Zhenhui Tan

Received-Signal-Strength-Based Indoor Positioning Using Compressive Sensing

The recent growing interest for indoor Location-Based Services (LBSs) has created a need for more accurate and real-time indoor positioning solutions. The sparse nature of location finding makes the theory of Compressive Sensing (CS) desirable for accurate indoor positioning using Received Signal Strength (RSS) from Wireless Local Area Network (WLAN) Access Points (APs). We propose an accurate RSS-based indoor positioning system using the theory of compressive sensing, which is a method to recover sparse signals from a small number of noisy measurements by solving an `1-minimization problem. Our location estimator consists of a coarse localizer, where the RSS is compared to a number of clusters to detect in which cluster the node is located, followed by a fine localization step, using the theory of compressive sensing, to further refine the location estimation. We have investigated different coarse localization schemes and AP selection approaches to increase the accuracy. We also show that the CS theory can be used to reconstruct the RSS radio map from measurements at only a small number of fingerprints, reducing the number of measurements significantly. We have implemented the proposed system on a WiFi-integrated mobile device and have evaluated the performance. Experimental results indicate that the proposed system leads to substantial improvement on localization accuracy and complexity over the widely used traditional fingerprinting methods.

Multiple Target Localization Using Compressive Sensing

In this paper, a novel multiple target localization approach is proposed by exploiting the compressive sensing theory, which indicates that sparse or compressible signals can be recovered from far fewer samples than that needed by the Nyquist sampling theorem. We formulate the multiple target locations as a sparse matrix in the discrete spatial domain. The proposed algorithm uses the received signal strengths (RSSs) to find the location of targets. Instead of recording all RSSs over the spatial grid to construct a radio map from targets, far fewer numbers of RSS measurements are collected, and a data pre-processing procedure is introduced. Then, the target locations can be recovered from these noisy measurements, only through an l1-minimization program. The proposed approach reduces the number of measurements in a logarithmic sense, while achieves a high level of localization accuracy. Analytical studies and simulations are provided to show the performance of the proposed approach on localization accuracy.

Compressive Sensing Based Positioning Using RSS of WLAN Access Points

The sparse nature of location finding problem makes the theory of compressive sensing desirable for indoor positioning in Wireless Local Area Networks (WLANs). In this paper, we address the received signal strength (RSS)-based localization problem in WLANs using the theory of compressive sensing (CS), which offers accurate recovery of sparse signals from a small number of measurements by solving an

Channel Estimation for OFDM

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Performance Analysis of Alamouti Transmit Diversity with QAM in Imperfect Channel Estimation

-minimization problem. A pre-processing procedure of orthogonalization is used to induce incoherence needed in the CS theory. In order to mitigate the effects of RSS variations due to channel impediments, the proposed positioning system consists of two steps: coarse localization by exploiting affinity propagation, and fine localization by the CS theory. In the fine localization stage, access point selection problem is studied to further increase the accuracy. We implement the positioning system on a WiFi-integrated mobile device (HP iPAQ hx4700 with Windows Mobile 2003 Pocket PC) to evaluate the performance. Experimental results indicate that the proposed system leads to substantial improvements on localization accuracy and complexity over the widely used traditional fingerprinting methods.

Fine-grained channel access in wireless LAN

Orthogonal frequency division multiplexing (OFDM) has been widely adopted in modern wireless communication systems due to its robustness against the frequency selectivity of wireless channels. For coherent detection, channel estimation is essential for receiver design. Channel estimation is also necessary for diversity combining or interference suppression where there are multiple receive antennas. In this paper, we will present a survey on channel estimation for OFDM. This survey will first review traditional channel estimation approaches based on channel frequency response (CFR). Parametric model (PM)-based channel estimation, which is particularly suitable for sparse channels, will be also investigated in this survey. Following the success of turbo codes and low-density parity check (LDPC) codes, iterative processing has been widely adopted in the design of receivers, and iterative channel estimation has received a lot of attention since that time. Iterative channel estimation will be emphasized in this survey as the emerging iterative receiver improves system performance significantly. The combination of multiple-input multiple-output (MIMO) and OFDM has been widely accepted in modern communication systems, and channel estimation in MIMO-OFDM systems will also be addressed in this survey. Open issues and future work are discussed at the end of this paper.

Orientation-aware indoor localization using affinity propagation and compressive sensing

In this paper, the performance of multi-level quadrature amplitude modulation (M-QAM) systems is studied analytically when Alamouti space-time transmit diversity (STTD) coding is used for transmission over Rayleigh fading channels. The effect of self-interference (interference from another simultaneously transmitted symbol in the STTD scheme for the same user) due to imperfect channel estimation is investigated. Based on the characteristic function method, a closed-form expression of the bit error rate (BER) is derived. Numerical results for 16/64-QAM show that, with the Alamouti STTD technique, the BER performance of the QAM system can be improved significantly. The effect of receive antenna diversity is also investigated. It is shown that high-order QAM constellations can be employed even in low signal to noise ratio (SNR) with the transmit diversity technique in conjunction with receive antenna diversity.

Hybrid TOA/AOA Cooperative Localization in Non-Line-of-Sight Environments

With the increasing of physical-layer (PHY) data rate in modern wireless local area networks (WLANs) (e.g., 802.11n), the overhead of media access control (MAC) progressively degrades data throughput efficiency. This trend reflects a fundamental aspect of the current MAC protocol, which allocates the channel as a single resource at a time. This paper argues that, in a high data rate WLAN, the channel should be divided into separate subchannels whose width is commensurate with the PHY data rate and typical frame size. Multiple stations can then contend for and use subchannels simultaneously according to their traffic demands, thereby increasing overall efficiency. We introduce FICA, a fine-grained channel access method that embodies this approach to media access using two novel techniques. First, it proposes a new PHY architecture based on orthogonal frequency division multiplexing (OFDM) that retains orthogonality among subchannels while relying solely on the coordination mechanisms in existing WLAN, carrier sensing and broadcasting. Second, FICA employs a frequency-domain contention method that uses physical-layer Request to Send/Clear to Send (RTS/CTS) signaling and frequency domain backoff to efficiently coordinate subchannel access. We have implemented FICA, both MAC and PHY layers, using a software radio platform, and our experiments demonstrate the feasibility of the FICA design. Furthermore, our simulation results show FICA can improve the efficiency of WLANs from a few percent to 600% compared to existing 802.11.

The Effective Throughput of MISO Systems Over

The sparse nature of location finding makes it desirable to exploit the theory of compressive sensing for indoor localization. In this paper, we propose a received signal strength (RSS)-based localization scheme in Wireless Local Area Networks (WLANs) using the theory of compressive sensing (CS), which offers accurate recovery of sparse signals from a small number of measurements by solving an l1-minimization problem. In order to mitigate the effects of RSS variations due to channel impediments and mobile device orientation, a two-step localization scheme is proposed by exploiting affinity propagation for coarse localization followed by a CS-based fine localization to further improve the accuracy. We implement the localization algorithm on a WiFi-integrated mobile device to evaluate the performance. Experimental results indicate that the proposed system leads to substantial improvements on localization accuracy and complexity over the widely used traditional fingerprinting methods.

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The majority of the location estimation error in wireless communication systems comes from the effect of non-line-of-sight (NLOS) propagation. NLOS identification and correction are the main techniques of mitigating the NLOS impact on positioning accuracy. In this paper, we propose a cooperative localization algorithm that combines the hybrid time of arrival (TOA) / angle of arrival (AOA) measurements of all identified Line-of-Sight (LOS) base station (BS) - mobile station (MS) links with the TOA measurements of MS-MS links. Different cost functions are described according to the NLOS detection results based on existing identification methods. A NLOS correction model is also presented when the destination MS to be located is completely in NLOS propagation, whereas some BS - cooperative MS links are in LOS conditions. Simulation results demonstrate that the proposed algorithm outperforms other existing hybrid localization techniques, and its accuracy increases with the number of LOS BS-MS links, as well as the NLOS detection accuracy.