IEEE - Aerospace and Electronic Systems - April 2023 - 3

In This Issue -Technically
LASER INTERSATELLITE LINK RANGE IN FREE-SPACE OPTICAL SATELLITE NETWORKS:
IMPACT ON LATENCY
Laser communication terminals for establishing laser intersatellite links (LISLs) are in high
demand by companies, such as SpaceX and Telesat, who want to equip their satellites with LISLs
to realize free-space optical satellite networks (FSOSNs). Such terminals offer LISLs with ranges
spanning from 4500 to 45,000 km. How does LISL range affect network latency in FSOSNs? To
investigate this effect, we employ the satellite constellation for Phase I of SpaceX's Starlink and
examine six different LISL ranges for satellites in this constellation in three different scenarios for
long-distance intercontinental data communications. We observe that the satellite connectivity,
and thereby the network connectivity, improves with an increase in the LISL range as more and
farther satellites become available within the LISL range of a satellite. A higher LISL range leads
to better shortest paths offering lower average network latency. Furthermore, improvement in
average network latency with increase in LISL range is seen in all scenarios.
LPI WAVEFORM RECOGNITION USING ADAPTIVE FEATURE CONSTRUCTION AND
CONVOLUTIONAL NEURAL NETWORKS
Low probability of intercept (LPI) radar waveform recognition is one of the crucial functions in
electronic intelligence systems. Advances in artificial intelligence promote the performance of the
LPI waveform recognition with various signal features defined with analytical expressions. However,
noisy LPI waveform recognition is still a challenge for traditional approaches even with
noise elimination techniques, especially for the heavily contaminated noisy LPI signals. Recently,
the adaptive analysis techniques, such as variational mode decomposition (VMD) and empirical
mode decomposition (EMD) provide potential methods that explore the inherent features of signals
and formulate adaptive features for signal recognition. In this article, we propose an adaptive
feature construction framework that utilizes both the adaptive features (via VMD and EMD) and
predefined analytical features (via Wigner-Ville distribution, Choi-William distribution, and
wavelet analysis) to construct the fusion feature, which is further applied on the convolutional neural
networks-based LPI waveform recognition system. The experimental results show that the proposed
feature adaptive LPI network-based exploitation (FALPINE) approach achieves higher
probability of correct classification than state-of-the-art works, which demonstrates the superior
performance of the proposed approach.
NEURAL NETWORK-BASED CONTROLLER FOR TERMINAL GUIDANCE APPLIED
IN SHORT-RANGE ROCKETS
Precision during the guided terminal flight in short-range rockets is a key factor that must be
improved using several methodologies. Traditionally, inertial navigation systems allowed to
unbind the accuracy concerning the range. Unfortunately, in short-range rockets, the response of
the inertial-based controller has to be fast enough to correct the trajectory in a short time. This fact
implies that to achieve high precision, either the use ofsystems too large to be implemented in portable
rockets is required, or systems that are too expensive concerning the total cost of the rocket.
For these systems, artificial intelligence-based guidance tactics could improve the precision, since
once the network is trained, it is not necessary to know the dynamics of the vehicle, and the network
itself can react quickly, simplifying the system and reducing economic costs using relatively
simple electronics. The neural networks are trained using a nonlinear-dynamics model based on a
simulated flight dynamics and verified with real flight data. The simulation results show that the
proposed method works effectively in a six-degree-of-freedom simulation environment, with
excellent accuracy and robustness to parameter uncertainty. The appropriateness of the closedloop
performance is validated using Monte Carlo analysis across a wide range of uncertainty
scenarios.
APRIL 2023
IEEE A&E SYSTEMS MAGAZINE
3

IEEE - Aerospace and Electronic Systems - April 2023

Table of Contents for the Digital Edition of IEEE - Aerospace and Electronic Systems - April 2023