Distributed Kalman-Consensus Filtering for Sparse Signal Estimation

<table>The evolution of <svg height="16.887501" id="M218" style="vertical-align:-3.86845pt" version="1.1" viewbox="0 0 33.799999 16.887501" width="33.799999" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
<g transform="matrix(.017,-0,0,-.017,.062,12.013)"><path d="M162 -163h-60v866h60v-866zM309 -163h-60v866h60v-866z" id="x2016"></path></g><g transform="matrix(.017,-0,0,-.017,8.663,11.825)"><path d="M470 557l-12 -25q-103 25 -223 64q-128 -41 -223 -64l-12 25q172 77 224 109h22q57 -35 224 -109z" id="xF5C01"></path></g><g transform="matrix(.017,-0,0,-.017,7.05,12.013)"><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>
<g transform="matrix(.012,-0,0,-.012,16.55,16.088)"><path d="M244 607q0 -25 -15.5 -43t-37.5 -18q-19 0 -32 13t-13 35q0 21 15 41t39 20q20 0 32 -14t12 -34zM222 91q-29 -33 -79 -68t-75 -35q-13 0 -19 7.5t-6 31t10 65.5l62 253q5 26 -1 26q-21 0 -72 -43l-13 24q43 40 91 68t71 28q30 0 10 -78l-71 -274q-8 -30 3 -30
q16 0 76 48z" id="x1D456"></path></g>
<g transform="matrix(.017,-0,0,-.017,20.425,12.013)"><path d="M162 -163h-60v866h60v-866zM309 -163h-60v866h60v-866z" id="x2016"></path></g>
<g transform="matrix(.012,-0,0,-.012,27.412,16.825)"><path d="M384 0h-275v27q67 5 81.5 18.5t14.5 68.5v385q0 38 -7.5 47.5t-40.5 10.5l-48 2v24q85 15 178 52v-521q0 -55 14.5 -68.5t82.5 -18.5v-27z" id="x31"></path></g>
</svg> and <svg height="16.887501" id="M219" style="vertical-align:-3.86845pt" version="1.1" viewbox="0 0 31.3375 16.887501" width="31.3375" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
<g transform="matrix(.017,-0,0,-.017,.062,12.013)"><path d="M162 -163h-60v866h60v-866zM309 -163h-60v866h60v-866z" id="x2016"></path></g><g transform="matrix(.017,-0,0,-.017,7.049,12.013)"><path d="M391 364q0 -28 -20.5 -53.5t-50 -43.5t-70 -34t-73.5 -25t-68 -17v-29q0 -48 20 -81t65 -33q73 0 157 76l16 -23q-107 -113 -221 -113q-21 0 -40.5 6.5t-39 22t-31.5 47t-12 75.5q0 69 33.5 139.5t95.5 114.5q77 55 143 55q42 0 69 -24.5t27 -59.5zM313 350
q0 26 -14.5 40.5t-37.5 14.5q-32 0 -44 -7q-82 -46 -104 -175q200 49 200 127z" id="x1D452"></path></g>
<g transform="matrix(.012,-0,0,-.012,14.088,16.088)"><path d="M244 607q0 -25 -15.5 -43t-37.5 -18q-19 0 -32 13t-13 35q0 21 15 41t39 20q20 0 32 -14t12 -34zM222 91q-29 -33 -79 -68t-75 -35q-13 0 -19 7.5t-6 31t10 65.5l62 253q5 26 -1 26q-21 0 -72 -43l-13 24q43 40 91 68t71 28q30 0 10 -78l-71 -274q-8 -30 3 -30
q16 0 76 48z" id="x1D456"></path></g>
<g transform="matrix(.017,-0,0,-.017,17.962,12.013)"><path d="M162 -163h-60v866h60v-866zM309 -163h-60v866h60v-866z" id="x2016"></path></g>
<g transform="matrix(.012,-0,0,-.012,24.95,16.825)"><path d="M412 140l28 -9q0 -2 -35 -131h-373v23q112 112 161 170q59 70 92 127t33 115q0 63 -31 98t-86 35q-75 0 -137 -93l-22 20l57 81q55 59 135 59q69 0 118.5 -46.5t49.5 -122.5q0 -62 -29.5 -114t-102.5 -130l-141 -149h186q42 0 58.5 10.5t38.5 56.5z" id="x32"></path></g>
</svg> in all of nodes in the all-to-all network.</table>

Mathematical Problems in Engineering

Distributed Kalman-Consensus Filtering for Sparse Signal Estimation

Figure 8