TWS Tracking Techniques Based on Adaptive Gaussian Mixture Model in Phased Array Radar

XUE Jian-ru; ZHENG Nan-ning; ZHENG Zhao-hui; QUAN Wei

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TWS Tracking Techniques Based on Adaptive Gaussian Mixture Model in Phased Array Radar

更新时间：2025-07-16

- TWS Tracking Techniques Based on Adaptive Gaussian Mixture Model in Phased Array Radar
- Acta Electronica Sinica Vol. 31, Issue 3, Pages: 433-436(2003)
- 作者机构：
  
  1. 西安交通大学人工智能与机器人研究所,陕西,西安,710049
  2. 西安导航技术研究所,陕西,西安,710043
  3. 西安交通大学人工智能与机器人研究所陕西西安,710049
  4. 西安导航技术研究所陕西西安,710043
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391
- Published：2003
- 稿件说明：
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XUE Jian-ru, ZHENG Nan-ning, ZHENG Zhao-hui, et al. TWS Tracking Techniques Based on Adaptive Gaussian Mixture Model in Phased Array Radar[J]. Acta Electronica Sinica, 2003, 31(3): 433-436.
DOI：

XUE Jian-ru, ZHENG Nan-ning, ZHENG Zhao-hui, et al. TWS Tracking Techniques Based on Adaptive Gaussian Mixture Model in Phased Array Radar[J]. Acta Electronica Sinica, 2003, 31(3): 433-436. DOI：

摘要

本文提出了一种在相控阵雷达回波数据序列中用高斯混合体模型(GMM)检测与跟踪运动目标的在线算法.首先.回波数据中的每一固定位置的历史数据用点过程描述.然后用GMM逼近此点过程.GMM参数随新数据的到来不断更新.接着建立具有自适应特性的背景模型.将每帧回波数据分割为背景和前景.对已标记为前景的数据用连通分支进行分类.求出目标的中心位置、大小、径向运动速度、角速度.最后用卡尔曼滤波器对运动目标进行跟踪.试验结果表明.本文算法对于复杂场景中运动目标的检测与跟踪具有较好的鲁棒性和实时性.具有较强的实用价值.

Abstract

An on-line real-time tracking algorithm for radar image sequences is proposed.First

a GMM (Gaussian mixture model) is used to approximate values of a particular pixel of the radar image sequences

and parameters of the GMM are updated each time.Then

we use a simple heuristic to hypothesize which Gaussian of the mixture is most likely to be part of the adaptive background model.After the background model is established

pixel values that don't match the pixel's "background" Gaussians are grouped using connected components.Features of the targets such as center position

size

radial and angular velocities are also computed in the mean time.Finally

the connected components are tracked across frames using a Kalman filter-based tracker.The experimental result shows that the algorithm is robust in clutter and easy to implement on-line.

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