Point Target Research Articles

AC-LSTM: Anomaly State Perception of Infrared Point Targets Based on CNN+LSTM

Anomaly perception of infrared point targets has high application value in many fields, such as maritime surveillance, airspace surveillance, and early warning systems. This kind of abnormality includes the explosion of the target, the separation between stages, the disintegration caused by the abnormal strike, etc. By extracting the radiation characteristics of continuous frame targets, it is possible to analyze and warn the target state in time. Most anomaly detection methods adopt traditional outlier detection, which has the problems of poor accuracy and a high false alarm rate. Driven by data, this paper proposes a new network structure, called AC-LSTM, which combines Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM), and embeds the Periodic Time Series Data Attention module (PTSA). The network can better extract the spatial and temporal characteristics of one-dimensional time series data, and the PTSA module can consider the periodic characteristics of the target in the process of continuous movement, and focus on abnormal data. In addition, this paper also proposes a new time series data enhancement method, which slices and re-amplifies the long time series data. This method significantly improves the accuracy of anomaly detection. Through a large number of experiments, AC-LSTM has achieved higher scores on our collected datasets than other methods.

Strong scattering points-based joint detection and size estimation method for swarm targets

The paper considers the joint detection and size estimation of swarm targets. The echo energy of swarm targets is doubly-spread in the range-Doppler (RD) image due to spatially dispersed and relative motion between individuals. Therefore, the traditional constant false alarm rate (CFAR) detection strategies of point and range-spread rigid targets may suffer performance degradation because of failing to accumulate all energy of target in the RD image. In this paper, we first developed a swarm targets echo model. Then a novel detector based on the strong scattering points (SPs) estimation is proposed. The detector considers the swarm targets as a whole and estimates and accumulates all the target-like strong SPs to enhance the detection performance. Moreover, the radial size of swarm targets is calculated according to the range of SPs. Finally, the constant false alarm property is derived, and the strategy of detector parameters setting is discussed in detail. A thorough performance assessment is conducted by Monte Carlo simulation. The results show that the proposed method is superior to the modified cell averaging CFAR (MCA-CFAR) and deterministic scatterer model generalized likelihood ratio test (DSM-GLRT) detectors.

Simulation Study of Acoustic-Resolution-Based Photoacoustic Microscopy for Imaging Complex Blood Vessel Networks

The high-quality imaging of vascular networks in biological tissue is significant to accurate cancer diagnosis with acoustic-resolution-based photoacoustic microscopy (AR-PAM). So far, many new back-projection (BP) models have been proposed to improve the image quality of AR-PAM in the off-focal regions. However, many essential arguments are still open regarding the effectiveness of these methods. To settle these remaining questions and explore the potential and adaptability of these BP methods in vascular network imaging, we conducted extensive simulations of a complex vascular network based on a GPU-based data generation framework. Results show that the SAFT-CF algorithm effectively improves the reconstructed image but mainly highlights point targets. In contrast, the STR-BP algorithm can effectively balance the computational cost, signal-to-noise ratio (SNR), and consistency of target intensity for both point and line targets. Results proved that data interpolation for more A-line numbers would not improve the image quality due to information lost. Thus, the detector number in the scan should be sufficiently large. Results also showed that the STR-BP method improved the PSNR of the image by 4.7 to 7.5 dB, which helps the image withstand a noise level of higher than 25%. The proposed simulation framework and the intuitive findings will guide the design of AR-PAM systems and image reconstruction.

Incoherent Point Spread Function Estimation and Multipoint Deconvolution for Active Incoherent Millimeter-Wave Imaging

We present an approach to image deconvolution in active incoherent millimeter-wave (AIM) imaging. While traditional incoherent imaging systems capture thermal radiation from the scene, AIM imaging uses the transmission of noise signals to increase the signal-to-noise ratio (SNR) while maintaining the necessary space–time incoherence required for image reconstruction. Images formed by any imaging technique are corrupted by the impulse response, or point spread function (PSF), of the imaging system; however, the degradation of the image due to the PSF can be alleviated significantly via deconvolution if the PSF is characterized. Typically, estimation of the PSF is challenging due to spatial undersampling and the impact of noise. In this work, we experimentally demonstrate an approach to estimate the PSF of AIM imaging systems by averaging PSF estimates from multiple point targets using a shift-and-add method in a 38-GHz imaging system. We demonstrate image deconvolution using the CLEAN algorithm to reconstruct a scene with multiple targets.

A REMOTE SENSING SATELLITE LIGHTING DETECTION SYSTEM WITH HIGH DETECTION RATE AND LOW COMPLEXITY

Abstract. A payload was designed and implemented to do real-time and continuous lightning detection which can be used on remote sensing satellite. The payload consists of sensor unit and electronics unit. Based on the analysis of lighting signal spectral characteristics, the sensor unit adopts ultra-narrowband filtering technology to realize lightning signal spectral filtering, and uses high frame rate area array CMOS device as photosensitive element to obtain image information. In order to reduce the complexity and hardware resources of the electronics unit, a continuous mean elimination method is used to detect lightning signals according to the instantaneous characteristics of lightning. The self-adaptive double threshold recognition method is utilized to detect the recognized lightning signal, which significantly reduces the detection time with a detection rate up to 95%. The electronics unit has the function of quick adjustment of detection threshold through instruction, which provides a more flexible application space for lightning characteristics analysis on the ground. In addition, the system can be used to detect other point or surface target by the change of optical design according to the different characteristics of the detection target, such as the large-scale fire disaster or military target.

3D ultrasound imaging by synthetic transmit aperture beamforming using a spherically curved array transducer

Visualization of dermal circulation is important in the field of skin healthcare. We have developed a three-dimensional (3D) photoacoustic (PA) imaging system using a spherically curved array transducer that can visualize the microscale circulation in the skin layers, but limited anatomical information was available around the microvasculature. To provide such anatomical information, this study was aimed at devising a high-quality and high-speed ultrasound (US) imaging framework, particularly, for the spherical array transducer. We tested three synthetic transmit aperture (STA) methods, all-elements, outer-track, and inner-track, for transmission by evaluating the spatial resolution and uniformity of 3D images of point and copper-wire targets. The results demonstrated that the all-elements and outer-track STA methods could provide uniform and clear 3D images. In addition, the outer-track STA could be performed with fewer transmissions than the all-elements STA, and it will be useful for realizing real-time, high-resolution 3D PA/US imaging.

Knowledge graph and deep learning combined with a stock price prediction network focusing on related stocks and mutation points

Due to the interaction of many factors in the stock market, stock price prediction has always been a challenging problem in the field of machine learning. In particular, the mutation factors of the stock market often have a great impact on subsequent predictions. The existing prediction models seldom consider the impacts of other stocks in the stock market and mutation points on the prediction accuracy of target stocks. Therefore, this paper presents a new knowledge graph and deep learning method combined with a stock price prediction network focusing on related stocks and mutation points. First, the target stock price features are obtained through the ConvLSTM network. Second, the knowledge graph is used to mine the hidden relationships between stocks to find the stocks relevant to the target stock to obtain the market information vector and the market information features through the ConvLSTM network. Then, we find the mutation points according to the price change range, construct the mutation point distance weight matrix according to the distance from each trading day to the mutation points, and obtain the mutation point information features through the graph convolutional network (GCN). Finally, the features of market information, mutation point information and target stock price are fused to jointly predict the future stock price. The experimental results on the A share of Shenzhen from 2010 to 2019 show that the algorithm has good robustness and that the prediction accuracy is effectively improved.

Efficient imaging method for multireceiver SAS

With the Loffeld's bistatic formula, the point target reference spectrum (PTRS) of multireceiver synthetic aperture sonar (SAS) can be decomposed into the quasi monostatic term and bistatic deformation (BD) term. The former term is analogous to the monostatic SAS PTRS while the latter one describes the bistatic phase introduced by the bistatic displacement between the transmitter and receiver. The multireceiver SAS echo signal can be transformed into monostatic SAS equivalent data after correcting the BD term. Based on the range sub-block processing method, this data transformation is conducted by using the preprocessing step. Then, fast imaging processors based on monostatic SAS system can be directly exploited to process the monostatic SAS equivalent data. In this paper, the authors mainly focus on the application of non-linear chirp scaling algorithm. At last, the simulations and real data are used to validate the presented method. The processing results show that the imaging performance of presented method is nearly close to that of traditional method. Besides, the imaging efficiency is highly improved compared to the phase centre approximation (PCA) method.

Multi-sector discrete-time channel model for data link layer evaluation of CubeSat communications

Innovation in the space sector is going through a fruitful period. Among other factors, this has been a consequence of the CubeSat paradigm and its acceptance in the educational, scientific, and commercial sectors, as well as its relevant role in the integration of terrestrial and satellite communication systems. Nevertheless, there is no standardized communication protocol for CubeSat technology. One of the reasons for this lack of standardization is the nonexistence of a widely accepted communication channel model for fast development and simulation of CubeSat technology at the data link layer level. Aiming at filling this gap, we propose a communication channel model that captures both the dynamics of the CubeSat in the low earth orbit and the propagation environment where the terrestrial devices are located, taking into account the elevation angle. For this purpose, finite state Markov chains are used to model the time evolution of the channel fading through the satellite trajectory when it passes by the ground point target. Then, CCSDS recommendations are used as a study case for the data link layer of a CubeSat, while an adaptive automatic repeat request mechanism, according to the elevation angle, is simulated and optimized, showing the usefulness of the proposed channel model.

Frequency‐domain multireceiver synthetic aperture sonar imagery with Chebyshev polynomials

Based on Chebyshev polynomials, the two-way slant range is first approximated by the fourth-order polynomial approximation. With the series reversion method, the point target reference spectrum (PTRS) which is further used for the development of imaging algorithm in this letter is obtained. The presented method processes the echoed signal corresponding to each transmitter/receiver pair. And then a high-resolution synthetic aperture sonar (SAS) image is obtained by coherently superposing all coarse-resolution images. Comparing the peak sidelobe ratio (PSLR) and integration sidelobe ratio (ISLR), the performance of the presented method is superior to that of the traditional method. Simulations are further validated by the presented method.

Tumor‐Suppressor Role of the Caveolar α1‐Na/K‐ATPase Signalosome in NASH related Hepatocellular Carcinoma

BackgroundHepatocellular Carcinoma (HCC) is the second and fastest‐growing cause of cancer‐ related mortality worldwide. In the Western countries, due to the epidemic of obesity, non‐alcoholic steatohepatitis (NASH) has become the major cause of HCC. Intriguingly, the molecular mechanisms that underlie the progression of NASH to HCC is still unclear.HypothesisWe hypothesized that, the growing uncoupled metabolism during NASH progression to ESLD and HCC manifested by lower cell oxi‐redox status and an apoptotic ‘switch’ activity, follows a dysregulation in the wild type Caveolar α1‐Na/K‐ATPase (NKA)/Src signalosome, promoting an amplification of the pSrc → PI3K→ Akt pathway, which mediates survivin overexpression driving local carcinogenesis.MethodsExpression of Cav‐1/Smac‐Diablo/Survivin proteins was performed by confocal‐microscopy on immuno‐stained HCC cell lines (Hep3B and SNU475), and livers in both NASH and NASH‐HCC rodent models, and in humans. Apoptotic activity was assayed via TUNEL. Tumor burden in rodents’ livers were evaluated using ImageJ software on H&E‐stained liver tissues and fibrosis via trichrome staining. Furthermore, signaling pathway studies were explored in‐vitro guided by RNA sequencing. Selective blockage of Src‐p at its kinase domain was performed by administration of a synthetic peptide (pNaKtide). Significant differences among groups were established at p<0.05 using ANOVA/Turkey’s post hoc test.ResultsBlockage of Src‐p at the α1‐NKA promoted apoptosis of Human HCC cell lines and tumors of disease murine models in a dose dependent manner (p< 0.01). pNaKtide at IC50 drove the downregulation and upregulation of Survivin and SMAC expressions (p<0.05), respectively in HCC cell lines. In‐vivo, liver tumor burden and fibrosis were significantly lower in animals treated with pNaKtide vs non‐treated animals (p<0.01). Furthermore, Cav‐1 and Survivin expressions were significantly higher, while SMAC protein expression was significantly lower in livers from rodents with NASH/HCC vs animals treated with pNaKtide (p<0.01). Similar pattern of proteins expressions was noted in tumors from patients with NASH±HCC vs liver tissue from healthy subjects or subjects with liver metastases (p<0.05). In‐vitro, Src‐p at the α1‐NKA activates PI3K/Akt pathway with concomitant inhibition of the FoxO cascade, favoring cell division and primary liver carcinogenesis.ConclusionsCaveolar Src‐phosphorylation at the α1‐Na/K‐ATPase regulated Survivin/SMAC expressions, leading to cellular “switch” from apoptosis to cell division via the activation of the PI3K/Akt pro‐survival pathway with concomitant inhibition of the FoxO cascade. Therefore, point target blockage may be explored as a treatment for tumor regression in HCC.

Distributed Aberration Correction Techniques Based on Tomographic Sound Speed Estimates.

Phase aberration is widely considered a major source of image degradation in medical pulse-echo ultrasound. Traditionally, near-field phase aberration correction techniques are unable to account for distributed aberrations due to a spatially varying speed of sound in the medium, while most distributed aberration correction techniques require the use of point-like sources and are impractical for clinical applications where diffuse scattering is dominant. Here, we present two distributed aberration correction techniques that utilize sound speed estimates from a tomographic sound speed estimator that builds on our previous work with diffuse scattering in layered media. We first characterize the performance of our sound speed estimator and distributed aberration correction techniques in simulations where the scattering in the media is known a priori. Phantom and in vivo experiments further demonstrate the capabilities of the sound speed estimator and the aberration correction techniques. In phantom experiments, point target resolution improves from 0.58 to 0.26 and 0.27 mm, and lesion contrast improves from 17.7 to 23.5 and 25.9 dB, as a result of distributed aberration correction using the eikonal and wavefield correlation techniques, respectively.

Synthetic Aperture Radar Active Decoy

Synthetic aperture radar (SAR) has been widely involved in military reconnaissance and detection of moving targets. SAR active decoy can generate deceptive jamming signal and it can severely affect the content of SAR focused image. SAR range Doppler algorithm (RDA) is considered as one of the most common algorithms utilized in the image formation processor (IFP) in SAR sensors. In this paper, the inverse RDA (IRDA) deception jamming technique and its evaluation criteria on SAR are demonstrated. Mathematical formulations for SAR RDA with and without deception jamming are presented. Matlab simulation and results of SAR RDA under the proposed deception scenario are discussed. SAR point target simulation of the proposed deceptive jamming and false target insertion into real large scene are also introduced. Comparison of the proposed deceptive jamming to signal power ratio (JSR) required to counter SAR with other SAR jamming techniques is analyzed.

Low-SNR Infrared Point Target Detection and Tracking via Saliency-Guided Double-Stage Particle Filter.

Low signal-to-noise ratio (SNR) infrared point target detection and tracking is crucial to study regarding infrared remote sensing. In the low-SNR images, the intensive noise will submerge targets. In this letter, a saliency-guided double-stage particle filter (SGDS-PF) formed by the searching particle filter (PF) and tracking PF is proposed to detect and track targets. Before the searching PF, to suppress noise and enhance targets, the single-frame and multi-frame target accumulation methods are introduced. Besides, the likelihood estimation filter and image block segmentation are proposed to extract the likelihood saliency and obtain proper proposal density. Guided by this proposal density, the searching PF detects potential targets efficiently. Then, with the result of the searching PF, the tracking PF is adopted to track and confirm the potential targets. Finally, the path of the real targets will be output. Compared with the existing methods, the SGDS-PF optimizes the proposal density for low-SNR images. Using a few accurate particles, the searching PF detects potential targets quickly and accurately. In addition, initialized by the searching PF, the tracking PF can keep tracking targets using very few particles even under intensive noise. Furthermore, the parameters have been selected appropriately through experiments. Extensive experimental results show that the SGDS-PF has an outstanding performance in tracking precision, tracking reliability, and time consumption. The SGDS-PF outperforms the other advanced methods.

Principles and techniques of microbubble localization and tracking in super-resolution ultrasound microvascular imaging

Super-resolution ultrasound microvascular imaging (SR-UMI) is an emerging technology that uses microbubbles as point targets to break the diffraction limit of ultrasound and track blood flow. Robust microbubble localization and tracking are essential for successful SR-UMI. However, in practice, microbubble localization and tracking are often hampered by the complex flow dynamics of microbubbles (e.g., microbubble signal overlap, merging and splitting) and suboptimal ultrasound imaging quality (e.g., low SNR, tissue motion). More importantly, the tradeoff between microbubble localization and tracking accuracy and SR-UMI imaging speed remains a key barrier for practical implementation of SR-UMI. In this presentation, I will first review the principles and state-of-the-art techniques of microbubble localization and tracking. I will then introduce several new methods recently developed by our group that demonstrate improved microbubble localization and tracking performance under high microbubble concentrations (which leads to accelerated SR-UMI). Finally, I will introduce a new deep learning-based SR-UMI method that is both microbubble localization- and tracking-free. Because this new method is no longer constrained by challenges associated with microbubble localization and tracking, it achieves very fast SR-UMI imaging speed (tens of milliseconds to several seconds on a moderate GPU) and has the potential to be implemented in real-time.

다항식 기반 오메가-케이 알고리즘을 이용한 곡선기하에서의 고해상도 바이스태틱 SAR 영상생성에 대한 연구

In this study, we propose a high-resolution synthetic aperture radar (SAR) imaging method for curved-orbit geometry in bistatic configurations with spatially separated transmitter and receiver. To accommodate large curved-path and bistatic configuration properties, we first formulate a B-SAR signal using a polynomial range model and derive its two-dimensional (2D) phase spectrum. Next, we extend the basic concept of the typical omega-K algorithm (OKA) for B-SAR by focusing on the polynomial range model, referred to as polynomial-OKA (P-OKA), enabling us to provide focused SAR images in curved-path and bistatic configurations. Using point target simulations, we verified the efficacy of the proposed P-OKA, where fully focused high-resolution B-SAR images were obtained in various curved-path scenarios.

Ocean Image Formation Algorithm Using Altimeter Data for Next Generation Satellite SAR

Synthetic aperture radar (SAR) obtains two-dimensional images of the Earth’s surface. Spatial resolution is one of the most important factors in evaluating SAR performance. Therefore, high operating frequencies are preferred for obtaining high resolution images. In fact, Ku-band and Ka-band are used in the automobile SAR (Auto-SAR) and the aircraft SAR (Airborne-SAR). However, the operating frequencies of the satellite SAR (Spaceborne-SAR) are limited to the X-band because of the wide Doppler frequency band caused by the satellite’s high velocity. To overcome this frequency limitation in satellite SAR, we propose a low-complexity SAR algorithm that forms images of the ocean using satellite altimeter data with an operating frequency of the Ku-band (13.575 GHz). The algorithm requires the burst mode pulse transmission technique frequently used in SAR altimeters. To validate the proposed algorithm as being suitable for ocean image formation, point target simulation and analysis are performed, and some quantitative results are provided.

Research on 3D geological modeling of fractured-vuggy carbonate reservoirs

Fractured-vuggy carbonate reservoirs are mainly composed of holes, caves, and fractures, with large disparity in scale, irregular shape, and discontinuous distribution. Quantitatively depicting the distribution of holes, caves, and fractures in three-dimensional space has always been a technical problem in the development of this type of reservoir, and there is currently no effective geological modeling method. Taking the Yijianfang Formation in the Halahatang area of the Tarim Basin as an example, this paper proposes to model the cavities and fractures separately. The hole model is generated by seismic resampling, and the attribute model uses the inverted porosity as the data volume combined with dynamic and static data constraints to simulate and generate. There are two main methods for fracture modeling. One is deterministic fracture modeling, which is mainly for large-scale fractures; the other is random fracture modeling, which is mainly for small-scale fractures. The deterministic fracture modeling method is based on the seismic data, by extracting the fracture slices of the seismic ant body data, combining the fracture characterization results, screening the fractures, and finally generating a large-scale fracture network; finally integrating hole, the hole model and fracture attributes model to establish a geological model of fracture-cavity reservoirs. Model the cavities and fractures separately was used to drill well Pro1 in yijianfang Formation, revealing that the target zone was 28 meters, and the high yield oil flow was obtained by sidetracking the high point target, which proved that the simulation results were credible.

Short-Range Millimeter-Wave Imaging in the Presence of Array Element Position Deviation

The focusing performance of an active short-range millimeter-wave imager is vulnerable to phase errors. As two major sources of phase error, delay uncertainty and array deformation are entangled when they are treated by conventional calibration methods, leading to defocusing. First, the defocusing effect caused by the array element position deviation (EPD) is graphically described in the form of ellipse deformation, indicating that the results of imaging a point target can inform on the distribution of EPD. Second, we analyze the phase error coupling problem to show that the EPD-caused phase error is spatially variant, therefore making the imaging performance sensitive to target location to varying extents bounded by the reference target location. Finally, a time-domain calibration method based on a constrained differential delay test is proposed to jointly mitigate the phase errors caused by both delay uncertainty and EPD. Numerical and experimental results demonstrate the efficacy of the proposed method.

Doppler Division Multiplexed Multiple-Input–Multiple-Output Imaging Using Cascaded Millimeter-Wave Radars

Radar-based near-field 3-D imaging technology has recently attracted increasing attention in the field of security systems. This article presents a novel multiple-input–multiple-output (MIMO) synthetic aperture radar imaging algorithm for security checkpoints attached to a moving walkway. The proposed algorithm is characterized by transmitting-signal multiplexing on the Doppler frequency domain, which allows for a reduced data acquisition time compared with that of conventional time-division multiplexed MIMO systems and therefore achieves the image reconstruction of dangerous objects with smaller motion errors. This algorithm can be applied to 1-D antenna array systems, and image reconstruction is based on a back-projection algorithm with some modifications. The mathematical derivation of the algorithm and its advantages and disadvantages in comparison with those of a conventional system are detailed in this article. Details of numerical simulation of a point target as well as the experimental results of image reconstruction of a pinball and toy gun, using cascaded millimeter-wave sensors, are also presented for verifying the performance of the algorithm.