TDAoA: A combination of TDoA and AoA localization with LoRaWAN

Aernouts, Michiel; BniLam, Noori; Berkvens, Rafael; Weyn, Maarten

doi:10.1016/j.iot.2020.100236

Cited by 31 publications

(16 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is a category of literates that uses a combination of two localization methods to improve estimation accuracy [80][81][82][83]. The authors in [80] present a message-passing algorithm for target tracking which exploits AoA and ranges information from RSSI measurements.…”

Section: Localization Accuracy Improvement Methodsmentioning

confidence: 99%

GNSS-Free Outdoor Localization Techniques for Resource-Constrained IoT Architectures: A Literature Review

et al. 2021

View full text Add to dashboard Cite

Large-scale deployments of the Internet of Things (IoT) are adopted for performance improvement and cost reduction in several application domains. The four main IoT application domains covered throughout this article are smart cities, smart transportation, smart healthcare, and smart manufacturing. To increase IoT applicability, data generated by the IoT devices need to be time-stamped and spatially contextualized. LPWANs have become an attractive solution for outdoor localization and received significant attention from the research community due to low-power, low-cost, and long-range communication. In addition, its signals can be used for communication and localization simultaneously. There are different proposed localization methods to obtain the IoT relative location. Each category of these proposed methods has pros and cons that make them useful for specific IoT systems. Nevertheless, there are some limitations in proposed localization methods that need to be eliminated to meet the IoT ecosystem needs completely. This has motivated this work and provided the following contributions: (1) definition of the main requirements and limitations of outdoor localization techniques for the IoT ecosystem, (2) description of the most relevant GNSS-free outdoor localization methods with a focus on LPWAN technologies, (3) survey the most relevant methods used within the IoT ecosystem for improving GNSS-free localization accuracy, and (4) discussion covering the open challenges and future directions within the field. Some of the important open issues that have different requirements in different IoT systems include energy consumption, security and privacy, accuracy, and scalability. This paper provides an overview of research works that have been published between 2018 to July 2021 and made available through the Google Scholar database.

show abstract

Section: Localization Accuracy Improvement Methodsmentioning

confidence: 99%

GNSS-Free Outdoor Localization Techniques for Resource-Constrained IoT Architectures: A Literature Review

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Localization and tracking services are considered a key feature that distinguishes IoT standards from each other. For instance, LoRa networks provide a localization solution based on the Time Difference of Arrival (TDoA) of the received signals [1]. Sigfox and NB-IoT, on the other hand, provide localization solutions based on the Received Signal Strength (RSS) values [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Angle of Arrival Estimation System for LoRa Technology based on Phase Detectors

BniLam

Nasser

Weyn

2022

2022 16th European Conference on Antennas and Propagation (EuCAP)

Self Cite

View full text Add to dashboard Cite

In this paper, we propose a comprehensive lowpower and low-cost Angle of Arrival (AoA) estimation system. The proposed system consists of a hardware architecture and an AoA estimation algorithm. The proposed hardware captures the phase values of the received signal and transmits them to the nearest LoRaWAN gateway. In the cloud, an AoA estimation algorithm will be performed, and the AoA of the received signal will be estimated. The proposed hardware is generic, and it can estimate the AoA of any received signal that is operating at a frequency below 2.7 GHz. However, we have validated its performance with LoRa signals that are operating at the 868 MHz frequency band. The experimental validation, which has been conducted in a controlled environment, reveals that the proposed system can estimate the AoA of the received signals from various azimuth angles with median and maximum estimation errors of 4 and 14 degrees, respectively.

show abstract

“…In actual application scenarios, time difference of arrival (TDOA) [2][3][4][5][6], received signal strength (RSS) [7][8][9][10][11], frequency difference of arrival (FDOA) [12], angle of arrival (AOA) [13][14][15][16][17][18][19][20][21][22], and their combined algorithms [23][24][25][26][27][28][29][30][31][32] are common passive localization techniques. However, TDOA and FDOA localization methoda require precise timing and synchronization, which increases the cost and complexity of the implementation.…”

Section: Introductionmentioning

confidence: 99%

Ant Colony Pheromone Mechanism-Based Passive Localization Using UAV Swarm

et al. 2022

View full text Add to dashboard Cite

The problem of passive localization using an unmanned aerial vehicle (UAV) swarm is studied. For multi-UAV localization systems with limited communication and observation range, the challenge is how to obtain accurate target state consistency estimates through local UAV communication. In this paper, an ant colony pheromone mechanism-based passive localization method using a UAV swarm is proposed. Different from traditional distributed fusion localization algorithms, the proposed method makes use of local interactions among individuals to process the observation data with UAVs, which greatly reduces the cost of the system. First, the UAVs that have detected the radiation source target estimate the rough target position based on the pseudo-linear estimation (PLE). Then, the ant colony pheromone mechanism is introduced to further improve localization accuracy. The ant colony pheromone mechanism consists of two stages: pheromone injection and pheromone transmission. In the pheromone injection mechanism, each UAV uses the maximum likelihood (ML) algorithm with the current observed target bearing information to correct the initial target position estimate. Then, the UAV swarm weights and fuses the target position information between individuals based on the pheromone transmission mechanism. Numerical results demonstrate that the accuracy of the proposed method is better than that of traditional localization algorithms and close to the Cramer–Rao lower bound (CRLB) for small measurement noise.

show abstract

TDAoA: A combination of TDoA and AoA localization with LoRaWAN

Cited by 31 publications

References 7 publications

GNSS-Free Outdoor Localization Techniques for Resource-Constrained IoT Architectures: A Literature Review

GNSS-Free Outdoor Localization Techniques for Resource-Constrained IoT Architectures: A Literature Review

Angle of Arrival Estimation System for LoRa Technology based on Phase Detectors

Ant Colony Pheromone Mechanism-Based Passive Localization Using UAV Swarm

Contact Info

Product

Resources

About