Henning Lenz

WLAN-Based Pedestrian Tracking Using Particle Filters and Low-Cost MEMS Sensors

Indoor positioning systems based on wireless LAN (WLAN) are being widely investigated in academia and industry. Meanwhile, the emerging low-cost MEMS sensors can also be used as another independent positioning source. In this paper, we propose a pedestrian tracking framework based on particle filters, which extends the typical WLAN-based indoor positioning systems by integrating low-cost MEMS accelerometer and map information. Our simulation and real world experiments indicate a remarkable performance improvement by using this fusion framework.

Fusion of Sensor Data in Siemens Car Navigation System

Car navigation systems have three main tasks, namely 1) positioning; 2) routing; and 3) navigation (guidance). Positioning of the car is carried out by appropriately combining information from several sensors and information sources, including odometers, gyroscopes, Global Positioning System (GPS) information, and digital maps. This paper describes two sensor-fusion steps implemented in commercial Siemens car navigation systems. The first step is the fusion of the odometer, gyroscope, and GPS sensory information. The dynamic model of the car movement is implemented in a Kalman filter, which relays the GPS signal as a teacher. In the second step, the available digital map is used to find the most likely position on the roads. Contrary to the standard application of the digital map, where the current estimated car position is just projected on the road map, the approach presented here compares the features of the integrated vehicle path with the features of the candidate roads from the digital map. In addition, this paper presents the results of the experimental drives. The developed car navigation system was awarded the best car navigation system among ten competing systems in 2002 by the Auto Build magazine

Fusion of Map and Sensor Data in a Modern Car Navigation System

The main tasks of car navigation systems are positioning, routing, and guidance. This paper describes a novel, two-step approach to vehicle positioning founded on the appropriate combination of the in-car sensors, GPS signals, and a digital map. The first step is based on the application of a Kalman filter, which optimally updates the model of car movement based on the in-car odometer and gyroscope measurements, and the GPS signal. The second step further improves the position estimate by dynamically comparing the continuous vehicle trajectory obtained in the first step with the candidate trajectories on a digital map. This is in contrast with standard applications of the digital map where the current position estimate is simply projected on the digital map at every sampling instant.

Initialization and Online-Learning of RSS Maps for Indoor / Campus Localization

Common approaches for indoor positioning based on cellular communication systems use as measurements the received signal strength (RSS). In order to work properly, such a system often requires many calibration points before its start. This paper presents a two-fold approach achieving high indoor localization accuracies without requiring too many calibration points. The basic idea is to use an initial propagation model with few parameters, which can be adapted by a few measurements, e.g. mutual measurements of access points. Then the model is refined by incorporating additional parameters and using online learning. Investigations on the requirements and potentials of different approaches and results for DECT and WLAN setups are given. The first approach uses predefined paths that should be passed through by a service technician with measurement equipment. The second approach uses a Kohonen-like learning algorithm to adapt the model on-the-fly. For both approaches linear propagation models and more involved dominant path models incorporating map information are applied for the initialization.

Enhancing the map usage for indoor location-aware systems

Location-aware systems are receiving more and more interest in both academia and industry due to their promising prospective in a broad category of so-called Location-Based-Services (LBS). The map interface plays a crucial role in the location-aware systems, especially for indoor scenarios. This paper addresses the usage of map information in a Wireless LAN (WLAN)-based indoor navigation system. We describe the benefit of using map information in multiple algorithms of the system, including radio-map generation, tracking, semantic positioning and navigation. Then we discuss how to represent or model the indoor map to fulfill the requirements of intelligent algorithms. We believe that a vector-based multi-layer representation is the best choice for indoor location-aware system.

Robust Control of the Chaotic Lorenz System

This paper presents a global approach for controlling the Lorenz system. The basic idea is to partially cancel the nonlinear cross-coupling terms such that the stability of the resulting system can be guaranteed by sequentially proving the stability of each individual state. The method combines ideas from feedback linearization, classical control theory, and Lyapunovs second method. Robust behavior with respect to model uncertainties in the feedback loop is proven. The performance of partial linearization compared to input-state linearization is illustrated on tracking of several trajectories including a periodic orbit and a steady state.

Nonlinear Speed-control for a Continuum Theory of Traffic Flow

Abstract A nonlinear controller creating a homogeneous flow is designed for a traffic model given by partial differential equations. To be able to apply nonlinear control techniques, a co-moving coordinate frame is set up leading to a reduced model of stop-and-go waves. The controller stabilizing this model is re-transformed and simplified. Properties of the controlled system indicating global stability are confirmed by simulations. Finally, a two-point controller is defined, bridging the gap to speed limits.

Sensor fusion in siemens car navigation system

Car navigation systems have three main tasks: positioning, routing and navigation (guidance). Positioning of the car is carried out by appropriately combining information from several sensors and information sources including odometers, gyroscopes, the GPS information and the digital map. This paper describes two-sensor fusion steps implemented in the commercial Siemens car navigation systems. The first step is the fusion of the odometer, gyroscope, and GPS sensory information. The dynamic model of the car movement is implemented in a Kalman filter, which relays on the GPS signal as a teacher. In the second step the available digital map is used to find the most likely position on the roads. Contrary to the standard application of the digital map where the current estimated car position is just projected on the road map, the herein presented approach compares the features of the integrated vehicle path with the features of the candidate roads from the digital map. In addition, this paper presents the results of the experimental drives. The developed car navigation system was awarded in 2002 by Auto Build magazine as the best car navigation systems among ten competing systems

Adaptive Sliding-Mode Control of the Absolute Gain

Abstract Nonlinear systems controlled by sliding-mode controllers exhibit robust behaviour towards model uncertainties and noise. However, the optimal estimation of the gain of the non-continuous control part remains an open problem. In general, one would tend to estimate a too large gain in order to reject all possible uncertainties, leading to an excessive control activity. Therefore, in this paper an adaptive sliding-mode controller automatically adjusting this gain is designed. No a priori knowledge about the estimation error is assumed. As a result, the gain approaches the optimal value. The adaptation is illustrated for a chaotic pendulum and the Duffing oscillator.

SLL: Relations to Kohonen SOMs

The simultaneous localization and learning (SLL) is an indoor localization technique based on existent radio communication networks. It originally takes received signal strength (RSS) as measured feature, used as input on an adaptive and iterative process based on Kohonen self organizing maps (SOMs) in order to learn and improve a feature map. The present paper points the main characteristics from both SLL and SOM, their differences and similarities. The somewhat generic formulation for SOMs acquire physical meanings with SLL that act as a constrainment, making the SLL a very particular case of SOM. The proofs for one dimensional SOMs are complemented by the proofs presented for the SLL by the authors in previous articles.