SCIENTIA SINICA Informationis, Volume 48, Issue 12: 1634-1650(2018) https://doi.org/10.1360/N112018-00033

A sparse estimation algorithm for the radar target range-direction under strong mainlobe jamming conditions

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  • ReceivedFeb 12, 2018
  • AcceptedApr 28, 2018
  • PublishedNov 27, 2018


In this paper, a sparse estimation algorithm for the radar target range and direction under strong mainlobe blanket jamming conditions is presented. This algorithm can effectively suppress multiple strong mainlobe blanket jammings (multi-MLJs) and extract the information about target distance and direction. First, the space domain adaptive processing for the received signal in each row (or column) is adopted to suppress multi-MLJs. Second, the sparse Bayesian learning is employed to obtain the information of the target distance and elevation (or azimuth). In addition, the proposed method still has an effective performance under the error condition and the applicable conditions of the new algorithm based on the performance analysis are provided in this paper.

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  • Table 1   The simulation parameters setting
    Parameter Setting
    The array antenna Consider a rectangular planar array which has 24 columns, and each column
    has 20 elements.
    The element spacing Half a wavelength.
    The beam boresight $\left(~\text{9}0{}^\circ~,30{}^\circ~~\right)$, the former is azimuth, and the latter is elevation.
    The 3 dB beam width $\left(~4.21{}^\circ~,5.05{}^\circ~~\right)$.
    The transmission signal Linear frequency modulation (LFM) signal, with bandwidth $B=5$ MHz,
    pulse width $\tau~=20~\mu$s and sampling rate ${{f}_{s}}=10$ MHz.
    The target One target with 5 dB is located at $\left(~\text{9}0{}^\circ~,30{}^\circ~~\right)$ and the 500-th sampling point.
    The jammings Two mainlobe blanket jammings with 50 dB are located at $\left(~\text{91}.05{}^\circ~,31.26{}^\circ~~\right)$,
    $\left(~88.95{}^\circ~,28.74{}^\circ~~\right)$, which are located at the one quarter of 3 dB beam width.
    Noise White Gaussian noise with 0 dB.
    The observation sample 5.
    number of sparse recovery

    Algorithm 1 Algorithm

    Inputs: the array received signal ${\boldsymbol~x}\in~{{\mathbb{C}}^{{{N}_{1}}\times~{{N}_{2}}}}$, angle dictionary $\mathbf{\varphi~}\in~{{\mathbb{C}}^{{{N}_{1}}\times~L}}$. Outputs: the sparsity coefficient $\mathbf{\omega~}$. 1. The outputs by the spatial row adaptive processing: ${\boldsymbol~r}(~n~)={\boldsymbol~a}\left(~{{\varphi~}_{0}}~\right)p{{s}_{0}}(~n~)+{{{\boldsymbol~v}}_{r}}(~n~)$; 2. Selecting $K$ training samples: ${\boldsymbol~r}\in~{{\mathbb{C}}^{{{N}_{1}}\times~K}}$; 3. Setting initial values: ${{\mathbf{\gamma~}}_{0}}=1$, $\sigma~_{0}^{2}={{10}^{-1}}$; 4. Calculating the posterior component:

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