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Fog backscattering interference suppression algorithm for FMCW laser fuze based on normalized frequency spectrum threshold
Optik 131 (2017) 188–193
Contents lists available at ScienceDirect
Optik journal homepage: www.elsevier.de/ijleo
Original research article
Fog backscattering interference suppression algorithm for FMCW laser fuze based on normalized frequency spectrum threshold Wei Zhang, Yinlin Li, Zhonghua Huang ∗ , Chao Ma National Key Laboratory of Mechatronic Engineering and Control, Beijing Institute of Technology, Beijing 100081, China
a r t i c l e
i n f o
Article history: Received 23 September 2016 Accepted 13 November 2016 Keywords: FMCW laser fuze Fog backscattering interference suppression False alarm Spread spectrum Normalized frequency spectrum threshold
a b s t r a c t Fog backscattering can cause false alarm for laser fuze. The characteristics of the target echo signal and the fog echo signal of FMCW laser fuze are comparatively analyzed. Based on the frequency domain feature of beat signal in these two cases, a fog backscattering suppression method based on the normalized frequency spectrum threshold of beat signal is proposed. Experimental results from simulated laboratory environment showed that the algorithm can identify the target and the fog interference with the correct rate of 91%. This algorithm provides a new method to suppress the fog backscattering for the frequency modulated continuous wave laser fuze. © 2016 Elsevier GmbH. All rights reserved.
1. Introduction Due to the unparalleled anti-electromagnetic interference capability, laser fuze has been widely adopted as detonation device in missiles and other smart ammunitions, such as AIM-9X, JAGM, Laser-SDB, etc. However, in obscurant conditions, such as fog, dust and smoke et.al, the laser fuze may suffer from its own problems such as signal noise ratio reduction [1], caused by light diffusion and attenuation. Moreover, the laser backscattering light generated by particulate of fog or cloud when entering the obscurant may be received by the detector and erroneously interpreted as echo signal from a target, resulting in a false alarm [2,3] and dysfunction. Therefore, it is critical to discriminate the obscurant return signal from the target return signal, especially when entering the obscurant cluster, in order to enable the applicability of the laser fuze under the low visibility conditions. Several studies have been conducted on suppression of clutter for laser fuze in obscurant conditions. Jason et al. [4] exploited the change rate of the received signal amplitude in multiple sectors to determine the cloud presence and point of entry/exit. Thomas et al. [5] presented a 3D flash lidar vision system that can selectively remove the 1 st return of the reflected laser pulse to differentiate an actual object (return) from the background noise of the obscurant. Ana Dijuricic et al. [6] detected the target in low visibility environment by calculating the time and amplitude of multiple-pulse echo. Yasser et al. [7] proposed a polarization discrimination technology to improve the signal to noise ratio of the echo signals, so as to separate the real target from the jamming. Ren et al. [8] presented a method of anti-interference based on dual-wavelength laser detection. Zhou et al. [9] put forward an algorithm to determine the true target position in the smoke environment based on cepstrum analysis. However, all of the above studies dealt with the pulse laser system. Compared with the pulse
∗ Corresponding author. E-mail address: [email protected] (Z. Huang). http://dx.doi.org/10.1016/j.ijleo.2016.11.084 0030-4026/© 2016 Elsevier GmbH. All rights reserved.
W. Zhang et al. / Optik 131 (2017) 188–193
189
Fig. 1. Spectrums of beat signals corresponding to the fog echo at a distance of 3m.
Fig. 2. Spectrums of beat signals corresponding to the fog echo at a distance of 3 m with different visibilities(v = 1 m, 3 m, 5 m, 7 m).
laser, the FMCW laser can provide better range resolution and accuracy [3,10]. In addition, the range information in FMCW system is carried in the beat frequency, which can be more robust in harsh visibility environment by using the spectrum domain signal processing methods. Thus, the FMCW technology has attracted the attention in the field. CM von der Lippe and Jony J. Liu [10,11] presented a FMCW laser fuze prototype. The FMCW laser ranging has also been used for vehicle navigation [12,13]. Nevertheless, the interference suppression method for FMCW laser fuze has not been addressed yet. Accordingly, the aim of the study is to investigate the clutter discrimination algorithm based on the normalized threshold of beat frequency spectrum to suppress the backscattering interference of laser fuze when entering the obscurants. The paper is organized as follows: section II analyzes the characteristics of FMCW laser echo signal, section III proposes a clutter discrimination algorithm, the experiment and result are presented in section IV, and section V discusses the tradeoffs of this technique. 2. Characteristics analysis of FMCW laser echo signal The surface of solid target can be regarded as rough solid, which reflects the light immediately after being illuminated by the laser beams. The laser photons are considered to reach the target surface simultaneously and be reflected by the target equidistantly. Different from the solid target, the obscurant is a discrete medium, in the process of the laser transmitting in the obscurant, the photon will collide with the obscurant particles, and thereby changes its directions due to light scattering. The direction of the photon is uncertain after every scattering, ranges from 0◦ to 360◦ [14], and the photon will keep colliding with the particles until it attenuates out of energy or be received by the receiver. Accordingly, the photons received by the detector include not only the single backscattering of the fog particles from the edge but also the multiple scattering of other particles. Compared with the single backscattering signal, the multiple scattering signals have varying delays in reaching the detector, which accounts for beat signals observed in spectrum. According to the literature [15], the spectrum of beat signal corresponding to the target echo at a distance of 3 m is shown in Fig. 1, and the spectrums of beat signals corresponding to the fog echo at a distance of 3 m with different visibilities(1 m, 3 m, 5 m and 7 m)are obtained and exhibited in Fig. 2. In Figs. 1 and 2, Ltarget and Lfog represents the distance between target-the detector, and fog edge-the detector respectively, and v is visibility through fog. It is obviously that the spectrums of beat signals are diffused and spread in fog condition, and the amplitude of beat spectrum corresponding to the backscattering signal from the fog edge became smaller with the increase of visibility, which can be interpreted as the backscattering signal of the fog edge decreases with the increase of visibility, and the laser can travel farther distance.
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W. Zhang et al. / Optik 131 (2017) 188–193
Fig. 3. The ratio of spectrum width with different visibilities(1 m, 3 m,5 m and 7 m) at 3 m, 6 m and 9 m.
Compared with the beat frequency signal corresponding to the target echo, the spectrums of beat signals are diffused and spread. The ratio of spectrum width of beat signals corresponding to the fog echo under different conditions is shown in Fig. 3. The Fig. 3 evidently shows that the spectrum width of beat signals corresponding to the fog echo is 9–13 times of the target echo under the same condition. Moreover, at the same distance, the spectrum width ratio decreased slightly with the increase of visibility, but when the visibility is increased to a certain extent, the ratio tends to be about 9 times, this observation can be rationalized as the fog related signal backscattering decreases with the increase of visibility, and the signals received by the detector are less. In addition, at the same visibility, the spectrum width ratio is inversely proportional to the distance, which can also be explained by the above reason. However, due to the random of scattering, in some cases, the amplitude of a backscattering signal is much larger than the other backscattering signals, which will result in the spread of spectrum is not obviously, and makes it difficult to discriminate the obscurant echo signal from the target return signal only by the ratio of spectrum width of the beat signal. Accordingly, this paper proposed an algorithm based on normalized frequency spectrum threshold to identify the target and fog when the laser fuze entering the obscurants. 3. Clutter discrimination algorithm At present, using the threshold of the time domain signal to suppress the interference in the operation of the laser fuze is common, but eliminating the interference from the obscurants by taking advantage of the frequency domain feature has not been reported. According to the principle of discrete Fourier transform, the beat signal can be transformed as below [16]: ∼
X [k] =
N−1 ∼
x [n] exp −j2kn/N , 0 ≤ k ≤ N − 1
(1)
n=0 ∼
where N is the sampling points, x [n] is the discrete beat signal, and the sampling frequency is Fs . The margin of beat frequency spectrum is affected by the beat signal amplitude, but normalize the frequency spectrum can make this effect indifferent. Hence, the spectrum coefficient can be processed as follow: ∼
[b1 , b2 , . . ., bN ] =
X [k] max
∼
(2)
X [k]
It is obvious that the maximum value of the normalized frequency spectrum is 1. Due to the expansion of the spectrum, the amplitude of the normalized spectrum is increased at other frequencies, which correspond to the different fog scattering signals. The sum of the normalized spectrum can be written as: Ef =
N
bi
(3)
i=1
Because of the same sampling points, the result of the spread of the spectrum is that the sum of the normalized spectrum is increased. Base on the given system parameters in the literature [15], the sums of the normalized frequency spectrum corresponding to target and fog at different distances are shown in Fig. 4. The Fig. 4 shows that the sums of the normalized frequency spectrum corresponding to target and fog are significantly different. The sum of the normalized frequency spectrum correspond to the fog is larger, ranges from 72 to 110, and the other case range is between 46 and 52 at the same situation. Therefore, a suitable threshold value Ef th can be set to distinguish the target and the fog clutter. If Ef < Efth , it can be considered to detect the target, and the system puts out the frequency
W. Zhang et al. / Optik 131 (2017) 188–193
191
Fig. 4. The sum of the normalized spectrum in two different conditions.
Fig. 5. Algorithmic data flow chart.
corresponding to the maximum value of the frequency spectrum, otherwise, it can be considered to be fog clutter, and the system puts out nothing. The flow chart of clutter discrimination algorithm is shown in Fig. 5. However, base on this algorithm, the system can be easy to misjudge the target as the fog interference if the thresholdEfth is chosen too small, and mistake fog clutter for target if the thresholdEfth is too large. In an ideal situation, the maximum value of the sum of the normalized frequency spectrum corresponding to the target or the minimum value of the sum of the normalized frequency spectrum corresponding to the fog can be selected as the threshold, but the simulation cannot exhaust all possible in fact, therefore, the average value can be chosen as the threshold based on the massive simulation data under certain system parameters. In this paper, the value of the threshold is 68. 4. Experiment and result In order to verify the efficiency of the algorithm, an experimental laboratory model was designed and experiments were performed. The set up of experiment is shown in Fig. 6. A plexiglass sealed chamber (2m in length and 30cm in diameter) equipped with spray machine to produce fog under controlled conditions along with camera to monitor the visibility of fog attached to computer was used to conduct the experiment. The detector is placed at adjustable distance on the other side of tube to collect the signal. First, record the beat signals of the target echo at 3m, 4m, 5m, 6m, 7 m and 8m.Then record the beat signals of the fog echo at these same distances after removing the solid target. The sums of the beat signals spectrum at different distances in these two cases are shown in Table 1.
192
W. Zhang et al. / Optik 131 (2017) 188–193
Fig. 6. Experimental setup. Table 1 Sums of the beat signals spectrum at different distances. R
3m
4m
5m
6m
7m
8m
target fog
54.0419 86.9172
48.9066 79.6300
57.2150 91.6115
58.5401 138.4917
66.4787 121.1481
70.7872 153.5996
Fig. 7. Measured distance VS actual distance.
According to the experimental results, the proposed algorithm can identify the target and interfere with the correct rate of 91.7% based on the threshold Efth = 68. The reliability of the experimental system and the authenticity of the experimental data are tested and verified by using t-test method [17].The results of the system measured distance and the actual distance are shown in Fig. 7. The t-test result shows that the value of P is 0.9496, which indicates that there is no significant difference between the experimental data and the actual data. Meanwhile, the value of correlation coefficientris 0.9979, which proves that the experiment is in accordance with the actual situation. 5. Conclusion In this paper, the characteristics of target echo signal and fog echo signal of FMCW laser fuze are comparative analyzed. It is found that multiple scattering will cause laser multipath transmission in fog and thus results in spreading the spectrum of beat signal. Based on this feature, a method based on the normalized frequency spectrum threshold of beat signal is proposed to discriminate the target and the fog clutter. The algorithm proposed in this paper is used for FMCW laser fuze, which is the main difference from the previous researchers. At present, the researches on fog clutter suppression algorithm for FMCW laser fuze are seldom seen, however, the characteristics of FMCW laser fuze make it a broad application prospects, therefore, it is significant to explore the suppression of the fog clutter algorithm for FMCW laser fuze. Although the simulation and test results are obtained under the conditions of typical system parameters, and the threshold is only applicable to the test system in this paper, but the algorithm provides a new method to suppress the fog interference for FMCW laser fuze. References [1] E. Trickey, P. Church, X. Cao, Characterization of the OPAL obscurant penetrating LiDAR in various degraded visual environments, Degraded Visual Environments: Enhanced, Synthetic, and External Vision Solutions (2013) 87370E. [2] V.K. Arora, Proximity Fuzes Theory and Technology, Defence Science Centre, 2010, pp. 189–205. [3] M. Pfennigbauer, C. Wolf, J. Weinkopf, A. Ullrich, Online waveform processing for demanding target situations, Proc. SPIE 9080 (2014), pp.90800J-90800J-10. [4] Jason Cowell, Weapon Systems and Technology Conference Fuzing Validation, 2012. [5] E. Thomas Laux, Chao-I. Chen, 3D flash LIDAR vision systems for imaging in degraded visual environments, Degraded Visual Environments: Enhanced, Synthetic, and External Vision Solutions 2014, Proc. of SPIE Vol. 9087 (2014) 908704.
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[6] A. Djuricic, B. Jutzi, Supporting UAVs in low visibility conditions by multiple-pulse laser scanning devices. High-resolution earth imaging for geospatial information, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 1 (2013) W1. [7] Y.Y. Hassebo, B. Gross, M. Oo, et al., Polarization-discrimination technique to maximize the lidar signal-to-noise ratio for daylight operations, Appl. Opt. 45 (22) (2006) 5521–5531. [8] Ren Hongguang, Yu Haishan, Huo Lijun, et al., Anti-interference of dual-wavelength laser fuze, J. Detect. Control 37 (2015) 1. [9] Zhou Yigang, Wang Yong, Anti-interference algorithm of clouds and smog for pulse laser range measurement based on cepstrum analysis, Infrared Laser Eng. 40 (2011) 9. [10] C.M. von der Lippe, J.J. Liu, Advanced optical fuzing technology, Proceedings of SPIE, the International Society for Optical Engineering, Society of Photo-Optical Instrumentation Engineers (2005), 58710I.1–58710I.8. [11] J. Jony Liu, C.M.V.D. Lippe, Advanced high-bandwidth optical fuzing technology, Proceedings of SPIE − The International Society for Optical Engineering (2005), 58710I.1–58710I.8. [12] Diego Pierrottet, Farzin Amzajerdian, Larry Petway, et al., Linear FMCW laser radar for precision range and vector velocity measurements, MRS Proc. 1076 (2008) (1076-K04-06). [13] https://www.thalesgroup.com/en/squire-ground-surveillance-radar. [14] C.F. Bohren, D.R. Huffman, Absorption and Scattering of Light by Small Particles, Wiley, New York, 1983. [15] W. Zhang, Y. Li, Z. Huang, Research on the characteristics of fog backscattering signals for frequency modulated continuous wave laser fuze, Optik—Int. J. Light Electron Optics 127 (20) (2016) 9046–9055. [16] W. Weisstein Eric, Discrete Fourier Transform, From MathWorld—A Wolfram Web, 2016 Resource. http://mathworld.wolfram.com/DiscreteFourierTransform.html. [17] Donald W. Zimmerman, A note on Interpretation of the paired-samples t-test, J. Educ. Behav. Stat. 22 (3) (1977) 349–360.
Contents lists available at ScienceDirect
Optik journal homepage: www.elsevier.de/ijleo
Original research article
Fog backscattering interference suppression algorithm for FMCW laser fuze based on normalized frequency spectrum threshold Wei Zhang, Yinlin Li, Zhonghua Huang ∗ , Chao Ma National Key Laboratory of Mechatronic Engineering and Control, Beijing Institute of Technology, Beijing 100081, China
a r t i c l e
i n f o
Article history: Received 23 September 2016 Accepted 13 November 2016 Keywords: FMCW laser fuze Fog backscattering interference suppression False alarm Spread spectrum Normalized frequency spectrum threshold
a b s t r a c t Fog backscattering can cause false alarm for laser fuze. The characteristics of the target echo signal and the fog echo signal of FMCW laser fuze are comparatively analyzed. Based on the frequency domain feature of beat signal in these two cases, a fog backscattering suppression method based on the normalized frequency spectrum threshold of beat signal is proposed. Experimental results from simulated laboratory environment showed that the algorithm can identify the target and the fog interference with the correct rate of 91%. This algorithm provides a new method to suppress the fog backscattering for the frequency modulated continuous wave laser fuze. © 2016 Elsevier GmbH. All rights reserved.
1. Introduction Due to the unparalleled anti-electromagnetic interference capability, laser fuze has been widely adopted as detonation device in missiles and other smart ammunitions, such as AIM-9X, JAGM, Laser-SDB, etc. However, in obscurant conditions, such as fog, dust and smoke et.al, the laser fuze may suffer from its own problems such as signal noise ratio reduction [1], caused by light diffusion and attenuation. Moreover, the laser backscattering light generated by particulate of fog or cloud when entering the obscurant may be received by the detector and erroneously interpreted as echo signal from a target, resulting in a false alarm [2,3] and dysfunction. Therefore, it is critical to discriminate the obscurant return signal from the target return signal, especially when entering the obscurant cluster, in order to enable the applicability of the laser fuze under the low visibility conditions. Several studies have been conducted on suppression of clutter for laser fuze in obscurant conditions. Jason et al. [4] exploited the change rate of the received signal amplitude in multiple sectors to determine the cloud presence and point of entry/exit. Thomas et al. [5] presented a 3D flash lidar vision system that can selectively remove the 1 st return of the reflected laser pulse to differentiate an actual object (return) from the background noise of the obscurant. Ana Dijuricic et al. [6] detected the target in low visibility environment by calculating the time and amplitude of multiple-pulse echo. Yasser et al. [7] proposed a polarization discrimination technology to improve the signal to noise ratio of the echo signals, so as to separate the real target from the jamming. Ren et al. [8] presented a method of anti-interference based on dual-wavelength laser detection. Zhou et al. [9] put forward an algorithm to determine the true target position in the smoke environment based on cepstrum analysis. However, all of the above studies dealt with the pulse laser system. Compared with the pulse
∗ Corresponding author. E-mail address: [email protected] (Z. Huang). http://dx.doi.org/10.1016/j.ijleo.2016.11.084 0030-4026/© 2016 Elsevier GmbH. All rights reserved.
W. Zhang et al. / Optik 131 (2017) 188–193
189
Fig. 1. Spectrums of beat signals corresponding to the fog echo at a distance of 3m.
Fig. 2. Spectrums of beat signals corresponding to the fog echo at a distance of 3 m with different visibilities(v = 1 m, 3 m, 5 m, 7 m).
laser, the FMCW laser can provide better range resolution and accuracy [3,10]. In addition, the range information in FMCW system is carried in the beat frequency, which can be more robust in harsh visibility environment by using the spectrum domain signal processing methods. Thus, the FMCW technology has attracted the attention in the field. CM von der Lippe and Jony J. Liu [10,11] presented a FMCW laser fuze prototype. The FMCW laser ranging has also been used for vehicle navigation [12,13]. Nevertheless, the interference suppression method for FMCW laser fuze has not been addressed yet. Accordingly, the aim of the study is to investigate the clutter discrimination algorithm based on the normalized threshold of beat frequency spectrum to suppress the backscattering interference of laser fuze when entering the obscurants. The paper is organized as follows: section II analyzes the characteristics of FMCW laser echo signal, section III proposes a clutter discrimination algorithm, the experiment and result are presented in section IV, and section V discusses the tradeoffs of this technique. 2. Characteristics analysis of FMCW laser echo signal The surface of solid target can be regarded as rough solid, which reflects the light immediately after being illuminated by the laser beams. The laser photons are considered to reach the target surface simultaneously and be reflected by the target equidistantly. Different from the solid target, the obscurant is a discrete medium, in the process of the laser transmitting in the obscurant, the photon will collide with the obscurant particles, and thereby changes its directions due to light scattering. The direction of the photon is uncertain after every scattering, ranges from 0◦ to 360◦ [14], and the photon will keep colliding with the particles until it attenuates out of energy or be received by the receiver. Accordingly, the photons received by the detector include not only the single backscattering of the fog particles from the edge but also the multiple scattering of other particles. Compared with the single backscattering signal, the multiple scattering signals have varying delays in reaching the detector, which accounts for beat signals observed in spectrum. According to the literature [15], the spectrum of beat signal corresponding to the target echo at a distance of 3 m is shown in Fig. 1, and the spectrums of beat signals corresponding to the fog echo at a distance of 3 m with different visibilities(1 m, 3 m, 5 m and 7 m)are obtained and exhibited in Fig. 2. In Figs. 1 and 2, Ltarget and Lfog represents the distance between target-the detector, and fog edge-the detector respectively, and v is visibility through fog. It is obviously that the spectrums of beat signals are diffused and spread in fog condition, and the amplitude of beat spectrum corresponding to the backscattering signal from the fog edge became smaller with the increase of visibility, which can be interpreted as the backscattering signal of the fog edge decreases with the increase of visibility, and the laser can travel farther distance.
190
W. Zhang et al. / Optik 131 (2017) 188–193
Fig. 3. The ratio of spectrum width with different visibilities(1 m, 3 m,5 m and 7 m) at 3 m, 6 m and 9 m.
Compared with the beat frequency signal corresponding to the target echo, the spectrums of beat signals are diffused and spread. The ratio of spectrum width of beat signals corresponding to the fog echo under different conditions is shown in Fig. 3. The Fig. 3 evidently shows that the spectrum width of beat signals corresponding to the fog echo is 9–13 times of the target echo under the same condition. Moreover, at the same distance, the spectrum width ratio decreased slightly with the increase of visibility, but when the visibility is increased to a certain extent, the ratio tends to be about 9 times, this observation can be rationalized as the fog related signal backscattering decreases with the increase of visibility, and the signals received by the detector are less. In addition, at the same visibility, the spectrum width ratio is inversely proportional to the distance, which can also be explained by the above reason. However, due to the random of scattering, in some cases, the amplitude of a backscattering signal is much larger than the other backscattering signals, which will result in the spread of spectrum is not obviously, and makes it difficult to discriminate the obscurant echo signal from the target return signal only by the ratio of spectrum width of the beat signal. Accordingly, this paper proposed an algorithm based on normalized frequency spectrum threshold to identify the target and fog when the laser fuze entering the obscurants. 3. Clutter discrimination algorithm At present, using the threshold of the time domain signal to suppress the interference in the operation of the laser fuze is common, but eliminating the interference from the obscurants by taking advantage of the frequency domain feature has not been reported. According to the principle of discrete Fourier transform, the beat signal can be transformed as below [16]: ∼
X [k] =
N−1 ∼
x [n] exp −j2kn/N , 0 ≤ k ≤ N − 1
(1)
n=0 ∼
where N is the sampling points, x [n] is the discrete beat signal, and the sampling frequency is Fs . The margin of beat frequency spectrum is affected by the beat signal amplitude, but normalize the frequency spectrum can make this effect indifferent. Hence, the spectrum coefficient can be processed as follow: ∼
[b1 , b2 , . . ., bN ] =
X [k] max
∼
(2)
X [k]
It is obvious that the maximum value of the normalized frequency spectrum is 1. Due to the expansion of the spectrum, the amplitude of the normalized spectrum is increased at other frequencies, which correspond to the different fog scattering signals. The sum of the normalized spectrum can be written as: Ef =
N
bi
(3)
i=1
Because of the same sampling points, the result of the spread of the spectrum is that the sum of the normalized spectrum is increased. Base on the given system parameters in the literature [15], the sums of the normalized frequency spectrum corresponding to target and fog at different distances are shown in Fig. 4. The Fig. 4 shows that the sums of the normalized frequency spectrum corresponding to target and fog are significantly different. The sum of the normalized frequency spectrum correspond to the fog is larger, ranges from 72 to 110, and the other case range is between 46 and 52 at the same situation. Therefore, a suitable threshold value Ef th can be set to distinguish the target and the fog clutter. If Ef < Efth , it can be considered to detect the target, and the system puts out the frequency
W. Zhang et al. / Optik 131 (2017) 188–193
191
Fig. 4. The sum of the normalized spectrum in two different conditions.
Fig. 5. Algorithmic data flow chart.
corresponding to the maximum value of the frequency spectrum, otherwise, it can be considered to be fog clutter, and the system puts out nothing. The flow chart of clutter discrimination algorithm is shown in Fig. 5. However, base on this algorithm, the system can be easy to misjudge the target as the fog interference if the thresholdEfth is chosen too small, and mistake fog clutter for target if the thresholdEfth is too large. In an ideal situation, the maximum value of the sum of the normalized frequency spectrum corresponding to the target or the minimum value of the sum of the normalized frequency spectrum corresponding to the fog can be selected as the threshold, but the simulation cannot exhaust all possible in fact, therefore, the average value can be chosen as the threshold based on the massive simulation data under certain system parameters. In this paper, the value of the threshold is 68. 4. Experiment and result In order to verify the efficiency of the algorithm, an experimental laboratory model was designed and experiments were performed. The set up of experiment is shown in Fig. 6. A plexiglass sealed chamber (2m in length and 30cm in diameter) equipped with spray machine to produce fog under controlled conditions along with camera to monitor the visibility of fog attached to computer was used to conduct the experiment. The detector is placed at adjustable distance on the other side of tube to collect the signal. First, record the beat signals of the target echo at 3m, 4m, 5m, 6m, 7 m and 8m.Then record the beat signals of the fog echo at these same distances after removing the solid target. The sums of the beat signals spectrum at different distances in these two cases are shown in Table 1.
192
W. Zhang et al. / Optik 131 (2017) 188–193
Fig. 6. Experimental setup. Table 1 Sums of the beat signals spectrum at different distances. R
3m
4m
5m
6m
7m
8m
target fog
54.0419 86.9172
48.9066 79.6300
57.2150 91.6115
58.5401 138.4917
66.4787 121.1481
70.7872 153.5996
Fig. 7. Measured distance VS actual distance.
According to the experimental results, the proposed algorithm can identify the target and interfere with the correct rate of 91.7% based on the threshold Efth = 68. The reliability of the experimental system and the authenticity of the experimental data are tested and verified by using t-test method [17].The results of the system measured distance and the actual distance are shown in Fig. 7. The t-test result shows that the value of P is 0.9496, which indicates that there is no significant difference between the experimental data and the actual data. Meanwhile, the value of correlation coefficientris 0.9979, which proves that the experiment is in accordance with the actual situation. 5. Conclusion In this paper, the characteristics of target echo signal and fog echo signal of FMCW laser fuze are comparative analyzed. It is found that multiple scattering will cause laser multipath transmission in fog and thus results in spreading the spectrum of beat signal. Based on this feature, a method based on the normalized frequency spectrum threshold of beat signal is proposed to discriminate the target and the fog clutter. The algorithm proposed in this paper is used for FMCW laser fuze, which is the main difference from the previous researchers. At present, the researches on fog clutter suppression algorithm for FMCW laser fuze are seldom seen, however, the characteristics of FMCW laser fuze make it a broad application prospects, therefore, it is significant to explore the suppression of the fog clutter algorithm for FMCW laser fuze. Although the simulation and test results are obtained under the conditions of typical system parameters, and the threshold is only applicable to the test system in this paper, but the algorithm provides a new method to suppress the fog interference for FMCW laser fuze. References [1] E. Trickey, P. Church, X. Cao, Characterization of the OPAL obscurant penetrating LiDAR in various degraded visual environments, Degraded Visual Environments: Enhanced, Synthetic, and External Vision Solutions (2013) 87370E. [2] V.K. Arora, Proximity Fuzes Theory and Technology, Defence Science Centre, 2010, pp. 189–205. [3] M. Pfennigbauer, C. Wolf, J. Weinkopf, A. Ullrich, Online waveform processing for demanding target situations, Proc. SPIE 9080 (2014), pp.90800J-90800J-10. [4] Jason Cowell, Weapon Systems and Technology Conference Fuzing Validation, 2012. [5] E. Thomas Laux, Chao-I. Chen, 3D flash LIDAR vision systems for imaging in degraded visual environments, Degraded Visual Environments: Enhanced, Synthetic, and External Vision Solutions 2014, Proc. of SPIE Vol. 9087 (2014) 908704.
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