Sensors fusion is a quite wide subtopic in signal processing. Apart from sensor fusion, it appears under different guises named for instance diversity enhancement, multisensor combination, ensemble averaging, to name a few.
If the sensors' outputs $y_i$ are in phase or synchronized (a sensitive hypothesis since your are talking about averaging), but possibly with noise $n_i$ or different amplitude levels (the $k_i$ factor):
$$y_i = k_i x+n_i$$
there is a quite old reference in Signal extraction from multiple noisy sensors, S. Palit, 1997. I haven't yet implemented it, and it is not quite cited, but I still have the feeling it deserves a closer look. In the same genre, a lot of recent works follow the track of "ensemble averaging", and the notion of "low-rank" decomposition sounds appealing: put your outputs in a matrix $X$. If there were no noise, the columns would be multiple of the single signal $x$. Hence, is $x$ is not zero, the matrix $X$ is rank one. The challenge, with noise, is to find a low-rank approximation of $X$, separating the signal $x$, noises $n_i$ and potentially localized disturbances, outliers with a sparse footprint. One of these methods is known as GoDec: Randomized Low-rank & Sparse Matrix Decomposition in Noisy Case:
Low-rank and sparse structures have been profoundly studied in matrix
completion and compressed sensing. In this paper, we develop ``Go
Decomposition'' (GoDec) to efficiently and robustly estimate the
low-rank part $L$ and the sparse part $S$ of a matrix $X=L+S+G$ with
noise $G$. GoDec alternatively assigns the low-rank approximation of
$X-S$ to $L$ and the sparse approximation of $X-L$ to $S$. The
algorithm can be significantly accelerated by bilateral random
projections (BRP). We also propose GoDec for matrix completion as an
important variant. We prove that the objective value $\|X-L-S\|_F^2$
converges to a local minimum, while $L$ and $S$ linearly converge to
local optimums. Theoretically, we analyze the influence of $L$, $S$
and $G$ to the asymptotic/convergence speeds in order to discover the
robustness of GoDec. Empirical studies suggest the efficiency,
robustness and effectiveness of GoDec comparing with representative
matrix decomposition and completion tools, e.g., Robust PCA and
OptSpace. GoDec can be extended to solve multi-label learning problem
by decomposing the multi-label data into the sum of several low-rank
part and a sparse residual, where each low-rank part corresponds to
the mapping of a particular label in the feature space. Then
prediction can be obtained by finding the group sparse representations
of a new instance on the subspaces defined by the low-rank parts.
If the outputs are not time-aligned, or with jitter, then you can explore convolutive blind source separation, superresolution (for BW extension), etc. but this margin is too thin to draw a complete panorama.