Fusing or combining signals from multiple sensors
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 &amp; 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.