Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Supervised Learning Based Online Tracking Filters: An XGBoost
  Implementation   The target state filter is an important module in the traditional target
tracking framework. In order to get satisfactory tracking results, traditional
Bayesian methods usually need accurate motion models, which require the
complicated prior information and parameter estimation. Therefore, the modeling
process has a key impact on traditional Bayesian filters for target tracking.
However, when encountering unknown prior information or the complicated
environment, traditional Bayesian filters have the limitation of greatly
reduced accuracy. In this paper, we propose a supervised learning based online
tracking filter(SLF). First, a complete tracking filter framework based on
supervised learning is established, which is directly based on data-driven and
establishes the mapping relationship between data. In other words, the proposed
filter does not require the prior information about target dynamics and clutter
distribution. Then, an implementation based on eXtreme Gradient Boosting
(XGBoost) is provided, which proves the portability and applicability of the
SLF framework. Meanwhile, the proposed framework will encourage other
researchers to continue to expand the field of combining traditional filters
with supervised learning. Finally, numerical simulation experiments prove the
effectiveness of the proposed filter.

8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat