Distributed detection, localization, and estimation in time-critical wireless sensor networks

In this thesis the problem of distributed detection, localization, and estimation (DDLE) of a stationary target in a fusion center (FC) based wireless sensor network (WSN) is considered. The communication process is subject to time-critical operation, restricted power and bandwidth (BW) resources operating over a shared communication channel Buffering from Rayleigh fading and phase noise. A novel algorithm is proposed to solve the DDLE problem consisting of two dependent stages: distributed detection and distributed estimation. The WSN performs distributed detection first and based on the global detection decision the distributed estimation stage is performed. The communication between the SNs and the FC occurs over a shared channel via a slotted Aloha MAC protocol to conserve BW. In distributed detection, hard decision fusion is adopted, using the counting rule (CR), and sensor censoring in order to save power and BW. The effect of Rayleigh fading on distributed detection is also considered and accounted for by using distributed diversity combining techniques where the diversity combining is among the sensor nodes (SNs) in lieu of having the processing done at the FC. Two distributed techniques are proposed: the distributed maximum ratio combining (dMRC) and the distributed Equal Gain Combining (dEGC). Both techniques show superior detection performance when compared to conventional diversity combining procedures that take place at the FC. In distributed estimation, the segmented distributed localization and estimation (SDLE) framework is proposed. The SDLE enables efficient power and BW processing. The SOLE hinges on the idea of introducing intermediate parameters that are estimated locally by the SNs and transmitted to the FC instead of the actual measurements. This concept decouples the main problem into a simpler set of local estimation problems solved at the SNs and a global estimation problem solved at the FC. Two algorithms are proposed for solving the local problem: a nonlinear least squares (NLS) algorithm using the variable projection (VP) method and a simpler gird search (GS) method. Also, Four algorithms are proposed to solve the global problem: NLS, GS, hyperspherical intersection method (HSI), and robust hyperspherical intersection (RHSI) method. Thus, the SDLE can be solved through local and global algorithm combinations. Five combinations are tied: NLS2 (NLS-NLS), NLS-HSI, NLS-RHSI, GS2, and GS-N LS. It turns out that the last algorithm combination delivers the best localization and estimation performance. In fact , the target can be localized with less than one meter error. The SNs send their local estimates to the FC over a shared channel using the slotted-Aloha MAC protocol, which suits WSNs since it requires only one channel. However, Aloha is known for its relatively high medium access or contention delay given the medium access probability is poorly chosen. This fact significantly hinders the time-critical operation of the system. Hence, multi-packet reception (MPR) is used with slotted Aloha protocol, in which several channels are used for contention. The contention delay is analyzed for slotted Aloha with and without MPR. More specifically, the mean and variance have been analytically computed and the contention delay distribution is approximated. Having theoretical expressions for the contention delay statistics enables optimizing both the medium access probability and the number of MPR channels in order to strike a trade-off between delay performance and complexity

Two-Dimensional AOA Estimation Based on a Constant Modulus Algorithm

We propose a two-dimensional (2D) angle of arrival (AOA) estimator using the algebraic constant modulus algorithm (ACMA). This algorithm was originally introduced to estimate the one-dimensional (1D) AOA. An extension to estimate and automatically pair the elevation and azimuth angles for different sources is derived and proved in this paper. The ACMA method factorises a matrix into two different matrices; one is of constant modulus and contains the azimuth AOA information; however the second was previously ignored. In this paper we will prove that this second matrix contains the elevation AOA information. Thus, 2D AOA estimation is proved possible using the ACMA method. Simulation results are presented to illustrate the proposed method’s performances under different conditions

Optimal quantization and power allocation for energy-based distributed sensor detection

We consider the decentralized detection of an unknown deterministic signal in a spatially uncorrelated distributed wireless sensor network. N samples from the signal of interest are gathered by each of the M spatially distributed sensors, and the energy is estimated by each sensor. The sensors send their quantized information over orthogonal channels to the fusion center (FC) which linearly combines them and makes a final decision. We show how by maximizing the modified deflection coefficient we can calculate the optimal transmit power allocation for each sensor and the optimal number of quantization bits to match the channel capacity

Distributed binary event detection under data-falsification and energy-bandwidth limitation

We address the problem of centralized detection of a binary event in the presence of falsifiable sensor nodes (SNs) (i.e., controlled by an attacker) for a bandwidth-constrained under-attack spatially uncorrelated distributed wireless sensor network (WSN). The SNs send their quantized test statistics over orthogonal channels to the fusion center (FC), which linearly combines them to reach a final decision. First (considering that the FC and the attacker do not act strategically), we derive (i) the FC optimal weight combining; (ii) the optimal SN to FC transmit power, and (iii) the test statistic quantization bits that maximize the probability of detection (Pd). We also derive an expression for the attacker strategy that causes the maximum possible FC degradation. But in these expressions, both the optimum FC strategy and the attacker strategy require

Distributed Localization in Censored Wireless Sensor Networks with Binary Data

In this paper we investigate distributed localization of an intruder in wireless sensor networks (WSNs), in which the sensor nodes (SNs) censor their transmission to the fusion center (FC). The SNs locally detect the intruder and send their decision only if it is positive. The FC, on the other hand, uses those binary data to localize the intruder. We present the censored maximum likelihood (cML) localization algorithm, Furthermore, we derive two computationally simple localization algorithms, the quadratic approximate ML (QAML) and the linear approximate ML (LAML). The performance of the ML-based algorithms significantly outperforms the heuristics-based algorithms, such as the centroid method (CM) and the center of maximum enclosing rectangle (CMER), as the simulation results show

AOA, Delay, and Complex Propagation Factor Estimation for the Monostatic MIMO Radar System

In this paper, we propose a solution to find the angle of arrival (AOA), delay, and the complex propagation factor for the monostatic multiple-input multiple-output (MIMO) radar system. In contrast to conventional iterative computationally demanding estimation schemes, we propose a closed form solution for most of the previous parameters. The solution is based on forming an approximate correlation matrix of the received signals at the MIMO radar receiver end. Then, an eigenvalue decomposition (EVD) is performed on the formed approximate correlation matrix. The AOAs of the received signals are deduced from the corresponding eigenvectors. Then, the delays are estimated from the received signal matrix properties. This is followed by forming structured matrices which will be used to find the complex propagation factors. These estimates can be used as initializations for other MIMO radar methods, such as the maximum likelihood algorithm. Simulation results show significantly low root mean square error (RMSE) for AOAs and complex propagation factors. On the other hand, our proposed method achieves zero RMSE in estimating the delays for relatively low signal-to-noise ratios (SNRs)

Two-Dimensional AOA Estimation Based on a Constant Modulus Algorithm

We propose a two-dimensional (2D) angle of arrival (AOA) estimator using the algebraic constant modulus algorithm (ACMA). This algorithm was originally introduced to estimate the one-dimensional (1D) AOA. An extension to estimate and automatically pair the elevation and azimuth angles for different sources is derived and proved in this paper. The ACMA method factorises a matrix into two different matrices; one is of constant modulus and contains the azimuth AOA information; however the second was previously ignored. In this paper we will prove that this second matrix contains the elevation AOA information. Thus, 2D AOA estimation is proved possible using the ACMA method. Simulation results are presented to illustrate the proposed method's performances under different conditions