Fabian Höflinger

A Wireless Micro Inertial Measurement Unit (IMU)

In this paper, we present a wireless micro inertial measurement unit (IMU) with the smallest volume and weight requirements available at the moment. With a size of 22 mm × 14 mm × 4 mm (1.2 cm3), this IMU provides full control over the data of a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer. It meets the design prerequisites of a space-saving design and eliminates the need for hard-wired data communication, while still being competitive with state-of-the-art commercially available MEMS IMUs. A CC430 microcontroller sends the collected raw data to a base station wirelessly with a maximum sensor sample rate of 640 samples/s. Thereby, the IMU performance is optimized by moving data post processing to the base station. This development offers important features in portable applications with their significant size and weight requirements. Due to its small size, the IMU can be integrated into clothes or shoes for accurate position estimation in mobile applications and location-based services. We demonstrate the performance of the wireless micro IMU in a localization experiment where it is placed on a shoe for pedestrian tracking. With sensor data-fusion based on a Kalman filter combined with the zero velocity update, we can precisely track a person in an indoor area.

Inertial Sensor Based Indoor Localization and Monitoring System for Emergency Responders

This paper presents a novel indoor localization and monitoring system based on inertial sensors for emergency responders. The system utilizes acceleration, angular rate and magnetic field sensors and consists of three parts. The first part is a modified Kalman filtering which implements the sensor data fusion and meanwhile detects and minimizes the magnetic field disturbances, so as to provide a long term stable orientation solution. The second part is zero velocity updating which resets the velocity within still phase to deliver accurate position information. The last part of the system is body movement monitoring, which is achieved by calculating the relative position of each body segment based on the transformation of coordinate frame of each body segment. The experimental result shows that the system is able to track person indoors in both walking and running cases, and to monitor the body movement during whole period of experiment.

Acoustic Self-calibrating System for Indoor Smartphone Tracking (ASSIST)

In this paper, we present a novel smartphone indoor localization system. The smartphone user is localized with small effort, affordable equipment and with high accuracy in indoor areas. The system uses commercially available smartphones generating high pitched acoustic chirp signals beyond the audible range. The chirp signals are received by sound receivers which identify the specific sound produced from each smartphone. The receivers are connected to a WiFi network, such that they synchronize their clocks and exchange the time differences of arrival (TDoA) of the received chirps. In this way, using an iterative multilateration algorithm, the location of the smartphones can be calculated and the receiver positions are calibrated automatically. For generating the specific sound signals from the smartphone and for user navigation an Android software application was developed. The user interface is simple and is invoked by starting the software application, which automatically connects to a server and receives an ID using the internet connection of the smartphone. Furthermore, the user is assigned specific parameters, such that several devices can be distinguished by the appearance of the chirps. The position of the user is displayed on the smartphone in context of the environment, with a map and surrounding items. In the presented work we have verified our system in a real-world scenario. We compared our trajectory of a pedestrian carrying smartphone to the reference positions. We could locate the smartphones with error margin of 30 cm. a centimeter margin of error.

Indoor localization using a smart phone

This paper presents a novel indoor localization solution using a smart phone. Instead of building the inertial measurement unit (IMU), the integrated calibrated sensors inside the smart phone provide all the sensor information needed. Meanwhile, we avoid the complicated calibration process, when the calibration machines or workstations are not available. Since smart phones are meant to be held in hand, algorithms and methods based on walking speed reset can not be utilized. Therefore, correct orientation and step length information are indispensable. In this study, a modified Kalman filter based sensor data fusion was used to achieve accurate orientation data by detecting and minimizing the effect of magnetic field disturbance. Three methods are presented and compared to calculate each step length based on vertical acceleration using biomechanic model or empirical relation. The experimental results show that the proposed solution is capable of tracking the person indoors and to achieve a tracking accuracy of less than 0.3m.

TDOA-Based Localization Using Interacting Multiple Model Estimator and Ultrasonic Transmitter/Receiver

This paper presents a novel indoor localization system using a self-built ultrasonic transmitter and a receiver. In comparison to commercial localization systems, our ultrasonic system is more robust against multipath propagation at indoor conditions and provides accurate time difference of arrival measurements. Besides, by improving the coverage of our ultrasonic system, the number of system components is significantly reduced. The actual position of the target is then determined by interacting multiple model estimator, which offers protection against the measurement noise at both line-of-sight and non-line-of-sight conditions through simultaneous running of extended Kalman filter and robust extended Kalman filter. The experimental results shows that our system is able to deliver the localization solution with higher accuracy compared to commercially available options.

Anchor-free TDOA self-localization

We present an approach for the localization of passive receiver nodes in a communication network. In our settings the positions of the nodes are unknown. The only source of information is the time when environmental sound or ultrasound signals are received. The discrete signals occur at unknown positions and times, but they can be distinguished. The clocks of the receivers are synchronized, so the time differences of arrival (TDOA) of the signals can be computed. The goal is to determine the relative positions of all receiver nodes and implicitly the positions and times of the environmental signals. Our novel approach, the Iterative Cone Alignment algorithm, solves iteratively a non-linear optimization problem of time differences of arrival (TDOA) by a physical spring-mass simulation. Here, our algorithm shows a smaller tendency to get stuck in local minima than a non-linear least-squares approach. The approach is tested in numerous simulations and in a real-world setting where we demonstrate and evaluate a tracking system for a moving ultrasound beacon without the need to initially calibrate the positions of the receivers. Using our approach we estimate the trajectory of a moving model train with a precision in the range of centimeters.

Calibration of an IMU Using 3-D Rotation Platform

This paper presents complete procedures to calibrate an inertial measurement unit (IMU) using a 3-D rotation platform. Previous works show that all the sensors can be calibrated separately using different calibration tools or methods. However, there is no guarantee that all the calibrated sensors are aligned to one coordinate system. In consequence, existence of axes correlations between the sensors can have big impact on orientation or position determination. Besides, very few literatures report the detail of gyroscope calibration without professional facilities. Different from the previous works, the calibration procedures reported in this paper not only focus on the scalar and bias removal, but also cover the correction of angle correlations between different sensors. In addition, the calibration of gyroscope relying only on 3-D rotation platform is also given. The calibration results have been validated using professional calibration tools and commercial IMU module. As a result, our calibration approaches are able to achieve a similar calibration performance as the one using professional tools as well as minimize the angle correlations between sensors.

Calibration-free TDOA self-localisation

We present an approach for the localisation of passive receiver nodes in a communication network. The only source of information is the time when environmental sound or ultrasound signals are received. The discrete signals occur at unknown positions and times, but they can be distinguished. The clocks of the receivers are synchronised, so the time differences of arrival (TDOA) of the signals can be computed. The goal is to determine the relative positions of all receiver nodes and implicitly the positions and times of the environmental signals. Our proposed approach, the Cone Alignment algorithm, solves iteratively a nonlinear optimisation problem of TDOA using a physical spring–mass simulation. We present a geometrical representation of the error function, which is modelled by physical springs. By iterative relaxation of the springs, the error function is minimised. The approach is tested in numerous simulations, whereby our algorithm shows a smaller tendency to get stuck in local minima than a nonlinear least-squares approach using gradient descent. In experiments in a real-world setting, we demonstrate and evaluate a tracking system for a moving ultrasound beacon without the need to initially calibrate the positions of the receivers. Using our algorithm, we estimate the trajectory of a moving model train and of an RC car with a precision in the range of few centimetres.

Indoor localization system for emergency responders with ultra low-power radio landmarks

In this paper we present a novel indoor localization approach based on 868MHz radio landmarks and inertial sensor data as a guidance system for emergency responders. For the first time we use low-power wake-up technology to develop real-time capable landmarks that overcome the problem of limited landmark lifetime. While in sleep mode our landmarks have an overall power consumption of 5.6μW making them ready-to-use in case of an emergency for up to 8 years. The landmarks are small and cost-efficient and may be integrated into the building infrastructure, e.g. into smoke detectors. Additionally, we have developed a handheld device for firefighters which communicates with our landmarks by an initial radio wake-up call and subsequent measuring of the received signal strength (RSSI) of the response. The measurements are used as an input to estimate and display the positions of the firefighter and the landmarks using local optimization algorithms. Furthermore, our handheld device communicates with a body-mounted wireless micro-inertial measurement unit (μIMU) to receive the angular rate, acceleration and magnetic field information in all three dimensions improving the accuracy of positioning. With this easy-to-setup guidance system emergency forces can be more effective, and the duration of rescue operations is reduced, hereby improving both the safety of rescue forces and increasing the chances of disaster victims.

Acoustic Self-Calibrating System for Indoor Smart Phone Tracking

This paper presents an acoustic indoor localization system for commercial smart phones that emit high pitched acoustic signals beyond the audible range. The acoustic signals with an identifier code modulated on the signal are detected by self-built receivers which are placed at the ceiling or on walls in a room. The receivers are connected in a Wi-Fi network, such that they synchronize their clocks and exchange the time differences of arrival (TDoA) of the received chirps. The location of the smart phone is calculated by TDoA multilateration. The precise time measuring of sound enables high precision localization in indoor areas. Our approach enables applications that require high accuracy, such as finding products in a supermarket or guiding blind people through complicated buildings. We have evaluated our system in real-world experiments using different algorithms for calibration-free localization and different types of sound signals. The adaptive GOGO-CFAR threshold enables a detection of 48% of the chirp pulses even at a distance of 30 m. In addition, we have compared the trajectory of a pedestrian carrying a smart phone to reference positions of an optic system. Consequently, the localization error is observed to be less than 30 cm.