<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yong-Shik Kim</style></author><author><style face="normal" font="default" size="100%">Keum-Shik Hong</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An IMM algorithm with federated information mode-matched filters for AGV</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Adaptive Control and Signal Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">September 2007</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">21 </style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div class=&quot;para&quot;&gt;&lt;p&gt;In this paper, a tracking algorithm for autonomous navigation of automated guided vehicles (AGVs) is presented. The developed navigation algorithm is an interacting multiple-model (IMM) algorithm used to detect other AGVs using fused information from multiple sensors. In order to detect other AGVs, two kinematic models were derived: A constant-velocity model for linear motion, and a constant-speed turn model for curvilinear motion. In the constant-speed turn model, a nonlinear information filter (IF) is used in place of the extended Kalman filter (KF). Being equivalent to the KF algebraically, the IF is extended to &lt;em&gt;N&lt;/em&gt;-sensor distributed dynamic systems. The model-matched filter used in multi-sensor environments takes the form of a federated nonlinear IF. In multi-sensor environments, the information-based filter is easier to decentralize, initialize, and fuse than a KF-based filter. In this paper, the structural features and information-sharing principle of the federated IF are discussed. The performance of the suggested algorithm using a Monte Carlo simulation is evaluated under the three navigation patterns. Copyright &amp;copy; 2006 John Wiley &amp;amp; Sons, Ltd.&lt;/p&gt;&lt;/div&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7</style></issue><section><style face="normal" font="default" size="100%">533–555</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">A tracking algorithm for autonomous navigation of AGVs in an automated container terminal</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Mechanical Science and Technology </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year><pub-dates><date><style  face="normal" font="default" size="100%">01/2005</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">19</style></volume><isbn><style face="normal" font="default" size="100%">1976-3824</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div class=&quot;abstract-content formatted&quot;&gt;&lt;p&gt;In this paper, a tracking algorithm for the autonomous navigation of the automated guided vehicles (AGVs) operated in a container terminal is investigated. The navigation system is based on sensors that detect range and bearing. The navigation algorithm used is an interacting multiple model algorithm to detect other AGVs and avoid obstacles using information obtained from the multiple sensors. In order to detect other AGVs (or obstacles), two kinematic models are derived: A constant velocity model for linear motion and a constant-speed turn model for curvilinear motion. For the constant-speed turn model, an unscented Kalman filter is used because of the drawbacks of the extended Kalman filter in nonlinear systems. The suggested algorithm reduces the root mean squares error for linear motions and rapidly detects possible turning motions.&lt;/p&gt;&lt;/div&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><section><style face="normal" font="default" size="100%">72-86 </style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yong-Shik Kim</style></author><author><style face="normal" font="default" size="100%">Keum-Shik Hong</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An IMM Algorithm for Tracking Maneuvering Vehicles in an Adaptive Cruise Control Environment </style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Control, Automation, and Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">September 2004</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">2</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div dir=&quot;ltr&quot; style=&quot;font-size: 17.5px; font-family: serif; left: 224.9px; top: 251.007px; transform: rotate(0deg) scale(1.00096, 1); transform-origin: 0% 0% 0px;&quot;&gt;&lt;div dir=&quot;ltr&quot; style=&quot;font-size: 17.5px; font-family: serif; left: 224.9px; top: 251.007px; transform: rotate(0deg) scale(1.00096, 1); transform-origin: 0% 0% 0px;&quot;&gt;&amp;nbsp;In this paper, an unscented Kalman filter (UKF) for curvilinear motions in an&lt;/div&gt;&lt;div dir=&quot;ltr&quot; style=&quot;font-size: 17.5px; font-family: serif; left: 154px; top: 271.108px; transform: rotate(0deg) scale(1.00113, 1); transform-origin: 0% 0% 0px;&quot;&gt;interacting multiple model (IMM) algorithm to track a maneuvering vehicle on a road is&lt;/div&gt;&lt;div dir=&quot;ltr&quot; style=&quot;font-size: 17.5px; font-family: serif; left: 154px; top: 291.208px; transform: rotate(0deg) scale(1.00068, 1); transform-origin: 0% 0% 0px;&quot;&gt;investigated. Driving patterns of vehicles on a road are modeled as stochastic hybrid systems.&lt;/div&gt;&lt;div dir=&quot;ltr&quot; style=&quot;font-size: 17.5px; font-family: serif; left: 154px; top: 311.309px; transform: rotate(0deg) scale(0.999569, 1); transform-origin: 0% 0% 0px;&quot;&gt;In order to track the maneuvering vehicles, two kinematic models are derived: A constant&lt;/div&gt;&lt;div dir=&quot;ltr&quot; style=&quot;font-size: 17.5px; font-family: serif; left: 154px; top: 331.409px; transform: rotate(0deg) scale(1.00016, 1); transform-origin: 0% 0% 0px;&quot;&gt;velocity model for linear motions and a constant-speed turn model for curvilinear motions. For&lt;/div&gt;&lt;div dir=&quot;ltr&quot; style=&quot;font-size: 17.5px; font-family: serif; left: 154px; top: 351.51px; transform: rotate(0deg) scale(0.99919, 1); transform-origin: 0% 0% 0px;&quot;&gt;the constant-speed turn model, an UKF is used because of the drawbacks of the extended&lt;/div&gt;&lt;div dir=&quot;ltr&quot; style=&quot;font-size: 17.5px; font-family: serif; left: 154px; top: 371.61px; transform: rotate(0deg) scale(1.00056, 1); transform-origin: 0% 0% 0px;&quot;&gt;Kalman filter in nonlinear systems. The suggested algorithm reduces the root mean squares&lt;/div&gt;&lt;div dir=&quot;ltr&quot; style=&quot;font-size: 17.5px; font-family: serif; left: 154px; top: 391.711px; transform: rotate(0deg) scale(1.00242, 1); transform-origin: 0% 0% 0px;&quot;&gt;error for linear motions and rapidly detects possible turning motions.&amp;nbsp;&lt;/div&gt;&lt;/div&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><section><style face="normal" font="default" size="100%">310-318</style></section></record></records></xml>