Low-power excitation and/or low sensitivity transducers,
such as electromagnetic acoustic transducers (EMATs),
piezoelectric paints, air-coupled transducers, or small elements
of dense arrays may produce signals below the noise threshold
at the receiver. The information from those noisy signals can
be recovered after averaging or pulse-compression using binary
(one-bit) quantisation only without experiencing significant losses.
Hence, no analog-to-digital converter is required, which makes
the electronics faster, more compact and energy efficient. All this
is especially attractive for applications that require arrays with
many channels and high sampling rates, where the sampling rate
can be as high as the system clock. In this paper, the theory of
binary quantisation is reviewed, mainly from previous work on
wireless sensor networks, and the signal-to-noise ratio (SNR) of
the input signals under which binary quantisation is of practical
interest for ultrasound applications is investigated. The main
findings are that in most practical cases binary quantisation
can be used with small errors when the input SNR is in the
order of 8 dB or less. Moreover, the maximum SNR after binary
quantisation and averaging can be estimated as 10 log10 N −2 dB,
where N is the number of averages.