An Effective Technique for Enhancing Direction Finding Performance of Virtual Arrays

Li, Wen‐Xing; Mao, Xiaojun; Yu, Weikuan; Yue, Chongyi

doi:10.1155/2014/728463

Cited by 7 publications

(5 citation statements)

References 20 publications

(36 reference statements)

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“…To overcome this problem, T w is used instead which obeys a unitary transformation(T H w T w =I). The unitary prosperity of T w is obtained by using a prewhitening scheme embodied in the term (T H v T v ) (−1/2) to convert the color noise back into a white noise [90,91].…”

Section: Uca-root-musicmentioning

confidence: 99%

Accurate and Robust Localization Techniques for Wireless Sensor Networks

AlHajri,

Goian,

Darweesh

et al. 2018

Preprint

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The concept of wireless sensor network revolves around a group of sensor nodes that utilize the radio signals in order to communicate among each other. These nodes are typically made, of sensors, a memory, a multi-controller, a transceiver, and a power source to supply the energy to these components. There are many factors that restrict the design of wireless sensor network such as the size, cost, and functionality. The field of wireless sensor network, witnessed a remarkable revolution special after the environmental awakening in the last two decades. Its importance emanates from its capability to monitor physical and environmental conditions (such as sound, temperature, and pressure) with minimal power consumption. The principle of passing data cooperatively though a network to a main location played a vital role in the success of the methodology of WSN.This report explores the concept of wireless sensor network and how it is been made viable through the convergence of wireless communications and micro-electro-mechanical systems (MEMS) technology together digital electronics. The report addresses some of the factors that have to be considered when choosing the localization algorithm. It is very important to choose properly since the localization process may involves intensive computational load, based on many different criteria, as well as analysis method. The report also views the advantages and disadvantages of localization techniques. Nevertheless, it investigates the challenges associated with the Wireless Sensor Networks. The report categorizes the algorithms, depending on where the computational effort is carried out, into centralized and distributed algorithms. With minimal computational complexity and signaling overhead, the project aims to develop algorithms that can accurately localize sensor nodes in real-time with low computational requirements, and robustly adapt to channel and network dynamics. The report focuses on three areas in particular: the first is the Received Signal Strength indicator technique, Direction of Arrival technique, and the integration of two algorithms, RSS and DOA, in order to build a hybrid, more robust algorithms.In the Received Signal Strength (RSS), the unknown node location is estimated using trilateration. This report examines the performance of different estimators such as Least Square, Weighted Least Square, and Huber robustness in order to obtain the most robust performance. In the direction of arrival (DOA) method, the estimation is carried out using Multiple Signal Classification (MUSIC), Root-MUSIC, and Estimation of Signal Parameters Via Rotational Invariance Technique (ESPRIT) algorithms. We investigate multiple signal scenarios utilizing various antenna geometries, which includes uniform linear array (ULA) and uniform circular array (UCA). Specific attention is given for multipath scenarios in which signals become spatially correlated (or coherent). This required the use of pre-processing techniques, which include phase mode excitation (PME), spatial smoothing ...

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Section: Uca-root-musicmentioning

confidence: 99%

Accurate and Robust Localization Techniques for Wireless Sensor Networks

AlHajri,

Goian,

Darweesh

et al. 2018

Preprint

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“…Therefore, after forward–backward spatial smoothing [29, 30], we can use the full‐rank covariance matrix

R_{zz}^{fb}

for minimum variance distortionless response (MVDR) beamforming. The corresponding MVDR weight vector, denoted by w MVDR , is

w_{MVDR} = \frac{R_{zz}^{fb - 1} a_{s} false(r, θ, t false)}{a_{s}^{H} false(r, θ, t false) R_{zz}^{fb} a_{s} false(r, θ, t false)}

where

R_{zz}^{fb}

is the forward–backward spatial smoothing data covariance and a s ( θ ) is the sub‐array steering vector of the form

a_{s} false(r, θ, t false) = {[\begin{matrix} 1 & {normale}^{j φ_{0} (r, θ, t)} & \dots & {normale}^{j (P - 1) φ_{0} (r, θ, t)} \end{matrix}]}^{T} .

The eigendecomposition of

R_{zz}^{fb}

R_{zz}^{fb} ≜ E_{s} Λ_{s} E_{s}^{H}

…”

Section: Difference Co‐array Processing‐based Target Range and Anglmentioning

confidence: 99%

Nested array receiver with time‐delayers for joint target range and angle estimation

Wang

Zhu

2016

IET Radar, Sonar & Navigation

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“…However, this is specific to the interference deployment method and power requirements and cannot represent a generalized practical study. The results of the above studies may help suppress super-DOF interference, but there are limitations, such as algorithm complexity and hardware [ 16 , 17 , 18 , 19 ]. Currently, researchers are focusing on the study of multi-DOF anti-jamming performance without considering the characteristics and laws of multi-DOF jamming.…”

Section: Introductionmentioning

confidence: 99%

Distributed Sensitivity and Critical Interference Power Analysis of Multi-Degree-of-Freedom Navigation Interference for Global Navigation Satellite System Array Antennas

Jiang,

Fu,

et al. 2024

Sensors

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Current research on the interference of GNSS (Global Navigation Satellite System) array antennas focuses on the single interference effect and the improvement of interference hardware capability, while the multi-degree-of-freedom (DOF) interference model and mechanism remain to be fully studied. Aiming at this problem, this paper analyzes the preconditions for the definition of anti-jamming degrees of freedom and the characteristics of super-DOF interference through formula derivation and simulation. First, by analyzing the influence of the number of interfering signals on the angular resolution, the prerequisite of the definition of anti-interference degrees of freedom in the airspace is proposed. Second, the definition of anti-interference degrees of freedom is used to calculate the change rule of the critical power of the interference under different numbers of interfering signals. Finally, the influence of super-DOF interference on the array antenna is analyzed. The results show that the prerequisite for the anti-interference freedom of the array antenna is that the distribution interval of the interfering signal is greater than 15°, taking a four-array element uniform circular array antenna as an example. The critical interference power of the array antenna decreases by about 15 dB when the number of interfering signals exceeds the degrees of freedom of the array antenna’s interference immunity, provided that the interference resolution is satisfied. The conclusions of this paper give the critical power change rule of multi-DOF interference and the effect of super-DOF interference, as well as the prerequisites for the setting of interference signals, which can be used, for example, in the deployment of distributed interference sources and the development of anti-jamming algorithms.

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An Effective Technique for Enhancing Direction Finding Performance of Virtual Arrays

Cited by 7 publications

References 20 publications

Accurate and Robust Localization Techniques for Wireless Sensor Networks

Accurate and Robust Localization Techniques for Wireless Sensor Networks

Nested array receiver with time‐delayers for joint target range and angle estimation

Distributed Sensitivity and Critical Interference Power Analysis of Multi-Degree-of-Freedom Navigation Interference for Global Navigation Satellite System Array Antennas

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