CPC G01S 5/0252 (2013.01) [G01S 5/02213 (2020.05); G01S 5/0284 (2013.01); H04W 64/006 (2013.01)] | 12 Claims |
1. A system for selectively applying machine learning to data fusion of a plurality of localization and classification of radio frequency (RF) emitters in an area, the system comprising:
a sensor array of RF sensors deployed outdoors, wherein each sensor being a software-defined radio (SDR) component configured to perform synchronized sensor measurements of at least three types, wherein the synchronized sensor measurements comprise localization measurement;
at least one computer processor in communication with the sensor array and configured to apply a data fusion algorithm to the sensor measurements, to yield localization and classification data of the RF emitters; and
a computer memory having a machine learning module comprising a set of instructions that, when executed, cause the at least one computer processor to:
obtain over a training period, localization data collected from at least the localization measurement of the three types of sensor measurements;
train a model of to provide outputs of the localization measurement of the first type based on readings of the localization measurement of at least one of the two other types;
apply, in a production period, the model to readings of the localization measurements of the first type in absence of the localization measurement of at least one of the two other types, to yield the outputs of the localization measurement of the first type, only in an absence of a line-of-sight (LoS) between the RF emitters and the RF sensors.
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