GNSS Precise Point Positioning Outlier Accommodation with Risk-Averse State Estimation

Core Concepts

Risk-Averse State Estimation optimally selects measurements to minimize outlier risk while meeting performance constraints.

Abstract

This article discusses the importance of outlier accommodation in GNSS Precise Point Positioning (PPP) for Connected Automated Vehicles (CAV). It introduces the Risk-Averse Performance-Specified (RAPS) state estimation method to address outliers and achieve accurate positioning. The study compares RAPS with traditional methods, highlighting its effectiveness in challenging environments. Experimental results show significant improvements using RAPS over conventional approaches.
I. Introduction:

Reliable real-time positioning crucial for CAV applications.
GNSS provides foundation for absolute positioning.
II. GNSS Observation Model:

Pseudorange and Doppler measurements used for state estimation.
III. Real-Time PPP Corrections:

Correction sources and models for multi-GNSS RT-PPP discussed.
IV. State Estimation:

Description of state vector and system model.
V. Measurement Outlier Accommodation:

Comparison of Threshold Decisions (TD) and RAPS approaches.
VI. Experiment Performance and Analysis:

Evaluation of different estimators: EKF, TD, and RAPS.
VII. Conclusion and Discussion:

RAPS approach outperforms traditional methods in outlier accommodation.

Stats

GNSS receivers achieve positioning accuracy around 10 meters without corrections - Common-mode errors limit standalone GNSS accuracy - PPP corrections offer global coverage without local base stations - Diagonal performance specification reduces computational costs - Multipath errors impact GNSS measurements - RAPS minimizes risk while meeting performance constraints

Quotes

"RAPS consistently achieves the lowest risk compared to EKF and TD."
"RAPS adapts both the number of satellites removed based on available measurements."
"Experimental results demonstrate significant improvements using RAPS over traditional methods."

Key Insights Distilled From

Outlier Accommodation for GNSS Precise Point Positioning using Risk-Averse State Estimation

by Wang Hu,Jean... at arxiv.org 03-14-2024

https://arxiv.org/pdf/2402.01860.pdf

Deeper Inquiries

How can the RAPS approach be extended to incorporate carrier-phase measurements

RAPSのアプローチを拡張してキャリア位相測定を組み込む方法は、いくつかの重要なステップで実現できます。まず第一に、キャリア位相測定を使用する場合、信号処理とデータ解析が必要です。これには、周波数間の非整数曖昧性の解決やイオノスフェアおよびトロポスフィア遅延補正が含まれます。さらに、GNSSシグナルから得られる情報量が増加し、複雑な状況下でも高精度な位置情報を提供できるようにするために、多様な観測値とその特性（例：マルチパスエラー）を考慮する必要があります。

What are the implications of relying on single-frequency GNSS receivers for precise positioning

単一周波数GNSS受信機に依存することの意味は重大です。主な影響は以下の通りです：

位置誤差：単一周波数受信機では精度が低く（約10メートル）、外部補正無しではSAE仕様（水平1.5m, 垂直3.0m）を満たすことが困難です。
キャリア位相利用：単一周波数ではキャリア位相法（RTK）等高精度手法へ移行しづらいため、位置推定精度向上やインテグレーション能力制限される可能性あり。
多天体観測不足：車両内部または都市環境下で衛星視界不良時に影響受けやすく，障害物回避等CAV応用領域で問題発生。

How does outlier accommodation impact the overall reliability of CAV applications

異常値対応がCAV応用全体の信頼性に与える影響は大きいです。具体的な影響：

位置推定品質向上: 精密地点推定技術(RT-PPP)内異常値除去効果促進し，許容範囲内最小化．
安全保障確保: 異常値排除・修正操作通じて自動車安全基準(SAE J2945)達成率向上．
リアルタイム反映: 高速道路事故防止等即時判断極めて重要，異常値対策追求欠かせず．
以上