Abstract: Systems, devices, and methods for using Multi-Pattern Viterbi Algorithm for joint decoding of multiple patterns are disclosed. An exemplary method may receive a plurality of sets of time-sequential signal observations for each of a number K of signal repetitions. Further, each set of signal observations is associated with a respective dimension of a K-dimensional time grid having time-indexed points. Moreover, at each of a plurality of the time-indexed points, a state cost metric is calculated with a processor for each state in a set of states of a hidden Markov model (HMM). In addition, each state in the set of states and for a given time-indexed point, the state cost metric calculation provides a most-likely predecessor state and a corresponding most-likely predecessor time-indexed point. The exemplary method may also determine a sequence of states using the calculated state cost metrics and determine a corresponding cumulative probability measure for the HMM.

Abstract: Systems, devices, and methods for using Multi-Pattern Viterbi Algorithm for joint decoding of multiple patterns are disclosed. An exemplary method may receive a plurality of sets of time-sequential signal observations for each of a number K of signal repetitions. Further, each set of signal observations is associated with a respective dimension of a K-dimensional time grid having time-indexed points. Moreover, at each of a plurality of the time-indexed points, a state cost metric is calculated with a processor for each state in a set of states of a hidden Markov model (HMM). In addition, each state in the set of states and for a given time-indexed point, the state cost metric calculation provides a most-likely predecessor state and a corresponding most-likely predecessor time-indexed point. The exemplary method may also determine a sequence of states using the calculated state cost metrics and determine a corresponding cumulative probability measure for the HMM.