In this study, a class of linear system, which is with no exogenous input and suffered from constant measurement delay and uncertain model‐parameter errors, is under consideration. To combat both the parametric uncertainties and constant measurement delay, a novel robust state estimator is proposed. Accounting for the constant measurement delay, a clever approach is utilised to expand the state vector and the system model is converted into an augmented delay‐free model. Considering the deterioration of estimation performance caused by stochastic model‐parameter errors, the sensitivity penalisation function of model‐parameter errors is defined and introduced into the objective function of the regularised least‐squares (RLS) problem, whose solution is the standard Kalman filter. Furthermore, by restricting the range of introduced parameter, the objective function of the modified RLS problem is converted into a strict convex function. Then, the recursive procedure of the proposed estimator is derived. The asymptotic stability conditions of the proposed estimator and the conditions for boundness of the estimation error matrix are given. Numerical simulations show the effectiveness of the estimator proposed in this paper.

In heterogeneous environments, the snapshot under test (SUT) and the corresponding training samples are usually not independent and identically distributed, which seriously degrades the clutter suppression performance of space‐time adaptive processing (STAP). To solve this problem, this paper proposes a method which can select the training samples with similar clutter characteristics to that of the SUT. The proposed method constructs a novel united generalised inner product (UGIP) statistic with the sub‐aperture clutter covariance matrix (CCM) of the SUT and that of any other snapshot. The smaller the statistic is, the more similar the corresponding two snapshots are. Therefore, the snapshots with smaller UGIPs will be selected as training samples. The proposed method effectively improves the quality of the selected training samples for STAP and a better estimate of the CCM can be obtained. Simulation experiments verify the effectiveness of the proposed method with both simulated data and measured data.

The Doppler signature of a man walking in a forested area analysed at L‐band is presented here. The aim is twofold: to assess the best time‐frequency distribution to characterise the activity; to highlight the similarity of the simulated data to the measured ones to validate the simulation tool. Indeed, the Doppler‐Time (DT) signal variation represents the main characteristic of Artificial Neural Networks (ANNs) for classification. The more accurately the DT characterises the activity, the higher the machine’s accuracy in classifying it. Besides, in the training data frame, reliable simulated models may supply the amount of data needed by ANN applications. Thus, a short‐time Fourier transform (STFT), a reassigned spectrogram (RE‐Spect), and a pseudo‐Wigner–Ville distribution have been applied to the measured and simulated data. The measurements have been performed using a bistatic radar working at 1 GHz. Then, the measurement setup has been replicated in simulation, and 3‐D human bodies walking in free space have been computed using physical optics. The results show that the STFT is the most suitable time‐frequency method for recognising and classifying the walk. Moreover, the simulated data are in agreement with the measured data, regardless of the chosen Cohen’s technique.

The measurement noise covariance R plays a vital role in the Kalman filter (KF) algorithm. Traditionally, a constant R is obtained by experience or experiments. However, the KF cannot achieve optimal estimation using constant R under non‐Gaussian conditions. A robust strategy for adaptive estimation of R is proposed to suppress the influence of non‐Gaussian noise on the in‐motion alignment performance of the Doppler velocity log (DVL) velocity‐aided strapdown inertial navigation system (SINS). Furthermore, an improved Sage–Husa robust adaptive KF algorithm (SHRAKF) based on the Mahalanobis distance (MD) algorithm is proposed to handle the outliers that frequently occur within the complicated underwater environment. The contributions of this work are twofold—the SHRAKF (1) designs a robust strategy to adaptively estimate R in real time and (2) further improves filtering robustness and adaptability with the MD algorithm, conditional on the DVL outputs being contaminated by non‐Gaussian noise. A semi‐physical simulation experiment for SINS/DVL in‐motion alignment based on the test data is carried out, and the experimental results show that the SHRAKF adaptively estimates R in real time and effectively suppresses observational outliers. For non‐Gaussian noise pollution, including outliers and heavy‐tailed noise, the SHRAKF performs better than traditional methods.

The vortex electromagnetic (EM) wave radar has the potential to obtain more accurate micro‐motion parameters for target recognition. However, with the existing algorithms of micro‐motion parameter extraction it is difficult to obtain the real rotation radius and tilt angle of a rotational target in the presence of multiple scattering points in the radar beam. A micro‐motion parameter extraction algorithm for rotating targets based on the vortex EM wave radar is proposed in this article. The angular Doppler is obtained from the dual‐mode vortex EM echoes. The time interval between the maximum and minimum angular Doppler frequency is derived. The relationship between the time interval and micro‐motion parameters is shown. By combining the linear Doppler and the angular Doppler, the micro‐motion parameters are roughly estimated. Then, fine micro‐motion parameters are obtained by using an iterative soft threshold algorithm. The proposed algorithm can extract the real rotation radius and tilt angle in the case of multiple scattering points. The performance and robustness of the algorithm are proved by simulations.