Demodulation of Light Sensing Overlapping Spectral Signal by Improved Particle Swarm Optimization Algorithm

Yong Chen; Yanan Cheng; Huanlin Liu

doi:10.3788/CJL201845.0710003

Chinese Journal of Lasers, Volume. 45, Issue 7, 0710003(2018)

Demodulation of Light Sensing Overlapping Spectral Signal by Improved Particle Swarm Optimization Algorithm

Yong Chen^1、*, Yanan Cheng¹, and Huanlin Liu²

¹ Key Laboratory of Industrial Internet of Things & Network Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;

² Key Laboratory of Optical Fiber Communication Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

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Abstract Get PDF(in Chinese)

Figures & Tables(22)

Fig. 1. Schematic of large capacity FBG sensing system

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Fig. 2. Flow chart of demodulating overlapping spectra using DPSO algorithm

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Fig. 3. Schematic of sensing system of FBG spectral shape multiplexing

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Fig. 4. FBG temperature sensing system

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Fig. 5. Two FBG signal-to-noise signal diagram at different temperatures

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Fig. 6. Two FBG de-noising signal diagram at different temperatures

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Fig. 7. Particle motion graphs when the number of iterations is (a) 5, (b) 10, (c) 15, (d) 20, (e) 25, (f) 30 in DPSO algorithm

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Fig. 8. Average time of seven algorithms runing 100 times at different temperatures

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Fig. 9. Run-time of seven algorithms runing 100 times at 60 ℃

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Fig. 10. Mean error of central wavelength of FBG11 and FBG12

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Fig. 11. Three FBG signal-to-noise signal diagram at different temperatures

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Fig. 12. Three FBG de-noising signal diagram at different temperatures

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Fig. 13. Average time of seven algorithms runin 100 times at different temperatures

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Fig. 14. Mean error of central wavelength of (a) FBG21, (b) FBG22 and (c) FBG23

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Fig. 15. Central wavelength fitting diagram of FBG21

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Table 1. Parameter setting of DPSO algorithm
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Table 1. Parameter setting of DPSO algorithm
Parameter N T w_max w_min
Value 30 100 0.9 0.4

Table 2. Average time of seven algorithms runing 100 times at different temperatures

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Table 2. Average time of seven algorithms runing 100 times at different temperatures

Temperature /℃	Average time /s
Temperature /℃	BPSOalgorithm	IPSOalgorithm	GPSOalgorithm	MPSOalgorithm	I-PSOalgorithm	GS-PSOalgorithm	DPSOalgorithm
35	14.6786	4.9726	4.5585	4.6775	4.5597	4.8182	4.5042
40	14.5516	5.0568	4.5699	4.7037	4.5793	4.7900	4.4900
45	14.7227	5.1258	4.5406	4.7963	4.6503	4.8031	4.5296
50	13.5309	5.1115	4.5958	4.7387	4.5546	4.7858	4.5347
55	13.2217	4.9579	4.5261	4.7910	4.5663	4.7661	4.5203
60	13.7655	5.0931	4.5889	4.6754	4.6091	4.7706	4.4764
65	14.7105	4.9948	4.6055	4.7256	4.5859	4.7767	4.5489

Table 3. Average fitness value of seven algorithms at different temperatures

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Table 3. Average fitness value of seven algorithms at different temperatures

Temperature /℃	Average fitness value
Temperature /℃	BPSOalgorithm	IPSOalgorithm	GPSOalgorithm	MPSOalgorithm	I-PSOalgorithm	GS-PSOalgorithm	DPSOalgorithm
35	0.66026	0.13412	0.00622	0.01324	0.00261	0.00634	0.00014
40	0.59764	0.02150	0.00578	0.01532	0.00554	0.00433	0.00015
45	0.52226	0.03873	0.00615	0.00970	0.00513	0.00341	0.00083
50	0.33318	0.00899	0.00532	0.01884	0.00790	0.00242	0.00018
55	0.07022	0.02352	0.00926	0.00908	0.00999	0.00281	0.00038
60	0.00845	0.00102	0.00427	0.00364	0.00304	0.00215	0.00075
65	0.09333	0.11302	0.00103	0.00136	0.00543	0.00230	0.00029

Table 4. Mean error of central wavelength of FBG11 and FBG12 of seven algorithms at different temperatures

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Table 4. Mean error of central wavelength of FBG11 and FBG12 of seven algorithms at different temperatures

Temperature /℃	FBG	Mean error /pm
Temperature /℃	FBG	BPSOalgorithm	IPSOalgorithm	GPSOalgorithm	MPSOalgorithm	I-PSOalgorithm	GS-PSOalgorithm	DPSOalgorithm
35	FBG₁₁	22.57272	6.32606	4.83910	2.40530	2.11034	3.33901	0.46600
	FBG₁₂	20.68532	7.57752	1.82794	1.82682	1.06209	1.49709	0.22706
40	FBG₁₁	29.05570	3.31489	4.36729	2.26857	2.97235	1.97205	0.33901
	FBG₁₂	18.04285	3.23710	2.47584	1.69534	1.34976	1.66905	0.27001
45	FBG₁₁	33.02502	2.60064	4.37743	2.44212	2.31071	2.69559	0.29102
	FBG₁₂	13.89648	3.23602	1.66914	2.33777	1.80997	1.08977	0.21798
50	FBG₁₁	46.23337	1.93749	5.74438	2.73671	3.78118	2.13112	0.63405
	FBG₁₂	21.18343	1.87464	2.28360	2.07933	1.74745	0.99112	0.18101
55	FBG₁₁	38.11705	33.86866	3.97333	3.17216	3.33848	1.97924	0.37621
	FBG₁₂	19.71244	18.66961	1.13299	1.96425	2.20601	1.27305	0.34102
60	FBG₁₁	52.30492	37.92883	30.23874	45.75969	31.88482	31.40478	0.68021
	FBG₁₂	30.04594	22.18308	18.46024	27.08714	19.05338	18.63080	0.84891
65	FBG₁₁	0.23620	2.21232	7.02901	3.75874	7.98747	7.77933	0.12090
	FBG₁₂	0.14371	2.20489	4.38922	2.45340	4.93424	4.16690	0.41114

Table 5. Mean error of central wavelength of FBG21, FBG22 and FBG23 of seven algorithms at different temperatures

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Table 5. Mean error of central wavelength of FBG21, FBG22 and FBG23 of seven algorithms at different temperatures

Temperature /℃	FBG	Mean error /pm
Temperature /℃	FBG	BPSOalgorithm	IPSOalgorithm	GPSOalgorithm	MPSOalgorithm	I-PSOalgorithm	GS-PSOalgorithm	DPSOalgorithm
	FBG₂₁	8.38650	9.10530	2.72147	9.57363	6.79582	6.56160	0.96455
45	FBG₂₂	11.58824	12.53189	9.64521	1.63202	6.42892	6.99971	0.71972
	FBG₂₃	11.20920	11.09653	6.11469	3.47095	5.44031	5.86538	0.69962
	FBG₂₁	10.68530	7.21927	12.92242	5.36296	7.56493	4.78567	0.26188
50	FBG₂₂	14.47487	16.38932	13.15051	11.07756	5.99276	7.89094	0.45558
	FBG₂₃	14.21533	15.26720	13.11181	8.80817	5.61857	7.87242	0.87062
	FBG₂₁	16.82183	14.04566	8.60896	11.21754	6.71527	5.26786	0.17213
55	FBG₂₂	34.27955	14.34737	11.06934	6.96918	3.95016	4.52220	0.47508
	FBG₂₃	10.39403	12.44230	9.11939	8.65444	4.70099	5.02434	0.83007
	FBG₂₁	8.03439	11.29509	8.45115	8.56031	5.35927	7.38787	0.76288
60	FBG₂₂	28.72941	23.45011	9.54285	7.10752	7.64219	4.63546	0.38900
	FBG₂₃	28.43478	16.60299	8.31982	5.55027	4.46734	4.69984	0.58287
	FBG₂₁	12.20465	14.26785	7.57924	9.91237	9.05138	6.26613	0.31380
65	FBG₂₂	22.17935	19.65278	7.84885	8.51009	4.25926	8.17510	0.52530
	FBG₂₃	10.21387	13.44579	8.33526	9.10327	3.33736	7.69325	0.67328
	FBG₂₁	9.02049	12.18091	8.49254	8.51531	6.79582	6.19848	0.41727
70	FBG₂₂	11.53099	6.37280	6.62709	7.36652	7.84856	6.99971	0.62785
	FBG₂₃	19.78399	5.85381	8.07413	9.51944	5.00661	5.86538	0.87348
	FBG₂₁	7.46752	3.52821	0.62172	5.31275	6.71527	3.10960	0.34889
75	FBG₂₂	6.44565	8.05850	6.84367	0.35084	3.74282	4.52220	0.51788
	FBG₂₃	13.66537	9.78516	7.31501	1.09412	7.68816	5.02434	0.72392

Table 6. Average time of seven algorithms runing 100 times at different temperatures

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Table 6. Average time of seven algorithms runing 100 times at different temperatures

Temperature /℃	Average run-time /s
Temperature /℃	BPSOalgorithm	IPSOalgorithm	GPSOalgorithm	MPSOalgorithm	I-PSOalgorithm	GS-PSOalgorithm	DPSOalgorithm
45	23.2612	7.0915	6.5037	6.3583	6.4666	6.4696	6.3217
50	23.6881	7.0439	6.5037	6.3871	6.4822	6.5786	6.2668
55	23.2014	7.0035	6.5582	6.3583	6.5219	6.5300	6.4370
60	23.5454	6.9705	6.4848	6.4181	6.4314	6.5097	6.2667
65	23.2283	6.9097	6.5220	6.3584	6.4439	6.5600	6.2499
70	23.1230	6.9522	6.4221	6.3717	6.4666	6.5197	6.1719
75	23.3226	6.9977	6.5158	6.3872	6.5219	6.6509	6.2592

Table 7. Average fitness value of seven algorithms runing 100 times at different temperatures

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Table 7. Average fitness value of seven algorithms runing 100 times at different temperatures

Temperature /℃	Average fitness value
Temperature /℃	BPSOalgorithm	IPSOalgorithm	GPSOalgorithm	MPSOalgorithm	I-PSOalgorithm	GS-PSOalgorithm	DPSOalgorithm
45	0.17330	0.13032	0.02611	0.01497	0.33687	0.20176	0.00421
50	0.36988	0.38277	0.28561	0.17255	0.21914	0.25709	0.00499
55	0.22092	0.25575	0.23362	0.19523	0.16857	0.13419	0.00208
60	0.28744	0.38277	0.09036	0.06344	0.15295	0.08014	0.00268
65	0.24345	0.13032	0.18841	0.02156	0.08820	0.02967	0.00218
70	0.40986	0.13032	0.07079	0.06789	0.05644	0.01267	0.00347
75	0.31010	0.25575	0.02387	0.00000	0.07077	0.11934	0.00350

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Yong Chen, Yanan Cheng, Huanlin Liu. Demodulation of Light Sensing Overlapping Spectral Signal by Improved Particle Swarm Optimization Algorithm[J]. Chinese Journal of Lasers, 2018, 45(7): 0710003

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Paper Information

Category: remote sensing and sensor

Received: Feb. 7, 2018

Accepted: --

Published Online: Sep. 11, 2018

The Author Email: Chen Yong (chenyong@cqupt.edu.cn)

DOI:10.3788/CJL201845.0710003

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Parameter setting of DPSO algorithm

Table 1. Parameter setting of DPSO algorithm

Table 2. Average time of seven algorithms runing 100 times at different temperatures

Table 2. Average time of seven algorithms runing 100 times at different temperatures

Table 3. Average fitness value of seven algorithms at different temperatures

Table 3. Average fitness value of seven algorithms at different temperatures

Table 4. Mean error of central wavelength of FBG11 and FBG12 of seven algorithms at different temperatures

Table 4. Mean error of central wavelength of FBG11 and FBG12 of seven algorithms at different temperatures

Table 5. Mean error of central wavelength of FBG21, FBG22 and FBG23 of seven algorithms at different temperatures

Table 5. Mean error of central wavelength of FBG21, FBG22 and FBG23 of seven algorithms at different temperatures

Table 6. Average time of seven algorithms runing 100 times at different temperatures

Table 6. Average time of seven algorithms runing 100 times at different temperatures

Table 7. Average fitness value of seven algorithms runing 100 times at different temperatures

Table 7. Average fitness value of seven algorithms runing 100 times at different temperatures