.png){kind=icon}
.png){kind=icon}
Return
Self-Position Awareness Based on Cascade Direct Localization Over Multiple Source Data
.png){kind=icon}
PRE
AI
DOI：10.1109/TITS.2022.3170465.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The global positioning system (GPS), which provides ubiquitous location-awareness with a constellation of satellites, has become an instrumental function of multiple mass-market applications. Satellite signals, however, may not be capable of penetrating obstacles in harsh environments (e.g., urban canyons, tree canopies, and flyovers). Hence, GPS may not provide adequate localization accuracy for applications like autonomous vehicles. Resorting to data fusion of heterogeneous signals emanating from multiple anchors, we advocate a self-localization method that provides accurate estimates of the vehicle position. To be more specific, several heterogenous emitters whose positions are known are used as anchors to determine the vehicle's position based on the weighted direct position determination (DPD) method that eliminates nonhomogeneity among different emitters. However, the weighted DPD method requires an exhaustive search of the parameter search space and is thus time-consuming. To reduce the computational burden, we propose a weighted cascade compensation estimator (WCCE) that is tailored for real-time tracking and self-localization. The proposed WCCE outperforms traditional DPD methods in terms of computational complexity while achieving nearly comparable localization accuracy. The effectiveness of the proposed method is corroborated by extensive simulated examples.
Keywords:
Location awareness
Global Positioning System
Wireless sensor networks
Maximum likelihood estimation
Matching pursuit algorithms
Satellites
Position measurement
Array processing
direct position determination
self-localization
target tracking
Journal
IF:
8.4
Papers:
9.5K
Citations:
6.3W
Organization
No organization information available