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Array signal processing

Reactive matching is extended to wide bandwidths using the impedance property of LC-ladder filters. In this paper, we present a systematic method to design wideband low-noise amplifiers. An SiGe amplifier with on-chip matching network spanning 3-10 GHz delivers 21-dB peak gain, 2.5-dB noise figure, and -1-dBm input IP3 at 5 GHz, with a 10-mA bias current.

A 3-10-Ghz low-noise amplifier with wideband LC-ladder matching network

Inverse Synthetic Aperture Radar Imaging with MATLAB Algorithms

1. Random Variables and Stochastic Processes

Inverse synthetic aperture radar (ISAR) imaging systems produce electromagnetic images of targets in the range-Doppler domain. In order to rescale the image in a homogeneous range-cross range domain (meters by meters), the modulus of the target effective rotation vector must be known. Although in some cases it can be retrieved by means of ancillary data, in most cases the modulus of the target effective rotation vector must be estimated. A blind technique is proposed for estimating the modulus of the target effective rotation vector that exploits information carried by the chirp rate of scattering centres. A technique based on image segmentation, local polynomial Fourier transform (LPFT), and image contrast (IC) maximisation is used in order to extract the scattering centres and estimate their chirp rate. Simulated and real data analyses are provided to confirm the effectiveness of the proposed algorithm.

Novel approach for ISAR image cross-range scaling

This paper presents a new target recognition scheme via adaptive Gaussian representation, which uses adaptive joint time-frequency processing techniques. The feature extraction stage of the proposed scheme utilizes the geometrical moments of the adaptivity spectrogram. For this purpose, we have derived exact and closed form expressions of geometrical moments of the adaptive spectrogram in the time, frequency, and joint time-frequency domains. Features obtained by this method can provide substantial savings of computational resources, preserving as much essential information for classifying targets as possible. Next, a principal component analysis is used to further reduce the dimension of feature space, and the resulting feature vectors are passed to the classifier stage based on the multilayer perceptron neural network. To demonstrate the performance of the proposed scheme, various thin-wire targets are identified. The results show that the proposed technique has a significant potential for use in target recognition.

Efficient radar target classification using adaptive joint time-frequency processing

In this work, we present a novel two-dimensional (2-D) scattering center extraction method, called 2-D evolutionary programming (EP)-based CLEAN. The 2-D EP-based CLEAN uses the ideal point scattering model and EP for optimization. Our algorithm has accuracy, robustness and better resolution than the fast Fourier transform (FFT)-based CLEAN. Further, it does not require the transformation of measured data from the polar frequency domain to the Cartesian frequency domain or to the image domain. Numerical simulations are performed to verify the characteristics of the proposed algorithm, such as resolution, robustness and accuracy.

Two-dimensional evolutionary programming-based CLEAN

Time domain response-based neural networks and frequency domain response-based neural networks have been proposed for radar target recognition. We propose a natural frequency-based neural network for radar target recognition. Our scheme takes advantage of an aspect angle independence of a natural frequency. It is shown from experimental results that a natural frequency based-neural network using the first natural frequency pair is superior to a time domain response-based neural network in the case of a single aspect angle and that a natural frequency based-neural network using the first natural frequency pair or the first two natural frequency pairs is superior to a time domain response-based neural network in the case of a multiple aspect angle.

Natural frequency-based neural network approach to radar target recognition

A differential common gate low noise amplifier (LNA) has been widely used for a wideband LNA. However, it has poor linearity due to a nonlinear transconductance in a MOSFET and poor noise performances from the common gate configuration. We propose a differential common gate LNA with a negative g m cell for the improvement of the linearity and noise figure. The cell comprises cross coupled transistors instead of a current source. The negative g m cell creates the opposite phased harmonic, canceling the distortion. The noise figure is improved by canceling the noise from the common gate transistors through the negative g m cell. The LNA is fabricated in 0.13 μm RF CMOS. The LNA has the bandwidth of 0.7 ∼ 3.5 GHz frequency and has provided the expected characteristics for linearity and noise figure.

Wideband LNA Using a Negative gm Cell for Improvement of Linearity and Noise Figure

Many time–frequency analysis techniques have been used for ISAR motion compensation. One of them, the adaptive wavelet transform (AWT), is cross-term free and has high resolution, but its accuracy is limited because it uses a bisection search method and fast Fourier transform (FFT) for parameter extraction. A novel time–frequency (T–F) analysis, called the evolutionary adaptive wavelet transform (EAWT), is used for ISAR motion compensation. The EAWT is more accurate than the conventional AWT. Using artificially created data from three time–frequency centres and simulated MIG-25 ISAR data, this algorithm is compared with the conventional AWT for accuracy. From the constructed ISAR image using AWT and EAWT, it is demonstrated that the EAWT algorithm can obtain a more clear motion-compensated ISAR image than the conventional AWT.

In-Sik Choi

Papers

Efficient radar target classification using adaptive joint time-frequency processing

Two-dimensional evolutionary programming-based CLEAN

Natural frequency-based neural network approach to radar target recognition

Wideband LNA Using a Negative gm Cell for Improvement of Linearity and Noise Figure

ISAR motion compensation using evolutionary adaptive wavelet transform