Dae-Young Chae

Hyperbolic Localization in MIMO Radar Systems

This letter addresses an effective algorithm for target localization in multiple-input-multiple-output (MIMO) radar systems with widely separated antennas. The algorithm derived uses time-of-arrival (TOA) measurements from multiple transmitter-receiver pairs and is based on a hyperbolic method suitable for radiation source localization in passive sensor networks. It does not have the local convergence problem as the conventional iterative method. Some combinations of the derived algorithm and the conventional iterative method are presented. In a numerical example, it is shown that the proposed methods can achieve the Cramer-Rao lower bound (CRLB) in the range of moderate processed measurement noise and obtain the better localization performance as the number of transmitters and receivers increases. Furthermore, some remarks are made on the robustness of the proposed methods.

Two-stage localization method in multistatic radar systems

In this paper, we present a new localization method in multistatic radar systems. Based on two-stage algorithm, which is used to estimate the location of a target that transmits a signal in passive sensor networks, we reformulate the target localization problem and derive the new two-stage algorithm which suits our situation. Simulation results demonstrate that the proposed method outperforms conventional methods and achieves the Cramer-Rao lower bound (CRLB) with the knowledge of noise statistics over the range of moderate noise power.

A novel feature extraction method for radar target classification using fusion of early-time and late-time regions

Abstract This paper proposes a feature vector fusion of early-time and late-time regions, which improves the performance of radar target classification. For verifying the performance of the proposed method, we use the calculated radar cross section (RCS) of four full-scale targets and measured the RCS of three scale model targets. Then, we extract a feature vector from a waveform structure in the early-time region. The resonance frequencies are extracted using an evolutionary programming (EP)-based CLEAN algorithm in the late-time region. The extracted feature vectors are passed through the feature fusion process and then used as inputs for a neural network classifier. The results show that the proposed method exhibits better performance than those that use either early-time or late-time features.

Target Classification from JEM Signal Using Frequency Masking

This paper deals with a technique for classifying jet aircrafts from JEM (Jet Engine Modulation) signal. A novel method to recognize an engine model by analyzing JEM spectrum using frequency mask is proposed. The frequency mask extracts and analyses the spectral component at the frequencies that are predicted from the blade number of a jet engine and the estimated spool rate. The proposed method does not need a complicated logical algorithm for finding the chopping frequency or the pre-simulated engine spectra used in previous methods. In addition, we suggest a method to precisely estimate the spool rate in the spectrum domain of JEM signal, which plays an important role in generating the frequency mask. The classification experiments using the JEM signals measured from two fabricated engine models verify that the proposed algorithm has good performance in the recognition of jet aircrafts.

Spatiotemporal radar target identification using radar cross-section modeling and hidden Markov models

We propose a target identification scheme exploiting the temporal dependency and spatial structure of radar cross-section (RCS) measurements in low-frequency, passive, or long-range surveillance radar systems. We employ target RCS modeling integrated into a hidden Markov model (HMM) with state-duration modeling. Assuming that the radar system ensures a sufficient sampling time, it is possible to use the spatial characteristics of airborne targets, because there is consistency between successively sampled RCS measurements. In addition, to exploit the whole temporal characteristic of the sequence of RCS measurements,which has rarely been considered in the literature, we adopt the HMM and target RCS modeling. To accomplish this task, we accurately develop target RCS models and establish the relationship between the target-sensor orientations, which are the hidden states of the HMM, and the corresponding RCS measurements. The proposed target identification scheme, which only uses the sequence of RCS measurements, is demonstrated with simulation results and an analysis for various signal-to-noise ratios and target-fluctuation models.

An effective GLRT-based method for extended target detection

In this paper a simple and effective GLRT algorithm is proposed to detect distributed target embedded in unknown Gaussian noise, under unknown Doppler frequency. Targets signature and velocity are accurately and simultaneously estimated during detection procedure without resorting to conventional algorithms like MUSIC, root MUSIC or ESPIRIT. Simulation results revealed high detection performance and target estimation under relative low SNR.

Natural frequency-based recursive LRT detection using the Lagrange polynomial

We consider the performance analysis of the natural frequency-based radar target detection. By making the Lagrange polynomial approximation of the standard normal distribution, the probability of detection for an augmented input vector can be recursively calculated. We present the bound of the error due to the Lagrange polynomial approximation, and it is illustrated that the actual error is within the derived error bound. We also present how to determine the optimal first-order Lagrange polynomial.

Dependence of performance on threshold value in natural frequency-based target recognition: frequency domain approach

In this paper, we address natural frequency-based radar target recognition in the frequency domain. We propose how to improve the performance of natural frequency-based radar target recognition in the frequency domain by extending the previous scheme in the time domain to the frequency domain. The probability of correct classification is expressed in terms of the probability density function (PDF) or the cumulative density function of difference of projections. The PDF can be numerically determined from the characteristic function of difference of projections. From the expression of the probability of correct classification, we show how to determine an optimal threshold in the sense that the probability of correct classification is maximized. The effectiveness of the proposed scheme is verified using numerical examples.