Jingjing Pan

Recently, exploiting signals of opportunity (SOPs) emitted by low Earth orbit (LEO) satellites has drawn great interest in target localization. In this work, we propose a deterministic maximum likelihood based direct position determination (DML DPD) that jointly exploits the spatial and frequency components of SOPs captured by sensor arrays for localization of the stationary target. Fusing these two features of the received SOPs yields a DML estimator based cost function whose minimum directly corresponds to the target coordinate; localization is therefore achieved by min imizing this cost function. Compared with the existing approaches, the proposed DML-DPD delivers accurate localization from even a single SOP and stays robust to LEO ephemeris and velocity errors. Extensive systems tool kit (STK) generated simulations demonstrate the robustness of the proposed method.

Recently, passive sensing exploiting low Earth orbit (LEO) satellites as non-cooperative emitters has attracted considerable attention in the field of positioning, navigation, and timing (PNT). However, as LEO satellites are not dedicated for PNT, we cannot obtain the crucial information, such as signals structure and clock synchronization from the LEO signals of opportunity (SOPs), bringing challenges to LEO-PNT based techniques, such as Doppler tracking and code pseudo-range. Moreover, Doppler positioning requires ≥ 3 SOPs and the process of signal recording is time-consuming, often requiring several minutes to complete. In this paper, we address the LEO-PNT problem from the perspectives of sensor data processing and passive source localization. A uniform circular array (UCA) is deployed to acquire both spatial (angles-of-arrival) and spectral (Doppler shift) features of SOPs, which offers uniform coverage in all directions, resulting in highly effective signal detection from any AOAs; then, a joint direct position determination (JDPD) through spatial and spectral fusion of SOPs is proposed for target localization by formulating a comprehensive cost function that maps to the target coordinate. Its coordinate can then be obtained by minimizing this cost function. Compared with the existing approaches, our method enables precise target positioning with a single SOP and more robust to the LEO ephemeris and velocity errors. The efficiency and reliability of our JDPD are demonstrated via numerical simulations, where the data are generated by the systems tool kit (STK) of Analytical Graphics.

Uniform circular array (UCA) is widely employed in the field of direction-of-arrival (DOA) estimation, due to its uniform coverage in all directions and its ability to estimate both azimuth and elevation angles. Conventional methods, such as two-dimensional MUSIC (2D-MUSIC) or beam-space transform based methods, either require computationally intensive multi-dimensional searches, or suffer from residual error and bias in DOA estimation, which limit their practicality in real time applications. To address these challenges, we devise a reduced-dimension MUSIC (RD-MUSIC) for DOA estimation by investigating both odd and even numbers of UCA sensors. The proposed method rearranges the sensor positions, and then splits the steering vector into two components, allowing for separate 1D searches for the elevation and azimuth angles. Our solution can achieve high estimation accuracy in a computation ally attractive manner. The effectiveness of the RD-MUSIC is evaluated through numerical simulations as well as laboratory and field experiments, demonstrating its robustness at different signal-to-noise ratio levels and number of snapshots. The results highlight the potential of RD-MUSIC for real-time applications where computational resources are limited and high accuracy is required.

Coprime array enables derivation of an extended array with the number of virtual elements beyond the number of physical sensors, resolving the underdetermined direction of-arrival (DOA) estimation problem while mitigating mutual coupling effects. However, the virtual array is discontinuous and contains some holes. To fully exploit the information of the virtual array, conventional methods typically employ complex matrix completion (MC) to iteratively recover the data of the covariance matrix, resulting in high complexity, particularly with large-scale coprime arrays. In this letter, a real-valued DOA estimation via the construction of Toeplitz covariance matrix and spline interpolation is proposed. The algorithm complexity is reduced by real-valued Toeplitz matrix, and then the holes are filled by spline interpolation. The recovered matrix can be used for DOA estimation by conventional subspace approaches. The superior computational performance of our approach is validated through comparative simulations with established methods.

Near field (NF) source localization plays an essential role in the field of array signal processing. However, for coherent sources, conventional SSP and Topelitz matrix reconstruction algorithms cannot be applied directly, because of the nonlinear phase variations in near-field source. Although probability model-based methods like enhanced maximum likelihood method (EML) can deal with coherent near-field sources, they are fundamentally constrained by computational complexity or resolution performance. To address these issues, this paper proposes an enhanced method for near-field coherent source localization using a two-step alternating oblique projection (AOP) and polynomial root-finding, comprising a coarse localization stage followed by a fine estimation. Firstly, the coarse estimates of DOA and range parameters are derived by applying the anti-diagonal components of the covariance matrix through root-MUSIC and root-Capon, respectively, followed by an iterative refinement process that alternately applies the AOP method and root-finding to achieve fine estimation. The performance of the proposed algorithm is validated through simulations. Results demonstrate that the algorithm achieves accurate localization of NF coherent sources with reduced computational complexity and improved resolution. The accuracy of the proposed method is also validated by a filed experiment.

Finding directions-of-arrival (DOAs) of targets by using co-prime array, extensively investigated in the scenario of Gaussian disturbances, faces issues with impulsive background noise. Existing solutions, like fractional low order moment (FLOM) and correntropy, aim to construct a statistic from the received signals but they require knowledge of background noise and under-perform in highly impulsive noise environments. To address these limitations, in this paper, we develop an adaptive correntropy based correlation (ACRCO), which can operate in all impulsive noise scenarios and do not demand the knowledge of the noise characteristics. The proposed scheme utilizes the adaptive kernel size function to process the impulsive noise and constructs correntropy based covariance matrix as statistical measures of the received signals. Within the scope of the kernel size function, we create numerous pseudo-snapshots from the co-prime virtual array with different kernel size function values to enhance DOA finding accuracy and resolution. Afterwards, multiple signal classification with de-correlation preprocessing is applied for computing the DOAs. Experiment results demonstrate that the proposed ACRCO provides superior DOA estimation in the presence of impulsive noise, compared with existing algorithms.

Direction of arrival (DOA) estimation for signal sources in indoor environments has become increasingly important in wireless communications and smart home applications. However, complex indoor conditions, such as multipath effects and noise interference, pose significant challenges to estimation accuracy. This issue is further complicated by domain discrepancies in data collected from different environments. To address these challenges, we propose a deep domain-adaptation-based DOA estimation method. The approach begins with deep feature extraction using a Convolutional Autoencoder (CAE) and employs a Domain-Adversarial Neural Network (DANN) for domain adaptation. By integrating Gradient Reversal Layer (GRL) and Maximum Mean Discrepancy (MMD) loss functions, the model effectively reduces distributional differences between the source and target domains. The CAE-DANN enables transfer learning between data with similar features from different domains. With minimal labeled data from the target domain incorporated into the source domain, the model leverages labeled source data to adapt to unlabeled target data. GRL counters domain shifts, while MMD refines feature alignment. Experimental results show that, in complex indoor environments, the proposed method outperforms other methods in terms of overall DOA prediction performance in both the source and target domains. This highlights a robust and practical solution for high-precision DOA estimation in new environments, requiring minimal labeled data.

Time-delay estimation (TDE) holds great significance in pavement surveys, especially in the modern transportation system. Established on the ideal Dirac pulse and white Gaussian noise, the performance of the existing TDE methods may degrade in practical ground penetrating radar (GPR) detection, where radar pulse and noise distribution are diverse. In this letter, we develop a gridless TDE method considering the radar pulse and noise pattern in GPR detection. Co-prime sampling strategy is applied to reduce the number of frequency samples compared with conventional uniform sampling. With the prior knowledge of radar pulse and noise distribution, an enhanced measurement vector is generated from the data covariance matrix, thus improves the signal quality compared with the conventional methods which are based on ideal Dirac pulse and white Gaussian noise. Subsequently, the time-delays are estimated by the proposed atomic norm minimization (ANM) method, where the complexity is further reduced compared with the previous works using multiple measurements. Simulation results show the advantages of the proposed method in terms of running time, weak echo detection, and estimation accuracy.

Time-delay estimation (TDE) using ground penetrating radar (GPR) is of great importance in roadway surveys. The conventional GPR methods apply uniform sampling strategy for TDE, which require numerous frequency sampling points, leading to lengthy data acquisition time and large data storage, especially for ultra-wideband (UWB) radar. Moreover, detecting the overlapped backscattered echoes from thin layer of roadways remains a challenge in TDE, due to the limited resolution of GPR and the characteristics of GPR signals. To address these issues, we derive a co-prime sampling strategy based TDE for thin layers in roadway survey by exploiting off-grid sparse Bayesian learning (OGSBL), referred as co-prime-OGSBL. In our scheme, the sampling rate of GPR signals with co-prime sampling strategy is greatly reduced compared with the uniform sampling, which therefore reduces the data acquisition burden and computational complexity. The estimation performance of time-delays and thickness is also enhanced with OGSBL by utilizing radar pulse, co-prime sampling, and non-circularity of GPR signals. Both simulation and experimental results demonstrate the efficiency and accuracy of the proposed method in the estimation of time-delays and thickness.