Maximum likelihood registration for multiple dissimilar sensors

Okello N.; Ristic B.

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Maximum likelihood registration for multiple dissimilar sensors

机译：多种异常传感器的最大似然注册

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摘要

A study of the maximum likelihood registration (MLR) algorithm for spatial alignment of multiple, possibly dissimilar (active or passive) sensors is presented. The MLR algorithm is a batch algorithm which outputs estimates of the registration parameters, registered sensor measurements and registered target location estimates, expressed in a common coordinate system. The Cramer-Rao type bound for registration of multiple dissimilar sensors is discussed and some numerical examples for sensor registration are presented in support of the theory.

机译：呈现了对多重，可能不同（有源或无源）传感器的空间对准的最大似然登记（MLR）算法的研究。 MLR算法是一种批量算法，其输出在公共坐标系中表示的登记参数，注册传感器测量和登记的目标位置估计的估计。讨论了用于配准的多异种传感器的登记的克拉梅-RAO类型，并呈现了传感器配准的一些数值示例，以支持该理论。

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来源
《IEEE Transactions on Aerospace and Electronic Systems》 |2003年第3期|p.1074-1083|共10页
作者
Okello N.; Ristic B.;
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作者单位

Dept. of Electr. & Electron. Eng. Melbourne Univ. Parkville Vic. Australia;

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原文格式 PDF
正文语种 eng
中图分类航空;
关键词
maximum likelihood estimation; sensor fusion; random noise; maximum likelihood registration; multiple dissimilar sensors; MLR; spatial alignment; batch algorithm; Cramer-Rao type bound; registration parameters; registered sensor measurements; common coordinate system;

机译：最大似然估计;传感器融合;随机噪声;最大似然登记;多种异常传感器;MLR;空间对齐;批量算法;克拉梅-RAO类型绑定;登记参数;注册传感器测量;公共坐标系;

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1. Maximum likelihood registration for multiple dissimilar sensors [J] . Okello N., Ristic B. IEEE Transactions on Aerospace and Electronic Systems . 2003,第3期

机译：多个异种传感器的最大似然配准
2. Maximum likelihood registration for multiple dissimilar sensors [J] . Okello N., Ristic B. IEEE Transactions on Aerospace and Electronic Systems . 2003,第3期

机译：多个异种传感器的最大似然配准
3. Dual-mode multiple-target tracking in wireless sensor networks based on sensor grouping and maximum likelihood estimation [J] . International journal of communication systems . 2020,第8期

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4. A Maximum Likelihood Registration Algorithm for Moving Dissimilar Sensors [C] . Wei Zixiang, Wei Shaoming, Luo Feixiang, IEEE Joint International Information Technology and Artificial Intelligence Conference . 2019

机译：移动异种传感器的最大似然配准算法
5. Adaptive sensing in uncertain environments: Maximum likelihood, sensor networks, and reinforcement learning. [D] . Blatt, Doron. 2006

机译：不确定环境中的自适应感测：最大可能性，传感器网络和强化学习。
6. Maximum Likelihood and Restricted Likelihood Solutions in Multiple-Method Studies [O] . Andrew L. Rukhin 2011

机译：多方法研究中的最大似然和受限似然解
7. A Distributed Recursive Maximum Likelihood Implementation for Sensor Registration [O] . Nikolas Kantas, Sumeetpal S. Singh, Arnaud Doucet 2016

机译：传感器配准的分布式递推最大似然实现

Maximum likelihood registration for multiple dissimilar sensors

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