1
A NOVEL METHOD FOR AUTOMATICALLY
IDENTIFYING PRI PATTERNS OF COMPLEX
RADAR SIGNALS
Yuwen Tang
1
, Minghao He
1
, Xiaojie Tang
1
, Jun Han
1*
, Xikun Fan
2
1
Air Force Early Warning Academy, Wuhan, China
2
Air Force Engineering University, Xi’an, China
*dspbuilder_t@163.com
Keywords: PRI, FEATURE EXTRACTION, MODULATION RECOGNITION, RADAR EMITTER
RECOGNITION
Abstract
Aimed at the problem of automatically identifying PRI
patterns of complex radar signals, a novel method is proposed
in this paper. The roughness, normalized mean jump energy,
and
-curve characteristic are extracted as feature set based
on the analysis of PRI sequences in time domain. Finally, the
decision tree is employed to recognize different PRI patterns.
The simulation results show that the algorithm has high
recognition accuracy and good robustness to spurious pulses
and pulse missing.
1 Introduction
The radar pulse repetition interval (PRI) is a time series
parameter of the radar pulse train. It is a key parameter for
analysing the radar's main tactical performance and identifying
the radar operation mode. However, with the development of
modern radar technologies, the PRI modulations are becoming
more and more complex, which puts forward higher
requirements for radar emitter signal recognition technologies.
Therefore, it is very necessary to find a new reliable algorithm
for PRI pattern recognition.
The algorithm studied in this paper is performed on the rough-
sorted radar signal PRI sequences, so that, pulse deinterleaving
processing is not considered. Existing PRI pattern recognition
methods can be roughly divided into three major categories:
one is the methods based on statistical histograms or plane
transformations. Typical examples include cumulative
difference histogram (CDIF)[1], sequential sequence
difference histogram (SDIF)[2], and PRI transform[3,4], etc.
However, the histogram-based methods have a good
performance only when deinterleaving the Constant and
Stagger PRI, and are ineffective for other patterns[5].
Moreover, these methods require manual analysis of a large
amount of pulse data, and it is difficult to identify small data
samples effectively. On the other hand, although the PRI
transform can handle small data samples, it needs constantly
adjust the width of the plane to obtain a clear image and it is
difficult to achieve automatic processing[6]. Another kind of
methods are based on the shape features of autocorrelation
function[5,7,8]. However, the autocorrelation function figure
of PRI sequences will change with the modulation parameters,
and it is sensitive to the pulse missing. The third kind of
methods are based on sequence differential characteristics. For
example, the [9] proposed a method based on the first-order
difference of PRI sequences and uses multi-layer perceptron to
identify. But its feature dimension is up to 64, and the
calculation cost is too high. The [10] obtained a good result by
combining the histogram-based methods and the sequence
difference methods, but the recognition accuracy decreases
significantly when both pulse missing and spurious pulses are
present[11].
The above literatures provide a good idea for the PRI pattern
recognition. This paper starts with the analysis of the time-
domain features of the PRI patterns and extracts three features
of the PRI sequences, namely, roughness, normalized mean
jump energy, and
-th pulse
front. In order to realize its rich operation modes, modern
multi-function radars have developed many complex PRI
patterns, and each pattern for a different function. The
common PRI patterns are mainly divided into six types:
Constant, Stagger, Jitter, Dwell and Switch(D&S), Sliding,
and Periodic. Since the histogram-based methods have been
able to recognize the Constant and Stagger PRI patterns, this
paper mainly focuses on the other four.
1.1.1 Jitter: Jitter is mainly used for anti-reconnaissance and
anti-jamming. It is the pattern that the PRI jittered around a
constant value randomly within a certain range.