高分求助：谁能帮我翻译一下这篇外文 。感激不尽
AbstractRecently,methodsbasedonArtificialIntelligence(AI)havebeensuggestedtopro...
Abstract Recently,methods based on Artificial Intelligence (AI) have been suggested to provide reliable positioning information for different land vehicle navigation applications integrating the Global Positioning System(GPS)with the Inertial Navigation System (INS). All existing AI-based methods are based on relating the INS error to the corresponding INS output at certain time instants and do not consider the depen- dence of the error on the past values of INS. This study, therefore, suggests the use of Input-Delayed Neu- ral Networks (IDNN) to model both the INS position and velocity errors based on current and some past samples of INS position and velocity, respectively. This results in a more reliable positioning solution dur- ing long GPS outages.The proposed method is evaluated using road test data of different trajectories while both navigational and tactical grade INS are mounted inside land vehicles and integrated with GPS receivers. The performance of the IDNN – based model is also compared to both conventional (based mainly on Kalman filtering) and recently published AI – based techniques. The results showed significant improvement in positioning accuracy especially for cases of tactical grade INS and long GPS outages.
Most of today’s land vehicles are equipped with Global Position- ing Systems (GPS) to provide accurate position and velocity infor- mation.However, there are several situations where GPS experience either total system outage (due to satellite signal block- age) or deterioration of accuracy (due to multipath effects and clock bias error). Therefore, GPS is usually combined with Inertial Navigation System (INS), which is a self-contained system incorpo- rating three orthogonal accelerometers and three orthogonal gyro- scopes. These monitor the vehicle’s linear accelerations and rotation rates. A set of mathematical transformations and integra- tions with respect to time are applied to these raw measurements to determine position, velocity and attitude information. However, the INS accuracy deteriorates with time due to possible inherent sensor errors (white noise, correlated random noise, bias instabil- ity, and angle random walk) that exhibit considerable long-term growth .
The integration of GPS and INS, therefore, provides a navigation system that has superior performance in comparison with either aGPS or an INS stand-alone system. For instance, GPS position com- ponents have approximately white noise characteristics with bounded errors and can therefore be used to update INS and im- prove its long-term accuracy. On the other hand, INS provides posi- tioning information during GPS outages thus assisting GPS signal reacquisition after an outage and reducing the search domain re- quired for detecting and correcting GPS cycle slips. INS is also capa- ble of providing positioning and attitude information at higher data rates than GPS. 展开
Most of today’s land vehicles are equipped with Global Position- ing Systems (GPS) to provide accurate position and velocity infor- mation.However, there are several situations where GPS experience either total system outage (due to satellite signal block- age) or deterioration of accuracy (due to multipath effects and clock bias error). Therefore, GPS is usually combined with Inertial Navigation System (INS), which is a self-contained system incorpo- rating three orthogonal accelerometers and three orthogonal gyro- scopes. These monitor the vehicle’s linear accelerations and rotation rates. A set of mathematical transformations and integra- tions with respect to time are applied to these raw measurements to determine position, velocity and attitude information. However, the INS accuracy deteriorates with time due to possible inherent sensor errors (white noise, correlated random noise, bias instabil- ity, and angle random walk) that exhibit considerable long-term growth .
The integration of GPS and INS, therefore, provides a navigation system that has superior performance in comparison with either aGPS or an INS stand-alone system. For instance, GPS position com- ponents have approximately white noise characteristics with bounded errors and can therefore be used to update INS and im- prove its long-term accuracy. On the other hand, INS provides posi- tioning information during GPS outages thus assisting GPS signal reacquisition after an outage and reducing the search domain re- quired for detecting and correcting GPS cycle slips. INS is also capa- ble of providing positioning and attitude information at higher data rates than GPS. 展开
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Recently,methods based on Artificial Intelligence (AI) have been suggested to provide reliable positioning information for different land vehicle navigation applications integrating the Global Positioning System(GPS)with the Inertial Navigation System (INS).
最近,基于人工智能(AI)的方法已被建议为各种不同的整合了全球定位系统(GPS)的惯性导航系统(INS)的陆地行驶车辆提供可靠的定位信息。
All existing AI-based methods are based on relating the INS error to the corresponding INS output at certain time instants and do not consider the dependence of the error on the past values of INS.
所有现存的基于人工智能的方法，都是基于这样的过程，即惯性系统（INS）的误差与某一时段的瞬间惯性系统（INS）的输出相对应，而不考虑惯性系统（INS）的过往数值这一误差的影响狂。
This study, therefore, suggests the use of Input-Delayed Neural Networks (IDNN) to model both the INS position and velocity errors based on current and some past samples of INS position and velocity, respectively. This results in a more reliable positioning solution during long GPS outages.
因此,本研究建议使用输入延时神经网络系统（IDNN），基于INS定位和速度的当前值和一些过去值对INS的定位和速度分别建模。这样，可以远程在GPS通讯中断期间更加可靠地进行定位。
The proposed method is evaluated using road test data of different trajectories while both navigational and tactical grade INS are mounted inside land vehicles and integrated with GPS receivers. The performance of the IDNN – based model is also compared to both conventional (based mainly on Kalman filtering) and recently published AI – based techniques. The results showed significant improvement in positioning accuracy especially for cases of tactical grade INS and long GPS outages.
利用不同运行轨迹的道路试验数据对提出的方法进行了评价，在试验中，陆行车辆中同时安装了导航和战术级INS，并集成了GPS接收器。对基于IDNN的模型的性能也同时与常规技术(主要基于卡尔曼滤波)和最近发表的基于AI的技术进行了比较。结果表明，定位精度得到明显改善，尤其对于战术级INS和远程GPS中断状态下。
Most of today’s land vehicles are equipped with Global Positioning Systems (GPS) to provide accurate position and velocity information.However, there are several situations where GPS experience either total system outage (due to satellite signal blockage) or deterioration of accuracy (due to multipath effects and clock bias error). Therefore, GPS is usually combined with Inertial Navigation System (INS), which is a self-contained system incorporating three orthogonal accelerometers and three orthogonal gyroscopes. These monitor the vehicle’s linear accelerations and rotation rates. 当今大多数的陆上车辆都配备了全球卫星定位系统(GPS)，以提供精确的位置和速度信息。然而，可能出现的几种情况是，GPS或者整个系统通讯中断(由于卫星信号被阻挡)或者数据的准确性恶化(由于多通道效应和时钟偏置误差)。因此，GPS通常结合了惯性导航系统(INS)，它是一个独立的系统，包含三个正交加速器和三个正交陀螺仪。这些系统监控车辆的线性加速度和转动速率。
A set of mathematical transformations and integrations with respect to time are applied to these raw measurements to determine position, velocity and attitude information. However, the INS accuracy deteriorates with time due to possible inherent sensor errors (white noise, correlated random noise, bias instability, and angle random walk) that exhibit considerable long-term growth .
一套依据时间进行的数学变换和积分被运用到进行定位、速度和高度等信息的原始数据的测量中。然而，由于内部传感器误差（白噪音、相关的不规则噪音、偏置不稳定性和角度随机游走）使得INS准确性随时间而恶化。
The integration of GPS and INS, therefore, provides a navigation system that has superior performance in comparison with either aGPS or an INS stand-alone system. For instance, GPS position components have approximately white noise characteristics with bounded errors and can therefore be used to update INS and improve its long-term accuracy. On the other hand, INS provides positioning information during GPS outages thus assisting GPS signal reacquisition after an outage and reducing the search domain required for detecting and correcting GPS cycle slips. INS is also capable of providing positioning and attitude information at higher data rates than GPS.
因此，与单独的一个GPS系统或INS系统相比，GPS与INS的集成提供了一种表现超群的导航系统。例如，GPS定位组件具有误差有限的白噪声的特征，因此可以被用来更新INS和提高其长期的准确性。另一方面，INS在GPS通讯中断时可以提供定位信息，因而辅助GPS在通讯中断后再次获得信号，并减少探测和修正GPS周跳所需要搜索的区域。 INS还可以比GPS高的速度提供位置和高度信息。
最近,基于人工智能(AI)的方法已被建议为各种不同的整合了全球定位系统(GPS)的惯性导航系统(INS)的陆地行驶车辆提供可靠的定位信息。
All existing AI-based methods are based on relating the INS error to the corresponding INS output at certain time instants and do not consider the dependence of the error on the past values of INS.
所有现存的基于人工智能的方法，都是基于这样的过程，即惯性系统（INS）的误差与某一时段的瞬间惯性系统（INS）的输出相对应，而不考虑惯性系统（INS）的过往数值这一误差的影响狂。
This study, therefore, suggests the use of Input-Delayed Neural Networks (IDNN) to model both the INS position and velocity errors based on current and some past samples of INS position and velocity, respectively. This results in a more reliable positioning solution during long GPS outages.
因此,本研究建议使用输入延时神经网络系统（IDNN），基于INS定位和速度的当前值和一些过去值对INS的定位和速度分别建模。这样，可以远程在GPS通讯中断期间更加可靠地进行定位。
The proposed method is evaluated using road test data of different trajectories while both navigational and tactical grade INS are mounted inside land vehicles and integrated with GPS receivers. The performance of the IDNN – based model is also compared to both conventional (based mainly on Kalman filtering) and recently published AI – based techniques. The results showed significant improvement in positioning accuracy especially for cases of tactical grade INS and long GPS outages.
利用不同运行轨迹的道路试验数据对提出的方法进行了评价，在试验中，陆行车辆中同时安装了导航和战术级INS，并集成了GPS接收器。对基于IDNN的模型的性能也同时与常规技术(主要基于卡尔曼滤波)和最近发表的基于AI的技术进行了比较。结果表明，定位精度得到明显改善，尤其对于战术级INS和远程GPS中断状态下。
Most of today’s land vehicles are equipped with Global Positioning Systems (GPS) to provide accurate position and velocity information.However, there are several situations where GPS experience either total system outage (due to satellite signal blockage) or deterioration of accuracy (due to multipath effects and clock bias error). Therefore, GPS is usually combined with Inertial Navigation System (INS), which is a self-contained system incorporating three orthogonal accelerometers and three orthogonal gyroscopes. These monitor the vehicle’s linear accelerations and rotation rates. 当今大多数的陆上车辆都配备了全球卫星定位系统(GPS)，以提供精确的位置和速度信息。然而，可能出现的几种情况是，GPS或者整个系统通讯中断(由于卫星信号被阻挡)或者数据的准确性恶化(由于多通道效应和时钟偏置误差)。因此，GPS通常结合了惯性导航系统(INS)，它是一个独立的系统，包含三个正交加速器和三个正交陀螺仪。这些系统监控车辆的线性加速度和转动速率。
A set of mathematical transformations and integrations with respect to time are applied to these raw measurements to determine position, velocity and attitude information. However, the INS accuracy deteriorates with time due to possible inherent sensor errors (white noise, correlated random noise, bias instability, and angle random walk) that exhibit considerable long-term growth .
一套依据时间进行的数学变换和积分被运用到进行定位、速度和高度等信息的原始数据的测量中。然而，由于内部传感器误差（白噪音、相关的不规则噪音、偏置不稳定性和角度随机游走）使得INS准确性随时间而恶化。
The integration of GPS and INS, therefore, provides a navigation system that has superior performance in comparison with either aGPS or an INS stand-alone system. For instance, GPS position components have approximately white noise characteristics with bounded errors and can therefore be used to update INS and improve its long-term accuracy. On the other hand, INS provides positioning information during GPS outages thus assisting GPS signal reacquisition after an outage and reducing the search domain required for detecting and correcting GPS cycle slips. INS is also capable of providing positioning and attitude information at higher data rates than GPS.
因此，与单独的一个GPS系统或INS系统相比，GPS与INS的集成提供了一种表现超群的导航系统。例如，GPS定位组件具有误差有限的白噪声的特征，因此可以被用来更新INS和提高其长期的准确性。另一方面，INS在GPS通讯中断时可以提供定位信息，因而辅助GPS在通讯中断后再次获得信号，并减少探测和修正GPS周跳所需要搜索的区域。 INS还可以比GPS高的速度提供位置和高度信息。
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