Performance Analysis of AOA-Based Localization Using the LS Approach: Explicit Expression of Mean-Squared Error

Son, Byung-Kwon; An, Do-Jin; Lee, Joon-Ho

doi:10.1155/2020/9346142

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…At present, the measurement information for passive localization is mainly acquired from the air domain, frequency domain, and time domain. Direction cosine intersection localization locates targets based on information in the air domain, where target localization is achieved by measuring the angle of arrival (AOA) of the received signal [4][5][6][7]. However, this method has high requirements for measuring the attitude of the moving platform.…”

Section: Introductionmentioning

confidence: 99%

Neighborhood Selection Synchronization Mechanism-Based Moving Source Localization Using UAV Swarm

et al. 2023

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To obtain the accurate time difference of arrival (TDOA) and frequency difference of arrival (FDOA) for passive localization in an unmanned aerial vehicle (UAV) swarm, UAV swarm network synchronization is necessary. However, most of the traditional distributed time synchronization protocols are based on iteration, which hinders efficiency improvement. High communication costs and long convergence times are often required in large-scale UAV swarm networks. This paper presents a neighborhood selection-all selection (NS-AS) synchronization mechanism-based moving source localization method for UAV swarms. First, the NS-AS synchronization mechanism is introduced, to model the UAV swarm network synchronization process. Specifically, the UAV neighbors are first grouped by sector, and the most representative neighbors are selected from each sector for the state update calculation. When the UAV swarm network reaches a fully connected state, the synchronization mechanism is switched to select all neighbors, to improve the convergence speed. Then, the TDOA-FDOA joint localization algorithm is employed to locate the moving radiation source. Through simulation, the effectiveness of the proposed method is verified by the system convergence and localization performance under different parameters. Experimental results show that the synchronization mechanism based on NS-AS effectively improves the convergence speed of the system while ensuring the accuracy of moving radiation source localization.

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Section: Introductionmentioning

confidence: 99%

Neighborhood Selection Synchronization Mechanism-Based Moving Source Localization Using UAV Swarm

et al. 2023

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“…In actual application scenarios, time difference of arrival (TDOA) [2][3][4][5][6], received signal strength (RSS) [7][8][9][10][11], frequency difference of arrival (FDOA) [12], angle of arrival (AOA) [13][14][15][16][17][18][19][20][21][22], and their combined algorithms [23][24][25][26][27][28][29][30][31][32] are common passive localization techniques. However, TDOA and FDOA localization methoda require precise timing and synchronization, which increases the cost and complexity of the implementation.…”

Section: Introductionmentioning

confidence: 99%

“…One of the most commonly used algorithms is the pseudo-linear estimator (PLE), which concentrates the nonlinear term into a noise term and estimates the target position using the least-square (LS) optimization algorithm [13,14]. By calculating the PL equation error, a distance weighted least-squares (DWLS) algorithm can be developed to more precisely measure the target position [15]. Meanwhile, how to reduce the estimation bias in AOA localization becomes an important problem.…”

Section: Introductionmentioning

confidence: 99%

Ant Colony Pheromone Mechanism-Based Passive Localization Using UAV Swarm

et al. 2022

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The problem of passive localization using an unmanned aerial vehicle (UAV) swarm is studied. For multi-UAV localization systems with limited communication and observation range, the challenge is how to obtain accurate target state consistency estimates through local UAV communication. In this paper, an ant colony pheromone mechanism-based passive localization method using a UAV swarm is proposed. Different from traditional distributed fusion localization algorithms, the proposed method makes use of local interactions among individuals to process the observation data with UAVs, which greatly reduces the cost of the system. First, the UAVs that have detected the radiation source target estimate the rough target position based on the pseudo-linear estimation (PLE). Then, the ant colony pheromone mechanism is introduced to further improve localization accuracy. The ant colony pheromone mechanism consists of two stages: pheromone injection and pheromone transmission. In the pheromone injection mechanism, each UAV uses the maximum likelihood (ML) algorithm with the current observed target bearing information to correct the initial target position estimate. Then, the UAV swarm weights and fuses the target position information between individuals based on the pheromone transmission mechanism. Numerical results demonstrate that the accuracy of the proposed method is better than that of traditional localization algorithms and close to the Cramer–Rao lower bound (CRLB) for small measurement noise.

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Detection of apple moldy cores using transmittance spectroscopy combined with supervised classifier comparison and parameter optimization

Tian

2023

IFS

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It is difficult to determine which apples have moldy cores just by looking at the outside of the apple. In the present study, we investigated identifying moldy cores using near-infrared transmittance spectra. First, input spectral features selected by noise adjusted principal component analysis (NAPCA) for back propagation artificial neural network (BP ANN) was used to reduce the dimensions of the original data. Then, four factors and five levels uniform design of the input nodes, training functions, transfer layer functions and output layer functions for NAPCA-BP ANN optimization is proposed. And the original data were input into NAPCA-BP ANN to obtain the recognition accuracy and NAPCA-support vector machine (SVM) was as a comparative recognition model. The results showed that through the uniform design-based NAPCA-BP ANN optimization, the NAPCA method had higher identification accuracy, precision, recall and F1 score, than either full spectrum or principal component analysis. Being assessed by different ratio of model test, functions in the hidden layer and output layer of NAPCA-BP ANN, the proposed method achieved the best accuracy to 98.03%. The accuracy, precision, recall and F1 score based on NAPCA-BP ANN were 3.92%, 2.86%, 2.78% and 2.82% higher than those based on NAPCA-SVM, respectively. This method provides a theoretical basis for the development of on-line monitoring of the internal quality of apples.

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Performance Analysis of AOA-Based Localization Using the LS Approach: Explicit Expression of Mean-Squared Error

Cited by 3 publications

References 20 publications

Neighborhood Selection Synchronization Mechanism-Based Moving Source Localization Using UAV Swarm

Neighborhood Selection Synchronization Mechanism-Based Moving Source Localization Using UAV Swarm

Ant Colony Pheromone Mechanism-Based Passive Localization Using UAV Swarm

Detection of apple moldy cores using transmittance spectroscopy combined with supervised classifier comparison and parameter optimization

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