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We propose a measurement setup reaching Heisenberg scaling precision for the estimation of any distributed parameter $φ$ (not necessarily a phase) encoded into a generic $M$-port linear network composed only of passive elements. The scheme proposed can be easily implemented from an experimental point of view since it employs only Gaussian states and Gaussian measurements. Due to the complete generality of the estimation problem considered, it was predicted that one would need to carry out an adaptive procedure which involves both the input states employed and the measurement performed at the output; we show that this is not necessary: Heisenberg scaling precision is still achievable by only adapting a single stage. The non-adapted stage only affects the value of a pre-factor multiplying the Heisenberg scaling precision: we show that, for large values of $M$ and a random (unbiased) choice of the non-adapted stage, this pre-factor takes a typical value which can be controlled through the encoding of the parameter $φ$ into the linear network.