A Robust Wi-Fi Fingerprint Positioning Algorithm Using Stacked Denoising Autoencoder and Multi-Layer Perceptron

Abstract

With the increasing demand for location-based services, Wi-Fi-based indoor positioning technology has attracted much attention in recent years because of its ubiquitous deployment and low cost. Considering that Wi-Fi signals fluctuate greatly with time, extracting robust features of Wi-Fi signals is the key point to maintaining good positioning accuracy. To handle the dynamic fluctuation with time and sparsity of Wi-Fi signals, we propose an SDAE (Stacked Denoising Autoencoder)-based feature extraction method, which can obtain a robust and time-independent Wi-Fi fingerprint by learning the reconstruction distribution from a raw Wi-Fi signal and an artificial-noise-added Wi-Fi signal. We also leverage the strong representation ability of MLP (Multi-Layer Perceptron) to build a regression model, which maps the extracted features to the corresponding location. To fully evaluate the performance of our proposed algorithm, three datasets are applied, which represent three different scenarios, namely, spacious area with time interval, no time interval, and complex area with large time interval. The experimental results confirm the validity of our proposed SDAE-based feature extraction method, which can accurately reflect Wi-Fi signals in corresponding locations. Compared with other regression models, our proposed regression model can better map the extracted features to the target position. The average positioning error of our proposed algorithm is 4.24 m when there is a 52-day interval between training dataset and testing dataset. That confirms that the proposed algorithm outperforms other state-of-the-art positioning algorithms when there is a large time interval between training dataset and testing dataset. present, due to the wide deployment and availability of Wi-Fi infrastructure, Wi-Fi fingerprint-based localization has become one of the most dominant indoor positioning techniques.There are two main types of Wi-Fi-based indoor positioning technologies: RSSI (Received Signal Strength Indicator)-based ranging positioning algorithm [9][10][11], and fingerprint-based positioning algorithm [12][13][14]. The RSSI-based ranging positioning algorithm [11] usually adopts the received Wi-Fi signal to estimate the distance between the target (its location is unknown) and the access point (its location is known) using the wireless radio signal propagation model, and then estimates the target position using trilateration or multilateration methods. The fingerprint-based positioning algorithm [14] adopts the signal matching algorithm to estimate the user location. It first collects environmental Wi-Fi signals and constructs a Wi-Fi fingerprint database during the offline phase. During the online positioning phase, the fingerprint-based positioning algorithm compares the current Wi-Fi observation with the recorded fingerprint in the database to obtain the target position using the optimum matching criterion. Compared with the fingerprint-based positioning algorithm, the RSSI-based ranging positioning algorithm struggles to meet...

A Novel Outdoor Positioning Technique Using LTE Network Fingerprints

“…Fingerprint-based positioning technologies can be divided into two main categories: infrastructure-free and infrastructure-based approaches [19]. Infrastructure-free approaches rely on utilizing existing infrastructure, such as CDMA2000, LTE, Frequency Modulation (FM), Wi-Fi, and sound signals, for positioning purposes [20]. On the other hand, infrastructure-based fingerprint positioning technologies focus on dedicated infrastructure, such as Bluetooth, radio frequency identification (RFID) or visible light for localization [21].…”

Deep Learning for Fingerprint Localization in Indoor and Outdoor Environments

“…The localization accuracy will deteriorate greatly in indoor environments, because the signal will often be blocked by objects and refracted [14]. However, fingerprint-based localization can overcome these drawbacks, and it has been proven to have a satisfactory localization performance [12]. Therefore, the fingerprint-based localization technique has attracted widespread attention.…”

“…In recent years, deep learning has made great progress both in academics and industry. Deep learning with multiple layers has beaten other techniques in speech recognition, image classification, and so on [11,12]. Therefore, in this work, deep residual network (Resnet) and transfer learning are introduced to develop a highly accurate localization system.…”

“…However, fingerprint-based localization can overcome these drawbacks, and it has been proven to 122 have a satisfactory localization performance [12]. Therefore, the fingerprint-based localization 123 technique has attracted widespread attention.…”

Comparison of CNN Applications for RSSI-Based Fingerprint Indoor Localization

“…To enhance location estimation, the centroid method was used in [20], and Hsieh [21] attempted to construct a recurrent neural network for indoor positioning. Variants of neural networks have been used in Wi-Fi positioning (e.g., deep belief networks [22], DNNs [23], fuzzy neural networks [24], and artificial synaptic networks [25]).…”