A system for identifying radio frequency, RF, transmitters in an environment, the apparatus comprising: at least one RF listening station configured to receive RF signals and acquire measurements of their RF power as a function of reception time, t, and frequency f; and a processing hub configured to identify RF transmitters operating in the environment by: receiving the RF power measurements; fitting to the RF power measurements a probability mixture model, PMM, that provides a probability density as a function of magnitude of the RF power measurements, the PMM comprising a plurality of probability density functions, PDFs, at least two of which PDFs are of a different type; identifying RF transmitters in the environment responsive to the PMM; wherein fitting comprises determining a set of defining parameters θ PMM for the PMM, the defining parameters comprising a number K of the plurality of PDFs, and for each PDF, parameters defining the PDF; iteratively partitioning the RF power measurements into a plurality of sub-blocks of power measurements, wherein at each iteration i+ 1 each data sub-block SB(i) of the i-th iteration is partitioned into a plurality of sub-blocks SB(i+1); for each given sub-block in an i-th iteration fitting a sub-block PMM comprising a plurality of sub-block PDFs to the RF power measurements in the given sub-block independent of RF power measurements in other sub-blocks of the i-th iteration to determine a set of defining parameters for the sub-block PMM, the set of defining parameters for the sub-block PMM having a number for the plurality of PDFs in the sub-block PMM; clustering sub-block PDFs from different sub-blocks to determine clusters of PDFs; and determining K responsive to a number of clusters. at least one RF listening station configured to receive RF signals and acquire measurements of their RF power as a function of reception time, t, and frequency f; and a processing hub configured to identify RF transmitters operating in the environment by: receiving the RF power measurements; fitting to the RF power measurements a probability mixture model, PMM, that provides a probability density as a function of magnitude of the RF power measurements, the PMM comprising a plurality of probability density functions, PDFs, at least two of which PDFs are of a different type; identifying RF transmitters in the environment responsive to the PMM; wherein fitting comprises determining a set of defining parameters θ PMM for the PMM, the defining parameters comprising a number K of the plurality of PDFs, and for each PDF, parameters defining the PDF; receiving the RF power measurements; fitting to the RF power measurements a probability mixture model, PMM, that provides a probability density as a function of magnitude of the RF power measurements, the PMM comprising a plurality of probability density functions, PDFs, at least two of which PDFs are of a different type; identifying RF transmitters in the environment responsive to the PMM; wherein fitting comprises determining a set of defining parameters θ PMM for the PMM, the defining parameters comprising a number K of the plurality of PDFs, and for each PDF, parameters defining the PDF; iteratively partitioning the RF power measurements into a plurality of sub-blocks of power measurements, wherein at each iteration i+ 1 each data sub-block SB(i) of the i-th iteration is partitioned into a plurality of sub-blocks SB(i+1); for each given sub-block in an i-th iteration fitting a sub-block PMM comprising a plurality of sub-block PDFs to the RF power measurements in the given sub-block independent of RF power measurements in other sub-blocks of the i-th iteration to determine a set of defining parameters for the sub-block PMM, the set of defining parameters for the sub-block PMM having a number for the plurality of PDFs in the sub-block PMM; clustering sub-block PDFs from different sub-blocks to determine clusters of PDFs; and determining K responsive to a number of clusters.