Recently, smart mobile devices, such as smartphone and tablet PC, have become so prevalent. Most of them are equipped with a set of sensors including a global positioning system (GPS) receiver, a digital magnetic compass, a gyroscope, and an ac-celerometer. Unlike traditional vehicle-fixed sensors, smartphone-embedded sensors can be utilized as a user-friendly and portable measurement probe for vehicle positioning systems, owing to their flexibility and mobility. However, GPS modules and inertial navigation system (INS) sensors, such as an accelerometer and a gyroscope, on smartphones consume a lot of battery power. Continued use of the battery for a long time may cause the battery to discharge immediately. Therefore, one of the main concerns for smartphone-based GPS/INS positioning algorithms is energy efficiency. Furthermore, low-cost INS sensors on smartphones may result in large localization errors due to sensor drift and bias. Unlike smartphone-based GPS/INS positioning algorithms, we use only the GPS receiver and digital compass without INS sensors. This makes it possible to offer more accurate positioning results and to save more energy. However, GPS receivers and digital compasses on smart-phones may continue to experience positional errors due to multi-path fading and disturbances in GPS signals and magnetic sources. Therefore, we propose an enhanced vehicle positioning method that provides more reliable localization results by fusing measurements from GPS receiver and digital compass based on a Bayesian filter, called a simplified Kalman filter (SKF). Compared to existing Bayes filters, such as Kalman filter (KF), unscented Kalman filter (UKF), and particle filter (PF), while the SKF is simple and intuitive to be implemented, it can achieve competitive positioning accuracy with less computational cost. Experimental results through various road configurations using the smartphone and test vehicle in real environments show that the SKF-based vehicle localization scheme can achieve about 92% higher energy-efficiency and about 31 % higher positioning accuracy than GPS/INS localization methods based on the KF, UKF, and PF.

中文翻译：

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为智能手机简化基于 KF 的节能车辆定位

近来，智能手机和平板电脑等智能移动设备变得如此普遍。它们中的大多数都配备了一组传感器，包括全球定位系统 (GPS) 接收器、数字磁罗盘、陀螺仪和加速计。与传统的车辆固定传感器不同，智能手机嵌入式传感器由于其灵活性和移动性，可以用作车辆定位系统的用户友好和便携式测量探头。但是，智能手机上的 GPS 模块和惯性导航系统 (INS) 传感器（例如加速度计和陀螺仪）会消耗大量电池电量。长时间继续使用电池可能会导致电池立即放电。因此，基于智能手机的 GPS/INS 定位算法的主要问题之一是能源效率。此外，由于传感器漂移和偏差，智能手机上的低成本 INS 传感器可能会导致较大的定位误差。与基于智能手机的 GPS/INS 定位算法不同，我们只使用 GPS 接收器和数字罗盘，没有 INS 传感器。这使得可以提供更准确的定位结果并节省更多能源。然而，由于 GPS 信号和磁源中的多径衰落和干扰，智能手机上的 GPS 接收器和数字罗盘可能会继续出现位置错误。因此，我们提出了一种增强型车辆定位方法，通过融合来自 GPS 接收器和基于贝叶斯滤波器（称为简化卡尔曼滤波器 (SKF) 的数字罗盘）的测量结果，提供更可靠的定位结果。与现有的贝叶斯滤波器相比，如卡尔曼滤波器（KF）、无迹卡尔曼滤波器（UKF）、和粒子滤波器 (PF)，虽然 SKF 实现起来简单直观，但它可以以较少的计算成本实现具有竞争力的定位精度。使用智能手机和测试车辆在真实环境中的各种道路配置的实验结果表明，基于 SKF 的车辆定位方案比基于 KF 的 GPS/INS 定位方法可实现约 92% 的能效和约 31% 的定位精度。 、UKF 和 PF。