Tao Zeng

Evaluating radar detection performance for complex, non-stationary targets is challenging. To address this problem, we propose a semi-analytical detection performance evaluation framework for noncoherent radars that leverages the efficiency of analytical methods and the adaptability of statistical simulations. First, we formulate the framework by adopting a compound fluctuation model to decompose the detection probability into tractable conditional terms. Then, within this framework, we develop an adaptive semi-analytical (ASA) method, which uses adaptive importance sampling to efficiently approximate the conditional probability summation over the joint parameter space without exhaustively enumerating all parameter combinations. Next, we derive a theoretical upper bound on the estimation error of ASA, providing guarantees on its reliability. Finally, we validate the proposed framework and ASA through numerical experiments on representative scenarios and a real-data example, showing that ASA achieves lower root mean square error than conventional methods. The proposed framework and ASA thus provide an accurate and efficient tool for noncoherent detection performance evaluation in advanced radar system design.

High-precision three-dimensional (3D) imaging is essential for accurately perceiving environments, providing critical depth and spatial awareness. Among various approaches, solid-state LiDAR systems have garnered significant attention. However, depth precision, detection range and pixel scalability remain key challenges for their widespread adoption. Here, we report a large-array coherent flash 3D imaging system that achieves a sub-millimeter range precision through stepped-frequency modulation and coherent detection with CCD sensors. A coherent image sensor is developed, and a prototype system is demonstrated, providing 3D imaging with a depth precision as high as 0.47 mm over a range of 30.50 m at an optical power of 15.86 mW and a maximum frame rate of 10 Hz. Our system features high range precision, exceptional sensitivity across long distances, and robust pixel scalability by directly leveraging well-established CCD sensors. This advancement introduces a scalable approach to long-range high-precision 3D imaging, with substantial implications for deformation monitoring, virtual reality, and cultural heritage preservation.

In many extended object tracking applications (e.g., tracking vehicles using a millimeter-wave radar), the shape of an extended object (EO) remains unchanged while the orientation angle varies over time. Thus, tracking the shape and the orientation angle as individual parameters is reasonable. Moreover, the tight coupling between the orientation angle and the heading angle contains information on improving estimation performance. Hence, this paper proposes a constrained filtering approach utilizing this information. First, an EO model is built using an orientation vector with a heading constraint. This constraint is formulated using the relation between the orientation vector and the velocity vector. Second, based on the proposed model, a variational Bayesian (VB) approach is proposed to estimate the kinematic, shape, and orientation vector states. A pseudo-measurement is constructed from the heading constraint and is incorporated into the VB framework. The proposed approach can also address the ambiguous issue in orientation angle estimation. Simulation and real-data results are presented to illustrate the effectiveness of the proposed model and estimation approach.

For tracking a rectangular vehicle, real-world automotive radar position measurements are distributed not uniformly over the vehicle extension but typically around the edges of the vehicle, i.e., the distribution of measurements is shape-dependent. To describe this phenomenon, a shape-dependent Gaussian mixture measurement model is presented, with each mixture component being used to describe a sub-rectangle region by introducing a shape scaling factor. The shape scaling factor is also shape-dependent and can characterize the measurement spread across the corresponding edge. In this model, parameters and mixture structure are highly shape-dependent, and the rectangular shape prior information is also incorporated. Based on the proposed model, a variational Bayesian approach is derived, which recursively and efficiently estimates the kinematic, shape, shape scaling factors, and orientation states of a vehicle. Additionally, the Doppler velocity measurement can also be integrated into the variational Bayesian framework by introducing a latent variable. This approach can effectively and adaptively describe the complex measurement distribution. From the simulation and real experimental results, the proposed approach has a great improvement in the tracking performance, and the superior performance of the proposed model is more significant in estimating the centroid position compared with the state-of-the-art approaches.

High-precision three-dimensional (3D) imaging is essential for accurately perceiving environments, providing critical depth and spatial awareness. Among the various reported approaches, solid-state LiDAR systems have garnered significant attention. However, depth precision, detection range and pixel scalability remain key challenges for their widespread adoption. Here, we report a large-array coherent flash 3D imaging system that achieves a sub-millimeter range precision through stepped-frequency modulation and coherent detection with CCD sensors. A coherent image sensor is developed, and a prototype system is demonstrated, providing 3D imaging with a depth precision as high as 0.47 mm over a range of 30.50 m at an optical power of 15.85 mW. Our system features high range precision, exceptional sensitivity across long distances, and robust pixel scalability by directly leveraging well-established CCD sensors. This advancement introduces a scalable and versatile approach to long-range high-precision 3D imaging, with substantial implications for virtual reality, cultural heritage restoration, biomedical imaging, and industrial applications.

Time-series InSAR is an advanced technology for real-time monitoring and precise analysis of object deformation. However, large-span steel truss structure bridges have complex bridge structures and deformation forms, which make it difficult for the traditional persistent scatterer interferometry (PSI) method to realize the precise detection of their deformation, resulting in a series of limitations. In order to solve these problems, an improved PSI method is proposed in this article, which is first to carry out preliminary elevation measurement by a small temporal baseline, use the preliminary elevation results to PS-arcs construction based on 3-D spatial network, and utilize the optimal model coherence coefficient adaptive selection based on constant false alarm rate to carry out the parameter estimation so as to realize the accurate monitoring of deformation of large-span steel truss structure bridges. In this article, 24 ascending CSK stripmaps are used to monitor the deformation of Dongting Lake Bridge of the Menghua Railway, and the monitoring results show that the method not only realizes the accurate detection of deformation of large-span steel truss structure bridges but also improves the accuracy and robustness of parameter estimation. The results show that the proposed method improves the accuracy of deformation parameter estimation by comparing with the deformation parameters solved by the traditional method, and the method reduces the average residual phase standard deviation by 10%, which provides the experience and reference for the deformation monitoring of similar bridges.

For extended object tracking (EOT), the shape of an extended object (EO) is usually fixed (e.g., tracking vehicles), but the orientation varies. Thus, an accurate estimate of the time-varying orientation is important. The orientation and the heading are not always identical but highly dependent, which can be used as additional prior information to improve estimation accuracy, including the orientation. In view of this, this letter proposes a velocity-dependent orientation estimation approach to EOT utilizing this information. First, we model the quantity between the orientation and the heading as a Gaussian noise with zero mean and adaptive variance. Second, based on the proposed model and the integration of a pseudo-measurement, a variational Bayesian (VB) approach is proposed to estimate the kinematic, shape, and orientation states. The proposed approach can adapt to most dynamic scenarios without the need for a sophisticated mathematical model. The effectiveness of the proposed model and estimation approach is demonstrated by using simulated data.

Earth-based radar is a pivotal instrument in deep space exploration to obtain radar images of desired celestial bodies. However, the system performance and image resolution of conventional integrated earth-based radars with only one radar are limited by the power-aperture product and cannot meet the higher demands of deep space exploration. Distributed coherent radar is a new radar system composed of multiple radar units and a central control system, and its system performance can be further improved by increasing the number of radar units. Distributed coherent radar provides a reliable way to build a high-performance and high-resolution earth-based deep space exploration system. This paper introduces several key technologies about distributed coherent radar astronomical imaging: 1) high-precision coherence parameter estimation, which ensures full coherence performance of the distributed coherent radar; 2) high-precision non-ideal effect compensation, which eliminates the image offset and defocusing induced by the non-ideal effects; 3) fast factorization back projection (FFBP) algorithm, which achieves high-resolution fast imaging of celestial bodies. Moreover, based on a distributed coherent radar prototype system composed of four radar units with antenna aperture of 16m, high-resolution imaging experiments of the moon are conducted, and the effectiveness of the distributed coherent radar is successfully validated, which could not only provide a reference for the distributed coherent radar system but also provide a reliable solution for detection and imaging of other celestial bodies in the solar system in the future.

The swath of the conventional spaceborne synthetic aperture radar (SAR) is parallel-to-orbit, making it inefficient to observe long curved terrain, like coastlines, railways, etc.. Imaging long curved terrains with the terrain matching (TM) curved swath is a promising technique for efficient data acquisition. The key feature is the employment of a long curved swath matching with the orientations of the long curved terrains. This paper reports the first demonstration of the spaceborne SAR TM curved imaging with the LJ2-01 satellite. A TM curved swath of 161.7 km is imaged with an azimuth resolution of 0.6 m. The main technical contributions are: First, a new electrical-mechanical-combined beam control method is proposed to achieve uniform azimuth resolution; Second, a new non-uniform pulse repetition frequency sequence is used to mitigate the data loss caused by the violent spatial variation of the slant range; Third, a new swath-adaptive sub-aperture time-domain imaging algorithm is proposed for efficient TM curved swath imaging. These innovations contribute to successful data acquisition and imaging of the spaceborne SAR TM curved imaging with the LJ2-01 satellite.

Tomographic synthetic aperture radar (TomoSAR), as a 3-D imaging technique, is widely applied in urban mapping. In our previous work, a multi-master (MM) TomoSAR approach was proposed for the long-baseline observation configuration. This article focuses on the MM TomoSAR for a common observation configuration. The main contribution includes two aspects: theoretical complement and performance extension. For the theoretical complement, the theory of virtual difference coarray (VDCA) in array signal processing (ASP) is introduced to the MM TomoSAR. First, we prove that the MM TomoSAR processing is equivalent to constructing a VDCA in the elevation direction, which essentially explains its principle. Then, to solve the redundancy problem in MM TomoSAR, the equivalent VDCA in the MM model is constructed by selecting the elements from the covariance matrix of the observed signal. Finally, the improved MM TomoSAR processing method is proposed. For performance extension, it is demonstrated that the performance improvement of the MM model is extended from the original two aspects (sidelobe suppression and high estimation accuracy) to include the third aspect (aperture expansion, which improves elevation resolution). The computer simulation and experiment based on TerraSAR-X data verify the proposed method effectively.