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Using the dual-polarized array for underdetermined estimation of partially polarized (PP) signal parameters can lead to limited signal-to-ratio (SNR) and biased reconstruction of the coherency matrix. In this paper, a new non-iterative method is proposed with the tri-polarized nested array. With the sub-covariance addition, the power of different polarized components of a signal can be completely accumulated, which improves the SNR. Besides, it is proven that with the optimized tri-polarized nested array, noise variance estimation without iterations becomes possible in the underdetermined case, which is critical for unbiased coherency matrix reconstruction. The subspace-based method is adopted to estimate the direction-of-arrival (DOA), and the polarization parameters can be obtained based on the reconstructed coherency matrices. The proposed method is validated by numerical experiments and compared with other representative methods. It has relatively high accuracy and is about one order of magnitude faster than its competitor.

The calibration of the angularly dependent array error is a challenging task for signal processing. In this paper, we propose a neural network (NN)-based two-dimensional (2D) calibration method for a linear array. Firstly, the array steering vectors are measured on an azimuth grid at different elevations in an anechoic chamber, and the off-grid steering vectors are derived by the proposed local manifold interpolation (LMI) technique to reduce the risk of model overfitting. Then, the phase differences are extracted to form the features of the training data. At last, noise is added to the training data to enable the NN model to generalize well to the noisy data. The proposed method is evaluated by the indoor and outdoor measured data from a 77 GHz automotive radar and is compared with the conventional signal processing-based methods. The evaluation results show that a single NN model trained at the lowest signal-to-noise ratio (SNR) outperforms conventional methods by at least 55% on average over the entire SNR range and gives close performance to the perfect array without array error at low to medium SNR.

The coprime array provides the possibility of resolving more signals than the sensors for the direction-of-arrival (DOA) estimation application. However, the non-consecution of its virtual array raises challenges for making full use of the degree of freedom (DOF). In this paper, we propose a new underdetermined DOA estimation method with coprime array where the non-consecutive virtual array can be converted into a virtual uniform linear array (ULA) with the same aperture. Firstly, all elements in the vectorized signal covariance matrix corresponding to the same virtual array positions are averaged to construct the output signals of the virtual array. Then, an atomic norm minimization (ANM) based optimization problem is formed for denoising the output signals of the virtual array and for interpolating the missing signals at the virtual array holes. At last, the ANM problem is solved by the semidefinite programming (SDP) and the DOAs are obtained by applying the subspace method on the reconstructed signal covariance matrix of the interpolated virtual ULA. The proposed algorithm is gridless and makes full use of the DOF and the information provided by the coprime array. The simulation results compared with the other representative methods are given to demonstrate the superiority of the proposed method with respect to the resolution and estimation accuracy.

In this paper, we address the problem of direction-of-arrival (DOA) estimation with the arbitrary array. The manifold separation technique (MST) is employed to transform the arbitrary array into a virtual array with Vandermonde manifold on which the spatial annihilating filter reconstruction method can be applied. When building the optimization problem for annihilating filter reconstruction, we propose the general solution modeling which can reduce the truncation error in MST to a negligible level. Finally, the spatial annihilating filter is reconstructed under the structural total least square (STLS) framework with the multiple measurement vectors structural total least norm (MMV-STLN) approach and the DOAs are estimated from the filter coefficients. Numerical simulations have verified the new proposed method adapts well to the low signal-to-noise ratio (SNR), limited snapshots and closely-spaced sources scenarios and can handle the coherent signals.

The time-consuming phase ambiguity resolution makes the uniform circular array (UCA) interferometer not suitable for real-time direction-of-arrival (DOA) estimation. This paper introduces the lookup table (LUT) method to solve this problem. The key of the method is that we look up the ambiguity numbers instead of the eventual DOA from the table, and then the DOA is obtained by relatively small amount of calculation. This makes it possible that we are able to shrink the table size while maintain the DOA estimation accuracy. The table addresses cover all possible measured phase differences (PDs), which enables the method to be free of spatial scanning. Moreover, without adding frequency index to the lookup table, the estimator can realize wideband application. As an example, a field-programmable gate array (FPGA) based DOA estimator with the estimation time of 180 ns is presented, accompanied by the measured results. This method possesses the advantages of ultra-high speed, high accuracy and low memory usage.

A novel quasi-coherent noise jamming method is proposed against linear frequency modulation (LFM) signal and pulse compression radar. Based on the structure of digital radio frequency memory (DRFM), the jamming signal is acquired by the pseudo-random sequence phase-modulation of sampled radar signal. The characteristic of jamming signal in time domain and frequency domain is analyzed in detail. Results of ambiguity function indicate that the blanket jamming effect along the range direction will be formed when jamming signal passes through the matched filter. By flexible controlling the parameters of interrupted-sampling pulse and pseudo-random sequence, different covering distances and jamming effects will be achieved. When the jamming power is equivalent, this jamming obtains higher process gain compared with non-coherent jamming. The jamming signal enhances the detection threshold and the real target avoids being detected. Simulation results and circuit engineering implementation validate that the jamming signal covers real target effectively.