Stereo Perception Optimization of Line-of-Sight and Non-Line-of-Sight Sensor Networks

Qinglong Wang; Ningning Qin

doi:10.3788/LOP213047

Laser & Optoelectronics Progress, Volume. 60, Issue 6, 0628005(2023)

Stereo Perception Optimization of Line-of-Sight and Non-Line-of-Sight Sensor Networks

Qinglong Wang¹ and Ningning Qin^1,2、*

¹Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, Jiangsu, China

²Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, Jiangsu, China

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Abstract Get PDF(in Chinese)

Figures & Tables(10)

Fig. 1. Node model

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Fig. 2. Perceptual signal occlusion model

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Fig. 3. Effect of perception optimization under combined action of line-of-sight and non-line-of-sight.(a) Random; (b) SCA; (c) GWO; (d) CTDCT

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Fig. 4. Impact of network size on CovRatio

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Fig. 5. Algorithm energy consumption comparison chart

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Fig. 6. Effect of perception optimization under combined action of line-of-sight and non-line-of-sight.

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Fig. 7. Comparison of algorithm coverage performance and mobile energy consumption in multiple scenarios

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Table 0. [in Chinese]

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Table 0. [in Chinese]

$C T D C T (N, D, α^{1} {[j]}_{m a x}, α^{1} {[j]}_{m i n}, t_{m a x}, M)$
1	$α^{1} = S F R M M (N, D, α^{1} {[j]}_{m a x}, α^{1} {[j]}_{m i n})$
2	The sensor randomly generates $M$ and determines the line-of-sight and non-line-of-sight targets，the CovRatio of the sensor is calculated and generated $β^{1}, δ^{1}$ ，and the remaining sensor are denoted as $ω^{1}$
3	For $t = 1, \dots, t_{m a x}$
4	Calculate $ξ_{i j}^{α^{t + 1}}, ξ_{i j}^{β^{t + 1}}, ξ_{i j}^{δ^{t + 1}}$ based on formula（6）-（9）
5	$ξ_{i j}^{ω^{t + 1}} = (ξ_{i j}^{α^{t + 1}} + ξ_{i j}^{β^{t + 1}} + ξ_{i j}^{δ^{t + 1}}) / 3$
6	Line-of-sight and non-line-of-sight targets are defined，and the CovRatio of the sensor is calculated and generated $α^{t + 1}, β^{t + 1}, δ^{t + 1}$ ，and the remaining sensor are denoted as $ω^{t + 1}$
7	End
8	$P = α^{t_{m a x}}$
9	For $η = 1, \dots, η_{m a x}$
10	If $η = 1$ ，then $S e n s o r = P, f (S e n s o r) = f (P)$
11	If $f (S e n s o r) > f (P)$ ，then $P = S e n s o r, f (P) = f (S e n s o r)$
12	$S_{i} \in S e n s o r$ ，calculate $S_{i} = (x_{i (η + 1)}, y_{i (η + 1)}, z_{i (η + 1)}, φ_{i (η + 1)})$ based on formula（10）-（12）
13	End

Table 1. Scene feasible region mapping process
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Table 1. Scene feasible region mapping process
$S F R M M (N, D, α^{1} {[j]}_{m a x}, α^{1} {[j]}_{m i n})$
1 $F o r i = 1, \dots, N, j = 1, \dots, D$
2 $r_{i j} = i e x p (j) - ⌊i e x p (j)⌋$
3 $α^{1} [i] [j] = α^{1} {[j]}_{m i n} + r_{i j} (α^{1} {[j]}_{m a x} - α^{1} {[j]}_{m i n})$
4 $E n d$
5 $R e t u r n$ $α^{1}$

Table 2. Network parameter configuration
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Table 2. Network parameter configuration
Parameter Configuration Parameter Configuration
$d$ 15 $ϑ$ $π / 6$
$R_{c}$ $15$ $d_{M a x M o v e}$ 8
$t_{m a x}$ $100$ $η_{m a x}$ $50$
$M$ $100$ $N$ $140$

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Qinglong Wang, Ningning Qin. Stereo Perception Optimization of Line-of-Sight and Non-Line-of-Sight Sensor Networks[J]. Laser & Optoelectronics Progress, 2023, 60(6): 0628005

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