Multifunctional and waveform agnostic antenna apertures having multiple simultaneous RF beams are necessary for emerging electromagnetic situational awareness applications. A multibeam aperture operating across the entire 1-6 GHz band is crucial for both military and commercial wireless applications. Spectrum perception refers to the application of artificial intelligence (AI) and machine learning (ML) to wireless applications to extract intelligence on spectral activity as a function of both direction and waveform parameters (frequency, modulation, and waveform shape). Efficient and accurate RF sensing is a necessary precursor to higher level AI/ML spectral perception algorithms for detecting particular waveforms, behaviors, or signatures. This paper explores multibeam beamforming for RF sensing as a joint analog-digital hybrid approximate computing problem that can be efficiently implemented using multiple chiplets. The first chiplet includes both a multiport LNA with Δ-Σ spatial noise shaping to improve resilience to high power jammers, and an approximate DFT (ADFT) based analog multi-beamformer with reduced circuit complexity. The second chiplet includes ADCs and ADFT-based digital beamformers with reduced computational complexity compared to conventional DFT-based designs. Initial results on the design of both chiplets are presented.
