Maximum likelihood (ML) based localization algorithm for multi-static passive radar using range-only measurements

2015 IEEE Radar Conference Pub Date : 2015-10-01 DOI:10.1109/RADARCONF.2015.7411876

Mubashir Alam, K. Jamil

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引用次数: 9

Abstract

Passive radar has been finding increasing application and use in last decade. Passive radar uses the "opportunistic" commercial signals for their operation. The passive radar does provide good Doppler resolution but suffer from bad range resolution. One way to improve the range resolution is to use passive radar in a multi-static fashion. By using the data collected at various multi-static sites, the range resolution can be improved. Therefore, there is a need for an algorithm for target localization using these multi-static measurements. Typically, these algorithms use range-only measurements. Various multi-static configurations are possible in passive radar setup. However, this paper will only consider the setup with single receiver, and multiple spatially distributed transmitter locations. Localization algorithm based on Maximum likelihood (ML) estimate will be presented, along with its efficient implementation using gradient and Newton's decent algorithms. The performance bounds for ML estimate in terms of Fisher information are also given. All the algorithms are verified using simulated data in 2D settings.

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基于最大似然(ML)的多静态无源雷达测距定位算法

近十年来，无源雷达得到了越来越多的应用和应用。无源雷达使用“机会主义”商业信号进行操作。无源雷达具有较好的多普勒分辨率，但距离分辨率较差。提高距离分辨率的一种方法是以多静态方式使用无源雷达。利用在不同多静态站点采集的数据，可以提高距离分辨率。因此，需要一种基于多静态测量的目标定位算法。通常，这些算法只使用范围测量。在无源雷达设置中，各种多静态配置是可能的。然而，本文将只考虑单接收机和多个空间分布的发射机位置的设置。提出了基于最大似然估计的定位算法，并利用梯度和牛顿体面算法进行了有效的实现。给出了基于Fisher信息的机器学习估计的性能界限。所有算法都使用2D设置中的模拟数据进行验证。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2015 IEEE Radar Conference

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