Reliable Localization in Maritime Search and Rescue

Operation by Utilizing Unmanned Aerial Vehicle

Sheikh Salman Hassan, Choong Seon Hong∗

Department of Computer Science and Engineering, Kyung Hee University, Yongin, 446-701 Korea

Email: {salman0335, cshong}@khu.ac.kr

Abstract

Maritime search and rescue (MSAR) systems worldwide primarily depend on foreign ships to support those in trouble at sea.

Signals of distress sea can now rapidly transmitted by satellite and terrestrial communication techniques to coastal search and rescue

authorities and ships in the immediate vicinity. However, in case of emergencies, the critical thing is the rescue response time. In

this framework, the MSAR operation done by utilizing unmanned aerial vehicles (UAV), which recently gained much technological

advancement. Due to UAV fast and flexible deployment in the rough sea, it can perform MSAR tasks easily. We present the

localization of missing persons from a vessel or boat based on transmission signals which their connected devices transmit for

communication. We utilize the same frequency band of communication that marine user equipment (MUE) utilizes during their

travel in the sea with maritime satellite or coastal base stations. Based on the transmitted signal, UAV can easily localize the

position of the person in sea faster than the rescue team. UAV performs rescue operations by dropping life jackets and send the

exact location of a person to rescue authorities onshore. Simulation results validated our proposed framework.

I. INTRODUCTION

Recently UAV has adopted in the number of applications for

networking, surveillance, and communication [1]. The MSAR

in the ocean can get great potential by utilizing UAVs in

their operations. Due to its diverse range of applications, UAV

can serve MSAR operations for the fast and effective search

of ships, boats, vessels, yachts, and persons in deep waters

to deliver services of rescue and communication [2]. Next-

generation cellular networks fulfill the requirements of the con-

sumers. To this end, 5G has introduced several key technologies

to meet such QoS requirements, including LTE-U [3]. However,

during the oceans’ emergency events, the maritime rescue team

needs to respond quickly where every moment counts for the

death or life of a person. Their operations are mostly dependent

upon the awareness of the situation which they have in the

control room. For effective MSAR operation, technologies

such as thermal imaging or radio-based navigation are high

ranked candidates. MSAR aerial strategy, i.e., the helicopter,

effectively localizes missing persons, but their operation cost

is a barrier. Therefore, UAV based MSAR is more effective

due to less cost on the operation. Moreover, UAV based MSAR

operation is safe for rescue teams due to remote control of UAV.

Our goal is to provide rapid support to the operation through the

deployment of a UAV by utilizing radio wave communication.

The task is to localize users focused solely on their elec-

tronic MUE without specialized MSAR tools. Because of the

limited utilization of specified MSAR facilities in water-based

sports and other fun activities, new methods of rescue are the

need of time. Wide high-sea vessels expect to be fit with MSAR

equipment such as radio beacons (EPIRB) showing the satellite

emergency location. Cargo Ships run the Automatic Identifica-

tion System (AIS) [4], to enable them for the reception of alert

or emergency signals from the respective MSAR transmitter

(AIS-MSART). Although radar transponders (radar-MSART)

use is coming from a long time ago, the global navigation

satellite system (GNSS) has allowed transponders to provide

greater importance, since they are now compact, which suitable

to built into life jackets or smaller sport vessels. Although

it can not grab specified MSAR devices as mentioned in the

global maritime distress and safety system (GMDSS), several

water-based fun activities contain these types of equipment.

Because of this, in cases where people are missing at sea,

finding consumer apparatus to enable MSAR operation is a

feasible choice. A simple outline of the solution suggested is

given in Fig. 1. The MUE position is approximate utilizing

successive measures of the signal power.

II. LOCALIZATION FRAMEWORK MODELING

A collection of criteria must be identified as a priori, so

identify the missing or drowner person. An emergency request

should put either by drowning person or by the connected party

sending the below cautions:

• Identification of mobile users by registration.

• Position coordinates in the sea before the incident.

• Details of last contacting information.

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MSAR area is identified by utilizing above data and present

sea situations i.e., waves and wind speed details, and UAV is

fly and targeted at the emergency region. The UAV has onboard

equipment capable of both listening to cellular signals for

communication between MSAR network when the marine UE

is located in the domain of coastal base station (BS) or spanning

a cellular BS that allows the marine UE to link with a flying

ad-hoc network (FANET). Alternative approach can be applied

as such that it bears comparable features to the catchers used

in law enforcement for international mobile subscriber identity

(IMSI). By this the UAV is allowed to calculate the received

signal strength indicator (RSSI) from MUE [5] texts. The UAV

locations and the corresponding RSSI can be used as virtual

anchor nodes to successively estimate the MUE location due

to the various dynamics of the UAV and the localization goal.

Status alerts are uncommon in traditional networks, and often

fail to include adequate samples to identify the MUE in distress.

We propose both to make a request to the individual through

drowning and use the calculation reports to receive regular

RSSI alerts or use empty SMS and use a accompanying ACK

messages to exploit them. In this research, we will concentrate

on absolute distance estimation for obtaining the location from

the MUE through the virtual anchor node (UAV) by using a

concept of free-space path-loss channel model in network,

PLFS = 20 ∗ log10(f) + 20 ∗ log10(dU ) + 92.45. (1)

Estimated distances from the RSSI are De. Furthermore the

specific aspect and allocation of a real distance Da utilize

estimation error ε based on received RSSI,

ε = |De −Da|. (2)

A. Model for Signal Propagation

The model for signal propagation will be addressed here

inside wireless communication network (WCN). The free space

model [6] is the most popular signal propagation technique in

WSN. The free space model suggests the recipient will accept

the data packet inside the range of contact. One possibility

to gain node distance from another node is by calculating the

signal intensity obtained from the receiving radio signals. A

theory behind RSSI is the Pt transmitted power optimized at

the transmitting unit directly influences the Pr obtained power

at the receiving system. According to the Friis free space path

loss the detected RSSI decreases quadratically with the distance

to the transmitting node,

Pr(d) =PtGtGrλ

2

(4πd)2, (3)

where Pr(d) is representing receiving power of UAV, Pt is

MUE transmit power, Gt is gain of MUE, Gr is the gain of

UAV, λ is representing wavelength of radio wave between both.

Fig. 1: Illustration of Localization Framework

B. Model for Power Law

The model calculates the pathloss between transmitting and

receiving node by distance di is,

PL(di) = PL(do)[dB] + 10n logdido

, (4)

where n represents the pathloss exponent, PL(do) is represents

pathloss at reference distance do. In case of free space path loss

model, the pathloss exponent can be equate to 2. RSSI from

distance can be found by the following equation,

RSSI[dBm] = −10n log(d) + P [dBm], (5)

where n represents propagation pathloss exponent, d distance

of a sender and P is the RSSI at distance of one meter.

TABLE I: Simulation Parameters

Parameters ValuesUAV Height h = 120m

UAV Communication Power PT = 0.01W

Operational Frequency f = 2GHz

Estimated Marine Users Location x = 500km, y = 500km

UAV Initial Location X = 0, y = 0 (On-shore)

III. FRAMEWORK EVALUATION AND RESULTS

A network scenario is implemented according to the simula-

tion table using a python framework as seen in Fig. 5. Accord-

ing to the framework given in [7], UAV has a limited resource

of energy to reach the required rescue location, so we optimize

the route and located the spot. We mark some checkpoints

for UAV to calculate distance and move accordingly, which

helps in maintaining the right direction towards rescue point.

We experiment with two different carrier frequencies for UAV

to MUE communication given in Fig. 2 3 and 4. According

to results of both frequencies, we observed that Giga Hertz

frequency has better performance in case of pathloss.

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Fig. 2: Pathloss vs Distance

Fig. 3: Channel Gain vs Distance

IV. CONCLUSION

This paper suggested a framework for assisting MSAR

operations by utilize the UAV wireless localization. A descrip-

tion provided how to combine with simulations that show the

applicability of the proposed solution using predefined search

patterns and current trajectory generation using either existing

methods or direct active search. Moreover, the simulations for

the channel structures tested over the given framework in a

scaled experimental configuration. It could demonstrate that a

UAV has the potential to identify the coordinates of the missing

person with the aid of RSSI.

ACKNOWLEDGMENT

This work was supported by Institute of Information &

communications Technology Planning & Evaluation (IITP)

grant funded by the Korea government(MSIT) (No.2019-0-

01287, Evolvable Deep Learning Model Generation Platform

for Edge Computing) *Dr. CS Hong is the corresponding

author.

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Fig. 4: RSSI vs Distance

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