111 research outputs found
RSS-based sensor localization with unknown transmit power
Received signal strength (RSS)-based single source localization when there is not a prior knowledge about the transmit power of the source is investigated. Because of nonconvex behavior of maximum likelihood (ML) estimator, convoluted computations are required to achieve its global minimum. Therefore, we propose a novel semidefinite programming (SDP) approach by approximating ML problem to a convex optimization problem which can be solved very efficiently. Computer simulations show that our proposed SDP has a remarkable performance very close to ML estimator. Linearizing RSS model, we also derive the partly novel least squares (LS) and weighted total least squares (WTLS) algorithms for this problem. Simulations illustrate that WTLS improves the performance of LS considerably
Hybrid algorithm for locating mobile station in cellular network
Locating mobile stations have been attracting an increasing attention from both researchers and industry communities and it is one of the most popular research areas of cellular network.Locating mobile stations using Time of
Arrival, Time Difference of Arrival, Angle of
Arrival and Received Signal Strength techniques have been widely used.However, more accurate results have been achieved by combining two or more of these techniques.A hybrid algorithm for locating mobile station is proposed by combining Received Signal Strength, Signal Attenuation and Time Difference of Arrival in this paper
Real-time localization using received signal strength
Locating and tracking assets in an indoor environment is a fundamental requirement for several applications which include for instance network enabled manufacturing. However, translating time of flight-based GPS technique for indoor solutions has proven very costly and inaccurate primarily due to the need for high resolution clocks and the non-availability of reliable line of sight condition between the transmitter and receiver. In this dissertation, localization and tracking of wireless devices using radio signal strength (RSS) measurements in an indoor environment is undertaken. This dissertation is presented in the form of five papers.
The first two papers deal with localization and placement of receivers using a range-based method where the Friis transmission equation is used to relate the variation of the power with radial distance separation between the transmitter and receiver. The third paper introduces the cross correlation based localization methodology. Additionally, this paper also presents localization of passive RFID tags operating at 13.56MHz frequency or less by measuring the cross-correlation in multipath noise from the backscattered signals. The fourth paper extends the cross-correlation based localization algorithm to wireless devices operating at 2.4GHz by exploiting shadow fading cross-correlation. The final paper explores the placement of receivers in the target environment to ensure certain level of localization accuracy under cross-correlation based method. The effectiveness of our localization methodology is demonstrated experimentally by using IEEE 802.15.4 radios operating in fading noise rich environment such as an indoor mall and in a laboratory facility of Missouri University of Science and Technology. Analytical performance guarantees are also included for these methods in the dissertation --Abstract, page iv
Target localization using RSS measurements in wireless sensor networks
The subject of this thesis is the development of localization algorithms for target localization in
wireless sensor networks using received signal strength (RSS) measurements or Quantized RSS
(QRSS) measurements.
In chapter 3 of the thesis, target localization using RSS measurements is investigated. Many
existing works on RSS localization assumes that the shadowing components are uncorrelated.
However, here, shadowing is assumed to be spatially correlated. It can be shown that
localization accuracy can be improved with the consideration of correlation between pairs of RSS
measurements. By linearizing the corresponding Maximum Likelihood (ML) objective function,
a weighted least squares (WLS) algorithm is formulated to obtain the target location. An iterative
technique based on Newtons method is utilized to give a solution. Numerical simulations show
that the proposed algorithms achieves better performance than existing algorithms with reasonable
complexity.
In chapter 4, target localization with an unknown path loss model parameter is investigated. Most
published work estimates location and these parameters jointly using iterative methods with a good
initialization of path loss exponent (PLE). To avoid finding an initialization, a global optimization
algorithm, particle swarm optimization (PSO) is employed to optimize the ML objective function.
By combining PSO with a consensus algorithm, the centralized estimation problem is extended to
a distributed version so that can be implemented in distributed WSN. Although suboptimal, the
distributed approach is very suitable for implementation in real sensor networks, as it is scalable,
robust against changing of network topology and requires only local communication. Numerical
simulations show that the accuracy of centralized PSO can attain the Cramer Rao Lower Bound
(CRLB). Also, as expected, there is some degradation in performance of the distributed PSO with
respect to the centralized PSO.
In chapter 5, a distributed gradient algorithm for RSS based target localization using only
quantized data is proposed. The ML of the Quantized RSS is derived and PSO is used to provide an
initial estimate for the gradient algorithm. A practical quantization threshold designer is presented
for RSS data. To derive a distributed algorithm using only the quantized signal, the local estimate
at each node is also quantized. The RSS measurements and the local estimate at each sensor
node are quantized in different ways. By using a quantization elimination scheme, a quantized
distributed gradient method is proposed. In the distributed algorithm, the quantization noise in the
local estimate is gradually eliminated with each iteration. Simulations show that the performance
of the centralized algorithm can reach the CRLB. The proposed distributed algorithm using a
small number of bits can achieve the performance of the distributed gradient algorithm using
unquantized data
Adaptive Wireless Biomedical Capsule Localization and Tracking
Wireless capsule endoscopy systems have been shown as a gold step to develop future
wireless biomedical multitask robotic capsules, which will be utilized in micro surgery, drug
delivery, biopsy and multitasks of the endoscopy. In such wireless capsule endoscopy systems,
one of the most challenging problems is accurate localization and tracking of the capsule inside
the human body. In this thesis, we focus on robotic biomedical capsule localization and
tracking using range measurements via electromagetic wave and magnetic strength based
sensors. First, a literature review of existing localization techniques with their merits and
limitations is presented. Then, a novel geometric environmental coefficient estimation technique
is introduced for time of flight (TOF) and received signal strength (RSS) based range
measurement. Utilizing the proposed environmental coefficient estimation technique, a 3D
wireless biomedical capsule localization and tracking scheme is designed based on a discrete
adaptive recursive least square algorithm with forgetting factor. The comparison between
localization with novel coefficient estimation technique and localization with known coefficient
is provided to demonstrate the proposed technique’s efficiency. Later, as an alternative
to TOF and RSS based sensors, use of magnetic strength based sensors is considered. We
analyze and demonstrate the performance of the proposed techniques and designs in various
scenarios simulated in Matlab/Simulink environment
Indoor Cooperative Localization for Ultra Wideband Wireless Sensor Networks
In recent years there has been growing interest in ad-hoc and wireless sensor networks (WSNs) for a variety of indoor applications. Localization information in these networks is an enabling technology and in some applications it is the main sought after parameter. The cooperative localization performance of WSNs is ultimately constrained by the behavior of the utilized ranging technology in dense cluttered indoor environments. Recently, ultra-wideband (UWB) Time-of-Arrival (TOA) based ranging has exhibited potential due to its large bandwidth and high time resolution. However, the performance of its ranging and cooperative localization capabilities in dense indoor multipath environments needs to be further investigated. Of main concern is the high probability of non-line of sight (NLOS) and Direct Path (DP) blockage between sensor nodes, which biases the TOA estimation and degrades the localization performance. In this dissertation, we first present the results of measurement and modeling of UWB TOA-based ranging in different indoor multipath environments. We provide detailed characterization of the spatial behavior of ranging, where we focus on the statistics of the ranging error in the presence and absence of the DP and evaluate the pathloss behavior in the former case which is important for indoor geolocation coverage characterization. Parameters of the ranging error probability distributions and pathloss models are provided for different environments: traditional office, modern office, residential and manufacturing floor; and different ranging scenarios: indoor-to-indoor (ITI), outdoor-to-indoor (OTI) and roof-to-indoor (RTI). Based on the developed empirical models of UWB TOA-based OTI and ITI ranging, we derive and analyze cooperative localization bounds for WSNs in the different indoor multipath environments. First, we highlight the need for cooperative localization in indoor applications. Then we provide comprehensive analysis of the factors affecting localization accuracy such as network and ranging model parameters. Finally we introduce a novel distributed cooperative localization algorithm for indoor WSNs. The Cooperative LOcalization with Quality of estimation (CLOQ) algorithm integrates and disseminates the quality of the TOA ranging and position information in order to improve the localization performance for the entire WSN. The algorithm has the ability to reduce the effects of the cluttered indoor environments by identifying and mitigating the associated ranging errors. In addition the information regarding the integrity of the position estimate is further incorporated in the iterative distributed localization process which further reduces error escalation in the network. The simulation results of CLOQ algorithm are then compared against the derived G-CRLB, which shows substantial improvements in the localization performance
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