# CM Sequence based Trajectory Modeling with Destination

arXiv: https://arxiv.org/abs/1811.08021 · HTML: https://mlsanity.com/paper/1811.08021/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2018.

## Built on by (most cited first, 1 of 1)

- [A Sequential Detection and Tracking of Very Low SNR Objects](https://mlsanity.com/paper/2312.15823/) (2023)

## Abstract (arXiv)

In some problems there is information about the destination of a moving object. An example is an airliner flying from an origin to a destination. Such problems have three main components: an origin, a destination, and motion in between. To emphasize that the motion trajectories end up at the destination, we call them textitdestination-directed trajectories. The Markov sequence is not flexible enough to model such trajectories. Given an initial density and an evolution law, the future of a Markov sequence is determined probabilistically. One class of conditionally Markov (CM) sequences, called the CML sequence (including the Markov sequence as a special case), has the following main components: a joint endpoint density (i.e., an initial density and a final density conditioned on the initial) and a Markov-like evolution law. This paper proposes using the CML sequence for modeling destination-directed trajectories. It is demonstrated how the CML sequence enjoys several desirable properties for destination-directed trajectory modeling. Some simulations of trajectory modeling and prediction are presented for illustration.