In unmanned aerial vehicle (UAV)-based time difference of arrival (TDOA) positioning technique, baselines are limited due to communication constraints. In this case, the accuracy is highly sensitive to the TDOA measurements’ error. This article primarily addresses the problem of short-baseline high-precision time synchronization and TDOA measurement. We conducted a detailed analysis of error models in TDOA systems, considering both the time and phase measurement. We utilize the frequency division wireless phase synchronization technique in TDOA systems. Building upon this synchronization scheme, we propose a novel time delay estimation method that relies on phase measurements based on the integer least squares method. The performance of this method is demonstrated through Monte Carlo simulations and outdoor experiments. The standard deviations of synchronization and TDOA measurements in experiments are 1.12 ps and 1.66 ps, respectively. Furthermore, the circular error probable (CEP) accuracy is improved from 0.33%R to 0.02%R, offering support for the practical application of distributed short-baseline high-precision passive location techniques.

With the rapid development of lightweight unmanned aerial vehicles (UAVs), the combination of UAVs and ground moving target indication (GMTI) radar systems has received great interest. In GMTI, moving target relocation is an essential requirement, because the positions of the moving targets are usually displaced. For a multichannel radar system, the position of moving targets can be accurately obtained by estimating their interferometric phase. However, the high position accuracy requirements of antennas and the computational resource requirements of algorithms limit the applications of relocation algorithms in UAV-borne GMTI radar systems. In addition, the clutter’s interferometric phase can be severely affected by the undesired phase error in the site. To overcome these issues, we propose an improved knowledge-based (KB) algorithm. In the algorithm, moving targets can be relocated by comparing their interferometric phase with the clutter’s phase. As for the undesired phase error, the algorithm first employs a random sample consensus (RANSAC) algorithm to iteratively filter the outliers. Compared with other classic relocation algorithms, the proposed algorithm shows better relocation accuracy and can be applied in real-time applications. The performance of the proposed improved KB algorithm was evaluated using both simulated and real experimental data.

High-precision, robust, and rapid three-dimensional (3D) passive positioning of the radiation source is critical for modern reconnaissance systems. While synthetic aperture technology has advanced 2D passive positioning performance, existing methods fail to achieve full 3D positioning with sufficient accuracy and computational efficiency. This is because of the inherent limitations of the single-station platform in resolving elevation-angle ambiguity. To address this gap, we propose a Cross-Track Interferometric Synthetic Aperture (CISA) 3D passive positioning algorithm. The algorithm innovatively realizes robust elevation-angle measurement by recursively deriving the long baseline unambiguous phase difference step-by-step from a virtual short baseline. The 3D positioning is achieved by combining passive synthetic aperture and interferometric angle measurement. Furthermore, we establish the incoherence model of synthetic aperture passive positioning for the first time and propose a compensation method based on static acquisition data to improve the practicability of CISA. Simulation and experimental results demonstrate that the proposed CISA algorithm achieves a positioning accuracy of 4.73‰R, improves computational efficiency by 1–2 orders of magnitude compared to conventional methods, and exhibits superior robustness to noise. The research can provide a reference for the method research and engineering realization of synthetic aperture 3D passive positioning.

Synthetic aperture radar (SAR) systems, as wideband radar systems, are inherently susceptible to interference signals within their operational frequency band, which significantly affects SAR signal processing and image interpretation. Recent studies have demonstrated that semiparametric methods (e.g., the RPCA method) exhibit excellent performance in suppressing these interference signals. However, these methods predominantly focus on processing SAR’s raw echo data, which does not satisfy the sparsity requirements and entails extremely high computational complexity, complicating integration with imaging algorithms. This paper introduces an effective method for suppressing interference signals by leveraging the sparsity of the SAR image domain. It utilizes the sparsity of the interference signal in the two-dimensional frequency domain, following focusing processing, rather than relying on low-rank properties. This approach significantly reduces the computational complexity. Ultimately, the effectiveness and efficiency of the proposed algorithm are validated through experiments conducted with simulated and real SAR data.

Real-time Synthetic Aperture Radar (SAR) imaging for small Unmanned Aerial Vehicles (UAVs) has become a significant research focus. However, limitations in Size, Weight, and Power (SwaP) restrict the imaging quality and timeliness of small UAV-borne SAR, limiting its practical application. This paper presents a non-iterative real-time Feature Sub-image Based Stripmap Phase Gradient Autofocus (FSI-SPGA) algorithm. The FSI-SPGA algorithm combines 2D Constant False Alarm Rate (CFAR) for coarse point selection and spatial decorrelation for refined point selection. This approach enables the accurate extraction of high-quality scattering points. Using these points, the algorithm constructs a feature sub-image containing comprehensive phase error information and performs a non-iterative phase error estimation based on this sub-image. To address the multifunctional, low-power, and real-time requirements of small UAV SAR, we designed a highly efficient hybrid architecture. This architecture integrates dataflow reconfigurability and dynamic partial reconfiguration and is based on an ARM + FPGA platform. It is specifically tailored to the computational characteristics of the FSI-SPGA algorithm. The proposed scheme was assessed using data from a 6 kg small SAR system equipped with centimeter-level INS/GPS. For SAR images of size 4096 × 12,288, the FSI-SPGA algorithm demonstrated a 6 times improvement in processing efficiency compared to traditional methods while maintaining the same level of precision. The high-efficiency reconfigurable ARM + FPGA architecture processed the algorithm in 6.02 s, achieving 12 times the processing speed and three times the energy efficiency of a single low-power ARM platform. These results confirm the effectiveness of the proposed solution for enabling high-quality real-time SAR imaging under stringent SwaP constraints.

With the increasing demand for three-dimensional positioning in Synthetic Aperture Radar (SAR) systems, multi-view SAR technology is rapidly evolving. Airborne-distributed SAR systems, benefiting from multi-platform collaborative observation, flexible baseline configuration, and synchronous imaging, have become an ideal solution for realizing this technology. However, the flight paths of these platforms are not optimal, and the airborne navigation equipment also suffers from measurement errors, which severely deteriorates the multi-view SAR target positioning accuracy of the airborne-distributed platforms. Currently, research on this issue remains scarce. This paper is based on the multi-view normalized Range Doppler positioning model, introducing platform position errors to derive the Cramér-Rao Lower Bound (CRLB). A detailed positioning accuracy analysis is conducted for different flight paths and various sources of errors, demonstrating that platform position errors are a primary factor affecting target positioning accuracy. To address this, a target positioning method based on inter-platform ranging information is proposed, which imposes constraints on the position of the airborne-distributed platform using inter-platform ranging data, thereby reducing the dependence of target positioning accuracy on platform position errors and enhancing the robustness of three-dimensional positioning for multi-view SAR targets. The effectiveness of the proposed method is verified using measured data, which reduces the 3D positioning error of the target by nearly 60%.