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Qun Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Time-varying three-dimensional interferometric imaging for space rotating targets with stepped-frequency Chirp Signal
    IET Radar Sonar & Navigation, 2017
    Co-Authors: Qun Zhang, Jian Hu
    Abstract:

    Three-dimensional (3D) radar imaging can provide abundant information about space targets, thus playing a significant role in space target recognition, measurement and cataloguing. In this study, a time-varying interferometric 3D imaging method for space rotating targets is proposed based on stepped-frequency Chirp Signal. In this study, with L-shaped three-antenna configuration, the interferometric Signal model is first set up and high-resolution range profile (HRRP) series of the three antennas are obtained. Then through interferometric processing of HRRP series on different interferometric planes, the instantaneous spatial positions in the azimuth and pitching directions of each target scatterer are reconstructed. Combining with the instantaneous positions in range direction extracted from HRRP series, the time-varying 3D image of target can be reconstructed accordingly. Compared to existing 3D imaging method for space rotating targets, the proposed method can obtain real time-varying 3D image of target with one multi-antenna set radar. Simulation results verify the validity and robustness of the proposed method.

  • a novel cognitive isar imaging method with random stepped frequency Chirp Signal
    Science in China Series F: Information Sciences, 2012
    Co-Authors: Qun Zhang, Kaiming Li, Fufei Gu
    Abstract:

    The random stepped frequency Chirp Signal (RSFCS) has better performance in anti-jamming than that of conventional stepped frequency Chirp Signal (SFCS). In combination with the theory of compressing sensing (CS), a novel ISAR imaging method is proposed based on RSFCS, in which the high resolution range profile (HRRP) is reconstructed by using the conventional OMP algorithm, whereas the cognitive approach is introduced to further reduce the number of sub-pulse in RSFCS. In the proposed method, via cognizing the characteristics of moving targets, the number of sub-pulse in each burst can be adjusted adaptively. Finally, in the cross-range direction, the accurate reconstruction of ISAR image by using CS theory is implemented, which can effectively accomplish unwrapping. With the proposed method, high quality HRRP and ISAR image can be achieved with fewer sub-pulses of RSFCS and lower burst repetition frequency (BRF). Some simulation results are given to validate the effectiveness and robustness of the proposed algorithm.

  • reconstruction of moving target s hrrp using sparse frequency stepped Chirp Signal
    IEEE Sensors Journal, 2011
    Co-Authors: Qun Zhang
    Abstract:

    This paper introduces a novel reconstruction method of high-resolution range profile (HRRP) using sparse frequency-stepped Chirp Signal (FSCS). In the method, Compressed Sensing (CS) theory is utilized to reconstruct the moving target's HRRP. With this method even if the subpulses number of FSCS is incomplete, the HRRP can still be reconstructed successfully, and the final reconstructed ISAR image is clear enough for identification and classification of the target. Furthermore, the effect of noise is also analyzed by corrupting the data via an uncorrelated additive white Gaussian process.

Ujjwal Das - One of the best experts on this subject based on the ideXlab platform.

  • Tests For the Parameters of Chirp Signal Model
    IEEE Transactions on Signal Processing, 2019
    Co-Authors: Subhra Sankar Dhar, Debasis Kundu, Ujjwal Das
    Abstract:

    In this paper, the testing of the hypothesis problem on the unknown parameters involved in one-dimensional Chirp Signal model is explored. To be precise, we here theoretically investigate whether the vector of unknown parameters is the same as the vector with specified parameters. For that purpose, we propose four tests based on the least squares and the least absolute deviation estimators of the unknown parameters using $L_1$ and $L_2$ distances. It is shown that the proposed tests are consistent (i.e., the power of the tests tend to one as the sample size tends to infinity). In addition, the asymptotic local power of the tests using contiguous (local) alternatives is also thoroughly studied. An extensive simulation study shows the satisfactory performance of the new tests, and the usefulness of the proposed tests is exhibited on a few benchmark real datasets that are closely associated with various Chirp Signal models.

Ananthram Swami - One of the best experts on this subject based on the ideXlab platform.

  • optimal particle filters for tracking a time varying harmonic or Chirp Signal
    IEEE Transactions on Signal Processing, 2008
    Co-Authors: Efthymios Tsakonas, Nicholas D Sidiropoulos, Ananthram Swami
    Abstract:

    We consider the problem of tracking the time-varying (TV) parameters of a harmonic or Chirp Signal using particle filtering (PF) tools. Similar to previous PF approaches to TV spectral analysis, we assume that the model parameters (complex amplitude, frequency, and frequency rate in the Chirp case) evolve according to a Gaussian AR(1) model; but we concentrate on the important special case of a single TV harmonic or Chirp. We show that the optimal importance function that minimizes the variance of the particle weights can be computed in closed form, and develop procedures to draw samples from it. We further employ Rao-Blackwellization to come up with reduced-complexity versions of the optimal filters. The end result is custom PF solutions that are considerably more efficient than generic ones, and can be used in a broad range of important applications that involve a single TV harmonic or Chirp Signal, e.g., TV Doppler estimation in communications, and radar.

Debasis Kundu - One of the best experts on this subject based on the ideXlab platform.

  • Chirp Signal Model
    Statistical Signal Processing, 2020
    Co-Authors: Swagata Nandi, Debasis Kundu
    Abstract:

    Chirp Signals have played an important role in the statistical Signal processing literature. An extensive amount of work has been done in analyzing different one dimensional Chirp, two dimensional Chirp and some related Signal processing models. These models have been used in analyzing different real-life Signals or images quite efficiently. It is observed that several sophisticated statistical and computational techniques are needed to analyze these models and in developing estimation procedures. In this chapter a comprehensive review of different models have been presented, and several open problems are discussed for future research.

  • Tests For the Parameters of Chirp Signal Model
    IEEE Transactions on Signal Processing, 2019
    Co-Authors: Subhra Sankar Dhar, Debasis Kundu, Ujjwal Das
    Abstract:

    In this paper, the testing of the hypothesis problem on the unknown parameters involved in one-dimensional Chirp Signal model is explored. To be precise, we here theoretically investigate whether the vector of unknown parameters is the same as the vector with specified parameters. For that purpose, we propose four tests based on the least squares and the least absolute deviation estimators of the unknown parameters using $L_1$ and $L_2$ distances. It is shown that the proposed tests are consistent (i.e., the power of the tests tend to one as the sample size tends to infinity). In addition, the asymptotic local power of the tests using contiguous (local) alternatives is also thoroughly studied. An extensive simulation study shows the satisfactory performance of the new tests, and the usefulness of the proposed tests is exhibited on a few benchmark real datasets that are closely associated with various Chirp Signal models.

  • On approximate least squares estimators of parameters on one-dimensional Chirp Signal
    arXiv: Applications, 2018
    Co-Authors: Rhythm Grover, Debasis Kundu, Amit Mitra
    Abstract:

    Chirp Signals are quite common in many natural and man-made systems like audio Signals, sonar, radar etc. Estimation of the unknown parameters of a Signal is a fundamental problem in statistical Signal processing. Recently, Kundu and Nandi \cite{2008} studied the asymptotic properties of least squares estimators of the unknown parameters of a simple Chirp Signal model under the assumption of stationary noise. In this paper, we propose periodogram-type estimators called the approximate least squares estimators to estimate the unknown parameters and study the asymptotic properties of these estimators under the same error assumptions. It is observed that the approximate least squares estimators are strongly consistent and asymptotically equivalent to the least squares estimators. Similar to the periodogram estimators, these estimators can also be used as initial guesses to find the least squares estimators of the unknown parameters. We perform some numerical simulations to see the performance of the proposed estimators and compare them with the least squares estimators and the estimators proposed by Lahiri et al., \cite{2013}. We have analysed two real data sets for illustrative purposes.

  • Efficient algorithm for estimating the parameters of a Chirp Signal
    Journal of Multivariate Analysis, 2012
    Co-Authors: Ananya Lahiri, Debasis Kundu, Amit Mitra
    Abstract:

    Chirp Signals play an important role in the statistical Signal processing. Recently Kundu and Nandi (2008) [8] derived the asymptotic properties of the least squares estimators of the unknown parameters of the Chirp Signals model in the presence of stationary noise. Unfortunately they did not discuss any estimation procedures. In this article we propose a computationally efficient algorithm for estimating different parameters of a Chirp Signal in presence of stationary noise. From proper initial guesses, the proposed algorithm produces efficient estimators in a fixed number of iterations. We also suggest how to obtain the proper initial guesses. The proposed estimators are consistent and asymptotically equivalent to least squares estimators of the corresponding parameters. We perform some simulation experiments to see the effectiveness of the proposed method, and it is observed that the proposed estimators perform very well. For illustrative purposes, we have performed the data analysis of a simulated data set. Finally, we propose some generalization in the conclusions.

Subhra Sankar Dhar - One of the best experts on this subject based on the ideXlab platform.

  • Tests For the Parameters of Chirp Signal Model
    IEEE Transactions on Signal Processing, 2019
    Co-Authors: Subhra Sankar Dhar, Debasis Kundu, Ujjwal Das
    Abstract:

    In this paper, the testing of the hypothesis problem on the unknown parameters involved in one-dimensional Chirp Signal model is explored. To be precise, we here theoretically investigate whether the vector of unknown parameters is the same as the vector with specified parameters. For that purpose, we propose four tests based on the least squares and the least absolute deviation estimators of the unknown parameters using $L_1$ and $L_2$ distances. It is shown that the proposed tests are consistent (i.e., the power of the tests tend to one as the sample size tends to infinity). In addition, the asymptotic local power of the tests using contiguous (local) alternatives is also thoroughly studied. An extensive simulation study shows the satisfactory performance of the new tests, and the usefulness of the proposed tests is exhibited on a few benchmark real datasets that are closely associated with various Chirp Signal models.