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Author: search.filters.author.Baghel V.Subject: search.filters.subject.Pulse compression

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    Development of an efficient hybrid model for range sidelobe suppression in pulse compression radar
    (2013) Baghel V.; Panda G.
    An efficient hybrid model that substantially reduces the sidelobe of the compressed output of the binary phase coded waveforms is suggested by suitably combining a matched filter (MF) and a radial function (RF). The sidelobe suppression is achieved by modulating the MF output by the RF output. Simulation study is carried out to evaluate the performance of standard MF, multilayer artificial neural network (MLANN) and radial basis function neural network (RBFNN) based pulse compressors for binary phase coded pulse compression. The evaluation is based on comparative analysis of the peak to sidelobe ratio (PSR) of the compressed output under noisy as well as Doppler shift conditions. The experimental results demonstrate that the performance of proposed method is significantly superior compared to that of the other standard methods. Further, the hardware requirement of the proposed model is significantly less and unlike other neural networks it does not require training operation. � 2012 Elsevier Masson SAS. All rights reserved.
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    Development and performance evaluation of an improved complex valued radar pulse compressor
    (2013) Baghel V.; Panda G.
    Pulse compression is an important and burning issue in radar signal processing. In the recent past, many adaptive and neural network based methods have been proposed to achieve effective pulse compression performance for real coded transmitted waveforms. Even though the radar signal is complex, it is mostly processed as real-valued in-phase and quadrature components. Hence it is desirable that for processing complex radar signal for pulse compression both the structure as well as the learning algorithm associated with it need to be complex in nature. Accordingly in this paper a novel adaptive method is proposed by employing a complex valued fully connected cascaded (CFCC) neural network. For training this network, a new complex Levenberg-Marquardt (CLM) algorithm is derived and used for imparting effective training of its weights. The new CLM based CFCC (CFCC-CLM) model offers superior convergence performance with the least residual mean squared error during training phase compared to those provided by the multilayer perceptron (MLP) trained with complex domain backpropagation (CDBP) and CLM based methods. Further the comparison of peak signal-to-sidelobe ratio (PSR) under noisy and Doppler shift conditions of the proposed method exhibits best performance compared to those offered by the MLP-CDBP, MLP-CLM and the matched filter (MF) based methods. � 2013 Elsevier Ltd. All rights reserved.
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    An efficient multi-objective pulse radar compression technique using RBF and NSGA-II
    (2009) Baghel V.; Panda G.; Srihari P.; Rajarajeswari K.; Majhi B.
    The task of radar pulse compression is formulated as a multi-objective optimization problem and has been effectively solved using radial basis function (RBF) network and multiobjective genetic algorithm (NSGA-II). The pulse compression performance of three different codes in terms of signal to peak side-lobe ratio (SSR) under noisy environment
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    A recurrent neural network approach to pulse radar detection
    (2009) Sailaja A.; Sahoo A.K.; Panda G.; Baghel V.
    Matched filtering of biphase coded radar signals create unwanted sidelobes which may mask some of the desired information. This paper presents a new approach for pulse compression using recurrent neural network (RNN). The 13-bit and 35-bit barker codes are used as input signal codes to RNN. The pulse radar detection system is simulated using RNN. The results of the simulation are compared with the results obtained from the simulation of pulse radar detection using Multilayer Perceptron (MLP) network. The number of input layer neurons is same as the length of the signal code and three hidden neurons are taken in the present systems. The Simulation results show that RNN yields better signal-to-sidelobe ratio (SSR) and doppler shift performance than neural network (NN) and some traditional algorithms like auto correlation function (ACF) algorithm. It is also observed that RNN based system converges faster as compared to the MLP based system. Hence the proposed RNN provides an efficient means for pulse radar detection.