This paper proposes an outliers-robust constant false-alarm rate (OR-CFAR) detector of Gaussian clutter based on the truncated-maximum-likelihood estimator (TMLE) in SAR imagery. The proposed method aims at elevating the detection performance in multiple-target environment, where the sea clutter samples are often contaminated by the interfering target pixels, the azimuth ambiguities, and the breakwater. As a consequence, the parameters used for statistical modeling are over-estimated, resulting in a degradation of the CFAR detection rate. Inspired by the traditional two-parameter CFAR (TP-CFAR) detector of Gaussian clutter, OR-CFAR designs an adaptive threshold-based clutter truncation method to eliminate the high-intensity outliers from the clutter samples in the local reference window, and the probability density function (PDF) of the sea clutter can be accurately modeled through the newly raised TMLE. Furthermore, the optimal truncation depth used for clutter truncation and PDF modeling is evaluated and selected properly to get the best detection results. The OR-CFAR greatly enhances the CFAR detection rate in multiple-target environment, and it is computationally simple and efficient, which has a great application value. The Chinese Gaofen-3 SAR data are used for experiments to show the better detection performance of OR-CFAR.

Traditional constant false alarm rate (CFAR) detectors suffer probability of detection (PD) degradation influenced by the outliers such as interfering ship targets, side lobes, and ghosts, especially in crowded harbors and busy shipping lines. In this paper, a new two-parameter CFAR detector based on adaptively truncated clutter statistics (TS-LNCFAR) is proposed. The new two-parameter CFAR detector uses log-normal as the statistical model; by adaptive-threshold-based clutter truncation in the background window, the outliers are removed from the clutter samples, while the real clutter is preserved to the largest degree. The log-normal model is accurately built using the truncated clutter statistics through the maximum-likelihood estimator. Compared with traditional CFAR detectors, the parameter estimation is more accurate, and TS-LNCFAR has a better false alarm regulation property and a high PD in a multiple-target environment. Furthermore, the parameter estimation and threshold calculation do not need iterative numerical calculation, and TS-LNCFAR has a high computational efficiency. The superiority of the proposed TS-LNCFAR detector is validated on the multilook Envisat-ASAR and TerraSAR-X data.

Objective: Currently, BP measurement devices are mainly cuff-based which are not portable or convenient for users. To simplify the measurement of BP, this article proposed a new framework for noninvasive BP estimation using single-channel PPG signals. Methods: Various PPG features that may be related to BP were extracted and a filter-wrapper collaborated feature selection method was used for rejecting irrelevant and redundant features. The features that maximize the correlation with BP were finally selected as the BP-oriented IFS, and a new LASSO-LSTM model was designed to estimate BP from the IFS. Results: Experiments were conducted on a public dataset and a self-collected clinical dataset, respectively. Results demonstrated that the proposed method is superior to previously reported methods in the literature, giving a mean absolute error of 4.95 mmHg for SBP and 3.15 mmHg for DBP which complies with the standard of the AAMI. Conclusion: The proposed filter-wrapper collaborated feature selection method could effectively reject weak correlation and redundant features, and the designed LASSO-LSTM model is capable of learning complicated nonlinear relations between the selected IFS and BP. The proposed method shows improved accuracy of noninvasive BP estimation.

Aiming at the problem that the amplitudes of false targets (FTs) are correlated among receivers in multistatic radar system (MSRS), a multi-jammer cooperative deception jamming method based on periodic multiple phases modulation (PMPM) is proposed. A FT string with random fluctuations in amplitude can be generated after the signal undergoes PMPM. In the distributed jammer system, each jammer adopts PMPM jamming method with different parameters, and then jointly transmit beamforming to forward jamming signals. Finally, the amplitude of the FTs between receivers is jointly affected by the azimuth angle and the phase modulation coefficient, which destroys the amplitude "coherence" of the FTs between receivers, and eliminates the amplitude symmetry and high-order attenuation characteristics of the FT strings generated by traditional jamming method. We study the amplitude compensation and amplitude correlation performance of the proposed jamming method, derive the rank of the ratio matrix of amplitude correlation factors, and prove that FTs have dispersiveness in the amplitude ratio space. In order to obtain the optimal and stable jamming effect, a genetic algorithm (GA)-based jamming modulation parameter optimization method is designed, which effectively drops the discrimination probability of physical targets (PTs) to 14% in the benchmark scenario. Simulation experiments demonstrate the feasibility and effectiveness of the jamming method.

Most of the existing photoplethysmography (PPG)-based blood pressure (BP) estimation methods aim at training a general BP model applicable to all individuals which neglected the vasculature and anatomical differences among individuals as well as the slow and subtle cardiovascular changes over time, thus, were hard to achieve high accuracy. This study aims at addressing this problem by constructing personalized BP models from PPG signals. First, the PPG features that can well reflect the changes of individual BP with the physiological state were extracted. Afterwards, an error feedback incremental support vector regression (EFISVR) model was designed to achieve high-accuracy BP measurement of a subject, which can quickly be adapted to new samples without retraining the whole model. Results show that the constructed model can accurately predict the BP values of a subject for at least three months. The mean absolute error (MAE) of BP estimation were 3.11 mmHg for systolic BP (SBP) and 2.47 mmHg for diastolic BP (DBP). The proposed EFISVR model is lightweight which can be integrated into wearables and other edge devices, as a part of Internet of Things (IoT) applications. The advantages of lightweight, few-shot learning and high precision make the model suitable for applications in real-life scenarios.