GPS integrated with inertial sensors in mobile phones becomes a reality

This algorithm can work in all types of vehicles and has the ability to adapt to different conditions during short learning phase when GPS is available. The real-time version of stop detection algorithm employs the probabilistic approach. It analyses the accelerometers data within a moving window.

Experimental results

Our portable car navigation system was tested in road tests in urban environment that included tunnels, parking garages, urban canyons, and road interchanges. Figure 5 displays the system positioning performance during driving test in Portland, OR that included a 300 m long tunnel. The direction of the car motion is shown by the arrow. Magenta dots correspond to the standalone GPS receiver position measurements. It should be noted that there are no GPS solution in the tunnel. The green crosses for the trajectory on the map correspond to the combined GPS+MEMS computed position. The black dots correspond to the dead-reckoned solution when GPS signals were not available. The first GPS position fix was received approximately 4 sec after the car passed the tunnel making the total GPS outage about 18 sec. The error of GPS+MEMS computed position at that time was less than 20 m. This test shows that the combined GPS+MEMS solution provides significant improvement; accurate position, velocity, and heading information were available even in the absence of GPS signal.

Figure 6 depicts the test route in Portland downtown. This test route included high-multipath urban canyon environment in the vicinity of high-rise buildings. In some places, GPS position error caused by multi-path was about 35 m. There are some time instances when the GPS signals were not available (black dots). This test shows improvements of the combined GPS+MEMS system performance in high-multipath environment, which is explained by the ability of the combined GPS+MEMS algorithm to filter out GPS position outliers.

Conclusions

This paper has shown that low-cost inertial sensors can significantly improve GPS positioning by continuing to output position during short GPS outages with sufficient accuracy for most of car navigation applications. The integrated GPS+MEMS system has also demonstrated improvement of position and velocity accuracy in high multipath urban canyon environment and the ability to provide continuous output of the vehicle heading even when vehicle is not moving. This is useful if we apply the map-matching algorithm. This device does not require any installation in the vehicle. It works in all vehicles and can be easily transferred between vehicles. Finally, it should be noted that our design is suitable for portable navigation devices since the cost, size, and power consumption of inertial sensors meet the requirements for mass market consumer electronics.

References

Zhang, X. , Wang, Q., and Wan, D., “Mapmatching in road crossings of urban canyons based on road traverses and linear heading change model,” IEEE Trans. Instrumentation and Measurement, vol. 56, no. 6, pp. 2795–2803, Dec. 2007.

Chowdhary , M., Colley, J., and Chansarkar, M., “Improving GPS location availability and reliability by using a suboptimal, low-cost MEMS sensor set,” in Proc. ION GNSS Int. Technical Meeting Satellite Division, Fort Worth, TX, Sept. 25–28 2007, pp. 610–614.

Davidson, P., Hautamäki J., Collin, J., and Takala, J., ”Improved Vehicle Positioning in Urban Environment through Integration of GPS and Low- Cost Inertial Sensors”, in Proc. ENCGNSS , Naples, Italy, May 2009.

Analog Devices Inc., “ADXRS150 ±150°/s single chip yaw rate gyro with signal conditioning,” datasheet, http://www.analog.com/en/ prod/0,2877,ADXRS150,00.html

VTI Technologies Oy, “SCA3000-D01 3-axis low power accelerometer with digital SPI interface,” datasheet, http:// www.vti.fi/en/products-solutions/products/ accelerometers/sca3000-accelerometers/

Fastrax L td., “IT03 development kit,” technical specification, http://www.fastrax.fi

		Pavel Davidson Researcher, Department of Computer Systems, Tampere University of Technology, Finland pavel.davidson@tuf.fi

		Jani Hautamäki Research Assistant, Department of Computer Systems, Tampere University of Technology, Finland

		Jussi Collin Senior Researcher, Department of Computer Systems, Tampere University of Technology, Finland

		Jarmo Takala Professor, Department of Computer Systems Tampere University of Technology, Finland

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