# Classification-Aided Robust Multiple Target Tracking Using Neural Enhanced Message Passing

arXiv: https://arxiv.org/abs/2310.12407 · HTML: https://mlsanity.com/paper/2310.12407/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 81 references that are in the map.
- First posted on arXiv 19 Oct 2023.

## Builds on (most cited first, 23 of 23)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Advances in Variational Inference](https://mlsanity.com/paper/1711.05597/) (2017)
- [Composing graphical models with neural networks for structured representations and fast inference](https://mlsanity.com/paper/1603.06277/) (2016)
- [Neural Enhanced Belief Propagation on Factor Graphs](https://mlsanity.com/paper/2003.01998/) (2020)
- [Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking](https://mlsanity.com/paper/2103.16178/) (2021)
- [Combining Generative and Discriminative Models for Hybrid Inference](https://mlsanity.com/paper/1906.02547/) (2019)
- [Belief Propagation Neural Networks](https://mlsanity.com/paper/2007.00295/) (2020)
- [Graph Neural Networks for Cross-Camera Data Association](https://mlsanity.com/paper/2201.06311/) (2022)
- [Scalable Detection and Tracking of Geometric Extended Objects](https://mlsanity.com/paper/2103.11279/) (2021)
- [Neural Enhanced Belief Propagation for Multiobject Tracking](https://mlsanity.com/paper/2212.08340/) (2022)
- [Neural Enhanced Belief Propagation for Data Association in Multiobject Tracking](https://mlsanity.com/paper/2203.09948/) (2022)
- [A Scalable Algorithm for Tracking an Unknown Number of Targets Using Multiple Sensors](https://mlsanity.com/paper/1607.07647/) (2016)
- [A Belief Propagation Algorithm for Multipath-Based SLAM](https://mlsanity.com/paper/1801.04463/) (2018)
- [Neural Enhanced Belief Propagation for Cooperative Localization](https://mlsanity.com/paper/2105.12903/) (2021)
- [Sigma Point Belief Propagation](https://mlsanity.com/paper/1309.0363/) (2013)
- [Classification-Aided Multitarget Tracking Using the Sum-Product Algorithm](https://mlsanity.com/paper/2008.01667/) (2020)
- [Cooperative Localization and Multitarget Tracking in Agent Networks with the Sum-Product Algorithm](https://mlsanity.com/paper/2108.02573/) (2021)
- [Multiple scan data association by convex variational inference](https://mlsanity.com/paper/1607.07942/) (2016)
- [A Message Passing Approach for Multiple Maneuvering Target Tracking](https://mlsanity.com/paper/1911.07466/) (2019)
- [Sequential Detection and Estimation of Multipath Channel Parameters Using Belief Propagation](https://mlsanity.com/paper/2109.05623/) (2021)
- [Fusion of Sensor Measurements and Target-Provided Information in Multitarget Tracking](https://mlsanity.com/paper/2111.13589/) (2021)
- [Robust Multitarget Tracking in Interference Environments: A Message-Passing Approach](https://mlsanity.com/paper/2212.07182/) (2022)
- [Message Passing-Based 9-D Cooperative Localization and Navigation with Embedded Particle Flow](https://mlsanity.com/paper/2301.01173/) (2023)

## Abstract (arXiv)

We address the challenge of tracking an unknown number of targets in strong clutter environments using measurements from a radar sensor. Leveraging the range-Doppler spectra information, we identify the measurement classes, which serve as additional information to enhance clutter rejection and data association, thus bolstering the robustness of target tracking. We first introduce a novel neural enhanced message passing approach, where the beliefs obtained by the unified message passing are fed into the neural network as additional information. The output beliefs are then utilized to refine the original beliefs. Then, we propose a classification-aided robust multiple target tracking algorithm, employing the neural enhanced message passing technique. This algorithm is comprised of three modules: a message-passing module, a neural network module, and a Dempster-Shafer module. The message-passing module is used to represent the statistical model by the factor graph and infers target kinematic states, visibility states, and data associations based on the spatial measurement information. The neural network module is employed to extract features from range-Doppler spectra and derive beliefs on whether a measurement is target-generated or clutter-generated. The Dempster-Shafer module is used to fuse the beliefs obtained from both the factor graph and the neural network. As a result, our proposed algorithm adopts a model-and-data-driven framework, effectively enhancing clutter suppression and data association, leading to significant improvements in multiple target tracking performance. We validate the effectiveness of our approach using both simulated and real data scenarios, demonstrating its capability to handle challenging tracking scenarios in practical radar applications.