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 efectiveness in maintaining synchronization, with simulation results demonstrating their potential in improving satellite communication performance. Time synchronization, distributed radar systems, satellite swarm, Two Way Time Transfer (TWTT), Inter-Satellite Link (ISL), ICYRIME 2024: 9th International Conference of Yearly Reports on
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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
applicaand ofset delays among various local oscillators
informational 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
iflters. The ISL is performed using a Time Division
Multiare often also established to find the optimal compromise
ple Access (TDMA) method: to each node is assigned the
between weight and technology [
To achieve fine synchronization, the i-th node must first ulated on a carrier of frequency and phase : broadcast a chirp signal, denoted as ( ), which is mod( ) = ( ) · − 2 ( )
· We can represent the signal received by the j-th sensor at a certain distance and sampled at as: , [] = − − + ,
︂)] To compute the time of arrival (and thus the delay time) during reception, it is necessary to perform the following correlation: , ( ) = , ( ) * * (− )
The maximum of this convolution correspond to the delay value associated with the clock temporal delays and the time of flight (TOF).
In this study, the classic Quadratic Least Squares method
was used because it was found to have acceptable
performance as shown in [
= − + , Once the time corresponding to the peak of the correlation has been found, the sample corresponding to this time can be determined using the sampling frequency :
:, = · ,
˜ = − 2 − 0 20 − 41 + 22
During the simulation, the correlation peaks initially appear separated because the average value of the pretransmission shift has not yet been processed. These peak separations indicate the delay of diferent transmission epochs (Fig. 3). This delay prevents synchronous transmission, making the system unusable. Subsequently, synchronization epochs will be performed to observe how the peaks’ distances converge, as shown in Fig. 2.2.
This algorithm is continuously iterated so that the chirps overlap as much as possible. Only in this way can we compensate for delays caused by temporal clock drifts among the various nodes.
The core issue is not the implementation of BPSK or QPSK encoding, but the conversion of arrival times into information suitable for use as a modulating signal for transmission. The chosen method involves converting these temporal values into voltage levels, which could be 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 enlowing 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 corresponding 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 secondary sensors; each secit 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 synprocess 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 ˆ at each step, which will be applied to different chirps before transmission. The goal is to achieve 3.1. Simulation model perfect synchronization by aligning all signals ideally; where: The simulated PLL transfer function, responsible for engaging the phase of the signal transmitted by the primary sensor, is: ˆ = 1 ∑︁ Φ ˆ , 0() = 5.78 1 + 8.1 · 10− 3 107
2
After a period of synchronization, the primary and secondary nodes have the same clock time. In this work, we used a carrier wave of 1 modulated with a 1.5 sinusoidal wave.
After clock synchronization, the next step is to synchronize the phases of the waveforms. This is important because in relation to the distance from the primary sensor, all nodes must create a beam with constructive interference on the target. The formula used to adjust the phase contribution is [15]: ( ) = 2
[1 − sin( )] The simulated system is composed by 2 nodes, a block that simulates an instantaneous phase noise, and the phase shift blocks, which are controlled by a high precision ranging system [18]. In fig.7 the PLL structure is shown while the complete system is shown in fig.8.
In the final step, we assessed the nodes’ 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 phase disturbances.
In this work, two diferent methods have been analyzed; after the implementation study and the simulation results we are able to determinate the advantages and disadvantages for both paths taken. The "TWTT Based ISL Link Method" we have the opportunity to develop both software, to face any implementation challenges, and hardware, to speed up algorithm processing. This algorithm convergence is ensured mathematically, indeed, it is wellknown that the arithmetic mean can influence a set of data, improving its precision as well. A problem could be the rising of complexity in defining a communication protocol with increasingly large system. For the "PLL Based ISL Method" we are able to perform beamforming without DSP, which is an worth implementation semplification, but we need an high precision measurement system in or band which could be dificult in ISL systems.
In future works, new optimization methods based on neural approaches will be explored [20, 21, 22]