A method, implemented by a location estimation module of a device, for estimating a target device's location, wherein the target device is operable to move in a wireless environment and to communicate with the wireless environment using signals each of which has at least one measurable signal value (x); the method comprising: - forming a plurality of calibration submodels of the wireless environment, each calibration submodel indicating a probability distribution (F 1 - F 3) for expected signal values at a respective calibrated location (Q 1 -Q 3) in the wireless environment, wherein the calibration submodels are based on calibration measurements; - combining the calibration submodels to provide a probabilistic model, the probabilistic model indicating a probability distribution (F 1 - F 3) for signal values at each one of a plurality of sample locations (Q 1 - Q Y) in the wireless environment, wherein the plurality of sample locations (Q 1 -Q Y) comprises the calibrated locations (Q 1 -Q 3) and one or more new locations (Q X -Q Y) for which actual calibration measurements are not available; - estimating the target device's location based on applying a set of target device observations of signal values (x) received in the wireless environment at the target device's location to the probabilistic model; wherein the combining step comprises: - for each one of the plurality of calibration submodels, forming its cumulative distribution function (31, 32) of expected signal values; - for each one of the plurality of sample locations (Q 1 - Q Y), assigning a respective relative weight (W 1, W 2, W i, 65) to each one of the plurality of calibration submodels, each relative weight selected based on a capability of the respective calibration submodel to predict the cumulative distribution of the signal value at the respective sample location, and for each one of the plurality of sample locations further forming a weighted combination of the cumulative distribution functions, wherein the weighted combination of the cumulative distribution functions is a normalized sum of products of an inverse cumulative distribution function of expected signal values of each of the plurality of the calibration submodels multiplied by its respective relative weight at the respective sample location, wherein the normalized sum of the product is normalized by dividing with a sum of all respective weights for the respective sample location; and - forming the probability distribution (34, 641-645) for the probabilistic model at each one of the plurality of sample points (Q1 - QY) by taking an inverse of its respective normalized weighted sum. - forming a plurality of calibration submodels of the wireless environment, each calibration submodel indicating a probability distribution (F 1 - F 3) for expected signal values at a respective calibrated location (Q 1 -Q 3) in the wireless environment, wherein the calibration submodels are based on calibration measurements; - combining the calibration submodels to provide a probabilistic model, the probabilistic model indicating a probability distribution (F 1 - F 3) for signal values at each one of a plurality of sample locations (Q 1 - Q Y) in the wireless environment, wherein the plurality of sample locations (Q 1 -Q Y) comprises the calibrated locations (Q 1 -Q 3) and one or more new locations (Q X -Q Y) for which actual calibration measurements are not available; - estimating the target device's location based on applying a set of target device observations of signal values (x) received in the wireless environment at the target device's location to the probabilistic model; - for each one of the plurality of calibration submodels, forming its cumulative distribution function (31, 32) of expected signal values; - for each one of the plurality of sample locations (Q 1 - Q Y), assigning a respective relative weight (W 1, W 2, W i, 65) to each one of the plurality of calibration submodels, each relative weight selected based on a capability of the respective calibration submodel to predict the cumulative distribution of the signal value at the respective sample location, and for each one of the plurality of sample locations further forming a weighted combination of the cumulative distribution functions, wherein the weighted combination of the cumulative distribution functions is a normalized sum of products of an inverse cumulative distribution function of expected signal values of each of the plurality of the calibration submodels multiplied by its respective relative weight at the respective sample location, wherein the normalized sum of the product is normalized by dividing with a sum of all respective weights for the respective sample location; and - forming the probability distribution (34, 641-645) for the probabilistic model at each one of the plurality of sample points (Q1 - QY) by taking an inverse of its respective normalized weighted sum.