Open-source optimization method for android smartphone single point positioning

Abstract

Nowadays, a chip-scale Global Navigation Satellite System (GNSS) receiver is ubiquitous in smartphones. In a smartphone GNSS receiver, the least square (LS) or Kalman Filter (KF) is implemented to estimate the position. With the aim to improve the smartphone GNSS position accuracy, we propose a position-smoothing method considering more historical information than the traditional methods, i.e., LS and KF. More past states are regarded as unknowns, and a cost function is constructed to optimize these states. An open-source smartphone dataset from Google was used for testing the proposed method. The experimental results indicate that the proposed method outperforms the other conventional methods in position errors. In addition, we open the source code. We expect that the optimization method implemented in the smartphone GNSS position smoothing application can be an illustrative example to clearly introduce such an optimization method and a reference for its implementation, which might inspire some other meaningful and exciting applications in GNSS.

Pedestrian Smartphone Navigation Based on Weighted Graph Factor Optimization Utilizing GPS/BDS Multi-Constellation

“…An overview of the factor graph of GPS/BDS positioning with the constraints of the pseudo-range, the velocity, and the height is shown in Figure 5. Unlike the EKF, the past states are also regarded as unknowns in the optimization method [36]. The state propagation model establishes a strong correlation between these states and measurements.…”

“…The FGO aims to find the minimal value of the cost function f (X, ρ, h). The Levenberg-Marquart (LM) algorithm is utilized to solve the optimal estimations [31,36,37]. The steps of the LM algorithm can be summarized as follows.…”

Localization with Noisy Android Raw GNSS Measurements

“…For example, an EKF-based GNSS positioning approach using Android data for maritime applications has been demonstrated in [5], but it fails to give a complete description of how to construct the process model and measurement equations with pseudoranges and pseudorange rates. A Least Squares (LS) localization engine followed by a Kalman filter is introduced in [6], but its connections to Android raw GNSS measurements are not explained in detail. Another work has proposed an EKF with inequality constraints, including vertical velocity, direction, and distance constraints, to process Android GNSS data [7].…”