How to Get the Best Out of Your Fingerprint Database: Hierarchical Fingerprint Indoor Positioning for Databases With Variable Density

Chang, Qiang; Velde, Samuel Van de; Steendam, Heidi

doi:10.1109/access.2019.2939545

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…In [32], the proposed solution requires little training to learn the model parameters and then generates extra RSSI values in new, virtual RPs. With the common goal of reducing training workload, in [33], the authors introduce the Hierarchical Positioning Algorithm (HPA). This algorithm creates several sub-dataset with different densities in virtual RPs.…”

Section: Related Workmentioning

confidence: 99%

“…Our solution completely eliminates the real-world training part of the fingerprint technique and replaces it with synthetic datasets. In particular, in the more directly related works [33] and [31], the authors present calibration-free positioning techniques, which exploit the floor plan/wall map of the environment for the construction of RSSI maps, calculating the path-loss of the signals using a signal propagation model. However, in this case, the authors generate only a single synthetic dataset to represent the signal behavior in the environment, with the same path-loss exponent in all RPs.…”

Section: Related Workmentioning

confidence: 99%

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Indoor Positioning System Using Synthetic Training and Data Fusion

et al. 2021

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Indoor Positioning Systems (IPSs) are used to estimate the position of mobile devices in indoor environments. Fingerprinting is the most used technique because of its higher accuracy. However, this technique requires a labor-intensive training phase that measures the Received Signal Strength Indicator (RSSI) at all Reference Points (RPs) locations. On the other hand, model-based IPSs use signal propagation models to estimate distances from RSSI. Thus, they do not require expensive training but result in higher positioning errors. In this work, we propose SynTra-IPS (Synthetic Training Indoor Positioning System), a hybrid approach between a fingerprint and a model-based IPS that uses synthetic, simulated datasets combined with data fusion techniques to eliminate the fingerprint collection cost. In our solution, we use the map of the scenario, with known anchor nodes' positions and the log-distance signal propagation model, to generate several synthetic, model-based, fingerprint training datasets. In the online phase of our solution, the positions estimated by the several synthetic datasets using K-Nearest Neighbors (KNN) are combined using data fusion techniques into a single, more accurate position. We evaluated the performance of our SynTra solution in a real-world, large-scale environment using mobile devices with Bluetooth Low Energy (BLE) technology, and we compared our solution to classic approaches from the literature. Our results show that SynTra can locate mobile devices with an average error of only 2.36 m while requiring no real-world environment training.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Indoor Positioning System Using Synthetic Training and Data Fusion

et al. 2021

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“…The fingerprinting approach has two crucial downsides that limit its applicability. First is the aforementioned calibration phase, which requires a large number of precisely measured spatial samples of the signal properties to assure the best positioning results [ 20 ]. In dynamic environments, the system needs to be recalibrated regularly as the radio map becomes outdated when the propagation environment changes over time.…”

Section: Introductionmentioning

confidence: 99%

Calibration-Free Single-Anchor Indoor Localization Using an ESPAR Antenna

Groth

Nyka

Kulas

2021

Sensors

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In this paper, we present a novel, low-cost approach to indoor localization that is capable of performing localization processes in real indoor environments and does not require calibration or recalibration procedures. To this end, we propose a single-anchor architecture and design based on an electronically steerable parasitic array radiator (ESPAR) antenna and Nordic Semiconductor nRF52840 utilizing Bluetooth Low Energy (BLE) protocol. The proposed algorithm relies on received signal strength (RSS) values measured by the receiver equipped with the ESPAR antenna for every considered antenna radiation pattern. The calibration-free concept is achieved by using inexpensive BLE nodes installed in known positions on the walls of the test room and acting as reference nodes for the positioning algorithm. Measurements performed in the indoor environment show that the proposed approach can successfully provide positioning results better than those previously reported for single-anchor ESPAR antenna localization systems employing the classical fingerprinting method and relying on time-consuming calibration procedures.

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Autonomous maneuver strategy of swarm air combat based on DDPG

Wang

Zhao

et al. 2021

Auton. Intell. Syst.

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Unmanned aerial vehicles (UAVs) have been found significantly important in the air combats, where intelligent and swarms of UAVs will be able to tackle with the tasks of high complexity and dynamics. The key to empower the UAVs with such capability is the autonomous maneuver decision making. In this paper, an autonomous maneuver strategy of UAV swarms in beyond visual range air combat based on reinforcement learning is proposed. First, based on the process of air combat and the constraints of the swarm, the motion model of UAV and the multi-to-one air combat model are established. Second, a two-stage maneuver strategy based on air combat principles is designed which include inter-vehicle collaboration and target-vehicle confrontation. Then, a swarm air combat algorithm based on deep deterministic policy gradient strategy (DDPG) is proposed for online strategy training. Finally, the effectiveness of the proposed algorithm is validated by multi-scene simulations. The results show that the algorithm is suitable for UAV swarms of different scales.

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How to Get the Best Out of Your Fingerprint Database: Hierarchical Fingerprint Indoor Positioning for Databases With Variable Density

Cited by 4 publications

References 17 publications

Indoor Positioning System Using Synthetic Training and Data Fusion

Indoor Positioning System Using Synthetic Training and Data Fusion

Calibration-Free Single-Anchor Indoor Localization Using an ESPAR Antenna

Autonomous maneuver strategy of swarm air combat based on DDPG

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