  The search for subsolar mass primordial black holes (PBHs) poses a
challenging problem due to the low signal-to-noise ratio, extended signal
duration, and computational cost demands, compared to solar mass binary black
hole events. In this paper, we explore the possibility of investigating the
mass range between subsolar and planetary masses, which is not accessible using
standard matched filtering and continuous wave searches. We propose a
systematic approach employing the Viterbi algorithm, a dynamic programming
algorithm that identifies the most likely sequence of hidden Markov states
given a sequence of observations, to detect signals from small mass PBH
binaries. We formulate the methodology, provide the optimal length for
short-time Fourier transforms, and estimate sensitivity. Subsequently, we
demonstrate the effectiveness of the Viterbi algorithm in identifying signals
within mock data containing Gaussian noise. Our approach offers the primary
advantage of being agnostic and computationally efficient.
