Random field-aided tracking of autonomous kinetically passive wireless agents

EURASIP Journal on Advances in Signal Processing

Table 3 Algorithm overview

Abbreviation	Proposed in	MPF LA	Time update (TUD)	Extra TUD parameters/ RF variable	Motion model	State variables
MPF	[16]	MCA [16]	\(\boldsymbol {f}(\boldsymbol {x}_{\mathbbm {i},k}, \boldsymbol {u}_{k,\mathbbm {i}} =0) + {{\boldsymbol {\nu }}_{\text {MPF}}}^{\dag }\)	—	(31)	[x,y,υ,ϕ]
IPF, \({{\sum }_{\text {MPF}}}\)	[13]	MCA [16]	\(\boldsymbol {f}(\boldsymbol {x}_{\mathbbm {i},k}, \boldsymbol {u}_{k,\mathbbm {i}}) + {{\boldsymbol {\nu }}_{\text {MPF}}}^{\dag }\)	\(\boldsymbol {u}_{k,\mathbbm {i}}={{\omega }}_{k,\mathbbm {i}}\)	(31)	[x,y,υ,ϕ]
IPF, \(\boldsymbol {\sum }_{\text {RF}}\)			\(\boldsymbol {f}(\boldsymbol {x}_{\mathbbm {i},k}, \boldsymbol {u}_{k,\mathbbm {i}}) + {{\boldsymbol {\nu }}_{\text {RF}}}^{\ddag }\)
RFaT	This work	MCA [16]	\(\boldsymbol {f}(\boldsymbol {x}_{\mathbbm {i},k}, \boldsymbol {u}_{k,\mathbbm {i}}) + {{\boldsymbol {\nu }}_{\text {RF}}}^{\dag }\)	\(\boldsymbol {u}_{k,\mathbbm {i}}={z}_{\boldsymbol {\theta }}(\boldsymbol {x}_{k,\mathbbm {i}})\)	(31)	[x,y,υ,ϕ]

†\({{\sum }_{\text {MPF}}}=\text {diag}[{0.05}\ {\mathrm {m}}, {0.05}\ {\mathrm {m}}, T^{2} {0.01}\ {\mathrm {m}/\mathrm {s}}^{2}, T^{2} {0.7}\ {\text {rad}/\mathrm {s}}^{2}]\) has been found to be the optimal covariance matrix of the zero-mean process noise for the MPF algorithm in results not reported in this work
‡\({{\sum }_{\text {RF}}}=\text {diag}[{0.02}\ {\mathrm {m}}, {0.02}\ {\mathrm {m}}, T^{2} {0.002}\ {\mathrm {m}/\mathrm {s}}^{2}, T^{2} {0.01}\ {\text {rad}/\mathrm {s}}^{2}]\) has been found to be the optimal covariance matrix of the zero-mean process noise for the RFaT algorithm in results not reported in this work