Compared with the frequency diverse array multiple-input multiple-output (FDA-MIMO) radar, monostatic polarimetric sensitive (PS)-FDA-MIMO radar can provide targets polarization information. While compared with PS-MIMO radar, monostatic PS-FDA-MIMO radar can distinguish targets, which is from the same range bin and spatial-polarization angle but different ambiguous range region. In practical applications, the high pulse repetition frequency will cause the range ambiguity problem. Hence, a resolving range ambiguous approach for joint spatial-polarization angle and range estimation are devised for the monostatic FDA-MIMO radar with electromagnetic vector sensor (EVS)-FDA-MIMO in this paper. In receiver, spatial-polarization angles are estimated with the ESPRIT-based algorithm. In transmitter, range dependence compensation can resolve the coupling problem between spatial angle with range due to the frequency increment. The monostatic EVS-FDA-MIMO radar is able to utilize degree-of-freedom in the spatial angle and range domains to jointly determine the spatial-polarization angle and range parameters of target by the maximum likelihood estimator method. In addition, the Cramér-Rao Bounds for spatial-polarization angle and range are derived, and the proposed approach performance is analyzed. Numerical results corroborate the effectiveness of the considered estimation strategies in some diverse simulated scenarios.

In real-world industrial scenarios, production equipment and process systems usually operate under variable working conditions, and abnormal/faulty samples are hard to collect, bringing great challenges in implementing intelligent fault diagnosis. This work presents a novel data augmentation-free multi-category contrastive learning framework specifically designed for small sample-oriented fault diagnosis under variable working conditions. The contributions are twofold. First, a sample tuple batch construction strategy (STBCS) is proposed to establish sample tuple batches without resorting to data augmentation. This is achieved by leveraging positive sample pairs from all fault categories within the limited small-sample training set, thereby enhancing the diversity and representativeness of the training data. Second, an adaptive multi-category contrastive learning method (AMCCLM) is proposed based on the constructed sample tuples to learn high-quality domain-invariant features, thereby enhancing the generalizability and robustness of the fault diagnosis system. Extensive confirmatory and comparative experiments were carried out on two publically available benchmark datasets: the Spectra Quest (SQ) and the Case Western Reserve University (CWRU) bearing dataset, and a real industrial application scenario: the axial flow fan fault diagnosis from a steel-making plant. The experimental results demonstrate the superiority and robustness of the proposed method compared to representative state-of-the-art methods, especially under conditions involving small samples and variable working conditions.

Pulse compression technology can augment the likelihood of target discernment without degradation and without amplifying system hardware requisites. However, radar-communication integrated waveforms may cause mismatches in reception due to communication modulation, leading to increased loss in processing gain (LPG). This method aims to achieve communication transmission while suppressing near-range sidelobe interference (NRSI) with a minor sacrifice in LPG. An environment-based weighted mismatched filter (EWMF) design methodology is proposed to attenuate NRSI to the requisite level, with further control of LPG possible by adjusting communication modulation parameters. Moreover, the alternating direction method of multipliers is employed to jointly optimize the integrated waveform and filter design. The effectiveness of this method is demonstrated using the average sidelobe level over a specified region as the performance metric. Theoretical evaluation and experimental results confirm the applicability of waveforms using EWMF, effectively suppressing NRSI, and this method is suitable for all waveforms based on pulse compression processing. Notably, it offers cost-reduction advantages without requiring modifications to the radar transmitter or receiver.

This paper investigates the estimation of parameters—including the elevation angle, azimuth angle, polarization auxiliary angle, polarization phase difference, frequency and range of near-field sources in a Polarimetric Uniform Linear Array (P-ULA) with defective electromagnetic vector sensors. The cross-distribution dipole pairs are alternately placed in the xoy plane and yoz plane, respectively, and the whole array is divided into two subarrays, where subarray 1 consists of all of the dipole pairs placed in the xoy plane, while the dipole pairs placed in yoz plane are gathered in subarray 2. Specifically, the polarization auxiliary angle and the polarization phase difference, as well as the elevation and azimuth angles of the sources, are firstly estimated based on the Fourth-Order Cumulant (FOC) matrix in each subarray. Moreover, a decoupling method is developed to obtain the elevation and azimuth. Subsequently, the frequency and range are estimated based on the FOC matrix. Then, the parameter pair matching method is performed in order to match the pairs. Finally, an analysis of the Cramér-Rao Bound (CRB) is provided, and comparisons of the root mean square error with respect to the different input signal-to-noise ratios and number of snapshots, among different estimation methods, are implemented in the environment of additive white gaussian noise. The simulation results are provided in order to verify the effectiveness and feasibility of the proposed method for multi-dimensional parameter estimation.