Separation of Radar Co-Frequency Signal Based on Improved Crow Search Algorithm

Yihan Chen; Yian Liu; Hailing Song

doi:10.3788/LOP221062

Journals >Laser & Optoelectronics Progress >Volume 60 >Issue 12 >Page 1228006 > Article

Laser & Optoelectronics Progress
Vol. 60, Issue 12, 1228006 (2023)

Separation of Radar Co-Frequency Signal Based on Improved Crow Search Algorithm

Yihan Chen^1,**, Yian Liu^1,*, and Hailing Song²

Author Affiliations

¹School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, Jiangsu, China

²Naval Research Institute, Beijing 100161, China

show less

DOI: 10.3788/LOP221062 Cite this Article Set citation alerts

Yihan Chen, Yian Liu, Hailing Song. Separation of Radar Co-Frequency Signal Based on Improved Crow Search Algorithm[J]. Laser & Optoelectronics Progress, 2023, 60(12): 1228006 Copy Citation Text

show less

Fig. 1. ICA principle

Download full size

Fig. 2. Dynamic AP

Download full size

Fig. 3. GSACSA-ICA flow chat

Download full size

Fig. 4. Convergence curves of text function. (a) Convergence curve of

f_{1}

; (b) convergence curve of

f_{2}

; (c) convergence curve of

f_{3}

; (d) convergence curve of

f_{4}

; (e) convergence curve of

f_{5}

; (f) convergence curve of

f_{6}

Download full size

Fig. 5. Radar signal waveforms. (a) Target echo signal; (b) co-frequency interference signal 1; (c) co-frequency interference signal 2

Download full size

Fig. 6. Radar signal frequency spectra. (a) Target echo signal; (b) co-frequency interference signal 1; (c) co-frequency interference signal 2

Download full size

Fig. 7. Waveform and frequency spectrum of observed signal

Download full size

Fig. 8. Function fitness value change curve

Download full size

Fig. 9. Signal output waveforms after separation. (a) Separate signal 1; (b separate signal 2; (c) separate signal 3

Download full size

Fig. 10. Signal output frequency spectra after separation. (a) Separate signal 1; (b) separate signal 2; (c) separate signal 3

Download full size

Fig. 11. Signal matching filtering effect

Download full size

Function name	Function expression	Dimension	Search space
Sphere	$f_{1} (x) = \sum_{i = 1}^{n} {x_{i}}^{2}$	30	$[- 100,100]$
Schwefel 2.22	$f_{2} (x) = \sum_{i = 1}^{n} \|x_{i}\| + \overset{n}{\prod_{i = 1}} \|x_{i}\|$	30	$[- 10,10]$
Quartic	$f_{3} (x) = \sum_{i = 1}^{n} i x_{i}^{4} + r a n d o m [0,1]$	30	$[- 1.28,1.28]$
Ackley	$f_{4} (x) = - 20 e x p (- 0.2 \sqrt[]{\frac{1}{n} \sum_{i = 1}^{n} x_{i}^{2}}) - e x p [\frac{1}{n} \sum_{i = 1}^{n} c o s (2 π x_{i})] + 20 + e$	30	$[- 32,32]$
Rastrigin	$f_{5} (x) = \sum_{i = 1}^{n} [{x_{i}}^{2} - 10 c o s (2 π x_{i}) + 10]$	30	$[- 5.12,5.12]$
Griewank	$f_{6} (x) = \frac{1}{4000} \sum_{i = 1}^{n} x_{i} - \overset{n}{\prod_{i = 1}} c o s (\frac{x_{i}}{\sqrt[]{i}}) + 1$	30	$[- 600,600]$

Table 1. Benchmark test function

Function	Algorithm	Optimal value	Worst value	Average value	Standard deviation
$f_{1}$	PSO	$1.0252 \times 10^{3}$	$2.8385 \times 10^{3}$	$2.0986 \times 10^{3}$	735.8144
	Gold-SA	$9.2358 \times 10^{- 304}$	$3.1718 \times 10^{- 215}$	$3.1718 \times 10^{- 214}$	0
	CSA	$2.1247 \times 10^{- 7}$	0.0112	0.0023	0.0041
	ICSA	$5.4221 \times 10^{- 70}$	$2.9862 \times 10^{- 41}$	$2.9864 \times 10^{- 42}$	$9.4430 \times 10^{- 42}$
	GSACSA	0	0	0	0
$f_{2}$	PSO	18.0383	31.1995	24.6222	4.4906
	Gold-SA	$8.7327 \times 10^{- 150}$	$2.0531 \times 10^{- 114}$	$1.0522 \times 10^{- 113}$	$4.3283 \times 10^{- 114}$
	CSA	0.0014	0.0383	0.0170	0.0149
	ICSA	$7.0486 \times 10^{- 34}$	$2.2356 \times 10^{- 24}$	$2.5050 \times 10^{- 25}$	$6.9985 \times 10^{- 25}$
	GSACSA	$2.0984 \times 10^{- 280}$	$3.8382 \times 10^{- 211}$	$3.8382 \times 10^{- 212}$	0
$f_{3}$	PSO	0.3081	1.9605	0.7914	0.5318
	Gold-SA	$1.5730 \times 10^{- 4}$	0.0016	0.0048	0.0017
	CSA	$9.3007 \times 10^{- 5}$	0.0022	$9.4556 \times 10^{- 4}$	$6.5185 \times 10^{- 4}$
	ICSA	$6.5843 \times 10^{- 5}$	$7.4332 \times 10^{- 4}$	$3.0638 \times 10^{- 4}$	$2.3329 \times 10^{- 4}$
	GSACSA	$7.6225 \times 10^{- 6}$	$4.9216 \times 10^{- 4}$	$1.9099 \times 10^{- 4}$	$1.4093 \times 10^{- 4}$
$f_{4}$	PSO	10.2037	14.2087	12.1394	1.2326
	Gold-SA	$8.8818 \times 10^{- 16}$	$1.5987 \times 10^{- 15}$	$4.4409 \times 10^{- 15}$	$1.4980 \times 10^{- 15}$
	CSA	$1.8075 \times 10^{- 5}$	0.0239	0.0097	0.0087
	ICSA	$8.8818 \times 10^{- 16}$	$4.4409 \times 10^{- 15}$	$1.2434 \times 10^{- 15}$	$1.1235 \times 10^{- 15}$
	GSACSA	$8.8818 \times 10^{- 16}$	$8.8818 \times 10^{- 16}$	$8.8818 \times 10^{- 16}$	0
$f_{5}$	PSO	104.4275	164.2278	132.3667	19.0805
	Gold-SA	0	0	0	0
	CSA	$6.2361 \times 10^{- 7}$	0.0063	0.0017	0.0023
	ICSA	0	0	0	0
	GSACSA	0	0	0	0
$f_{6}$	PSO	18.6614	42.1057	28.4701	6.4986
	Gold-SA	0	0	0	0
	CSA	$8.6892 \times 10^{- 8}$	0.1290	0.0217	0.0405
	ICSA	0	0	0	0
	GSACSA	0	0	0	0

Table 2. Simulation results of different algorithms for six functions

Method	Separation degree	Performance index	Number of iterations
FastICA	0.9327	0.1238	53
IPSO-ICA	0.9519	0.0875	34
IWOA-ICA	0.9556	0.0822	29
CSA-ICA	0.9482	0.0961	37
GSACSA-ICA	0.9628	0.0748	22

Table 3. Separation degree and performance index of signals separated by each method

Yihan Chen, Yian Liu, Hailing Song. Separation of Radar Co-Frequency Signal Based on Improved Crow Search Algorithm[J]. Laser & Optoelectronics Progress, 2023, 60(12): 1228006

Download Citation

Set citation alerts for the article

Tools

Set citation alerts for the article

Save the article for my favorites

Paper Information

微信扫一扫：分享

微信扫一扫：分享