrival (DoA) estimation and source localization is a well-researched topic. In this paper, we analyze virtual antenna arrays for DoA estimation where the antenna array geometry is acquired using data from a low-cost inertial measurement unit (IMU). Performance evaluation of an unaided inertial navigation system with respect to individual IMU sensor noise parameters is provided using a state space based extended Kalman filter. Secondly, using Monte Carlo simulations, DoA estimation performance of random 3-D antenna arrays is evaluated by computing Cramér-Rao lower bound values for a single plane wave source located in the far field of the array. Results in the paper suggest that larger antenna arrays can provide significant gain in DoA estimation accuracy, but, noise in the rate gyroscope measurements proves to be a limiting factor when making virtual antenna arrays for DoA estimation and source localization using single antenna devices.

A single antenna based virtual antenna array at the receiver can be used to find direction of different incoming radio signals impinging at the receiver. In this paper, we investigate the performance of random 3D virtual antenna arrays for DoA estimation. We have computed a Cramér-Rao Lower Bound (CRLB) for DoA estimation if the true antenna positions are not known, but these are estimated with an uncertainty. Position displacement is estimated with an extended Kalman filter (EKF) by using simulated data samples of acceleration and rotation rate which are corrupted by stochastic errors, such as, white Gaussian noise and bias drift. Furthermore, the effect of position estimation error on the DoA estimation performance is evaluated using the CRLB. The results show that the number of useful elements in the antenna array is limited, because the standard deviation of the position estimation error grows over time.

This article contains a study of how spatial errors and receiver imperfections affect the angle of arrival estimation accuracy for virtual antenna arrays. A virtual antenna array consists of one receiver element whose location is tracked as the element is moved and in this work, linear arrays are studied. If the location of the receiver is tracked using an inertial measurement unit, an interesting trade-off emerges. The array should extend as far as possible but since the position estimates from the unaided inertial measurement unit become increasingly uncertain over time, the angle of arrival estimation will deteriorate. Several algorithms are available for estimating the angle of arrival in such a scenario but the one used for evaluation here is a sparse enforcing least squares method.

In this paper, we have investigated the use of virtual antenna arrays at the receiver to do single antenna direction-of-arrival estimation. The array coordinates are obtained by doing simple dead reckoning using acceleration and angular speed measurements from a low cost micro-electro-mechanical system inertial measurement unit (IMU). The proposed solution requires no extra hardware in terms of receiver chains and antenna elements. Direction-of-arrival estimation results are obtained using a high resolution SAGE algorithm. Measurement results show that the direction-of-arrival can be estimated with a reasonable accuracy in an indoor environment.

Navigation using inertial measurement units (IMUs) is an interesting area of research. Due to the low cost hardware and simple implementation, the approach looks very attractive. But the performance of the IMUs to provide sub-meter accuracy over a longer period of time is still not sufficient, so different approaches have been adopted to increase the performance at the cost of extra hardware and/or infrastructure. Our solution is based on the use of already existing radio infrastructure, where amplitude and phase variations in a received radio signal at the user terminal is used together with the IMU to do a tightly coupled estimation of navigation and radio signalmultipath components. The results show that the approach has the potential to enhance the performance of IMU based navigation significantly.

A description and analysis of the sales process of ERP systems is presented; also the consequences for the parties involved are discussed. The chosen methodological basis is an inductive research design consisting of a case study based on interviews. The analysis of the empirical data is done with a proposed analytical framework. Based on the agency theory, the analysis is tightly knitted together with opportunism. There are three controlling factors: relationships (relationship marketing), contracts (principal-agent theory), and competition. Three results are proposed: First, two factors forms opportunism-safeguarding and fringe selling. Second, two possibilities of minimizing opportunism exist, the contract and the relationship, the former largely being overlooked, and the latter not being what it is thought to be. Third, there is a need for a feedback loop to correctly address opportunism.

This thesis investigates the topic of joint positioning and radio channel estimation and prediction. Both positioning and radio channel estimation have a long history of research with many publications but the combination of the two has so far at large been left unexplored. The reason for studying this topic is twofold: improvement of positioning and improvement of radio channel prediction. Positioning is of interest in many situations, such as, e.g., localization in an unknown environment. Better radio channel estimates and prediction enable improved transmission rates with fewer lost data packages in wireless networks. In this thesis, both areas are covered with analysis and simulations and the improvement in positioning performance is also demonstrated with measurements from experiments. A well established approach for positioning is using an inertial measurement unit (IMU) which contains sensors measuring, e.g., acceleration and angular velocity. Due to noise in the sensors, the dead reckoning performance of the stand-alone unit is quickly degraded. The degradation has previously been combated by fusing the accelerometer and gyroscope signals with other sensor information such as GPS or wheel encoders in order to correct for the errors of the IMU. This is achieved by establishing a model that combines the information from the sensors. In this thesis, such a model is established between the accelerometer and gyroscope readings and the radio channel estimates obtained from pilot signals transmitted in a wireless network. The transfer characteristics of the narrowband radio channel are described with multipath components, where amplitude and angle of arrival are associated with each component. Since it is believed that the performance of the solution is greatly affected by imperfections in the receiver, its frequency error is also included in the modeled.The joint model is estimated using Bayesian methods, suitable for nonlinear systems. By simultaneously estimating the variables of the multipath components, the frequency error, and the location of the receiver, it is shown that the positioning performance using an IMU, with similar quality found in a modern day cellular phone, can be greatly improved. Since all the signals needed are present in a typical cellular phone, the proposed solution does not require any extra infrastructure. Both simulations and experiments show that the technique has a potential to give a breakthrough in positioning performance using low-cost inertial measurement units.With the established model, the variables that describe the future radio channel can also be predicted. By knowing beforehand what signal reception the cellular phone can expect, the transmissions can be adjusted in terms of modulation and transmission power to suit the future channel condition that occurs at the moment when the transmission is received. This is commonly known as link adaptation. Simulations show that the data transmission rates to the end user can be greatly improved in communication systems such as the LTE system.The thesis also includes an investigation of performance bounds that extends previously known results for the angle of arrival estimation problem and alsocontributions to joint estimation of angle of arrival and frequency error estimation. These results give an intuitive understanding of how the receiver's trajectory of movement impacts the accuracy achievable when estimating the local radio channel landscape. In mathematical terms this can be stated as that the space-time moments of the trajectory determine the Cramer-Rao lower bound of the variables for joint estimation of angle of arrival and frequency error. (Less)