# A CFAR-Enhanced Spectral Whitening Method for Acoustic Sensing via UAVs

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## Abstract

**:**

## 1. Introduction

## 2. Background Information

## 3. CFAR-Enhanced Whitening

#### 3.1. Description

#### 3.2. Validation

## 4. Experimental Results & Discussion

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- Farnell, A. Designing Sound; MIT Press: Cambridge, MA, USA, 2010. [Google Scholar]
- Thomas, R. (Ed.) Springer Handbook of Acoustics; Springer: New York, NY, USA, 2007; p. 113. [Google Scholar]
- Michel, U.; Dobrzynski, W.; Splettstoesser, W.; Delfs, J.; Isermann, U.; Obermeier, F. Aircraft noise. In Handbook of Engineering Acoustics, 1st ed.; Muller, M.M.G., Ed.; Springer Heidelberg: New York, NY, USA, 2013; p. 489. [Google Scholar]
- Richards, M.A. Fundamentals of Radar Signal Processing; McGraw-Hill: New York, NY, USA, 2005. [Google Scholar]
- Sarma, A.; Tufts, D.W. Robust adaptive threshold for control of false alarms. IEEE Signal Process. Lett.
**2001**, 8, 261–263. [Google Scholar] [CrossRef] - Ohata, T.; Nakamura, K.; Mizumoto, T.; Taiki, T.; Nakadai, K. Improvement in outdoor sound source detection using a quadrotor-embedded microphone array. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, IL, USA, 14–18 September 2014. [Google Scholar]
- Okutani, K.; Yoshida, T.; Nakamura, K.; Nakadai, K. Outdoor auditory scene analysis using a moving microphone array embedded in a quadrocopter. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vilamoura, Portugal, 7–12 October 2012; p. 3288. [Google Scholar]
- Ferguson, B.; Wyber, R. Detection and localization of a ground based impulsive sound source using acoustic sensors onboard a tactical unmanned aerial vehicle. In Proceedings of the Battlefield Acoustic Sensing for ISR Applications, Neuilly-sur-Seine, France, 9–10 October 2006; pp. 16-1–16-8. [Google Scholar]
- Robertson, D.N.; Pham, T.; Edge, H.; Porter, B.; Shumaker, J.; Cline, D. Acoustic sensing from small-size UAVs. Proc. SPIE
**2007**, 6562. [Google Scholar] [CrossRef] - Harvey, B.; O’Young, S. Detection of continuous ground-based acoustic sources via unmanned aerial vehicles. J. Unmanned Veh. Syst.
**2015**, 4, 83–95. [Google Scholar] [CrossRef] - Harvey, B. Signal Processing Methods for the Detection & Localization of Acoustic Sources via Unmanned Aerial Vehicles. Ph.D., Memorial University of Newfoundland, St. John’s, NL, Canada, 2017. [Google Scholar]
- Harvey, B.; O’Young, S. Robust distribution-free CFAR detection of nonstationary narrowband signals. Dig. Signal Process.
**2017**. submitted. [Google Scholar] - Lyons, R.G. Understanding Digital Signal Processing; Prentice Hall: Upper Saddle River, NJ, USA, 2010. [Google Scholar]
- Lee, M.W. Spectral Whitening in the Frequency Domain; Open-File Report 86-108; United States Department of the Interior: Denver, CO, USA, 1986.
- Blake, S. OS-CFAR theory for multiple targets and nonuniform clutter. IEEE Trans. Aerosp. Electron. Syst.
**1988**, 24, 785–790. [Google Scholar] [CrossRef] - Finn, H.M.; Johnson, P.S. Adaptive detection mode with threshold control as a function of spatially sampled clutter estimation. RCA Rev.
**1968**, 29, 414–464. [Google Scholar] - Jalil, A.; Yousaf, H.; Baig, M.I. Analysis of CFAR techniques. In Proceedings of the 2016 13th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, Pakistan, 12–16 January 2016. [Google Scholar]
- Rohling, H. Radar CFAR thresholding in clutter and multiple target situations. IEEE Trans. Aerosp. Electron. Syst.
**1983**, AES-19, 608–621. [Google Scholar] [CrossRef] - Ritcey, J.A. Performance analysis of the censored mean-level detector. IEEE Trans. Aerosp. Electron. Syst.
**1986**, AES-22, 443–454. [Google Scholar] [CrossRef] - Gandhi, P.P.; Kassam, S.A. Analysis of CFAR processors in nonhomogeneous background. IEEE Trans. Aerosp. Electron. Syst.
**1988**, 24, 427–445. [Google Scholar] [CrossRef] - Tan, L.; Jiang, J. Novel adaptive IIR filter for frequency estimation and tracking [DSP Tips&Tricks]. IEEE Signal Process. Mag.
**2009**, 26, 186–189. [Google Scholar] - Tan, L.; Jiang, J. Real-time frequency tracking using novel adaptive harmonic IIR notch filter. Technol. Interface J.
**2009**, 9. [Google Scholar] - Tan, L.; Jiang, J. Simplified gradient adaptive harmonic IIR notch filter for frequency estimation and tracking. Am. J. Signal Process.
**2015**, 5, 6–12. [Google Scholar] - Tan, L.; Jiang, J.; Wang, L. Adaptive harmonic IIR notch filters for frequency estimation and tracking. In Adaptive Filtering; Garcia, L., Ed.; InTech: Rijeka, Croatia, 2011; p. 313. [Google Scholar]

Sampling Frequency (${\mathit{f}}_{\mathit{s}}$) | 48 kHz | Number of Signals | 4 |

Decimation Factor | 8 | FFT Window | 0.5 s |

IIR Step Size ($\mu $) | 5 × 10^{−4} | Window Overlap | 50% |

Notch Radius ($r$) | 0.995 | Padded Length (${L}_{fft}$) | 12,000 pts |

Harmonics Removed ($R$) | 8 | Spectral Resolution (${f}_{r}$) | 0.5 Hz/bin |

Detector Type | OS-CFAR | Noise Samples ($\mathit{N})$ | 101 |

Forgetting Factor ($\xi $) | 0.2 | Order Statistic ($k)$ | $0.75\text{}N$ |

Flooring Factor ($\delta $) | 0.5 | Guard Cell Band ($\stackrel{\rightharpoonup}{G}$) | 5.5 Hz |

Noise Band ($\stackrel{\rightharpoonup}{N}$) | 50 Hz | Guard Cells ($G$) | 12 |

Noise Sample Band ($\stackrel{\rightharpoonup}{\mathit{N}}$) | 1–1000 Hz | Consecutive Detections ($\mathit{D}$) | 2 |

Test Band ($\stackrel{\rightharpoonup}{B}$) | 150–550 Hz | Cell Deviation ($\Delta $) | 1 |

Guard Cell Band ($\stackrel{\rightharpoonup}{G}$) | 10.5 Hz | Maxima Tested ($M$) | 2 |

Noise Samples ($N$) | 1998 pts | ${P}_{FA}^{SC}$ | 1.0 × 10^{−3} |

Test Cells ($B$) | 801 pts | ${P}_{FA}^{ST}$ | 6.5 × 10^{−1} |

Guard Cells (G) | 22 pts | ${P}_{FA}^{BI}$ | 8.2 × 10^{−4} |

Order Statistic ($\overline{k}$) | 2 | ${P}_{FA}^{RBI}$ | 2.5 × 10^{−3} |

Consecutive Trials ($T$) | 2 |

${\mathit{f}}_{\mathit{o}}$ = 200 Hz | ${\mathit{f}}_{\mathit{o}}$ = 500 Hz | |||
---|---|---|---|---|

Unwhitened | Whitened | Unwhitened | Whitened | |

Detection Rate (ST, BI, RBI) | 64%, 54%, 55% | 100%, 97%, 99% | 69%, 37%, 51% | 100%, 63%, 83% |

Max SNR | 28 dB | 38.3 dB | 26.5 dB | 47.4 dB |

Average SNR | 12.4 dB | 19.7 dB | 8.2 dB | 32.5 dB |

Initial Detection | 13.25 s | 13 s | 11 s | 10.5 s |

Second Detection | 20.25 s | 13.25 s | 12 s | 10.75 s |

Observed Frequency Range | 212–190 Hz | 523–479 Hz |

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**MDPI and ACS Style**

Harvey, B.; O’Young, S.
A CFAR-Enhanced Spectral Whitening Method for Acoustic Sensing via UAVs. *Drones* **2018**, *2*, 1.
https://doi.org/10.3390/drones2010001

**AMA Style**

Harvey B, O’Young S.
A CFAR-Enhanced Spectral Whitening Method for Acoustic Sensing via UAVs. *Drones*. 2018; 2(1):1.
https://doi.org/10.3390/drones2010001

**Chicago/Turabian Style**

Harvey, Brendan, and Siu O’Young.
2018. "A CFAR-Enhanced Spectral Whitening Method for Acoustic Sensing via UAVs" *Drones* 2, no. 1: 1.
https://doi.org/10.3390/drones2010001