Reciprocal Sequences as CM Sequences
For researchers in signal processing and control, this work offers a unified framework and practical tools for reciprocal sequences, but the results are incremental as they extend existing CM theory.
The paper studies reciprocal sequences from the conditionally Markov (CM) perspective, providing new insights and results. It shows that a nonsingular Gaussian sequence is reciprocal iff it is both CM_L and CM_F, and presents new white-noise-driven dynamic models that are easier to apply than existing colored-noise-driven models.
The conditionally Markov (CM) sequence contains several classes, including the reciprocal sequence. Reciprocal sequences have been widely used in many areas of engineering, including image processing, acausal systems, intelligent systems, and intent inference. In this paper, the reciprocal sequence is studied from the CM sequence point of view, which is different from the viewpoint of the literature and leads to more insight into the reciprocal sequence. Based on this viewpoint, new results, properties, and easily applicable tools are obtained for the reciprocal sequence. The nonsingular Gaussian (NG) reciprocal sequence is modeled and characterized from the CM viewpoint. It is shown that an NG sequence is reciprocal if and only if it is both $CM_L$ and $CM_F$ (two special classes of CM sequences). New dynamic models are presented for the NG reciprocal sequence. These models (unlike the existing one, which is driven by colored noise) are driven by white noise and are easily applicable. As a special reciprocal sequence, the Markov sequence is also discussed. Finally, it can be seen how all CM sequences, including Markov and reciprocal, are unified.