# Effectiveness of Mobile Emitter Location by Cooperative Swarm of Unmanned Aerial Vehicles in Various Environmental Conditions

^{*}

## Abstract

**:**

## 1. Introduction

## 2. SDF Method and Simulation Study Procedure

#### 2.1. SDF Method in Location

#### 2.2. Diagram of Simulation Procedure

- generation of the sensor trajectory division into sections with LOS and NLOS conditions;
- on the route sections with LOS conditions, the DFS estimation for the resultant velocity vector for the analyzed sensor and the mobile emitter;
- estimating the current emitter position relative to the vector of the sensor.

#### 2.3. Input Data for Simulation Studies

- the coordinates of the initial position of each sensor, ${x}_{s}\left(0\right)=\left({x}_{s}\left(0\right),{y}_{s}\left(0\right),{z}_{s}\left(0\right)\right),$ and monitored emitter, ${x}_{e}\left(0\right)=\left({x}_{e}\left(0\right),{y}_{e}\left(0\right),{z}_{e}\left(0\right)\right);$
- the velocity vectors of individual sensors, ${v}_{s}\left(t\right)=\left[{v}_{sx}\left(t\right),{v}_{sy}\left(t\right),{v}_{sz}\left(t\right)\right],$ and the emitter, ${v}_{e}\left(t\right)=\left[{v}_{ex}\left(t\right),{v}_{ey}\left(t\right),{v}_{ez}\left(t\right)\right];$
- the probability ${P}_{LOS}\left(Env,\beta \right)$ of the LOS conditions occurring on the sensor trajectory as a function of the type of propagation environment $Env$ and the elevation angle $\beta $ of the sensor relative to the emitter position (see Figure 1);
- the carrier frequency ${f}_{c},$ an emission type and the bandwidth $B$ of the transmitted signal;
- processing parameters of the received signals such as a sampling rate ${f}_{s}$ and minimum acquisition time $\mathsf{\Delta}t$ conditioning the determination of a single DFS.

#### 2.4. Stage 1. Generation of LOS/NLOS Sections on Sensor Trajectory

#### 2.5. Stage 2. Estimation of Doppler Frequency Shift in Received Signal

#### 2.6. Stage 3. Estimation of Emitter Position by Individual Sensors

#### 2.7. Estimation of Weighted Average Emitter Position by Swarm

#### 2.8. Calculation of Efficiency Metrics for Monte-Carlo Process

- a cumulative distribution function (CDF) of the location error, $F\left(\mathsf{\Delta}{R}_{s}\right),$ obtained for the Monte-Carlo process;
- an average percentage of flight-time under the LOS conditions for sensors in a swarm:
- an effectiveness factor (EF) of the emitter monitoring by a swarm:$${\tau}_{LOS}=\frac{1}{M}{\displaystyle \sum _{m=1}^{M}\frac{{t}_{LOS,m}}{T}}\cdot 100\%,$$$$EF=\frac{D}{RMSE}\cdot \frac{{\tau}_{LOS}}{100\%}.$$

## 3. Simulation Studies and Results

#### 3.1. Assumptions and Scenario in Simulation Tests

- the dimensions of the urbanized area were ${X}_{UA}\times {Y}_{UA}=3000\mathrm{m}\times 3000\mathrm{m};$
- three types of propagation environments, $Env,$ were analyzed: suburban, urban, and dense urban; to evaluate the occurrence probability of LOS/NLOS conditions for these areas, we used ${d}_{f}=500\mathrm{m}$ as described in Section 2.4; ${P}_{LOS}\left(Env,\beta \right)$ for the analyzed $Env$ and specific sensor elevation $\beta $ (see Figure 1) were determined based on the distributions presented in Figure 2 of [44];
- the emitter antenna height was equal to $h=2\mathrm{m};$
- the considered emitter speeds were ${v}_{e}=\left\{0,1,2,5,10\right\}\mathrm{m}/\mathrm{s};$
- an angular width of the monitored sector was $2{\phi}_{\mathrm{max}}=90\xb0\Rightarrow {\phi}_{\mathrm{max}}=45\xb0;$
- the considered number of sensors in the swarm were $1\le J\le 10;$
- the trajectory length of each mobile sensor was equal $D=3000\mathrm{m};$
- the flight time along the trajectory for each sensor was $T=D/{v}_{s}=30\mathrm{s};$
- the radio channel including attenuation, CIR and Rician factor was modeled under the methodology described in [45];
- the minimum signal-to-noise ratio (SNR) for the signals received by the mobile sensors was $SN{R}_{\mathrm{min}}=3\mathrm{dB}$ (e.g., [58]);
- the receiver parameters used in each sensor were: ${B}_{s}=500\mathrm{kHz}$—the bandwidth of the received signal, ${f}_{s}=2{B}_{s}=1000\mathrm{kS}/\mathrm{s}$—sample rate, ${B}_{d}=10\mathrm{kHz}$—a bandwidth of a decimation filter, $\mathsf{\Delta}f=0.05\mathrm{Hz}$—a spectrum resolution (i.e., the basic frequency of signal analysis);
- the estimation method of the DFS in the received signal was analogous to that presented in [59];
- the signal recording time required to determine a single DFS value was equal $\mathsf{\Delta}t=0.1\mathrm{s};$
- the number of Monte-Carlo runs was $M=200.$

#### 3.2. Sample Simulation Results for Determined Position of Emitter

- suburban or urban area;
- the initial emitter position was ${x}_{e}\left(0\right)=\left(2000,500,2\right)\mathrm{m};$
- the emitter velocity was defined by ${v}_{e}=1\mathrm{m}/\mathrm{s}$ and $\alpha =90\xb0;$
- the number of mobile sensors in the swarm was $J=5;$
- the flight altitude of the mobile sensors was $H=500\mathrm{m}.$

#### 3.3. Impact of Swarm Size

#### 3.4. Influence of Propagation Environment

#### 3.5. Impact of Sensor Flight Altitude

#### 3.6. Influence of Emitter Speed

## 4. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Adamy, D.L. EW 101: A First Course in Electronic Warfare; Artech House: Boston, MA, USA, 2001; ISBN 978-1-58053-169-6. [Google Scholar]
- Adamy, D.L. EW 102: A Second Course in Electronic Warfare; Artech House: Boston, MA, USA, 2004; ISBN 978-1-58053-686-8. [Google Scholar]
- Stefański, J. Asynchronous time difference of arrival (ATDOA) method. Pervasive Mob. Comput.
**2015**, 23, 80–88. [Google Scholar] [CrossRef] - Sadowski, J.; Stefański, J. Asynchronous phase-location system. J. Mar. Eng. Technol.
**2017**, 16, 400–408. [Google Scholar] [CrossRef] [Green Version] - Ziółkowski, C.; Kelner, J.M. Doppler-based navigation for mobile protection system of strategic maritime facilities in GNSS jamming and spoofing conditions. IET Radar Sonar Navig.
**2020**, 14, 643–651. [Google Scholar] [CrossRef] - Alotaibi, E.T.; Alqefari, S.S.; Koubaa, A. LSAR: Multi-UAV collaboration for search and rescue missions. IEEE Access
**2019**, 7, 55817–55832. [Google Scholar] [CrossRef] - Kelner, J.M.; Ziółkowski, C. Portable beacon system for emergency mountain landing pad. In Proceedings of the 2019 European Navigation Conference (ENC), Warsaw, Poland, 9–12 April 2019; pp. 1–5. [Google Scholar] [CrossRef]
- Tabbane, S. An alternative strategy for location tracking. IEEE J. Sel. Areas Commun.
**1995**, 13, 880–892. [Google Scholar] [CrossRef] - Akyildiz, I.F.; Ho, J.S.M.; Lin, Y.-B. Movement-based location update and selective paging for PCS networks. IEEEACM Trans. Netw.
**1996**, 4, 629–638. [Google Scholar] [CrossRef] [Green Version] - Li, B.; Fei, Z.; Zhang, Y. UAV communications for 5G and beyond: Recent advances and future trends. IEEE Internet Things J.
**2019**, 6, 2241–2263. [Google Scholar] [CrossRef] [Green Version] - Li, B.; Fei, Z.; Zhang, Y.; Guizani, M. Secure UAV communication networks over 5G. IEEE Wirel. Commun.
**2019**, 26, 114–120. [Google Scholar] [CrossRef] - Wang, D.; Zhang, P.; Yang, Z.; Wei, F.; Wang, C. A novel estimator for TDOA and FDOA positioning of multiple disjoint sources in the presence of calibration emitters. IEEE Access
**2020**, 8, 1613–1643. [Google Scholar] [CrossRef] - Ho, K.C.; Xu, W. An accurate algebraic solution for moving source location using TDOA and FDOA measurements. IEEE Trans. Signal Process.
**2004**, 52, 2453–2463. [Google Scholar] [CrossRef] - Mušicki, D.; Kaune, R.; Koch, W. Mobile emitter geolocation and tracking using TDOA and FDOA measurements. IEEE Trans. Signal Process.
**2010**, 58, 1863–1874. [Google Scholar] [CrossRef] - Yu, H.; Huang, G.; Gao, J.; Liu, B. An efficient constrained weighted least squares algorithm for moving source location using TDOA and FDOA measurements. IEEE Trans. Wirel. Commun.
**2012**, 11, 44–47. [Google Scholar] [CrossRef] - Sathyan, T.; Sinha, A.; Kirubarajan, T. Passive geolocation and tracking of an unknown number of emitters. IEEE Trans. Aerosp. Electron. Syst.
**2006**, 42, 740–750. [Google Scholar] [CrossRef] - Yu, Z.; Wei, J.; Liu, H. An energy-efficient target tracking framework in wireless sensor networks. EURASIP J. Adv. Signal Process.
**2009**, 2009, 524145. [Google Scholar] [CrossRef] [Green Version] - Kim, W.C.; Song, T.L.; Mušicki, D. Mobile emitter geolocation and tracking using correlated time difference of arrival measurements. In Proceedings of the 2012 15th International Conference on Information Fusion (FUSION), Singapore, 9–12 July 2012; pp. 700–706. [Google Scholar]
- Kaniewski, P. Extended Kalman filter with reduced computational demands for systems with non-linear measurement models. Sensors
**2020**, 20, 1584. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Masazade, E.; Fardad, M.; Varshney, P.K. Sparsity-promoting extended Kalman filtering for target tracking in wireless sensor networks. IEEE Signal Process. Lett.
**2012**, 19, 845–848. [Google Scholar] [CrossRef] - Pathirana, P.N.; Savkin, A.V.; Jha, S. Location estimation and trajectory prediction for cellular networks with mobile base stations. IEEE Trans. Veh. Technol.
**2004**, 53, 1903–1913. [Google Scholar] [CrossRef] - Schmidhammer, M.; Gentner, C.; Siebler, B.; Sand, S. Localization and tracking of discrete mobile scatterers in vehicular environments using delay estimates. Sensors
**2019**, 19, 4802. [Google Scholar] [CrossRef] [Green Version] - Yan, L.; Lu, Y.; Zhang, Y. An improved NLOS identification and mitigation approach for target tracking in wireless sensor networks. IEEE Access
**2017**, 5, 2798–2807. [Google Scholar] [CrossRef] - Mušicki, D. Bearings only multi-sensor maneuvering target tracking. Syst. Control Lett.
**2008**, 57, 216–221. [Google Scholar] [CrossRef] - Cao, Y.; Fang, J. Constrained Kalman filter for localization and tracking based on TDOA and DOA measurements. In Proceedings of the 2009 International Conference on Signal Processing Systems (ICSPS), Singapore, 15–17 May 2009; pp. 28–33. [Google Scholar] [CrossRef]
- Lin, C.-M.; Hsueh, C.-S. Adaptive EKF-CMAC-based multisensor data fusion for maneuvering target. IEEE Trans. Instrum. Meas.
**2013**, 62, 2058–2066. [Google Scholar] [CrossRef] - Kumar, A.A.; Sivalingam, K.M. Target tracking in a WSN with directional sensors using electronic beam steering. In Proceedings of the 2012 4th International Conference on Communication Systems and Networks (COMSNETS), Bangalore, India, 3–7 January 2012; pp. 1–10. [Google Scholar] [CrossRef]
- Li, L.; Xie, W. Bearings-only maneuvering target tracking based on fuzzy clustering in a cluttered environment. AEU—Int. J. Electron. Commun.
**2014**, 68, 130–137. [Google Scholar] [CrossRef] - Parvin, J.R.; Vasanthanayaki, C. Particle swarm optimization-based energy efficient target tracking in wireless sensor network. Measurement
**2019**, 147, 106882. [Google Scholar] [CrossRef] - Tahat, A.; Kaddoum, G.; Yousefi, S.; Valaee, S.; Gagnon, F. A look at the recent wireless positioning techniques with a focus on algorithms for moving receivers. IEEE Access
**2016**, 4, 6652–6680. [Google Scholar] [CrossRef] - Jing, Z.; Pan, H.; Li, Y.; Dong, P. Non-Cooperative Target Tracking, Fusion and Control: Algorithms and Advances; Springer: Cham, Switzerland, 2018; ISBN 978-3-319-90716-1. [Google Scholar] [CrossRef]
- Kelner, J.M. Cooperative system of emission source localization based on SDF. In Proceedings of the 2018 19th International Conference on Military Communications and Information Systems (ICMCIS), Warsaw, Poland, 22–23 May 2018; pp. 1–6. [Google Scholar] [CrossRef] [Green Version]
- Cheng, L.; Wang, Y.; Sun, X.; Hu, N.; Zhang, J. A mobile localization strategy for wireless sensor network in NLOS conditions. China Commun.
**2016**, 13, 69–78. [Google Scholar] [CrossRef] - Tepedelenlioğlu, C.; Abdi, A.; Giannakis, G.B. The Ricean K factor: Estimation and performance analysis. IEEE Trans. Wirel. Commun.
**2003**, 2, 799–810. [Google Scholar] [CrossRef] [Green Version] - Tang, P.; Zhang, J.; Molisch, A.F.; Smith, P.J.; Shafi, M.; Tian, L. Estimation of the K-factor for temporal fading from single-snapshot wideband measurements. IEEE Trans. Veh. Technol.
**2019**, 68, 49–63. [Google Scholar] [CrossRef] - Skokowski, P.; Malon, K.; Kelner, J.M.; Dołowski, J.; Łopatka, J.; Gajewski, P. Adaptive channels’ selection for hierarchical cluster based cognitive radio networks. In Proceedings of the 2014 8th International Conference on Signal Processing and Communication Systems (ICSPCS), Gold Coast, QLD, Australia, 15–17 December 2014; pp. 1–6. [Google Scholar] [CrossRef]
- Malon, K.; Skokowski, P.; Łopatka, J. The use of a wireless sensor network to monitor the spectrum in urban areas. In Proceedings of the SPIE 10418, 2016 XI Conference on Reconnaissance and Electronic Warfare Systems (CREWS), Ołtarzew, Poland, 21–23 November 2016; Volume 10418, p. 1041809. [Google Scholar] [CrossRef]
- Wu, S.; Xu, D.; Wang, H. Adaptive NLOS mitigation location algorithm in wireless cellular network. Wirel. Pers. Commun.
**2015**, 84, 3143–3156. [Google Scholar] [CrossRef] - Yin, F.; Fritsche, C.; Gustafsson, F.; Zoubir, A.M. EM- and JMAP-ML based joint estimation algorithms for robust wireless geolocation in mixed LOS/NLOS environments. IEEE Trans. Signal Process.
**2014**, 62, 168–182. [Google Scholar] [CrossRef] [Green Version] - Thomä, R.S.; Hampicke, D.; Richter, A.; Sommerkorn, G.; Schneider, A.; Trautwein, U.; Wirnitzer, W. Identification of time-variant directional mobile radio channels. IEEE Trans. Instrum. Meas.
**2000**, 49, 357–364. [Google Scholar] [CrossRef] [Green Version] - Zhu, Q.; Yang, Y.; Chen, X.; Tan, Y.; Fu, Y.; Wang, C.; Li, W. A novel 3D non-stationary vehicle-to-vehicle channel model and its spatial-temporal correlation properties. IEEE Access
**2018**, 6, 43633–43643. [Google Scholar] [CrossRef] - Kelner, J.M.; Ziółkowski, C.; Nowosielski, L.; Wnuk, M.T. Localization of emission source in urban environment based on the Doppler effect. In Proceedings of the 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), Nanjing, China, 15–18 May 2016; pp. 1–5. [Google Scholar] [CrossRef]
- Chan, Y.-T.; Tsui, W.-Y.; So, H.-C.; Ching, P.-C. Time-of-arrival based localization under NLOS conditions. IEEE Trans. Veh. Technol.
**2006**, 55, 17–24. [Google Scholar] [CrossRef] - Al-Hourani, A.; Kandeepan, S.; Lardner, S. Optimal LAP altitude for maximum coverage. IEEE Wirel. Commun. Lett.
**2014**, 3, 569–572. [Google Scholar] [CrossRef] [Green Version] - Matolak, D.W.; Sun, R. Air-ground channel characterization for unmanned aircraft systems—Part III: The suburban and near-urban environments. IEEE Trans. Veh. Technol.
**2017**, 66, 6607–6618. [Google Scholar] [CrossRef] - Koohifar, F.; Guvenc, I.; Sichitiu, M.L. Autonomous tracking of intermittent RF source using a UAV swarm. IEEE Access
**2018**, 6, 15884–15897. [Google Scholar] [CrossRef] - Arafat, M.Y.; Moh, S. Localization and clustering based on swarm intelligence in UAV networks for emergency communications. IEEE Internet Things J.
**2019**, 6, 8958–8976. [Google Scholar] [CrossRef] - Wan, P.; Huang, Q.; Lu, G.; Wang, J.; Yan, Q.; Chen, Y. Passive localization of signal source based on UAVs in complex environment. China Commun.
**2020**, 17, 107–116. [Google Scholar] [CrossRef] - Gajewski, P.; Ziółkowski, C.; Kelner, J.M. Using SDF method for simultaneous location of multiple radio transmitters. In Proceedings of the 2012 19th International Conference on Microwave Radar and Wireless Communications (MIKON), Warsaw, Poland, 21–23 May 2012; Volume 2, pp. 634–637. [Google Scholar] [CrossRef]
- Kelner, J.M.; Ziółkowski, C. SDF technology in location and navigation procedures: A survey of applications. In Proceedings of the SPIE 10418, 2016 XI Conference on Reconnaissance and Electronic Warfare Systems (CREWS), Ołtarzew, Poland, 21–23 November 2016; Volume 10418, p. 104180B. [Google Scholar] [CrossRef]
- Rafa, J.; Ziółkowski, C. Influence of transmitter motion on received signal parameters—Analysis of the Doppler effect. Wave Motion
**2008**, 45, 178–190. [Google Scholar] [CrossRef] - Fowler, M.L. Analysis of single-platform passive emitter location with terrain data. IEEE Trans. Aerosp. Electron. Syst.
**2001**, 37, 495–507. [Google Scholar] [CrossRef] - Gajewski, P.; Ziółkowski, C.; Kelner, J.M. Space Doppler location method of radio signal sources. Bull. Mil. Univ. Technol.
**2011**, 60, 187–200. (In Polish) [Google Scholar] - HiClipart—Free Transparent Background PNG Clipart. Available online: https://www.hiclipart.com/free-transparent-background-png-clipart-xddbw (accessed on 30 March 2020).
- Kelner, J.M.; Ziółkowski, C.; Nowosielski, L.; Wnuk, M. Reserve navigation system for ships based on coastal radio beacons. In Proceedings of the 2016 IEEE/ION Position, Location and Navigation Symposium (PLANS), Savannah, GA, USA, 11–14 April 2016; pp. 393–402. [Google Scholar] [CrossRef]
- Matolak, D.W.; Sun, R. Air-ground channel characterization for unmanned aircraft systems—Part I: Methods, measurements, and models for over-water settings. IEEE Trans. Veh. Technol.
**2017**, 66, 26–44. [Google Scholar] [CrossRef] - Sun, R.; Matolak, D.W. Air-ground channel characterization for unmanned aircraft systems—Part II: Hilly and mountainous settings. IEEE Trans. Veh. Technol.
**2017**, 66, 1913–1925. [Google Scholar] [CrossRef] - Fraser, B. Radio-frequency emitter localisation using a swarm of search agents. In Proceedings of the 2018 28th International Telecommunication Networks and Applications Conference (ITNAC), Sydney, NSW, Australia, 21–23 November 2018; pp. 1–6. [Google Scholar] [CrossRef]
- Kelner, J.M.; Ziółkowski, C. The use of SDF technology to BPSK and QPSK emission sources’ location. Przegląd Elektrotechniczny
**2015**, 91, 61–65. (In Polish) [Google Scholar] [CrossRef] - Skrzypecki, S.; Pierzchała, D.; Tarapata, Z. Distributed simulation environment of unmanned aerial systems for a search problem. In Modelling and Simulation for Autonomous Systems. Proceedings of 2018 5th International Conference on Modelling and Simulation for Autonomous Systems (MESAS), Prague, Czech, 17–19 October 2018; Mazal, J., Ed.; Springer: Cham, Switzerland, 2019; pp. 65–81. ISBN 978-3-030-14984-0. [Google Scholar] [CrossRef]
- Skrzypecki, S.; Tarapata, Z.; Pierzchała, D. Combined PSO methods for UAVs swarm modelling and simulation. In Modelling and Simulation for Autonomous Systems. Proceedings of 2019 6th International Conference on Modelling and Simulation for Autonomous Systems (MESAS), Palermo, Italy, 29–31 October 2019; Mazal, J., Fagiolini, A., Vasik, P., Eds.; Springer: Cham, Switzerland, 2020; pp. 11–25. ISBN 978-3-030-43890-6. [Google Scholar] [CrossRef]

**Figure 3.**Transformation of the coordinate system relative to the sensor position and the direction of its velocity vector.

**Figure 5.**Examples of instantaneous DFS, location errors and the estimated emitter position obtained based on the individual sensors and swarm for a suburban area.

**Figure 6.**Examples of instantaneous DFS, location errors and the estimated emitter position obtained based on the individual sensors and swarm for an urban area.

**Figure 9.**Percentage of flight-time under LOS conditions versus number of sensors in swarm for suburban area.

**Figure 11.**Cumulative distribution function (CDFs) of the location error versus the different types of urbanized areas.

Title 1 | Propagation Environment Type, Env | ||
---|---|---|---|

Suburban | Urban | Dense Urban | |

RMSE (m) | 187 | 280 | 295 |

${\tau}_{LOS}$ (%) | 96 | 68 | 43 |

EF (−) | 15.4 | 7.2 | 4.4 |

Title 1 | Sensor Flight Altitude, H | ||
---|---|---|---|

100 m | 200 m | 500 m | |

RMSE (m) | 654 | 518 | 187 |

${\tau}_{LOS}$ (%) | 31 | 69 | 96 |

EF (−) | 1.4 | 4.0 | 15.4 |

Title 1 | $\mathbf{Emitter}\mathbf{Speed},{\mathit{v}}_{\mathit{e}}$ | ||||
---|---|---|---|---|---|

0 | 1 m/s | 2 m/s | 5 m/s | 10 m/s | |

RMSE (m) | 142 | 187 | 252 | 988 | 2 773 |

${\tau}_{LOS}$ (%) | 96 | ||||

EF (–) | 20.4 | 15.4 | 11.5 | 2.9 | 1.1 |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Kelner, J.M.; Ziółkowski, C.
Effectiveness of Mobile Emitter Location by Cooperative Swarm of Unmanned Aerial Vehicles in Various Environmental Conditions. *Sensors* **2020**, *20*, 2575.
https://doi.org/10.3390/s20092575

**AMA Style**

Kelner JM, Ziółkowski C.
Effectiveness of Mobile Emitter Location by Cooperative Swarm of Unmanned Aerial Vehicles in Various Environmental Conditions. *Sensors*. 2020; 20(9):2575.
https://doi.org/10.3390/s20092575

**Chicago/Turabian Style**

Kelner, Jan M., and Cezary Ziółkowski.
2020. "Effectiveness of Mobile Emitter Location by Cooperative Swarm of Unmanned Aerial Vehicles in Various Environmental Conditions" *Sensors* 20, no. 9: 2575.
https://doi.org/10.3390/s20092575