Implementation and evaluation of particle filtering for indoor positioning

Chateau, Johan; Rousseau, Pierre; Albiston, Gregory; Cook, Beverley; Papanastasiou, Stylianos; Peytchev, Evtim

doi:10.1109/iscc.2014.6912587

Cited by 4 publications

(7 citation statements)

References 8 publications

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“…This method finds the location of the target node with the help of the angle of arrival the signal makes with the antenna array at the target node [15], [49], [50]. In this method, phase difference technique and the signal amplitude of the main beam are utilized to determine the angle of arrival.…”

Section: Angle Of Arrival (Aoa)mentioning

confidence: 99%

“…A direct path is considered between the transmitter and the receiver and ray tracing and empirical measurements for RFID ranging were carried out by using POA ranging equations. [15] Advantages:…”

Section: Phase Of Arrival (Poa)mentioning

confidence: 99%

“…Fingerprinting provides higher accuracy if large amount of offline data is collected accurately in the training phase. Fingerprinting is mainly used with measured RSSI and CSI data [15]. However, it can be used in conjunction with any other distance measurement techniques like TOA, AOA and POA.…”

Section: Fingerprintingmentioning

confidence: 99%

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Neural Network Based Recursive Least Square Technique for Indoor Wireless Positioning

Adhikari

2023

Preprint

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Location Based Services (LBS) in the realms of Smart City realizations require accurate real time positioning information of objects and people indoors. Both VLC based and the RF based positioning techniques have been studied in the literature. In RF system, we use Recursive Least Square (RLS) multilateration algorithm to estimate the position of unknown target indoor. The RLS multilateration algorithm is based on the trilateration solution using algebraic method. Solution obtained from the trilateration is used in RLS algorithm and the solution is updated recursively. Since Machine Learning (ML) is one of the widely used approaches to improve the accuracy in indoor positioning, we have used Artificial Neural Network (ANN) to fine tune the simulated distances with Rician noise. Therefore, the proposed technique hybridizes RLS with Artificial Neural Network (ANN) to solve a multilateration problem of different sets of anchor nodes. We use MATLABTM for the simulation purpose. While simulating distances, Rician noise is considered for anchor-target distance and, noisy distances are fed into ANN filter. Instead of using directly simulated distances, estimated distances from ANN are used for RLS implementation. Results from the hybrid RLS-ANN are compared with pure RLS, pure least square (LS) and LS-ANN approaches. Hybrid RLS-ANN provides the least Root Mean Square Error (RMSE) among all the techniques and improves the accuracy up to 80% compared to the pure LS multilateration technique. Complexity of the proposed technique is relatively low with significantly increased accuracy. We also compare the results with different sets of anchor nodes for RLS implementation. Hybrid RLS-ANN algorithm is used for anchor nodes N = 4 to N = 10. Comparing the results, it has been shown that as the number of anchor nodes increases, the RMSE decreases steadily. RLS multilateration with large number of anchor nodes performs better than LS multilateration. On the other hand, distance error is largely reduced by using ANN training and RLS with ANN estimated distances further improves the accuracy. In VLC system, we analyze the impact of SNR on indoor positioning. VLC indoor positioning is analyzed based on SNR distribution on the room indoor. Distance measurement is analyzed with three parameters: angle of incidence, angle of irradiance and transmitter FOV. Since SNR has the direct impact on the VLC indoor distance measurement, a comparative study of the SNR variation with respect to transmitter FOV, receiver FOV, angles of incidence and irradiance is presented, and corresponding distance measurement error is analyzed.

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Section: Angle Of Arrival (Aoa)mentioning

confidence: 99%

Section: Phase Of Arrival (Poa)mentioning

confidence: 99%

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Neural Network Based Recursive Least Square Technique for Indoor Wireless Positioning

Adhikari

2023

Preprint

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“…Although the RSS values are inherently unstable at a single level, packets of RSS values show stable distribution. Previous works try to establish a Gaussian distribution based on the statistical property of packets of RSS data to estimate the target localization, the possibility-based method [17] improves the localization accuracy compared with traditional determine-based method. However, determine-based [18] method still faces greatly swift phenomenon in localization results due to the multi-path effects.…”

Section: Fig 1 Cdf Of Correlation Coefficient Of Rss Between Adjacementioning

confidence: 99%

A novel indoor localization scheme based on refined fingerprint-based autoencoder network

Mei

2018

MATEC Web Conf.

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Abstract. Indoor positioning systems have attracted increasing interests for the emergency of location based service in indoor environments. Wi-Fi fingerprint-based localization scheme has become a promising indoor localization technique due to the availability of access point (AP) and its low cost. However, the received signal strength (RSS) values are easily fluctuated by the interference of multi-path effects, which introduce propagation errors into localization results. In order to address the issue, a fingerprint-based autoencoder network scheme is proposed to learn the essential features from the measured coarse RSS values and extract the trained weight parameters of autoencoder network as refined fingerprints. The extracted fingerprints are able to represent the environmental properties and display strong robustness with fluctuated signals. The proposed scheme is further implemented in complex indoor scenes, which substantiate the effectiveness and accuracy improvement compared with other RSS-based schemes.

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“…PF [ 33 ] is a powerful Monte Carlo-based method addressing dynamic Bayesian system state estimation problems and widely applied in tracking and navigation [ 34 ]. The probability density function (PDF) of system states based on a series of observations is recursively approximated by a group of particles with sampling, weight calculation and resampling processes.…”

Section: Proposed Hiln Systemmentioning

confidence: 99%

A Hybrid Indoor Localization and Navigation System with Map Matching for Pedestrians Using Smartphones

Tian

Salčić

Wang

et al. 2015

Sensors

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Pedestrian dead reckoning is a common technique applied in indoor inertial navigation systems that is able to provide accurate tracking performance within short distances. Sensor drift is the main bottleneck in extending the system to long-distance and long-term tracking. In this paper, a hybrid system integrating traditional pedestrian dead reckoning based on the use of inertial measurement units, short-range radio frequency systems and particle filter map matching is proposed. The system is a drift-free pedestrian navigation system where position error and sensor drift is regularly corrected and is able to provide long-term accurate and reliable tracking. Moreover, the whole system is implemented on a commercial off-the-shelf smartphone and achieves real-time positioning and tracking performance with satisfactory accuracy.

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Implementation and evaluation of particle filtering for indoor positioning

Cited by 4 publications

References 8 publications

Neural Network Based Recursive Least Square Technique for Indoor Wireless Positioning

Neural Network Based Recursive Least Square Technique for Indoor Wireless Positioning

A novel indoor localization scheme based on refined fingerprint-based autoencoder network

A Hybrid Indoor Localization and Navigation System with Map Matching for Pedestrians Using Smartphones

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