multiple object phase tracking and ranging

简明释义

多目标相位跟踪和测距导航

英英释义

例句

1.In autonomous vehicles, multiple object phase tracking and ranging is essential for detecting nearby obstacles.

在自动驾驶汽车中，多目标相位跟踪和测距对于检测附近障碍物至关重要。

2.The military uses multiple object phase tracking and ranging for target acquisition in complex environments.

军方在复杂环境中使用多目标相位跟踪和测距进行目标获取。

3.The new radar system employs multiple object phase tracking and ranging to accurately monitor several aircraft simultaneously.

新雷达系统采用多目标相位跟踪和测距技术，能够同时准确监控多架飞机。

4.Researchers are developing algorithms for multiple object phase tracking and ranging to improve drone navigation.

研究人员正在开发用于多目标相位跟踪和测距的算法，以提高无人机导航能力。

5.The fishing industry benefits from multiple object phase tracking and ranging to locate schools of fish more efficiently.

渔业利用多目标相位跟踪和测距技术更有效地定位鱼群。

作文

In the realm of modern technology, the ability to track and measure multiple objects simultaneously has become increasingly vital. This capability is often referred to as multiple object phase tracking and ranging, which can be defined as the process of detecting, tracking, and determining the distance of several objects in motion at the same time. The significance of this technology spans various fields, including robotics, autonomous vehicles, and aerospace applications. To understand the implications of multiple object phase tracking and ranging, we must first consider its components. Tracking involves monitoring the position and movement of objects over time, while ranging refers to the measurement of distances between the observer and the objects being tracked. When combined, these functions allow for a comprehensive understanding of dynamic environments where multiple entities interact. In robotics, for example, robots equipped with sensors that utilize multiple object phase tracking and ranging can navigate complex terrains while avoiding obstacles. By continuously updating their position relative to surrounding objects, these robots can make real-time decisions to ensure safe and efficient movement. This is particularly crucial in applications such as warehouse automation, where robots must work alongside human workers and other machines. Similarly, in the field of autonomous vehicles, multiple object phase tracking and ranging plays a critical role in ensuring passenger safety. Advanced driver-assistance systems (ADAS) rely on this technology to detect pedestrians, cyclists, and other vehicles on the road. By accurately tracking the speed and trajectory of these objects, autonomous systems can predict potential collisions and respond accordingly, either by alerting the driver or taking corrective actions. Moreover, aerospace applications also benefit from multiple object phase tracking and ranging. For instance, air traffic control systems employ this technology to monitor the positions and movements of numerous aircraft simultaneously. This ensures safe distances between planes and helps prevent mid-air collisions. Additionally, satellites equipped with advanced sensors can use this technology to track space debris, which poses a significant threat to operational spacecraft. The development of multiple object phase tracking and ranging has been made possible through advancements in sensor technology, data processing algorithms, and machine learning techniques. Sensors such as radar, lidar, and cameras are commonly used to gather data about the environment. Once this data is collected, sophisticated algorithms analyze it to identify and track multiple objects, even in challenging conditions such as poor visibility or high-density environments. Furthermore, machine learning enhances the capabilities of multiple object phase tracking and ranging by enabling systems to learn from past experiences. By training algorithms on vast datasets, these systems can improve their accuracy and reliability over time, adapting to new scenarios and environments. This adaptability is essential for applications where conditions can change rapidly, such as in urban traffic situations. In conclusion, the concept of multiple object phase tracking and ranging represents a significant advancement in our ability to interact with and understand complex environments. Its applications across various industries highlight its importance in enhancing safety, efficiency, and functionality. As technology continues to evolve, we can expect further innovations in this area, leading to even more sophisticated systems capable of navigating the intricacies of our world. The future of tracking and ranging holds immense potential, making it an exciting field to watch as it develops.

在现代科技领域，同时追踪和测量多个物体的能力变得越来越重要。这种能力通常被称为多目标相位跟踪和测距，可以定义为同时检测、跟踪和确定多个运动物体距离的过程。这项技术的重要性跨越了多个领域，包括机器人技术、自动驾驶汽车和航空航天应用。要理解多目标相位跟踪和测距的含义，我们首先必须考虑其组成部分。跟踪涉及监测物体随时间的位置和运动，而测距则是指测量观察者与被跟踪物体之间的距离。当这两种功能结合时，它们能够全面了解动态环境中多个实体的互动。例如，在机器人技术中，配备利用多目标相位跟踪和测距的传感器的机器人可以在复杂地形中导航，同时避免障碍物。通过持续更新它们相对于周围物体的位置，这些机器人可以实时做出决策，以确保安全和高效的移动。这在仓库自动化等应用中尤为重要，因为机器人必须与人类工人和其他机器一起工作。同样，在自动驾驶汽车领域，多目标相位跟踪和测距在确保乘客安全方面发挥着关键作用。先进的驾驶辅助系统（ADAS）依赖于该技术来检测道路上的行人、自行车和其他车辆。通过准确跟踪这些物体的速度和轨迹，自动系统可以预测潜在的碰撞并作出相应反应，或者通过警告驾驶员或采取纠正措施。此外，航空航天应用也受益于多目标相位跟踪和测距。例如，空中交通管制系统使用该技术同时监控多个飞机的位置和运动。这确保了飞机之间的安全距离，并帮助防止空中碰撞。此外，配备先进传感器的卫星可以利用该技术跟踪太空垃圾，这对在轨道运行的航天器构成了重大威胁。多目标相位跟踪和测距的发展得益于传感器技术、数据处理算法和机器学习技术的进步。雷达、激光雷达和摄像头等传感器通常用于收集环境数据。一旦收集到数据，复杂的算法会分析这些数据，以识别和跟踪多个物体，即使在能见度差或高密度环境等挑战条件下。此外，机器学习通过使系统能够从过去的经验中学习，增强了多目标相位跟踪和测距的能力。通过在海量数据集上训练算法，这些系统可以随着时间的推移提高其准确性和可靠性，适应新的场景和环境。这种适应性对于条件可能迅速变化的应用至关重要，例如城市交通情况。总之，多目标相位跟踪和测距的概念代表了我们与复杂环境互动和理解能力的显著进步。它在各个行业中的应用突显了其在提高安全性、效率和功能性方面的重要性。随着技术的不断发展，我们可以期待这一领域的进一步创新，导致更复杂的系统能够驾驭我们世界的复杂性。跟踪和测距的未来蕴含着巨大的潜力，使其成为一个值得关注的激动人心的领域。