Genetic algorithm assisted DS-UWB BPSK MMSE receiver

Doshi, Twinkle V.; Dalal, Upena D.

doi:10.1016/j.ijleo.2015.10.149

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…The inertia weight will gradually decrease with the iteration. Then initialize the particle swarm: Randomly initialize the position of the particle swarm within the corresponding range: ( , , , , ) i X k n l lr b   k represents the size of convolution kernel in TCN, with the range of [2,8]  n represents the number of convolution kernels in TCN network, with the range of [10,200]  l represents the number of layers in TCN network, with the range of [2,6]  lr represents the learning rate, with the value range of [0.001, 0.01]  b represents the batch processing size, with a value range of [64,256] Randomly initialize velocity of particles in each dimension. And the velocity of particles in a certain dimension is always limited to 20% of the range of the dimension Then update the particle swarm: In each iteration, the network determined by the position of each particle is trained.…”

Section: Network Parameter Selection Based On Particle Swarm Optimiza...mentioning

confidence: 99%

A NLOS Identification Algorithm of UWB Signal via Temporal Convolutional Neural Network and Attention Mechanism

Wang

Tan

2022

Preprint

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The accurate identification of Non-line of Sight (NLOS) propagation is an important premise to ensure the positioning accuracy in UWB indoor positioning system. In this paper, a network which takes the channel impulse response (CIR) as the input and combines the temporal convolutional network (TCN) and attention mechanism is proposed to identify the NLOS propagation. Based on the framework, particle swarm optimization (PSO) algorithm is used to select the key parameters of the network to achieve higher accuracy and faster process speed. Experiments on the open source dataset show that the identification accuracy of the network reaches 89.80%, which is better than the existing mainstream long short-term memory neural network. The proposed network is further improved by using six extra characteristics and its validation accuracy can reach 91.08% with a relatively high convergence rate. Also, the accuracy and computational amount of the network can be balanced by adjustment of CIR length according to the needs in practical application, indicating that the network has a good application prospect.

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