Low-Cost MEMS-Based Pedestrian Navigation  Technique for GPS-Denied Areas

<table class="table-group" width="800px"><tr class="fig-image" id="a"><td><img alt="197090.fig.005a" src="https://static.hindawi.com/articles/js/volume-2013/197090/figures/197090.fig.005a.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(a) </b>Gauss Markov parameters  </td></tr></table></td></tr><tr class="fig-image" id="b"><td><img alt="197090.fig.005b" src="https://static.hindawi.com/articles/js/volume-2013/197090/figures/197090.fig.005b.jpg"/></td></tr><tr class="fig-caption"><td align="center"><table><tr><td><b>(b) </b>Autocorrelation fit for the <svg height="7.9499998" id="M63" style="vertical-align:-0.1638pt" version="1.1" viewbox="0 0 9.625 7.9499998" width="9.625" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
<g transform="matrix(.017,-0,0,-.017,.062,7.675)"><path d="M536 404q0 -17 -13.5 -31.5t-26.5 -14.5q-8 0 -15 10q-11 14 -25 14q-22 0 -67 -50q-47 -52 -68 -82l37 -102q31 -88 55 -88t78 59l16 -23q-32 -48 -68.5 -78t-65.5 -30q-19 0 -37.5 20t-29.5 53l-41 116q-72 -106 -114.5 -147.5t-79.5 -41.5q-21 0 -34.5 14t-13.5 37
q0 16 13.5 31.5t28.5 15.5q12 0 17 -11q5 -10 25 -10q22 0 57.5 36t89.5 111l-40 108q-22 58 -36 58q-21 0 -67 -57l-19 20q81 107 125 107q17 0 30 -22t39 -88l22 -55q68 92 108.5 128.5t74.5 36.5q20 0 32.5 -14t12.5 -30z" id="x1D465"></path></g>
</svg> gyroscope </td></tr></table></td></tr></table>

<table>Gauss-Markov model representation.</table>

Journal of Sensors

fig5

Figure 5

Figure 5: Low-Cost MEMS-Based Pedestrian Navigation  Technique for GPS-Denied Areas 