Citation: Song, J.; Lu, Z.; Xiao, Z.; Li,
B.; Sun, G. Optimal Order of
Time-Domain Adaptive Filter for
Anti-Jamming Navigation Receiver.
Remote Sens. 2022, 14, 48. https://
doi.org/10.3390/rs14010048
Academic Editors: Kamil Krasuski
and Damian Wierzbicki
Received: 25 November 2021
Accepted: 21 December 2021
Published: 23 December 2021
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Technical Note
Optimal Order of Time-Domain Adaptive Filter for
Anti-Jamming Navigation Receiver
Jie Song, Zukun Lu * , Zhibin Xiao, Baiyu Li and Guangfu Sun
College of Electronic Science and Technology, National University of Defense Technology,
Changsha 410073, China; songjie16@nudt.edu.cn (J.S.); xiaozhibin@nudt.edu.cn (Z.X.);
lby0505@nudt.edu.cn (B.L.); gfsun@nudt.edu.cn (G.S.)
* Correspondence: luzukun@nudt.edu.cn; Tel.: +86-155-7499-3958
Abstract:
Adaptive filtering algorithms can be used on the time-domain processing of navigation
receivers to suppress interference and maintain the navigation and positioning function. The filter
length can affect the interference suppression performance and hardware utilization simultaneously.
In practical engineering, the filter length is usually set to a large number to guarantee anti-jamming
performance, which means a high-performance receiver requires a high-complexity anti-jamming
filter. The study aims at solving the problem by presenting a design method for the optimal filter
order in the time-domain anti-jamming receiver, with no need for detailed interference information.
According to interference bandwidth and jam-to-signal ratio (JSR), the approach designed a band-
stop filter by Kaiser window for calculating the optimal filter order to meet interference suppression
requirements. The experimental results show that the time-domain filtering processing has achieved
good interference suppression performance for engineering requirements with optimal filter order in
satellite navigation receivers.
Keywords:
Global Navigation Satellite System (GNSS) receiver; time-domain anti-jamming; adaptive
filter processing; Kaiser window; optimal filter order
1. Introduction
With the development of satellite navigation systems, navigation interference and
anti-interference have become necessary means of navigation confrontment and compe-
tition
[1,2]
. Narrowband interference (NBI) and wideband interference (WBI) are critical
issues for receivers’ acquisition and tracking processing, which are classified by interfer-
ence bandwidth [
3
,
4
]. NBI has become the most common method in preventing receivers’
positioning because of its advantages of low power and easy realization [
5
]. Time-domain
anti-jamming processing is one of the most common techniques to suppress narrowband
interference in navigation receivers [
6
]. It takes advantage of the difference between the
signal and interference in the frequency spectrum and designs a filter to filter out the
interference signal in the time domain [
7
] to ensure the normal ranging and positioning
function [
8
]. With the development of digital signal processing in navigation applications,
navigation receivers often face different hardware problems such as speed, design scale,
and power consumption during digital signal processing, which put forward high require-
ments of filter complexity [
9
]. However, the main parameter affecting the filter complexity
is the filter length [
10
], which heavily relies on engineering practices instead of being
adjusted according to the actual interference suppression requirements. The hardware
resources of satellite navigation receivers are usually limited, so the hardware complexity
of adaptive filters should be reduced as much as possible while meeting the anti-jamming
requirements [
11
]. Therefore, the research of low complexity time-domain anti-jamming
filters is an essential subject for navigation receiver applications.
The adaptive algorithm concludes mainly with two branches: the adaptive algorithm
based on non-parametric spectrum estimation and the adaptive algorithm based on linear
Remote Sens. 2022, 14, 48. https://doi.org/10.3390/rs14010048 https://www.mdpi.com/journal/remotesensing
