High sensitivity multi-channel digital receiver for wideband very weak signal direction-finding classified by machine learning

For weak signal detection with direction-finding (DF), this article presents a new receiver design approach that combines our accumulatively increasing receiver sensitivity (AIRS) signal detection algorithm with the compressive-sensing (CS)-based DF-array/algorithm. The former uses the concept of timeslot (TS)-based signal threshold detection, whereas the latter employs a frequency-independent array with randomly located elements, whose bandwidth (BW) largely determines the DF-array BW. To estimate the direction of a signal, the AIRS algorithm generates the array steering vectors in each TS when the amplitude of any frequency bins exceeds the predetermined threshold of the TS. The aim of this paper is to demonstrate the ability of the new receiver to detect low probability of intercept radar signals with high DF accuracy, fine frequency resolution, and good time-of-arrival measurement resolution. To discriminate accurate emitter directions from many false estimations created by the DF-array in very low signal-to-noise ratio environments, K-means clustering was also applied. In a scenario, the frequency modulated signals from several 165-mW X-band radars were in the field of view of a 6-element DF-array. Simulation results show that the receiver can accurately estimate all the emitters' directions with root mean squared error of less than 1°, when the separation between the DF-array and radars is about 100 km.