Joan Bordoy

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.

Unsynchronized ultrasound system for TDOA localization

Indoor localization based on time difference of arrival (TDOA) has been recently a promising field of study. We consider the previously unsolved problem of locating a moving target receiver by using unsynchronized stationary beacons without requirement of manual calibration. Thus, the received signals and their time of arrival (TOA) have to be assigned to a beacon. Besides, in order to automatically calibrate the system it is required to estimate the time offsets between the senders, their positions and the initial receiver position. We present an approach to estimate all the variables of the scenario using the gradient descent and the Gauss-Newton method, two local optimization algorithms which use the derivative of a system of hyperbolic error equations. Besides, we present an ultrasound transmission system approach which fulfils the requirements of this scenario, being robust against multipath and estimating the reception time with high accuracy. In order to avoid interference by echoes the packet size is reduced by using two frequencies in Orthogonal Frequency Division Multiplex (OFDM). Further, the transmission system enables distinction of the beacons, as the ultrasound signals are used both for localization and for information transmission. The simulations show the local optimization algorithms are capable of estimating the positions of the beacons, receivers and offsets. They require only a rough knowledge of the sender positions. Further, real experiments show that the timestamps are measured with a standard deviation of only 1.19 μs for a SNR of 10 dB, which corresponds to standard deviation of about 0.4 mm for the distance measurement.

Robust tracking of a mobile receiver using unsynchronized time differences of arrival

Localization based on the time difference of arrival (TDoA) has turned out to be a promising approach for indoor environments, especially in combination with innovative self-calibrating TDoA algorithms that eliminate the need to measure the positions of reference receivers. We consider the previously unsolved problem of locating a moving target receiver by discrete signals from stationary beacons at unknown locations. We assume that the beacons are small and inexpensive and they require no further communication, i.e. they are unsynchronized. They only emit short discrete signals at regular intervals, of which we assume that they can be distinguished. The moving target travels on an unknown trajectory, receiving signals from the beacons and calculating the TDoA of the signals. First, we discuss adaptions of two TDoA algorithms by which the senders can be located from unknown signals. Second, we propose two novel approaches based on probabilistic state estimation to enable robust localization of the mobile receiver using the discrete arrival times, once the senders have been located. The probabilistic algorithms use the particle filter and the unscented Kalman filter to estimate the position and velocity of the target, as well as the unknown synchronization offsets of the senders. We provide a motion model and a sensor model for which we take into account that the signals of the beacons are received as singles, each at a different time. We verify the feasibility and robustness of our approach in extensive simulations, where we analyze the reliability of localization and compare both algorithms.

Smartphone-Based Indoor Localization System Using Inertial Sensor and Acoustic Transmitter/Receiver

This paper presents a novel smartphone-based indoor localization system that integrates an infrastructure-based acoustic localization system with inertial sensor-based dead reckoning. A fuzzy inference system is developed to achieve a short-term high accuracy tracking by using inertial sensors. The acoustic positioning and the inertial sensor-based dead reckoning are then fused by a Kalman filter with a carefully designed decision making algorithm. Hence, long-term stable and precise indoor localization, which is also robust against short-term measurement noise of the acoustic system, can be achieved. The experimental results show that the proposed system is able to accurately follow the true trajectory meanwhile to maintain the robustness and stability even if the position data of acoustic localization system are missing or erroneous.

Single transceiver device-free indoor localization using ultrasound body reflections and walls

In this paper we present a novel indoor localization approach based on measuring the time of flight (TOF) of ultrasound signal reflections in human bodies and walls. This is a stand off detection approach, it does not require the user to carry any wireless device. Only a static co-located speaker and microphone pair is used. The speaker sends a chirp, which is reflected to the environment. Then, the microphone receives the chirp and the reflections, measuring the reception times. Subsequently, the position of the target is estimated by the line of sight signal and the second reflections from 2 walls. Fast normalized correlation is used to estimate the timestamps of the received signals. The human being reflections are detected by subtracting the correlated signal in consecutive intervals, exploiting the fact that even a static human being moves slightly due to his breathing. Besides, we use a polynomial approximation of the reflections to increase the accuracy of this process. The choice among the remaining peaks is done by minimizing the error of the geometrical equations involved. When the target is moving continuously, an unscented Kalman filter is used to predict the most reliable peaks and estimate the position of the target. Real measurements show the capability of the proposed approach of localizing a person standing still with a median error of 0.15m and a moving human being with a median error of 0.08m and a standard deviation of 0.13 m.

Data Fusion of Time Stamps and Transmitted Data for Unsynchronized Beacons

Unsynchronized localization systems based on the measurement of time (difference) of arrival require reliable time stamps of the received signal. Noise, frequency shifts, and echoes disturb the signal and induce measurement errors of the time stamp, which leads to localization errors. Furthermore, the line of sight (LOS) signal has to be distinguished from the echoes to avoid false signal tracking. The proposed method combines the information of an ultrasound transmission with the measured time stamp and estimates the identifier. In our approach, the ultrasound transmission system uses phase-shift keying to modulate the signal. The received symbols and the time stamps are tracked and fused by the Kalman filter to increase the signal-to-noise ratio of the fused symbols and improve the validity of the decoding. Hence, the bias of the received symbols is tracked and the tracking allows to distinguish between the LOS signal and the echoes. As a result, the data fusion reduces the packet error rate from 70% at a distance of 21 m to 4.5%. Moreover, the median error of the localization is reduced from 7 to 4.6 cm.

Indoor-localization system for smart phones

In this paper we present an indoor-localization system using sound signals which are outside of the audible range with an error in the range of centimeters. The innovative distinctive feature of the proposed system is based on the high accuracy due to the acoustic run-time measurements. Therefore this approach is more precise than other indoor-localization systems based on Wi-Fi or Bluetooth. Furthermore, it is user-friendly as the user needs only his smartphone and no additional hardware. With its high precision the system allows entirely new applications in indoor localization. In particular, it has the technological potential to help blind people to navigate through public and administrative buildings without the need of assistance from others. In this novel approach we implement an unscented Kalman filter which proves to be capable of tracking not only the position of the target but also the time offset of the smartphone clock, reducing the required number of available receivers for localization. Besides, the precise synchronization of the receivers allows us to track the current position of the smartphone user with a median error of 9 cm and a standard deviation of 9 cm in a real-world scenario. In this way, the user is enabled to track his current location in a building with a very high accuracy.

Robust Extended Kalman filter for NLOS mitigation and sensor data fusion

This paper presents an algorithm based on the Robust Extended Kalman filter (REKF) for non-line-of-sight (NLOS) mitigation and sensor data fusion. The aim is to locate an off-the-shelf smartphone using only its speaker and its inertial measurement unit (IMU). The target emits inaudible sound signals which are detected by static receivers on the ceiling. Then, its position can be estimated using time differences of arrival (TDOA). The limiting factor of sound localization is the difficulty to identify the signals which travel directly from the target to the anchor nodes (line-of-sight) and the signals which are reflected to walls or other objects (non-line-of-sight). Nowadays, most of the smartphones are provided with an IMU which can be used for localization. However, its accumulative error deteriorates the result after a certain time. Then, a promising approach is to fuse the received sound timestamps and the IMU data in order to estimate the position of the target. The REKF reduces the effect of the non line-of-sight (NLOS) measurements by assigning them lower weights while the fusion with the IMU improves the accuracy, specially when only a reduced number of line-of-sight (LOS) signals are available.

Localization System based on Ultra Low-power Radio Landmarks.

In this paper we present a novel indoor localization system using external reference landmarks as a guidance system for emergency responders. The landmarks are based on low-power wake-up nodes which can be integrated into smoke detectors. The radio wake-up technology is equipped in the system to extend the lifetime of landmarks. While in sleep mode our landmarks have an overall power consumption of 66 μW making them ready-to-use in case of an emergency for up to 5 years. The landmarks are small and cost-efficient and may be integrated into the building infrastructure. The positioning is achieved by combining the radio ranging and IMU based dead reckoning to overcome the disadvantages of both systems. The experimental results show that the proposed system is able to outperform both standalone systems and meanwhile maintain the low power consumption.

Localization system based on handheld inertial sensors and UWB

In this paper we developed a novel indoor localization system based on Ultra-wideband (UWB) landmarks and inertial sensor as a guidance system for emergency responders. The wake-up technology is also equipped in the system to extend the lifetime of landmarks. While in sleep mode our landmarks have an overall power consumption of 66µW making them ready-to-use in case of an emergency for up to 5 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 wakes up the UWB-landmarks. The inertial sensors are integrated in the handheld device for tracking purpose. The IMU and the UWB based position information are then fused by unscented Kalman filter to enhance the tracking accuracy. The experimental results show that the proposed system is able to outperform both standalone systems.