Kyu-Ha Song

Recognition of PRI Modulation Types of Radar Signals Using the Autocorrelation

In electronic warfare support systems, the analysis of PRI (Pulse Repetition Interval) modulation characteristics for a radar pulse signal has attracted much interest because of the problem of the identification ambiguity in dense electronic warfare signal environments. A new method of recognizing the PRI modulation type of a radar pulse signal is proposed for electronic warfare support. The proposed method recognizes the PRI modulation types using classifiers based on the property of the autocorrelation of the PRI sequences for each PRI modulation type. In addition, the proposed method estimates the PRI modulation period for the PRI modulation type with the periodicity. Simulation results are presented to show the performance of the proposed method.

Pulse Repetition Interval Modulation Recognition Using Symbolization

Information on the pulse repetition interval (PRI) modulation of a radar signal plays an important role in detecting and identifying each radar signal in an electronic warfare support (ES) system. In this paper, we present a new method for recognizing the PRI modulation type of a radar signal using symbolization. The proposed method uses three key feature parameters extracted from symbol sequences in order to discriminate each PRI modulation type. The recognition capability of the method presented is verified through extensive simulations.

Denoising method for weak-power radar signals using the modified sinc wavelet

Electronic warfare (EW) is commonly divided into three subfields: electronic warfare support (ES), electronic attack (EA), and electronic protection (EP). Among them, ES systems take charge of collecting and analyzing the received signal from enemys emitters and then provide the processed information to friendly EA and EP systems. The information is of critical importance because it is the first prerequisite for the proper discharge of duties of EA and EP systems and is directly linked to the issue of a battle. However, ES systems have difficulty in carrying out their jobs because there are some cases of having no information about enemys operating frequency and the received signal power is sometimes seriously weak. In this paper, we propose a novel method to solve these problems at once based on the modified wavelet transform by using the unique scaling property of continuous time wavelet transform and the frequency shifting property. Computer simulation results show that the proposed method can be useful and practical to cope with low SNR environment.

A Kernel Density Window Clustering Algorithm for Radar Pulses

As radar signal environments become denser and more complex, the capability of high-speed and accurate signal analysis is required for ES (electronic warfare support) system to identify individual radar signals at real-time. In this paper, we propose the novel clustering algorithm of radar pulses to alleviate the load of signal analysis process and support reliable analysis. The proposed algorithm uses KDE (kernel density estimator) and its CDF (cumulative distribution function) to compose clusters considering the distribution characteristics of pulses. Simulation results show the good performance of the proposed clustering algorithm in clustering and classifying the emitters.

ANALYSIS ON THE EFFECT OF GEOMETRIC DEPLOYMENT AND VELOCITIES OF SENSORS BASED ON TDOA/FDOA MEASUREMENTS

In passive emitter location systems using the Time difference of arrival(TDOA) and Frequency Difference of arrival(FDOA) measurements, the estimation performance varies with the geometric deployment and velocities of the sensors due to the high non-linearity of the TDOA/FDOA signal models, even when the estimation algorithm and the measurement error are not changed. To achieve a desired performance, finding appropriate deployment and velocities of the sensors is required. The problem is how to find them because there are too many cases of operating the emitter location systems. To cope with this problem, we derive an error ellipse to evaluate the estimation accuracy according to geometric deployment of the sensors based on the signal model in a 3-dimensional space (3-D) and various velocities. By using the derived error ellipse, analysis on the performance in the cases of several practical scenarios is carried out. The reliability of our evaluation method is confirmed by comparing to the performance evaluation based on the root-mean square error for the corresponding simulation results. Our analysis results on the various practical situations will be very helpful for operating the passive emitter location systems.

Weak radar signal detection based on variable band selection

Electronic warfare support (ES) plays an important role in gathering and analyzing signals from enemy emitters to provide friendly electronic attack (EA) systems or electronic protection (EP) systems with crucial information. However, ES systems may have difficulty in gathering meaningful signals because there is no prior information about the signal in most cases, and the signal is sometimes too weak or distorted. In this paper, we propose a novel weak-signal detection method to solve these problems based on a modified sinc wavelet transform. This method exploits the frequency shifting property to change the center frequency of a band-pass filter and then applies the scaling property of the continuous-time wavelet transform to vary its bandwidth. This approach is distinguished from previous works in terms of the detection capability of weak signals with high-frequency or multifrequency components. Computer simulation results show that the proposed method can be used in practical implementation to deal with signals that have a low signal-to-noise ratio (SNR).

Emitter location based on the linearization of quadratic TDOA and FDOA curves

This paper proposes a straightforward method producing the coarse estimate of an emitter position without the constraints of initialization and sensor-pair combination. The method derives two straight lines from a pair of quadratic TDOA and FDOA curves, and then employs a least squares minimization to estimate the emitter position. Simulation results are included to evaluate the performance of the proposed algorithm with varying the emitter position.