This paper is focused on resolving the problem of suppressing mainbeam deceptive jammers in the element-pulse coding (EPC) radar. At the modeling stage, the element-pulse phase coding matrix (EPPCM) is employed at the transmit side, introducing extra coding degrees-of-freedom (DOFs) for the discrimination of the true target and mainbeam jammers. By utilizing the extra coding DOFs, nonadaptive and adaptive suppression method for mainbeam jammers are developed. Firstly, in order to suppress the jammers non-adaptively, the EPPCM is designed based on the coding steering vector orthogonalization (CSVO) method, which requires the identical angle of the true and false targets. Then, the adaptive suppression method is proposed for the general cases, where the angle deviation between the true and false targets occurs. To this end, the unitary EPPCM is designed to achieve the coding DOFs at the transmit side. Moreover, we take the towed radar active decoy (TRAD) jammer as an example and the sample selection scheme for the estimation of jammer-plus-noise covariance matrix is proposed. At the analysis stage, experimental and numerical results in terms of output signal-to-jammer-plus-noise ratio (SJNR) demonstrate the effectiveness of the devised methods for suppressing mainbeam deceptive jammers.

To achieve high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) images, this paper focuses on resolving the problem of separating range-ambiguous echoes with the space–time coding (STC) array. At the modeling stage, the transmit elements and pulses of the STC array are configured with time delay and phase coding modulation, which introduces extra degrees of freedom (DOFs) in the transmit domain. To separate the echoes corresponding to different range-ambiguity regions, the equivalent transmit beamforming is performed in the two-dimensional space–frequency domain. Moreover, in order to compensate for the loss of range resolution during the beamforming progress, the frequency splicing method is proposed. At the analysis stage, the distributed target simulation is provided to demonstrate the effectiveness of obtaining HRWS SAR images in the STC radar. Additionally, the performance of resolving range ambiguity is compared with the traditional radar in terms of the range-ambiguity-to-signal ratio (RASR).

This paper is focused on the problem of resolving the range ambiguity in a fixed phase coding-phased array (FPC-PA) radar. At the modeling stage, the FPC-PA radar is configured by a fixed phase coding in both transmit elements and pulses, where the transmit beam is modulated with a coding phase in each pulse. Then, after some appropriate signal processing procedures, the degrees-of-freedom (DoFs) in the transmit domain is obtained, and the range region numbers, namely the index of the range ambiguity region, are discriminated in the transmit spatial frequency domain. Particularly, echoes from different range ambiguity regions correspond to different pointing directions of the equivalent transmit beampattern. Moreover, in order to obtain the range ambiguity index and angle of the target, a three-stage algorithm is developed to enhance the performance against the effect of amplitude and phase errors. At the analysis stage, the parameter estimation performance is assessed in comparison with the Cramér-Rao lower bound (CRLB). Experimental and numerical results are provided to demonstrate the effectiveness of resolving range ambiguity and parameter estimation with the proposed method.

The multi-target tracking filter under the Bayesian framework has strict requirements on the prior information of the target, such as detection probability density, clutter density, and target initial position information. This paper proposes a novel robust measurement-driven cardinality balance multi-target multi-Bernoulli filter (RMD-CBMeMBer) for solving the multiple targets tracking problem when the detection probability density is unknown, the background clutter density is unknown, and the target’s prior position information is lacking. In RMD-CBMeMBer filtering, the target state is first extended, so that the extended target state includes detection probability, kernel state, and indicators of target and clutter. Secondly, the detection probability is modeled as a Beta distribution, and the clutter is modeled as a clutter generator that is independent of each other and obeys the Poisson distribution. Then, the detection probability, kernel state, and clutter density are jointly estimated through filtering. In addition, the correlation function (CF) is proposed for creating new Bernoulli component (BC) by using the measurement information at the previous moment. Numerical experiments have verified that the RMD-CBMeMBer filter can solve the multi-target tracking problem under the condition of unknown target detection probability, unknown background clutter density and inadequate prior position information of the target. It can effectively estimate the target detection probability and the clutter density.

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