Tianyi Jia

Saturation attacks and high-speed penetration have become common battlefield threats. Traditional radars face challenges such as difficulty in target initiation and poor tracking accuracy under resource-constrained conditions.This paper proposes a fast target confirmation mechanism based on multi-carrier mode for the target initiation process to coordinate time resources to handle the initiation problem of high-speed targets. Additionally, it establishes a joint optimization model of revisit time and dwell time for identified targets to significantly improve multi-target tracking accuracy. Specifically, we first establish a phase compensation mechanism based on Fast Fourier Transform to achieve coherent processing between multi-carrier echoes, assisting the radar in effectively detecting high-speed targets. We then derived the Bayesian Cramer-Rao lower bound for evaluating the tracking error of the initiated targets. By combining this with the asynchronous tracking time constraints of multiple targets, we develop a resource optimization model related to the revisit and dwell time of each target and design a fast solution method based on cycle minimization algorithm. Finally, the simulation results show that the proposed method not only better assists in target initiation but also achieves superior target tracking accuracy.

Unlike terrestrial environments, the inhomogeneity distribution of underwater sound speed poses significant challenges for underwater ranging and target localization. In the presence of sound ray bending and sensor node position errors in underwater acoustic sensor networks (UASNs), this paper proposes a joint TOA-AOA deep-sea target localization framework based on sound ray bending correction. From the perspective of information theory and time series analysis, the TOA measurements are time series signals carrying target position information, and the entropy-based analysis quantifies the fundamental limit on localization uncertainty. First, based on the TOA time series measurements and combined with the acoustic propagation characteristics of the deep sea, a sound ray bending correction method is adopted to improve the accuracy of slant range measurement. To enhance target localization accuracy, this paper proposes a two-step WLS closed-form solution based on TOA-AOA. To further reduce localization bias, a maximum likelihood estimation (MLE) method based on the Gauss-Newton is also derived. Subsequently, the paper derives and analyzes the Cramér-Rao lower bound (CRLB) for target localization, proving theoretically that jointly using TOA-AOA can improve localization accuracy. Simulations verify the performance of the proposed methods. The slant range estimation method based on sound ray bending correction effectively improves range measurement accuracy. The proposed closed-form solution enhances target localization accuracy, achieving the CRLB accuracy. The Gauss-Newton MLE solution can attain the CRLB accuracy under certain localization geometries and further reduces localization bias.

Unlike terrestrial environments, the unique characteristics of underwater acoustic channels and dynamic underwater environments pose significant challenges for precise clock synchronization between sensor nodes in underwater acoustic sensor networks (UASNs). Additionally, sensor nodes may drift due to ocean currents and other underwater activities. These two factors significantly degrade the target localization accuracy. This paper investigates a cooperative localization scheme based on information interaction for UASNs under asynchronous clocks and proposes a closed-form solution for joint sensor position calibration and target localization to improve the target localization accuracy. First, we design a cooperative localization scheme that enables range measurements between sensors and between sensors and target. Then, using a Gaussian noise model, we derive the Cramér-Rao lower bounds (CRLBs) for sensor position calibration and target localization. The CRLB theoretical analysis and simulation demonstrate that range measurements between sensors can effectively calibrate sensor positions and improve the target localization accuracy. Based on the scheme, we propose an algebraic closed-form solution for joint sensor position calibration and target localization, leveraging range measurements between sensors to calibrate sensor positions and enhance the target localization accuracy. Under two mild conditions, the theoretical analysis shows that the proposed closed-form solution can achieve the CRLB accuracy of sensor position calibration and target localization. Simulations validate the performance of the proposed method and the theoretical analysis, demonstrating that the proposed method can significantly calibrate sensor positions and improve target localization accuracy compared to other methods.

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.

When tracking maneuvering targets, model-driven approaches encounter difficulties in comprehensively delineating complex real-world scenarios and are prone to model mismatch when the targets maneuver. Meanwhile, contemporary data-driven methods have overlooked measurements' confidence, markedly escalating the challenge of fitting a mapping from measurement sequences to target state sequences. To address these issues, this paper presents a deep maneuvering target tracking methodology based on target state space projection. The proposed methodology initially establishes a projection from the target measurement sequence to the target state space by formulating the probability density function of measurement error and samples the distribution information of measurement noise within the target state space as a measurement representation. Under this representation, the sequential regression task of target state estimation can be transmuted into a task of detecting the target location in the state space. Subsequently, a deep detection network is devised to accomplish target location detection in the target state space. Finally, a loss function is designed to facilitate the network's training for attaining the desired network performance. Simulation experiments suggest that the proposed method can maintain satisfactory tracking performance even when the target maneuvers, and can rapidly converge and achieve higher estimation accuracy compared with existing methods after the target maneuvers.

This paper concerns the direction of arrival (DOA) estimation problem in Bayesian framework by using the sparse methods that incorporate the prior knowledge within the array observation data. The obtained prior of knowledge of DOAs is assumed to follow a prior distribution. Considering the unknown DOAs are random variables, we propose two sparse methods by effectively and efficiently exploiting the information from the observation data and prior knowledge. One is a grid-based sparse method using the second-order cone programming (SOCP) by discretizing the grids in the prescribed prior region where the targets occur with high probability. The other is a gridless sparse method using the atomic norm minimization by transforming the prior knowledge into semidefinite constraint. The first is computationally efficient, but it suffers from grid mismatch problem in high SNR. The second further improves the estimation performance with high computational complexity. Simulation results demonstrate the superiority of the proposed methods when comparing with the traditional DOA estimation methods together with the maximum a posterior (MAP) estimator and the Bayesian Cramer- ´ Rao lower bounds (BCRLB)

The measurement biases of sensors during the target tracking process, if not accounted for, can deteriorate significantly the localization accuracy. This paper focuses on the problem of target tracking using range and azimuth measurements obtained by asynchronous sensors when there are measurement biases and sensor position errors. A new online sequential estimation method is proposed by formulating the target states, sensor measurement biases and the sensor positions as an augmented state vector. The filtering concepts and the minimum mean square error (MMSE) criterion are employed to perform real-time processing, which can provide simultaneous estimates for the augmented state vector. Comparing with the existing batch processing methods, the computational complexity is reduced by applying real-time estimations. Simulation validates the effectiveness of the proposed method in achieving the BCRLB performance under the distance-dependent measurement noise.