Weak Target Research Articles

Translational Motion Compensation Method for ISAR Imaging of Air Maneuvering Weak Targets Based on CV-GRUNet

Translational Motion Compensation Method for ISAR Imaging of Air Maneuvering Weak Targets Based on CV-GRUNet

Displacement Measurement of Weak Targets with Imaging Radar

Displacement Measurement of Weak Targets with Imaging Radar

A Coherent TBD Algorithm for Remote Sensing of Weak Targets Using GNSS Reflected Signals

A Coherent TBD Algorithm for Remote Sensing of Weak Targets Using GNSS Reflected Signals

WTE-CGAN Based Signal Enhancement for Weak Target Detection

WTE-CGAN Based Signal Enhancement for Weak Target Detection

Target Detection in Single-Photon Lidar Using CNN Based on Point Cloud Method

To enhance the detection capability of weak targets and reduce the dependence of single-photon lidar target detection on the number of the time-correlated single-photon counting detection cycles, a convolutional neural network (CNN) based on the point cloud (CNN-PC) method is proposed in this paper for detecting targets in single-photon lidar. This approach utilizes the exceptional feature extraction capabilities offered by CNN. The CNN-PC method utilizes the feature extraction module of the trained CNN to simultaneously extract features from two-dimensional point cloud slices. Subsequently, it combines these features and feeds them into the classification module of the trained CNN for final target detection. By training the CNN using point cloud slices generated with a minimal number of detection cycles and employing a parallel structure to extract features from multiple point cloud slices, the CNN-PC method exhibits remarkable flexibility in adapting to varying numbers of detection cycles. Both simulation and experimental results demonstrate that the CNN-PC method outperforms the classical constant false alarm rate method in terms of the target detection probability at the same signal-to-noise ratio and in terms of the imaging rate and error rate at the same number of detection cycles.

Super-Resolution Reconstruction of Weak Targets on Water Surfaces: A Generative Adversarial Network Approach Based on Implicit Neural Representation

Super-Resolution Reconstruction of Weak Targets on Water Surfaces: A Generative Adversarial Network Approach Based on Implicit Neural Representation

A mitophagy sensor PPTC7 controls BNIP3 and NIX degradation to regulate mitochondrial mass

Mitophagy mediated by BNIP3 and NIX critically regulates mitochondrial mass. Cellular BNIP3 and NIX levels are tightly controlled by SCFFBXL4-mediated ubiquitination to prevent excessive mitochondrial loss and lethal disease. Here, we report that knockout of PPTC7, a mitochondrial matrix protein, hyperactivates BNIP3-/NIX-mediated mitophagy and causes perinatal lethality that is rescued by NIX knockout in mice. Biochemically, the PPTC7 precursor is trapped by BNIP3 and NIX to the mitochondrial outer membrane, where PPTC7 scaffolds assembly of a substrate-PPTC7-SCFFBXL4 holocomplex to degrade BNIP3 and NIX, forming a homeostatic regulatory loop. PPTC7 possesses an unusually weak mitochondrial targeting sequence to facilitate its outer membrane retention and mitophagy control. Starvation upregulates PPPTC7 expression in mouse liver to repress mitophagy, which critically maintains hepatic mitochondrial mass, bioenergetics, and gluconeogenesis. Collectively, PPTC7 functions as a mitophagy sensor that integrates homeostatic and physiological signals to dynamically control BNIP3 and NIX degradation, thereby maintaining mitochondrial mass and cellular homeostasis.

Fast and accurate wideband sparse spatial spectrum estimation in an underwater strong interference environment.

Wideband sparse spatial spectrum estimation is an important direction-of-arrival (DOA) estimation method that can obtain a high resolution with few snapshots and a low signal-to-noise ratio. However, in an underwater strong interference environment, the accuracy of DOA estimation may be seriously affected, and even the weak targets could be completely masked. In this paper, we propose a fast matrix filter design method based on truncated nuclear norm regularization to attenuate strong interferences while passing weak targets. The matrix filter operator and the exact covariance matrix after filtering can be obtained simultaneously by solving a convex optimization problem that contains the output power term and non-Toeplitz error propagation control term. Then the modified sparse spectrum fitting algorithm based on the matrix filter is used to estimate spatial spectrum over closely spaced wideband signals. Compared with existing methods, the proposed method achieves higher DOA estimation accuracy and lower computational time for matrix filter design. Meanwhile, the estimation accuracy of the proposed method is verified with the experimental results.

Line Spectrum Trajectory Detection Method of Underwater Acoustic Signal Based on Particle filter

Line spectrum detection is a significant task to detect, track, and classify weak target sources for passive sonar systems. In order to solve the problem of tracking time-varying frequency trajectories, a track-before-detect (TBD) algorithm based on the two-layer particle filter structure is proposed. The detect layer deals with the potential tracks by maintaining a low-threshold dynamic detection grid, which is used to determine the birth, death, and false alarm of targets. The tracking layer utilizes the particle filter to compute the joint multitarget probability density through a Bayesian framework. Simulation results show that the proposed algorithm can effectively detect and track time-varying frequency trajectories in a low signal-to-noise ratio situation, which has certain research and application value in weak signal detection.

Finding a Needle in a Haystack: Faint and Small Space Object Detection in 16-Bit Astronomical Images Using a Deep Learning-Based Approach

With the increasing interest in space science exploration, the number of spacecraft in Earth’s orbit has been steadily increasing. To ensure the safety and operational integrity of active satellites, advanced surveillance and early warning of unknown space objects such as space debris are crucial. The traditional threshold-based filter for space object detection heavily relies on manual settings, leading to limitations such as poor flexibility, high false alarm rates, and weak target detection capability in low signal-to-noise ratios. Therefore, detecting faint and small objects against a complex starry background remains a formidable challenge. To address this challenge, we propose a novel, intelligent, and accurate detection method called You Only Look Once for Space Object Detection (SOD-YOLO). Our method includes the following novel modules: Multi-Channel Histogram Truncation (MHT) enhances feature representation, CD-ELAN based on Central Differential Convolution (CDC) facilitates learning contrast information, the Space-to-Depth (SPD) module replaces pooling layer to prevent small object feature loss, a simple and parameter-free attention module (SimAM) expands receptive field for Global Contextual Information, and Alpha-EIoU optimizes the loss function for efficient training. Experiments on our SSOD dataset show SOD-YOLO has the ability to detect objects with a minimum signal-to-noise ratio of 2.08, improves AP by 11.2% compared to YOLOv7, and enhances detection speed by 42.7%. Evaluation on the Spot the Geosynchronous Orbit Satellites (SpotGEO) dataset demonstrates SOD-YOLO’s comparable performance to state-of-the-art methods, affirming its generalization and precision.

Analysis of the channel estimate model in passive radar using OFDM waveforms

The paper takes an in-depth analysis of the channel estimate model (CEM) in passive radar using the orthogonal frequency division multiplexing (OFDM) waveforms. The CEM has been intensively exploited in OFDM passive radar, where the channel estimates (CE) are obtained from the original digitized received signal (ODRS), and target detection is performed based on CEs. However, traditional CEM is derived neglecting the inter-carriers interference (ICI). The influence of the ICI on target detection has rarely been discussed previously. In fact, target with large power and Doppler frequency can induce strong ICI, which increases the noise floor, and thus imposes significant influence on the detection of the other targets, especially the weak targets. In this paper, we rederive the CEM taking the ICI into consideration, and obtain a new CEM. In the new CEM, a specific target has two components, i.e., the useful signal part, and ICI. We derive the useful signal to noise ratio (SNR) and ICI to noise ratio (ICINR) theoretically, and provide compact expressions. We reveal the relationship between the SNR and ICINR in the CE, and the SNR in the ODRS. Based on the theoretical derivation, the influence of ICI is analysed. We also discuss the elimination of the ICI. The influence of ICI can be eliminated by cancelling the target signal that induces the ICI from the CEM. A target signal cancellation method is developed based on the new CEM. Simulations demonstrate the effectiveness of the theoretical analysis of the CEM and the proposed cancellation method.

Detection of hypersonic weak targets by high pulse repetition frequency radar based on multi‐hypothesis fuzzy‐matching radon transform

AbstractFor the integration detection of near space hypersonic weak targets by high pulse repetition frequency (PRF) radar, a novel method named Multi‐Hypothesis Fuzzy‐Matching Radon transform (MHFM‐RT) is proposed for the near space hypersonic target detection and tracking. For remote hypersonic target detection, to avoid range ambiguity, current radars always use a low PRF mode, which limit the number of pulse accumulations. Using the high PRF mode, the fuzzy folding will appear in the target range measurements when target trajectory crosses range fuzzy intervals. Therefore, there is a contradiction between range ambiguity and energy accumulation. The proposed method is used to match the fuzzy measurements, so as to realise the correct integration in the condition of range ambiguity. Firstly, considering the need of range ambiguity resolution, the mode of staggered PRF is used. Secondly, the first frame measurements are periodically extended for multiple‐fuzzy hypothesis. Finally, the weak target track is accumulated in MHFM‐RT domain, and the signal integration and ambiguity resolution can be realised simultaneously. The proposed method expands the Variable‐Diameter‐Arc‐Helix Radon transform (VDAH‐RT) method to fuzzy folding conditions. Compared with the existing methods, for 7‐scan measurements non‐coherent integration, the detection sensitivity of the proposed method is about 0.5–1 dB higher than that of the IMM hybrid filter algorithm, and about 1 dB higher than that of the RHT‐TBD approach, and it needs less storage space and has higher detection probability.

Effective reduction of sidelobes in pulse compression radars using NLFM signal processing approaches

Pulse compression techniques are widely used in modern radar systems to enhance range resolution and detection capability. As signal design is one of the basic factors of an efficient radar system, the problem of designing a radar signal with good characteristics using pulse compression is addressed in this paper. A linear frequency modulated (LFM) waveform has been widely used for conventional radars. However, it has a higher sidelobe level which makes the detection of weak targets very difficult in presence of strong targets returns and as a result the problem of masking occurs. In order to get suppressed sidelobes of radar matched filter output as well as preserve the main lobe resolution and level to overcome the problem of masking, nonlinear frequency modulated (NLFM) signals are used in modern radars. In this paper, we will introduce two different approaches to design an optimized NLFM signal which is characterized by optimized sidelobe level (SLL). The first is the exponential piecewise linear function (EPWL) which is the modification of piecewise linear (PWL) functions relying on an exponential predistortioning function. The second is the odd-term polynomial approximation (OTPA) in which the generation of NLFM signal depends only on odd-powered terms of the polynomial function. Furthermore, the simulation results of autocorrelation functions (ACF) of the proposed signals show its superiority over the traditional LFM signals and significantly enhancement compared to the recent background work. The Doppler sensitivity of the designed signals has been evaluated, revealing that the first approach offers Doppler tolerance and is suitable for surveillance radar systems, while the second approach with a lower sidelobe level is used in applications such as Synthetic Aperture Radar (SAR). Finally, the ambiguity function of the designed signals has been measured to illustrate the effect of the Doppler effect relative to different velocities.

Coherent Accumulation for Measuring Maneuvering Weak Targets Based on Stepped Dechirp Generalized Radon–Fourier Transform

The problem of accurately measuring the motion parameters of low radar cross-section (RCS) maneuvering targets has long been a hurdle in the radar technology landscape. Small targets, due to their elusive characteristics, are particularly difficult to detect with conventional radar systems. In this paper, we investigate the capabilities of dechirp-receiving stepped-frequency radar, a modern system using a linear frequency modulation signal for downconversion. This permits the radar to function at reduced sampling rates while maintaining the transmission of large-bandwidth signals and achieving synthetic imaging. Our primary contribution is introducing the stepped dechirp generalized Radon–Fourier transform (stepped DGRFT) algorithm. This novel approach allows the radar system to perform coherent accumulation, enhancing the accuracy of motion parameter estimates for low-RCS maneuvering targets. Results from our simulations and measured data analysis validate the effectiveness of our proposed algorithm, demonstrating its superiority over other methods.

Broadband acoustic signal enhancement via gradient metamaterials coupled to crystals

In this work, a phononic crystal gradient metamaterial structure (PCGMs) is proposed based on the strong wave compression effect coupled with equivalent medium theory to achieve enhancement and directional sensing of weak target acoustic signals. Compared with the conventional gradient structure, PCGMs exhibit superior acoustic enhancement performance and wider range of acoustic response capability. Numerical analysis and experimental validation consistently demonstrate that PCGMs can effectively enhance the target frequency signals in harmonic signals. This study breaks through the detection limit of acoustic sensing systems and provides a great method for engineering applications of weak acoustic signal perception.

Weak non-line-of-sight target echoes extraction without accumulation.

Non-line-of-sight (NLOS) technology has been rapidly developed in recent years, allowing us to visualize or localize hidden objects by analyzing the returned photons, which is expected to be applied to autonomous driving, field rescue, etc. Due to the laser attenuation and multiple reflections, it is inevitable for future applications to separate the returned extremely weak signal from noise. However, current methods find signals by direct accumulation, causing noise to be accumulated simultaneously and inability of extracting weak targets. Herein, we explore two denoising methods without accumulation to detect the weak target echoes, relying on the temporal correlation feature. In one aspect, we propose a dual-detector method based on software operations to improve the detection ability for weak signals. In the other aspect, we introduce the pipeline method for NLOS target tracking in sequential histograms. Ultimately, we experimentally demonstrated these two methods and extracted the motion trajectory of the hidden object. The results may be useful for practical applications in the future.

The combined plasma membrane coating and cluster bombing strategy for improved tumor-targeting gene delivery of silicon nanoclusters

With the promising biosafety and favorable cell imaging efficiency, silicon quantum dots (SiQDs) was broadly exploited as non-viral gene carriers in recent years. However, the low transfection efficiency and weak targeting ability hindered its further clinical applications. In this study, the combined plasma membrane coating and cluster bombing strategy was adopted to enhance the gene delivery potential of silicon quantum dots nanoclusters (SiNC). Initially, SiNC was generated via 3, 3′-Dithiodipropionic acid (DipA) crosslinking of SiQDs, then the obtained nanoclusters were coated by distinct plasma membrane. Interestingly, cell membrane coated SiNC (CM-SiNC) underwent particle size change, the typical character of “cluster bombing”, when exposed to high GSH concentration, which was observed in the tumor microenvironment. Meanwhile, CM-SiNC can be efficiently uptaken by HEK 293T and HeLa cells, therefore transferring DNA into those cells. More importantly, among the particles coated by HeLa (HeLa-M), Red Blood (RBC-M) or RAW267.4 (RAW-M) cell membrane, HeLa cell membrane coating exhibited better cellular uptake and transfection efficiency in HeLa cells, which suggested the encouraging tumor targeting ability. In sum, these data suggested that cluster bombing of SiNC could be beneficial for physical stability and biodistribution, the additional plasma membrane coating further endowed SiNC the efficient gene delivery and tumor targeting ability. Therefore, CM-SiNC had the potential as a gene delivery vector and its application should be further addressed in vivo.

Robust dual-channel correlation algorithm for complex weak target detection with wideband radar

Robust dual-channel correlation algorithm for complex weak target detection with wideband radar

Unipolar carrier multiplication high-gain and low-noise AlGaN ultraviolet avalanche photodiode with periodically stacked structure

Highly sensitive avalanche photodetector (APD) has become a promising candidate for detecting extremely weak target signals. However, the impact ionization multiplication simultaneously triggered by electrons and holes will lead to large excess noise, thus significantly influencing device avalanche performance. Herein, we propose a distinctive AlGaN-based ultraviolet avalanche photodiode with AlN/Al0.2Ga0.8N periodically stacked multiplication region. The higher effective masses and density of states in valence band renders holes limited in the quantum-well region, where thermalization plays a dominant role during carrier transport process. On the contrary, in the atomic-scale AlN/AlGaN stacked structure with a periodic thickness of 10 nm, the electron mutualization motion is conductive to electron obtaining sufficient energy to induce impact ionization. Hence, the mechanism of unipolar carrier induced avalanche multiplication effectively reduces device noise and improving multiplication gain. Meanwhile, the high electric field intensity and tilted energy band in the AlGaN/AlN periodically stacked region significantly contribute to the carrier impact ionization. Consequently, the device achieves a superior avalanche gain of more than 105 at 74 V reverse bias. It is envisioned that the unipolar carrier triggering avalanche events offers a viable route to build high-performance APDs.

Diagnosing Orthotopic Lung Tumor Using a NTR-Activatable Near-Infrared Fluorescent Probe by Tracheal Inhalation.

Nitroreductase (NTR) is an enzyme that is upregulated under tumor-depleted oxygen conditions. The majority of studies have been conducted on NTR, but many existing fluorescent imaging tools for monitoring NTR inevitably suffer from weak targeting, low sensitivity, and simple tumor models. Research on diagnosing lung tumors has been very popular in recent years, but targeting assays in orthotopic lung tumors is still of great research value, as such models better mimic the reality of cancer in the organism. Here, we developed a novel near-infrared (NIR) fluorescent probe IR-ABS that jointly targets NTR and carbonic anhydrase IX (CAIX). IR-ABS has excellent sensitivity and selectivity and shows exceptional NTR response in spectroscopic tests. The measurements ensured that this probe has good biosafety in both cells and mice. A better NTR response was found in hypoxic tumor cells at the cellular level, distinguishing tumor cells from normal cells. In vivo experiments demonstrated that IR-ABS achieves a hypoxic response at the zebrafish level and enables rapid and accurate tumor margin distinguishment in different mouse tumor models. More importantly, we successfully applied IR-ABS for NTR detection in orthotopic lung tumor models, further combined with tracheal inhalation drug delivery to improve targeting. To the best of our knowledge, we present for the first time a near-infrared imaging method for targeting lung cancerous tumor in situ via tracheal inhalation drug delivery, in contrast to the reported literature. This NIR fluorescence diagnostic strategy for targeting orthotopic lung cancer holds exciting potential for clinical aid in cancer diagnosis.