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


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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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