Abstract
Two fundamental approaches to information averaging are based on linear and logarithmic combination, yielding the arithmetic average (AA) and geometric average (GA) of the fusing data, respectively. In the context of multi-sensor target tracking, the two most common formats of data to be fused are random variables and probability density functions, namely v-fusion and f-fusion, respectively. In this work, we analyze and compare the second-order statistics (including variance and mean square error) of AA and GA in terms of both v-fusion and f-fusion. The case of weighted Gaussian mixtures representing multitarget densities in the presence of false alarms and missed detections (whose weight sums are not necessarily unit) is also considered, the result of which turns out to be significantly different from that of a single target. In addition to exact derivation, exemplifying analyses and illustrations are also provided.
Original language | English |
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Pages (from-to) | 233-243 |
Number of pages | 11 |
Journal | Information Fusion |
Volume | 51 |
DOIs | |
State | Published - Nov 2019 |
Keywords
- Aggregation operator
- Arithmetic mean
- Average consensus
- Covariance intersection
- Distributed tracking
- Geometric mean
- Linear pool
- Log-linear pool
- Multisensor fusion