=Paper=
{{Paper
|id=Vol-3869/p06
|storemode=property
|title=Efficient Methods for Time Synchronization in Distributed
Radar Systems
|pdfUrl=https://ceur-ws.org/Vol-3869/p06.pdf
|volume=Vol-3869
|authors=Damodarin Udhaya Mugil,Federico Di Girolamo,Samuele Tanzini
|dblpUrl=https://dblp.org/rec/conf/icyrime/MugilGT24
}}
==Efficient Methods for Time Synchronization in Distributed
Radar Systems==
https://ceur-ws.org/Vol-3869/p06.pdf
Efficient Methods for Time Synchronization in Distributed
Radar Systems
Damodarin Udhaya Mugil1 , Federico Di Girolamo1 and Samuele Tanzini1
1
Department of Electronic Engineering, Tor Vergata University of Rome, 00133 Rome, Italy
Abstract
Recently, the push to develop high-performance antenna arrays for space applications has underscored the economic
and technical constraints associated with satellite missions. One cost-reducing strategy involves deploying a swarm of
smaller, lighter satellites, though this approach introduces synchronization complexities. This paper evaluates two time
synchronization techniques for distributed radar systems: the Two Way Time Transfer (TWTT) based Inter-Satellite Link
(ISL) method and a Phase-Locked Loop (PLL) based method. The TWTT method utilizes Time Division Multiple Access
(TDMA) for signal exchange among satellites, ensuring time alignment via delay filters. Conversely, the PLL method involves
a primary satellite transmitting a reference signal to secondary satellites, which then recover the clock signal. Both techniques
are analyzed for their implementation feasibility and effectiveness in maintaining synchronization, with simulation results
demonstrating their potential in improving satellite communication performance.
Keywords
Time synchronization, distributed radar systems, satellite swarm, Two Way Time Transfer (TWTT), Inter-Satellite Link (ISL),
Phase-Locked Loop (PLL).
1. Introduction 2. TWTT Based ISL Link Method
In recent years, a new technical trend has been spread- The TWTT Based ISL Link is a Method used to reach the
ing throughout the world. The objective is to develop time synchronization between sensors in a multistatic
increasingly high-performance antenna arrays for space system. The basic idea is to broadcast signal flight times
applications from both a technological and an applica- and offset delays among various local oscillators informa-
tional point of view. The greatest economic impact of tion between nodes and exploit this information to align
each mission is established by the weight of the launcher the flow of transmitted or received data with true delay
and the payload; therefore, implementation constraints filters. The ISL is performed using a Time Division Multi-
are often also established to find the optimal compromise ple Access (TDMA) method: to each node is assigned the
between weight and technology [1] used to perform the time intervals to listen to and transmit data within the
satellite or to improve its performance [2]. A possible formationas as reported in [11] and [12], for which the
solution to reduce satellite launch costs is to use a satel- implemented software has been refactored accordignly
lite swarm [3, 4] composed of many smaller and lighter [13]. In order to carry out the TDMA technique and to
satellites [5]. Although the implementation of these sys- ensure that the time intervals will be respected, all nodes
tems has the advantage of decreasing launch costs, they must first achieve coarse synchronization. This can be
have the disadvantage of complexity of synchronization achieved by using a highly stable frequency source and
[6, 7, 8, 9, 10]. In this paper, two different synchronization GPS pulse-per-second (PPS) tamed crystal oscillator.
techniques are analyzed. The first one is based on a direct To achieve fine synchronization, the i-th node must first
Inter-Satellite link (ISL) using Two Way Time Transfer broadcast a chirp signal, denoted as π π (ππ ), which is mod-
(TWTT), and the second is based on the transmission ulated on a carrier of frequency ππ and phase πΎππ‘π₯ :
of a reference signal from a primary to one or multiple π‘π₯
secondary that recover the clock with a PLL. π€π (ππ ) = π π (ππ ) Β· πβπ2πππ (ππ ) Β· πππΎπ
We can represent the signal received by the j-th sensor
at a certain distance and sampled at ππ as:
[οΈ (οΈ )οΈ]οΈ
π
π,π π
π,π err
ππ,π [π] = π π π β ππ ππ β ππ + πβπ2πππ (ππ βππ + π ) πππΎπ,π
π
ICYRIME 2024: 9th International Conference of Yearly Reports on
Informatics, Mathematics, and Engineering. Catania, July 29-August To compute the time of arrival (and thus the delay time)
1, 2024
during reception, it is necessary to perform the following
$ Udhaya.Mugil.Damodarin@uniroma2.it (D. U. Mugil);
federico.digirolamo@alumni.uniroma2.eu (F. D. Girolamo); correlation:
samuele.tanzini@alumni.uniroma2.eu (S. Tanzini)
Β© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0). ππ,π (ππ ) = ππ,π (ππ ) * π *π (βππ )
47
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Damodarin Udhaya Mugil et al. CEUR Workshop Proceedings 47β52
Figure 1: Simulink block diagram of sensor Rx section.
The maximum of this convolution correspond to the delay In this study, the classic Quadratic Least Squares method
value associated with the clock temporal delays and the was used because it was found to have acceptable perfor-
time of flight (TOF). mance as shown in [14]:
π¦2 β π¦0
π‘ππ:π,π = ππππππ₯ππ |ππ,π (ππ )| Λ ππ = πππ β
π
2π¦0 β 4π¦1 + 2π¦2
= ππ β ππ + π ππΉπ,π
2.2. Simulation results
Once the time corresponding to the peak of the correla-
tion has been found, the sample corresponding to this During the simulation, the correlation peaks initially
time can be determined using the sampling frequency ππ : appear separated because the average value of the pre-
transmission shift has not yet been processed. These
πππ:π,π = ππ Β· π‘πππ ,π peak separations indicate the delay of different transmis-
sion epochs (Fig. 3). This delay prevents synchronous
transmission, making the system unusable. Subsequently,
2.1. Simulation model
synchronization epochs will be performed to observe
The core issue is not the implementation of BPSK or how the peaksβ distances converge, as shown in Fig. 2.2.
QPSK encoding, but the conversion of arrival times into This algorithm is continuously iterated so that the chirps
information suitable for use as a modulating signal for overlap as much as possible. Only in this way can we
transmission. The chosen method involves converting compensate for delays caused by temporal clock drifts
these temporal values into voltage levels, which could be among the various nodes.
utilized with an encoder before transmission.
The proposed Rx architecture is shown in Fig. 1. Fol- In Fig. 2, the iteration of the algorithm throughout the en-
lowing the correlation using the matched filter, there is a tire working period is shown; it is clear that it is possible
comparison process aimed at resetting a set/reset counter to correct a few tenths of a sample.
in free-counting mode. The counter continues to count
indefinitely until reset by an external signal correspond-
ing to the correlation peak. The conversion to amplitude 3. PLL Based ISL Method
was achieved by implementing a memory that contains
various delay values in terms of samples. This memory The second method analyzed is an implementation of
is scanned by the previously introduced counter, and a PLL based architecture [15, 16, 17]. This architecture
through the "hit" signal generated by the counter itself, has a primary sensor and π secondary sensors; each sec-
it becomes possible to sample the time value as ampli- ondary sensor receives a wave from the primary and
tude. While in Simulink the transmission and reception synchronizes its clock on it. When all clocks are syn-
process was simulated, in Matlab the iterative true peak chronized each node will be in phase depending on the
detection algorithm was employed. This iterative process distance from the primary sensor [18, 19].
generates π Λ π at each step, which will be applied to dif-
ferent chirps before transmission. The goal is to achieve 3.1. Simulation model
perfect synchronization by aligning all signals ideally;
The simulated PLL transfer function, responsible for en-
where:
1 βοΈ gaging the phase of the signal transmitted by the primary
π
Λπ = Ξ¦Μπ,π sensor, is:
π π
1 + π 8.1 Β· 10β 3 7
πΊ0 (π ) = 5.78 10
π 2
48
Damodarin Udhaya Mugil et al. CEUR Workshop Proceedings 47β52
Figure 3: Iteration 1
Figure 2: Algorithm convergence.
After a period of synchronization, the primary and sec-
ondary nodes have the same clock time. In this work, Figure 4: Iteration 3
we used a carrier wave of 1 πΊπ»π§ modulated with a 1.5
π π»π§ sinusoidal wave.
After clock synchronization, the next step is to synchro-
nize the phases of the waveforms. This is important be-
cause in relation to the distance from the primary sensor,
all nodes must create a beam with constructive interfer-
ence on the target. The formula used to adjust the phase
contribution is [15]:
π
ππππ (π) = 2π [1 β sin(π)]
ππ
πΉ
The simulated system is composed by 2 nodes, a block Figure 5: Iteration 6
that simulates an instantaneous phase noise, and the
phase shift blocks, which are controlled by a high pre-
cision ranging system [18]. In fig.7 the PLL structure is
shown while the complete system is shown in fig.8.
3.2. Simulation results
In the final step, we assessed the nodesβ performance
by comparing the outputs of the phase shifters with the
inputs. In Fig.9 and 10 we note that the system converges
across all distances tested and remains resistant to small Figure 6: Iteration 8
phase disturbances.
Method" we have the opportunity to develop both soft-
4. Conclusions ware, to face any implementation challenges, and hard-
In this work, two different methods have been analyzed; ware, to speed up algorithm processing. This algorithm
after the implementation study and the simulation results convergence is ensured mathematically, indeed, it is well-
we are able to determinate the advantages and disadvan- known that the arithmetic mean can influence a set of
tages for both paths taken. The "TWTT Based ISL Link data, improving its precision as well. A problem could
49
Damodarin Udhaya Mugil et al. CEUR Workshop Proceedings 47β52
Figure 7: PLL block diagram.
Figure 8: Complete system
be the rising of complexity in defining a communication neural approaches will be explored [20, 21, 22]
protocol with increasingly large system. For the "PLL
Based ISL Method" we are able to perform beamforming
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