Skip to main content
Springer Nature Link
Log in

A passive detection algorithm for low-altitude small target based on a wavelet neural network

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

A passive detection algorithm is presented for multiple low-altitude small targets, which employs a wavelet neural network (WNN). The slope, kurtosis, and skewness are employed as the features for low-altitude small target detection, and an algorithm is given to determine the number of targets. A WNN is used to establish a relationship between signal classes and the signal characteristics using training signals. Then, signals are classified as either target present or target not present using the WNN. Indoor data from a research laboratory and outdoor data from a bridge in the Jimo District, Qingdao, were used for training and evaluation. The performance results show that the error rate with the proposed WNN-based algorithm is better than those based on the slope, skewness, and kurtosis of signal. Furthermore, the proposed algorithm is better than those based on other neural networks such as BPNN, RBFNN, SOMNN, and SVM. At a distance of 3 km, the recognition rate is greater than 84%, which is better than other techniques such as visual recognition, acoustic, and active radar.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9: a
Fig. 10: a
Fig. 11
Fig. 12: a
Fig. 13: a

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Balabin RM, Safieva RZ, Lomakina EI (2008) Wavelet neural network (WNN) approach for calibration model building based on gasoline near infrared (NIR) spectra. Chemometr Intell Lab 93(1):58–62

    Article  Google Scholar 

  • Chen YB, Yu JQ, Su XL et al (2015) Path planning for multi-UAV formation. J Intell Robot Syst 77(1):229–246

    Article  Google Scholar 

  • Goldman GH, Culver RL (2014) Tracking unmanned aerial vehicles using a tetrahedral microphone array. J Acoust Soc Am 136(4):2213

    Article  Google Scholar 

  • Hürlimann W (2014) On Mardia skewness and kurtosis of Soules basis matrices in ROM simulation. Linear Algebra Appl 473:284–296

    Article  MathSciNet  Google Scholar 

  • Kanellakis C, Nikolakopoulos G (2017) Survey on computer vision for UAVs: current developments and trends. J Intell Robot Syst 87:141–168

    Article  Google Scholar 

  • Klare J, Biallawons O, Cerutti-Maori D (2016) Detection of UAVs using the MIMO radar MIRA-CLE Ka. In: Proceedings of EUSAR 2016: 11th European conference on synthetic aperture radar, Hamburg, Germany, pp 731–734

  • Koohifar F, Kumbhar A, Guvenc I (2017) Receding horizon multi-UAV cooperative tracking of moving RF source. IEEE Commun Lett 21(6):1433–1436

    Article  Google Scholar 

  • Li J, Zhou Y, Lamont L et al (2013) Packet delay in UAV wireless networks under non-saturated traffic and channel fading conditions. Wireless Pers Commun 72(2):1105–1123

    Article  Google Scholar 

  • Liang XL, Zhang H, Gulliver TA et al (2017) An improved algorithm for through-wall target detection using ultra-wideband impulse radar. IEEE Access 5:22101–22118

    Article  Google Scholar 

  • Liu C, Chao J, Wei GU et al (2014) On the surface roughness characteristics of the land fast sea-ice in the Bohai Sea. J Oceanogr 33(7):97–106

    Google Scholar 

  • Liu Z, Li Z, Liu B, et al (2015) Rise of mini-drones: applications and issues. In: Proceedings of the 2015 workshop on privacy-aware mobile computing, Hangzhou, China, pp 7–12

  • Luo XB, Fan HQ, Song ZY, Fu Q et al (2014) Passive target tracking with intermittent measurement based on random finite set. J Cent South Univ 21(6):2282–2291

    Article  Google Scholar 

  • May R, Steinheim Y, Kvaløy P et al (2017) Performance test and verification of an off-the-shelf automated avian radar tracking system. Ecol Evol 7(15):5930–5938

    Article  Google Scholar 

  • Mei S, Ji J, Hou J et al (2017) Learning sensor-specific spatial-spectral features of hyperspectral images via convolutional neural networks. IEEE Trans Geosci Remote 55(8):4520–4533

    Article  Google Scholar 

  • Meng CM, Lim HS, Tan AWC (2015) Improved channel estimation for MIMO interference cancellation. IEEE Commun Lett 19(8):1355–1357

    Article  Google Scholar 

  • Nguyen P, Ravindranatha M, Nguyen A, et al (2016) Investigating cost-effective RF-based detection of drones. In: Proceedings of the 2nd workshop on micro aerial vehicle networks, systems, and applications for civilian use, Singapore, Singapore, pp 17–22

  • Noori FM, Naseer N, Qureshi NK et al (2017) Optimal feature selection from fNIRS signals using genetic algorithms for BCI. Neurosci Lett 647:61–66

    Article  Google Scholar 

  • Rabbani H, Nezafat R, Gazor S (2009) Wavelet-domain medical image denoising using bivariate laplacian mixture model. IEEE T Bio-Med Eng 59(12):2826–2837

    Article  Google Scholar 

  • Ren J, Jiang X (2017) Regularized 2-D complex-log spectral analysis and subspace reliability analysis of micro-Doppler signature for UAV detection. Pattern Recogn 69:225–237

    Article  Google Scholar 

  • Seo J, Kim Y, Kim S et al (2017) Collision avoidance strategies for unmanned aerial vehicles in formation flight. IEEE Trans Aeroso Electron Syst 53(6):2718–2734

    Article  Google Scholar 

  • Shabayek AER, Demonceaux C, Morel O et al (2012) Vision based UAV attitude estimation: progress and insights. J Intell Robot Syst 65:295–308

    Article  Google Scholar 

  • Unlu E, Zenou E, Riviere N (2018) Generic fourier descriptors for autonomous UAV detection. In: Proceedings of the 7th international conference on pattern recognition applications and methods, Madeira, Portugal, pp 550–554

  • Wiggerich B, Wiggerich B, Pfingsthorn M (2011) Safety, security, and rescue missions with an unmanned aerial vehicle (UAV). J Intell Robot Syst 64(1):57–76

    Article  Google Scholar 

  • Xiao L, Lu X, Xu D, et al (2018) UAV relay in VANETs against smart jamming with reinforcement learning. IEEE Trans Veh Technol (Early Access)

  • Yang X, Yu Q, He L et al (2013) The one-against-all partition based binary tree support vector machine algorithms for multi-class classification. Neurocomputing 113(7):1–7

    Google Scholar 

  • Zhang Y, Guo D, Li Z (2013) Common nature of learning between back-propagation and Hopfield-type neural networks for generalized matrix inversion with simplified models. IEEE Trans Neural Netw Learn 24(4):579–592

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61701462 and 41527901, in part by the Qingdao National Laboratory for Marine Science and Technology under Grant 2017ASKJ01, in part by the Qingdao Science and Technology Plan under Grant 17-1-1-7-jch, in part by the Fundamental Research Funds for the Central Universities under Grant 201713018.

Author information

Authors and Affiliations

  1. Department of Physics and Information Engineering, Jianghan University, Wuhan, 430056, China

    Conghui Cao & Qun Hou

  2. Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, V8W 2Y2, Canada

    Thomas Aaron Gulliver

  3. Department of Research and Development, Hisense Electric Co. Ltd, Qingdao, 266100, China

    Qiang Lan

Authors
  1. Conghui Cao
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Qun Hou
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Thomas Aaron Gulliver
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Qiang Lan
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Qun Hou.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest regarding the publication of this paper.

Ethical approval

This article does not contain any studies on human participants or animals.

Informed consent

Consent was obtained from all volunteers/individual participants included in the study.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cao, C., Hou, Q., Gulliver, T.A. et al. A passive detection algorithm for low-altitude small target based on a wavelet neural network. Soft Comput 24, 10693–10703 (2020). https://doi.org/10.1007/s00500-019-04574-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-019-04574-3

Keywords