16 research outputs found
Low-complexity three-dimensional AOA-cross geometric center localization methods via multi-UAV network
The angle of arrival (AOA) is widely used to locate a wireless signal emitter in unmanned aerial vehicle (UAV) localization. Compared with received signal strength (RSS) and time of arrival (TOA), AOA has higher accuracy and is not sensitive to the time synchronization of the distributed sensors. However, there are few works focusing on three-dimensional (3-D) scenarios. Furthermore, although the maximum likelihood estimator (MLE) has a relatively high performance, its computational complexity is ultra-high. Therefore, it is hard to employ it in practical applications. This paper proposed two center of inscribed sphere-based methods for 3-D AOA positioning via multiple UAVs. The first method could estimate the source position and angle measurement noise at the same time by seeking the center of an inscribed sphere, called the CIS. Firstly, every sensor measures two angles, the azimuth angle and the elevation angle. Based on that, two planes are constructed. Then, the estimated values of the source position and the angle noise are achieved by seeking the center and radius of the corresponding inscribed sphere. Deleting the estimation of the radius, the second algorithm, called MSD-LS, is born. It is not able to estimate angle noise but has lower computational complexity. Theoretical analysis and simulation results show that proposed methods could approach the Cramér–Rao lower bound (CRLB) and have lower complexity than the MLE
Majorization-Minimization based Hybrid Localization Method for High Precision Localization in Wireless Sensor Networks
This paper investigates the hybrid source localization problem using the four
radio measurements - time of arrival (TOA), time difference of arrival (TDOA),
received signal strength (RSS) and angle of arrival (AOA). First, after
invoking tractable approximations in the RSS and AOA models, the maximum
likelihood estimation (MLE) problem for the hybrid TOA-TDOA-RSS-AOA data model
is derived. Then, in the MLE, which has the least-squares objective, weights
determined using the range-based characteristics of the four heterogeneous
measurements, are introduced. The resultant weighted least-squares problem
obtained, which is non-smooth and non-convex, is solved using the principle of
the majorization-minimization (MM), leading to an iterative algorithm that has
a guaranteed convergence. The key feature of the proposed method is that it
provides a unified framework where localization using any possible merger out
of these four measurements can be implemented as per the
requirement/application. Extensive numerical simulations are conducted to study
the estimation efficiency of the proposed method. The proposed method employing
all four measurements is compared against a conventionally used method and also
against the proposed method employing only limited combinations of the four
measurements. The results obtained indicate that the hybrid localization model
improves the localization accuracy compared to the heterogeneous measurements.
The integration of different measurements also yields good accuracy in the
presence of non-line of sight (NLOS) errors
Efficient underwater acoustical localization method based on time difference and bearing measurements
This article addresses the underwater acoustical localization problem by using the time-difference-of-arrival (TDOA) and bearing-angle-of-arrival (BAOA) measurements. For the underwater acoustic equipment, such as the ultrashort baseline system (USBL), whose bearing measurements are different from the traditional angle-of-arrival (AOA) model, a closed-form solution for the hybrid TDOA/BAOA-based source localization problem is developed. However, the solution suffers from the measurement noise and cannot achieve the Cramer–Rao lower bound (CRLB) performance in the case of large measurement noise. Thus, an iterative constrained weighted least-squares method is presented to further minimize the error in the case of large noise. The CRLB for hybrid TDOA/BAOA source localization is analyzed, and the solution is proved to achieve the CRLB performance. Numerical simulations and field tests demonstrate that the proposed method outperforms the traditional methods in terms of estimation bias and accuracy. It can achieve the CRLB performance better
Two Rapid Power Iterative DOA Estimators for UAV Emitter Using Massive/Ultra-massive Receive Array
To provide rapid direction finding (DF) for unmanned aerial vehicle (UAV)
emitter in future wireless networks, a low-complexity direction of arrival
(DOA) estimation architecture for massive multiple input multiple output (MIMO)
receiver arrays is constructed. In this paper, we propose two strategies to
address the extremely high complexity caused by eigenvalue decomposition of the
received signal covariance matrix. Firstly, a rapid power-iterative rotational
invariance (RPI-RI) method is proposed, which adopts the signal subspace
generated by power iteration to gets the final direction estimation through
rotational invariance between subarrays. RPI-RI makes a significant complexity
reduction at the cost of a substantial performance loss. In order to further
reduce the complexity and provide a good directional measurement result, a
rapid power-iterative Polynomial rooting (RPI-PR) method is proposed, which
utilizes the noise subspace combined with polynomial solution method to get the
optimal direction estimation. In addition, the influence of initial vector
selection on convergence in the power iteration is analyzed, especially when
the initial vector is orthogonal to the incident wave. Simulation results show
that the two proposed methods outperform the conventional DOA estimation
methods in terms of computational complexity. In particular, the RPIPR method
achieves more than two orders of magnitude lower complexity than conventional
methods and achieves performance close to CRLB. Moreover, it is verified that
the initial vector and the relative error have a significant impact on the
performance of the computational complexity
Influence of measured radio map interpolation on indoor positioning algorithms
Indoor positioning and navigation increasingly has become popular and there are many different approaches, using different technologies. In nearly all of the approaches the locational accuracy depends on signal propagation characteristics of the environment. What makes many of these approaches similar is the requirement of creating a signal propagation Radio Map (RM) by analysing the environment. As this is usually done on a regular grid, the collection of Received Signal Strength Indicator (RSSI) data at every Reference Point (RP) of a RM is a time consuming task. With indoor positioning being in the focus of the research community, the reduction in time required for collection of RMs is very useful as it allows researchers to spend more time with research instead of data collection. In this paper we analyse the options for reducing the time required for the acquisition of RSSI information. We approach this by collecting initial RMs of Wi-Fi signal strength using 5 ESP32 micro controllers working in monitoring mode and placed around our office. We then analyse the influence the approximation of RSSI values in unreachable places has, by using linear interpolation and Gaussian Process Regression (GPR) to find balance between final positioning accuracy, computing complexity, and time requirements for the initial data collection. We conclude that the computational requirements can be significantly lowered, while not affecting the positioning error, by using RM with a single sample per RP generated considering many measurements.- (undefined
Energy-efficient mobile node localization using CVA technology and SAI algorithm
In the evolving landscape of the Internet of Things (IoT), Mobile Wireless Sensor Networks (MWSN) play a pivotal role, particularly in dynamic environments requiring mobile sensing capabilities. A primary challenge in MWSNs is achieving accurate node positioning with minimal energy consumption, as these networks often consist of battery-powered, mobile sensors where energy replenishment is difficult. This paper addresses the critical problem of energy-efficient node localization in MWSNs. We propose a novel positioning approach leveraging Cooperative Virtual Array (CVA) technology, which strategically utilizes the mobility of nodes to enhance positioning accuracy while conservatively using energy resources. The methodology revolves around optimizing the number of transceiver nodes, considering factors such as node moving speed, total energy consumption, and positioning errors. Central to our approach is the Signal Arrival and Interaction (SAI) algorithm, an innovative technique devised for efficient and precise mobile node localization, replacing traditional Time of Arrival (ToA) methods. Our simulations, conducted under various scenarios, demonstrate the significant advantages of the CVA-based positioning algorithm. Results show a marked reduction in energy consumption and robust performance in mobile node scenarios. Key findings include substantial improvements in localization accuracy and energy efficiency, highlighting the potential of our approach in enhancing the operational sustainability of MWSNs. The implications of this research are far-reaching for IoT applications, particularly those involving mobile sensors, such as in smart cities, industrial monitoring, and disaster management. By introducing a novel, energy-efficient positioning method, our work contributes to the advancement of MWSN technology, offering a sustainable solution to the challenge of mobile node localization
A quantum-inspired sensor consolidation measurement approach for cyber-physical systems.
Cyber-Physical System (CPS) devices interconnect to grab data over a common platform from industrial applications. Maintaining immense data and making instant decision analysis by selecting a feasible node to meet latency constraints is challenging. To address this issue, we design a quantum-inspired online node consolidation (QONC) algorithm based on a time-sensitive measurement reinforcement system for making decisions to evaluate the feasible node, ensuring reliable service and deploying the node at the appropriate position for accurate data computation and communication. We design the Angular-based node position analysis method to localize the node through rotation and t-gate to mitigate latency and enhance system performance. We formalize the estimation and selection of the feasible node based on quantum formalization node parameters (node contiguity, node optimal knack rate, node heterogeneity, probability of fusion variance error ratio). We design a fitness function to assess the probability of node fitness before selection. The simulation results convince us that our approach achieves an effective measurement rate of performance index by reducing the average error ratio from 0.17-0.22, increasing the average coverage ratio from 29% to 42%, and the qualitative execution frequency of services. Moreover, the proposed model achieves a 74.3% offloading reduction accuracy and a 70.2% service reliability rate compared to state-of-the-art approaches. Our system is scalable and efficient under numerous simulation frameworks
Low complexity DOA estimation for wideband off-grid sources based on re-focused compressive sensing with dynamic dictionary
Under the compressive sensing (CS) framework, a novel focusing based direction of arrival (DOA) estimation method is first proposed for wideband off-grid sources, and by avoiding the application of group sparsity (GS) across frequencies of interest, significant complexity reduction is achieved with its computational complexity close to that of solving a single frequency based direction finding problem. To further improve the performance by alleviating both the off-grid approximation errors and the focusing errors which are even worse for the off-grid case, a dynamic dictionary based re-focused off-grid DOA estimation method is developed with the number of extremely sparse grids involved in estimation refined to the number of detected sources, and thus the complexity is still very low due to the limited increased complexity introduced by iterations, while improved performance can be achieved compared with those fixed dictionary based off-grid methods