Martti Kirkko-Jaakkola

Bias Prediction for MEMS Gyroscopes

MEMS gyroscopes are gaining popularity because of their low manufacturing costs in large quantities. For navigation system engineering, this presents a challenge because of strong nonstationary noise processes, such as 1/f noise, in the output of MEMS gyros. In practice, on-the-fly calibration is often required before the gyroscope data are useful and comparable to more expensive optical gyroscopes. In this paper, we focus on an important part of MEMS gyro processing, i.e., predicting the future bias given calibration data with known (usually zero) input. We derive prediction algorithms based on Kalman filtering and the computation of moving averages, and compare their performance against simple averaging of the calibration data based on both simulations and real measured data. The results show that it is necessary to model fractional noise in order to consistently predict the bias of a modern MEMS gyro, but the complexity of the Kalman filter approach makes other methods, such as the moving averages, appealing.

Distributed Indoor Positioning System With Inertial Measurements and Map Matching

Accurate position information is nowadays very important in many applications. For instance, maintaining the situation awareness in command center in emergency operations is very crucial. Due to signal strength attenuation and multipath, Global Navigation Satellite Systems are not suitable for indoor navigation purposes. Radio network-based positioning techniques, such as wireless local area network, require local infrastructure that is often vulnerable in emergency situations. We propose here a distributed system for personal positioning based on inertial sensors. The system consists of an inertial measurement unit (IMU) connected to a radio carried by a person and the server connected to another radio. Step length and heading estimation is computed in the IMU and sent to the server. On the server side, the position is estimated using particle filter-based map matching. The benefit of the distributed architecture is that the computational capacity can be kept very low on the user side, which leads to long operation time as power consumption also remains very low.

A raim approach to GNSS outlier and cycle slip detection using L1 carrier phase time-differences

Cycle slips are a common error source in Global Navigation Satellite System (GNSS) carrier phase measurements. In this paper, the cycle slip problem is approached using Receiver Autonomous Integrity Monitoring (RAIM) methodology. Carrier phase measurements are used here in a single-receiver time-differential positioning method where integer ambiguities are canceled, but any cycle slips remain. The performance of the method was assessed by comparing the detection results to a Real-Time Kinematic (RTK) solution and by manual data examination. Postprocessing results obtained using authentic Global Positioning System (GPS) measurements logged by low-cost single-frequency receivers show that the method is able to reliably detect and identify single errors but fails in an exemplary multiple outlier scenario. As no reference receiver is needed, the method is a potential means to produce cycle-slip-corrected data usable in any postprocessing application.

Using a MEMS gyroscope to measure the Earth’s rotation for gyrocompassing applications

In this paper, a method and system for gyrocompassing based on a low-cost micro-electro-mechanical (MEMS) gyroscope are described. The proposed setup is based on the choice of a gyroscope with specified bias instability better than 2 deg h-1 and on careful error compensation. The gyroscope is aligned parallel to the local level, which helps to eliminate the g-sensitivity effect but also sacrifices a fraction of the Earth’s rotation rate that can be observed. The additive bias is compensated for by rotating the sensor mechanically and by extended Kalman filtering. In this paper, it is demonstrated that the proposed system is capable of observing the Earth’s rotation, and the north finding results show that a two-sigma accuracy of 4.03° was attained at latitude 61°N. With current MEMS gyroscopes, the system requires hours of time to achieve this accuracy, but the results demonstrate the theoretical accuracy potential of these small self-contained, low-cost sensors.

Indoor Localization Methods Using Dead Reckoning and 3D Map Matching

In order to navigate or localize in 3D space such as parking garages, we would need height information in addition to 2D position. Conventionally, an altimeter is used to get the floor level/height information. We propose a novel method for three-dimensional navigation and localization of a land vehicle in a multi-storey parking-garage. The solution presented in this paper uses low cost gyro and odometer sensors, combined with a 3D map by means of particle filtering and collision detection techniques to localize the vehicle in a parking garage. This eliminates the necessity of an altimeter or other additional aiding sources such as radio signalling. Altimeters have inherent dynamic influential factors such as temperature and environmental pressure affecting the altitude readings, and for radio signals we need extra infrastructure requirements. The proposed solution can be used without any such additional infrastructure devices. Other sources of information, such as WLAN signals, can be used to complement the solution if and when available. In addition we extend this proposed method to novel concept of non-stationary 3D maps, as moving maps, within which localization of a track-able object is required. We also introduce novel techniques that enable seamless navigation solution from vehicular dead reckoning (VDR) to pedestrian dead reckoning (PDR) and vice versa to reduce user involvement. For achieving this we collect relevant measurements such as vehicle ignition status and accelerometer signal variance, and user pattern recognition to select appropriate dead reckoning method.

Indoor 3D navigation and positioning of vehicles in multi-storey parking garages

We propose a novel method for three-dimensional navigation and localization of a land vehicle in a multi-storey parking-garage. In order to navigate or localize in 3D space we also need height information in addition to 2D position. Conventionally, an altimeter is used to get the floor level/height information. The solution presented in this paper uses low cost gyro and odometer sensors, combined with a 3D map by means of particle filtering and collision detection techniques to localize the vehicle in a parking garage. This eliminates the necessity of an altimeter or other additional aiding sources such as radio signaling. Thus the proposed solution can be used without any additional infrastructure devices. Other sources of information, such as WLAN signals, can be used to complement the solution if and when available.

Compact North Finding System

The knowledge of orientation of an object with respect to the earth-fixed reference coordinate system is crucial in many applications. For instance, in oil mining, it is very crucial to accurately know the orientation of the drilling equipment under the earth surface to drill through the desired path. In this context, we propose a compact inertial sensor system that estimates the instantaneous orientation of the system using an accelerometer and a gyroscope-derived tilt and azimuth angles. To keep the system size small, we use two-axis accelerometer and one-axis gyroscope. In addition, to avoid high sensor cost, the sensor biases are removed using the indexing method. The proposed system estimates the orientation of the compact system in almost all of the orientations, and, additionally, it also provides the measurement accuracy and integrity values that help in ascertaining the validity of the orientation estimate.

A Software Multi-GNSS Receiver Implementation for the Indian Regional Navigation Satellite System

ABSTRACT The Indian Regional Navigation Satellite System (IRNSS) is currently under development with four out of the total planned seven satellites deployed in space. The Department of Navigation and Positioning of the Finnish Geospatial Research Institute (FGI) has been an early adopter of this system in Europe through the development of its software-based multi-frequency multi-GNSS receiver, called FGI-GSRx. This paper presents the results of the first comprehensive IRNSS receiver implementation in Finland, if not in Europe, using the FGI-GSRx receiver. Following a brief description of the IRNSS system, the paper presents the receiver architecture, including the acquisition and tracking stages, and position computation. The results show that IRNSS satellites when used in multi-GNSS positioning can be beneficial in augmenting other satellite systems over north and east Europe. These benefits are expected to grow as more IRNSS satellites are deployed in space in the future. Therefore, the impact of these results is interesting to the positioning, navigation, and timing community even outside the intended service area of IRNSS.

Effect of Carouseling on Angular Rate Sensor Error Processes

Carouseling is an efficient method to mitigate the measurement errors of inertial sensors, particularly microelectromechanical systems (MEMS) gyroscopes. In this paper, the effect of carouseling on the most significant stochastic error processes of an MEMS gyroscope, i.e., additive bias, white noise, 1/f noise, and rate random walk (RRW) is investigated. Variance propagation equations for these processes under averaging and carouseling are defined. Furthermore, a novel approach to generating 1/f noise is presented. The experimental results show that carouseling reduces the contributions of additive bias, 1/f noise, and RRW significantly compared with plain averaging, which can be used to improve the accuracy of dead reckoning systems.

Monocular visual SLAM for tactical situational awareness

Tactical situational awareness for military applications should be based on infrastructure-free systems and should be able to form knowledge of the previously unknown environment. Simultaneous Localization and Mapping (SLAM) is a key technology for providing an accurate and reliable infrastructure-free solution for indoor situational awareness. However, indoor environments and the requirements , especially the size and weight limits of the system, make the implementation of SLAM using existing algorithms challenging. In particular, we aim to implement SLAM using a monocular camera, due to size limitations, whereas most existing algorithms use stereo images. The two major obstacles to be overcome are the unknown scale of translation observed using a monocular camera and the shortage of features indoors, complicating visual perception. Herein, a Kalman filter based SLAM solution is discussed, utilizing a concept called visual odometry that provides absolute translation information with a reduced number of features. The results show that our solution is feasible for performing SLAM indoors using a monocular camera.