IEEE - Aerospace and Electronic Systems - August 2023 - 3

In This Issue -Technically
AIRBUS A32X VERSUS BOEING 737 SAFETY OCCURRENCES
Safety is a major priority for airlines. To address passengers' perceptions of safety, airlines have randomly
assigned the Boeing 737 Max to routes and times. Historically, Boeing has been considered more
reliable and safer than Airbus. Hence, it is worth considering the differences in the safety occurrences of
the core narrow-body single-aisle aircraft of Boeing and Airbus; the 737 and A32x families of aircraft.
Utilizing the International Civil Aviation Organization safety occurrence data from 2008 to 2019, we
compared these aircrafts in terms of occurrence type, occurrence category, phase of flight, injury level,
and fatalities. It was found that Boeing had more accidents than expected, while Airbus had fewer (p ¼
0.015). In terms of fatalities, Boeing had more than expected, with Airbus fewer (p < 0.001). Looking at
accidents alone, only the number of fatalities was statistically significantly different. In both cases, the
increased number offatalities for Boeing appears to be the result oftwo Boeing 737 Max accidents (Lion
Air accident on 29 October 2018 and Ethiopian Airlines accident on 10 March 2019). Looking at the
reported fatal and hull loss accident rates, it was also found that the annual reduction for the Airbus A32x
aircraft were better than for the Boeing 737 aircraft.
AN INTEGRATED INS/GNSS SYSTEM WITH AN ATTENTION-BASED DEEP NETWORK FOR DRONES
IN GNSS DENIED ENVIRONMENTS
We propose a neural network-based approach to assist positioning in an integrated inertial navigation system/global
navigation satellite system (INS/GNSS) during GNSS interruptions. One can aid a navigation
system during GNSS outages by substituting the GNSS measurements with a well-trained neural network
(NN). Outputs of this NN can then be used in a Kalman filtering scheme to acquire the best consistent and
concise estimates according to the INS measurements. Since this problem has inherent spatial and temporal
aspects, the proposed NN should account for both aspects simultaneously. Convolutional long short-term
memory (CLSTM) structure is an excellent candidate to satisfy the aforementioned requirement. The
designed CLSTM architecture uses the angular rates and specific forces measured by INS to output the
pseudo GNSS position increment covering for the lost GNSS signal. An attention mechanism is added to
the final layer of the CLSTM to counter the gradient vanishing problem in long time-series prediction. A
field test utilizing a fixed-wing unmanned aerial vehicle is arranged to evaluate the proposed architecture's
performance. The field test result shows a significant 3D positioning improvement duringGNSS outages.
INCREMENTAL DEINTERLEAVING OF RADAR EMITTERS
In this article, we present a new paradigm for the deinterleaving ofradar signals in electronic support systems.
Traditional systems observe the electromagnetic spectrum for a longer period of time and perform
the deinterleaving operation batchwise. This article presents a new approach, where the deinterleaving is
performed after each detected radar pulse, which we call pulse-to-pulse incremental deinterleaving. It
enables minimum reaction time ofthe system for the detection ofradar emitters. For that purpose this article
presents three incremental clustering algorithms based on DBSCAN, adaptive resonance theory, and
the leader approach. The algorithms are evaluated using simulated PDW data of realistic complex radar
scenes. The results show that incremental approaches can achieve a similar accuracy as nonincremental
methods, but have much faster reaction times to the occurrence ofnew emitters.
THE MOONSHINE PULSE REPEATER JAMMER
Moonshine was a British airborne pulse repeater decoy jammer, first used in 1942 to counter German air
defense radars. It was later developed to be used on ships to spoof airborne maritime surveillance
radars, during the allied invasion of Europe in 1944. The development history of this system is
described, together with a description of its operating principles and hardware design.
AUGUST 2023
IEEE A&E SYSTEMS MAGAZINE
3

IEEE - Aerospace and Electronic Systems - August 2023

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