This letter focuses on the passive localization of a moving source in the presence of random sensor location errors. We propose a modified two-stage closed-form method that takes the sensor location errors into account, utilizing time difference of arrival and frequency difference of arrival measurements. This method utilizes the weighted spherical interpolation approach to obtain an initial estimation in the first stage. The initial estimation is substituted into the original criterion to get a final estimation in the second stage. Theoretical analysis shows the proposed method can reach the Cramer-Rao lower bound under the small Gaussian noise assumption. Numerical simulation results show high noise tolerances of the method.

The sensor position uncertainties and synchronization offsets can cause substantial performance degradation if the sensors are not properly calibrated. This paper investigates the localization of a constant velocity moving target and the self-calibration of sensors using a sequence of range and azimuth measurements observed at successive instants. A theoretical study by the Cramer-Rao Lower Bound (CRLB) reveals that the sensor positions can only be self-calibrated when there are at least two sensors and synchronization offsets can be handled by joint estimation. A low complexity sequential closed-form solution is proposed to estimate the target position and velocity first, and the coordinates of each sensor and synchronization offset afterward. While less intuitive, the analysis shows that the closed-form solutions for both the target and sensor parameters can reach the CRLB accuracy under small Gaussian noise. We also develop a semidefinite programming (SDP) solution by semidefinite relaxation (SDR) for joint localization and calibration from the Maximum Likelihood formulation, which exhibits higher noise tolerance than the closed-form solution. Simulations validate the analysis and the performance of the proposed methods.

In the field of radar, nonlinearity has always been significant challenge in target tracking algorithms. It is evident in the complexity of the target motion model, observation model, and maneuverability of the target. Traditional model-based algorithms often rely on numerical approximations or simulations to obtain suboptimal solutions, which may lead to conversion errors and increase algorithm complexity. Model-free methods based on deep neural networks have been continuously employed in nonlinear target tracking (NTT) to improve target state estimation performance. This paper introduces two nonlinear trackers based on the Transformer that are used for smoothing, filtering, and predicting target states in the NTT task. First, a classical Transformer-based method is proposed for smoothing and prediction, improving both inference efficiency and accuracy through parallel operation. After that, to handle the recursive operation required for filtering, we introduce a novel recursive Transformer for recursive filtering and predicting of the target state. This significantly reduces computational load compared to the classical Transformer method. Simulation results indicate that the proposed algorithm outperforms traditional and recurrent neural network-based methods.

In this study, the authors consider a two‐stage constant false alarm rate (TS‐CFAR) detection problem in a distributed multiple‐input–multiple‐output radar. This study is motivated by the fact that the clutter plus noise power and the signal to clutter plus noise ratio may be both unknown in practice. Two TS‐CFAR detectors are proposed, that is, TS generalised likelihood ratio test detector and TS adaptive matched filter detector. Each of the proposed detectors has two thresholds: the first one called local threshold controls the communication rate and the second one called global threshold adjusts the global probability of false alarm. It is proved that both of the proposed detectors have CFAR property. Numerical results illustrate that the two proposed detectors have good detection performance at a low communication rate.

In this article, a spatial-temporally varying generalized Pareto intensity distribution (STV-GPID) model is presented to characterize large-scene sea clutter in high-resolution maritime surveillance radars, and a multiscan recursive Bayesian bipercentile (MSRB-BiP) estimation method is proposed to implement the outlier-robust estimation of parameters in the STV-GPID model. Considering that sea clutter characteristics are affected by sea states and the viewing geometry of a radar, the large scene is segmented into clutter map cells based on an empirical backscattering coefficient model of sea surface to predict radar cross section (RCS) of sea surface per unit physical area. Sea clutter intensities on each clutter map cell are modeled by a GPID. In the parameter estimation, the data of previous scans are transformed into the prior information on the parameters to reduce the storage burden of radar systems. The MSRB-BiP estimator updates the parameters of the STV-GPID model recursively by a mixed sample set with the returns of the present scan and simulated data using the prior information. The mixture ratio adjusts the forgetting rate of data to adapt to temporally varying characteristics of sea clutter. At least, it brings three merits: low storage requirement, outlier robustness, and mitigation of spatial small sample size of a single scan. The convergence and robustness of the estimation method are verified by simulated data. The experimental results on two measured radar datasets verify the effectiveness of the MSRB-BiP estimators, and the errors at the steady state are reduced at least 17.7% and 66.7%, respectively.