In this letter, we address the performance prediction of the hybrid integration detector for radar moderately fluctuating Rayleigh targets in thermal noise. Initially, the moderately fluctuating Rayleigh target model is defined as a general form of well-known Swerling I and Swerling II models using an exponential correlation function. Based on this, an exact closed-form expression of detection probability for hybrid integration detector is derived. In extreme conditions (correlation coefficient is 1 or 0), the derived expression can degrade into the classical formulas, confirming it's validity. Finally, numerical examples are presented to verify the effectiveness of the derived theoretical model and to analyze the issue of optimal hybrid integration detector.

Due to Github’s closed-source nature and potential limitations like trade control regulation, numerous countries have established their own Verion Control Systems (VCSs) platforms to foster local open source software (OSS) and communities. However, it presents several challenges for new born VCSs to attract and retain users. To address these problems, this paper seeks solutions that consider developer motivations, automated tools, innovative proposals and localization to effectively address these pressing challenges, emphasizing the need for enhanced user engagement and contributions within native VCSs.

The adjacent cross correlation function (ACCF) is an effective tool to remove the range migration (RM) and reduce the order of Doppler frequency migration (DFM) in the pulsed-radar echoes of maneuvering targets, and thus has been employed in radar target detection and imaging. However, the ACCF is a nonlinear transform, which means that the signal-to-noise ratio (SNR) variation will be introduced by this operation. This letter presents a quantitative analysis of SNR for the ACCF. The frequency-domain derivation and analyses of ACCF are presented. Based on this, the SNR variation of the ACCF is analytically derived. The theoretical derivations and analyses are confirmed by numerical examples.

Abstract Bayesian compressive sensing (BCS) is an important sub‐class of sparse signal reconstruction algorithms. In this paper, a modified complex multitask Bayesian compressive sensing (MCMBCS) algorithm using the Laplacian scale mixture (LSM) prior is proposed. The LSM prior is first introduced into the complex BCS framework by exploiting its better sparse characteristic and flexibility than traditional Laplacian prior. Furthermore, by integrating out the noise variance analytically, the MCMBCS algorithm significantly improves the signal recovery performance than the original CMBCS. More importantly, the authors not only present the iterative algorithm but also develop the sub‐optimal fast implementation method based on the marginal likelihood maximisation, which dramatically reduce the computational complexity. Finally, sufficient numerical simulations validate the better performance of the proposed algorithm in reconstruction accuracy and computational effectiveness than existing work. It is revealed that the proposed algorithm has great potential in the complex‐valued signal processing field.

The phase scintillation induced by ionospheric irregularities can reduce azimuth decorrelation and cause defocusing in the low-frequency spaceborne synthetic aperture radar (SAR) imaging. Thus, it demands for an autofocus approach to correct the scintillation phase error (SPE) and to refocus the deteriorated SAR image. However, the azimuth variation of the SPE has been rarely considered and poorly tackled in previous research studies. In this letter, an ionospheric phase screen (PS) projection method of phase gradient autofocus (PSP-PGA) is proposed to deal with this issue and compensate the azimuth-varying SPE in SAR images. The PSP is used to project the SAR image onto the PS, by using the part decompression and spectral analysis, so as to realize the decoupling of the SAR data with SPE. Then, the projected image can be directly applied to the PGA to accurately estimate the SPE. The proposed PSP-PGA is validated by using several simulation processing experiments and its performance is finally evaluated with respect to the parameter of the PS height.

The spaceborne low-frequency (L-band and below) synthetic aperture radar (SAR) is very susceptible to ionospheric scintillation. The scintillation phase error (SPE) is a vital factor that brings about the azimuth decorrelation and leads to the imaging degradation. In this article, a methodology is described, which exploits the accurate SPE estimation to measure scintillation parameters from SAR images. First, the phase gradient autofocus (PGA) is applied to achieve the believable SPE estimate from the local area that contains a strong scatterer. Second, based on the estimated staggered index of two SPE estimates, the irregularity altitude can be derived by solving an established equation using an iteration process. Third, three parameters, including the spectrum index, the outer scale, and the integrated turbulence strength, can be fitted when the theoretical spectrum expression mostly approximates the derived spectrum. The methodology is validated on an ALOS-2 PALSAR-2 spotlight data, and its azimuth imaging was seriously degraded by ionospheric scintillation. Two existent corner reflectors ensure the accuracy of SPE estimates from PGA. The measurement results indicate that the local ionospheric irregularities are extremely turbulent and located at a lower altitude of about 220 km in the F2-layer.

Aircraft operations statistics have typically received significant attention from U.S. airport owners and operators and state, local, and federal agencies. Accurate operational data is beneficial in assessing airports’ performance efficiency and impact on the environment, but operational statistics at nontowered general aviation airports are, for the most part, limited or not available. However, the increasing availability and economy of capturing and processing Automatic Dependent Surveillance-Broadcast (ADS-B) data shows promise for improving accessibility to a wide variety of information about the aircraft operating in the vicinity of these airports. Using machine learning technology, specific operational details can be decoded from ADS-B data. This paper aims to develop a reliable and economical method for general aviation aircraft flight phase identification, thereby leading to improved noise and emissions models, which are foundational to addressing many public concerns related to airports.

As a typical propagation error, tropospheric delay has been fully considered during synthetic aperture radar (SAR) interferometry and differential interferometry, while the influence on the SAR imaging process is just concerned in advanced configurations, e.g., staring mode, high-resolution, geosynchronous/geostationary (GEO) SAR. In this article, a comprehensive model of turbulent tropospheric delay (TTD), involving atmospheric sciences and fluid mechanics, is introduced, and the influence of which on high-resolution SAR is analyzed. In order to compensate for the influence of TTD in advanced SAR missions, the autofocus method used in motion error compensation is extended, and a 2-D phase error estimation and compensation algorithm is proposed. Subsequently, the simulation experiments of both dot-matrix targets and TerraSAR-X data are performed to validate the effectiveness of the proposed algorithm. Conclusions and prospects are reached in the end.

As a branch of reinforcement learning, interactive reinforcement learning mainly studies the interaction process between humans and agents, allowing agents to learn from the intentions of human users and adapt to their preferences. In most of the current studies, human users need to intentionally provide explicit feedback via pressing keyboard buttons or mouse clicks. However, in our paper, we proposed an interactive reinforcement learning method that facilitates an agent to learn from human social signals - facial feedback via a ordinary camera and gestural feedback via a leap motion sensor. Our method provides a natural way for ordinary people to train agents how to perform a task according to their preferences. We tested our method in two reinforcement learning benchmarking domains - LoopMaze and Tetris, and compared to the state of the art - the TAMER framework. Our experimental results show that when learning from facial feedback the recognition of which is very low, the TAMER agent can get a similar performance to that of learning from keypress feedback with slightly more feedback. When learning from gestural feedback with a more accurate recognition, the TAMER agent can obtain a similar performance to that of learning from keypress feedback with much less feedback received. Moreover, our results indicate that the recognition error of facial feedback has a large effect on the agent performance in the beginning training process than in the later training stage. Finally, our results indicate that with enough recognition accuracy, human social signals can effectively improve the learning efficiency of agents with less human feedback.

An L-band geosynchronous synthetic aperture radar (GEO SAR) system has to be confronted by an intractable issue of the decorrelations imposed by ionospheric irregularities. On the one hand, the phase and amplitude scintillations will bring about the decorrelation within the synthetic aperture and result in azimuth-imaging degradation. On the other hand, the imposed scintillation history is spatially decorrelated across the ultra-large GEO SAR scene. In this article, a signal model of the GEO SAR acquisitionis established with the two-way ionospheric transfer function (ITF) modulation to incorporate these two types of decorrelations. This model meanwhile takes the anisotropic and flowing irregularities into account. By using this model, the L-band GEO SAR azimuth-imaging is evaluated in terms of five indexes, whose performances are dependent on nine ionospheric parameters. Furthermore, the spatial correlation of the phase and intensity scintillation histories is investigated for the L-band GEO SAR scene, both in simulation and statistics. The statistical result implies a sized scene, in which the phase scintillation history tends to be consistent. Finally, the interferometric performance is investigated between the pure and contaminated GEO SAR images. The simulation result shows that the degradation of the interferometric coherence results from the in-aperture decorrelation.

There has been a burgeoning prospect in developing a spaceborne P-band synthetic aperture radar (SAR) mission for its stronger penetrability through foliage and subsurface than the higher-frequency system. However, the transionospheric signals operating at P-band are more susceptible to scintillation impacts, which may bring about the decorrelation of the signal amplitude, phase, and frequency introduced by ionospheric irregularities. In this article, a comprehensive numerical model of the generalized ambiguity function (GAF) is established to evaluate SAR imaging deterioration. On the one hand, an improved two-frequency and two-position coherence function (TFTPCF) is integrated in the GAF to include the amplitude scintillation derived from the diffraction. On the other hand, the anisotropic characteristics of the irregular structure is introduced into TFTPCF by adopting the Rino's 2-D spectrum. Furthermore, the ambiguous resolution is redefined for a more strict numeration. Numerical analyses about the ionospheric coherence and an ambiguous resolution are performed to investigate their sensitivity to scintillation parameters. Results show that this model is capable of depicting more comprehensive effects of the anisotropic irregular ionosphere, which may exhibit a rod-like structure, including elongation by anisotropic scale, rotation by geomagnetic heading, and projection by geomagnetic inclination. At last, the signal-level simulations are operated to verify the effectiveness of numerical conclusions, which further confirm that the structural configuration of anisotropic irregularities has a complicated effect on spaceborne P-band SAR image resolution.

In this paper, the anisotropic generality of the ionospheric irregularities is incorporated in the generalized ambiguity function (GAF) to evaluate its impact on spaceborne P-band synthetic aperture radar (SAR) imaging. The configuration of the anisotropic ionosphere exhibits as a rod-like structure, which is elongated by anisotropic scale, rotated by magnetic heading and projected by geomagnetic inclination. Aiming at these three parameters, numerical analysis is implemented in terms of the coherence and ambiguous resolution. At last, signal-level simulation is carried out to validate the effectiveness of the numerical results.

As was indicated in the mentioned paper, the ionospheric stripes, in general, aligned well with the orientation of the projected ambient geomagnetic field vector. However, it is shown in our study that the calculated striping heading is not only dependent upon the orientation of the projected ambient geomagnetic field vector, namely, the geomagnetic heading but also the geomagnetic inclination, the system incident, and squint angle. It also confirms that the changing direction of the visible stripes in the mentioned Phased Array-type L-band Synthetic Aperture Radar (PALSAR) data is mainly due to the variation of the geomagnetic inclination, while the geomagnetic heading is nearly constant along the orbit. Therefore, the denotation and presentation in that research that the projected geomagnetic field vector elongates in the direction of the stripe orientation might not be feasible.

It is well known that spaceborne synthetic aperture radar (SAR) systems, which operate at L- and P-bands, are significantly influenced by the ionospheric effects. One of the severe effects is Faraday rotation (FR), which has a potential trend to cause the polarimetric distortion. Therefore, performing FR estimation is a prerequisite for the polarimetric SAR (PolSAR) application. A set of FR estimators have been proposed to solve this issue. Based on the Bickel and Bates's method, an improved FR estimator has been briefly mentioned, but has not been specified. The improved estimator's performance is verified by real SAR data, and experimental results indicate that the improved estimator shows the most robust performance in terms of system noise compared with other proposed estimators, which implies an applicable method for FR estimation.

The performance of spaceborne linearly polarized synthetic aperture radar (SAR) systems operating at low frequencies, such as L-band and P-band, is significantly affected by Faraday rotation (FR) effects. FR indicates the rotation of the polarization plane, which deteriorates the polarimetry accuracy of the polarization scattering matrix. A set of FR estimators have been proposed to solve this issue, but all estimators suffer from the ambiguity error of FR angle (FRA). Based on Bickel and Bates's estimator and the scatter distribution diagram of FR estimation, a novel correction method for eliminating FRA ambiguity error is proposed, which is divided into pixel-level correction and image-level correction. Experimental results verify the effectiveness of the novel correction method by processing the real data.

At L-band, the ionosphere is assumed to have considerable effects on differential Synthetic Aperture Radar Interferometry (D-InSAR) image quality [1]. This paper focuses on ionospheric propagation effects from difference of slant range total electron content (ΔSTEC) and error calibration for D-InSAR. Referring to International Reference Ionosphere (IRI) model, we simulate the distribution of ionosphere slant range total electron content (STEC) in Hainan. Then the phase error and deformation error from ionospheric effects are analyzed. The performance of two ionospheric correction methods is also given. The `group-phase delay difference method' [2] is expected to have a lower precision than the `spectrum-split method' [3] in the limit of regular image registration accuracy. Some suggestions about the smoothing window of the latter are given. Finally, with updated SBRAS simulator, an L-band D-InSAR simulation experiment is conducted and the `spectrum-split method' is validated. Experiment results show that about 0.02TECU ΔSTEC and less than 1cm residual deformation error from ionospheric effects are available for L-band D-InSAR.

Phase synchronization is a key problem which has to be carefully considered in the distributed radar systems. In this letter, the use of a continuous duplex phase synchronization method for the distributed frequency modulated continuous wave synthetic aperture radar system is investigated. The bistatic raw signal model containing the phase difference due to the spatially separated oscillators is introduced. The continuous duplex phase synchronization procedure is proposed later and the synchronization method performance is predicted.

This paper proposed an innovative bistatic radar system, named as GEO-UAV bistatic circular synthetic aperture radar (BCSAR). In GEO-UAV BCSAR, the geostationary satellite serves as the transmitter, while the receiver is mounted on the UAV platform moving along a circular trajectory. Combining the technical advantages of GEO SAR and that of UAV platform, GEO-UAV BCSAR benefits from low-cost, high operational flexibility and 3-D imaging ability. This paper introduced its system concept, and discussed its imaging ability and technical challenges. It is shown that the proposed GEO-UAV BCSAR could be able to provide a potential alternative for future remote sensing missions.

This paper analyzes the spatial decorrelation between repeat-pass bistatic synthetic aperture radar (BSAR) images with Global Navigation Satellite Systems as transmitters and a fixed receiver. This study is needed in the development of such a system to monitor temporal changes in a scene. The main challenge is that, in this bistatic configuration, spatial coherence heavily depends on the data acquisition geometry. The appropriate theoretical framework to describe spatial coherence for this case is developed by extending well-established monostatic models and, in principle, can be applied to any fixed-receiver BSAR with a spaceborne transmitter. Theoretical results are initially supported by Monte Carlo simulations. Finally, the validity of the model is confirmed by comparing real images.