Target Interference Research Articles

OBTAINING TRANSGENIC POTATO PLANTS FOR COUNTERING PHYTOPHTHORA INFESTANS VIA “HIGS” METHOD IMPLEMENTATION

Oomycetes are a group of organisms that include some of the most dangerous plant pathogens capable of causing epiphytoties that lead to reduced crop yields and famine in some countries. The only available method of combating them – repeated application of fungicides in fields and careful selection of seed material – is ineffective for most of these pathogens. This paper proposes to use the HIGS (host-induced gene silencing) approach so that genetically modified plants, when infected with late blight, would cause the pathogen to silence its effector protein genes, which would prevent the pathogen from growing and reproducing in plant. To implement the HIGS method against the pathogen Phytophthora infestans, we developed two genetically engineered constructs, one of which uses the P. infestans RXLR-effector gene AVR3a-b as the RNA interference target, and the other uses a region of the PITG_03155 effector gene, which is conserved for both P. infestans and P. cactorum. After Agrobacterium-mediated genetic transformation of potato plants, we obtained four transgenic plants carrying a construct based on the AVR3a-b gene (two each of the “Milena” and “Gala” varieties) and one of “Milena” variety carrying a construct based on the PITG_03155 gene. These plants will be micro clonally propagated and tested for the presence of interfering RNA to the effector genes of P. infestans, after which the plant lines are planned to be planted under natural pathogenic background conditions and a comparative analysis with control plants for disease incidence and resistance will be conducted.

Profiling of Phakopsora pachyrhizi transcriptome revealed co-expressed virulence effectors as prospective RNA interference targets for soybean rust management.

Soybean rust (SBR), caused by an obligate biotrophic pathogen Phakopsora pachyrhizi, is a devastating disease of soybean worldwide. However, the mechanisms underlying plant invasion by P. pachyrhizi are poorly understood, which hinders the development of effective control strategies for SBR. Here we performed detailed histological characterization on the infection cycle of P. pachyrhizi in soybean and conducted a high-resolution transcriptional dissection of P. pachyrhizi during infection. This revealed P. pachyrhizi infection leads to significant changes in gene expression with 10 co-expressed gene modules, representing dramatic transcriptional shifts in metabolism and signal transduction during different stages throughout the infection cycle. Numerous genes encoding secreted protein are biphasic expressed, and are capable of inhibiting programmed cell death triggered by microbial effectors. Notably, three co-expressed P. pachyrhizi apoplastic effectors (PpAE1, PpAE2, and PpAE3) were found to suppress plant immune responses and were essential for P. pachyrhizi infection. Double-stranded RNA coupled with nanomaterials significantly inhibited SBR infection by targeting PpAE1, PpAE2, and PpAE3, and provided long-lasting protection to soybean against P. pachyrhizi. Together, this study revealed prominent changes in gene expression associated with SBR and identified P. pachyrhizi virulence effectors as promising targets of RNA interference-based soybean protection strategy against SBR.

Persymmetric direction detector for range‐spread target in subspace interference plus homogeneous and partially homogeneous clutter

AbstractThe problem of detecting the direction of range‐spread targets in the presence of subspace interference, along with homogeneous and partially homogeneous Gaussian clutter backgrounds characterised by an unknown persymmetric covariance matrix, is being investigated. It is assumed that the interference subspace is linearly independent of the signal subspace, and the coordinates of both subspaces are unknown. Leveraging the property of persymmetry, the two‐step Generalised Likelihood Ratio Test and the two‐step Wald test are proposed to formulate three detectors in the presence of subspace interference, homogeneous, and partially homogeneous Gaussian clutter. Theoretical derivations and simulations demonstrate that three proposed detectors maintain a constant false alarm rate with respect to the unknown clutter covariance matrix. In comparison to existing detectors in similar backgrounds, particularly in scenarios with limited training data, these three detectors exhibit superior detection performance.

Multi-Person Action Recognition Based on Millimeter-Wave Radar Point Cloud

Human action recognition has many application prospects in human-computer interactions, innovative furniture, healthcare, and other fields. The traditional human motion recognition methods have limitations in privacy protection, complex environments, and multi-person scenarios. Millimeter-wave radar has attracted attention due to its ultra-high resolution and all-weather operation. Many existing studies have discussed the application of millimeter-wave radar in single-person scenarios, but only some have addressed the problem of action recognition in multi-person scenarios. This paper uses a commercial millimeter-wave radar device for human action recognition in multi-person scenarios. In order to solve the problems of severe interference and complex target segmentation in multiplayer scenarios, we propose a filtering method based on millimeter-wave inter-frame differences to filter the collected human point cloud data. We then use the DBSCAN algorithm and the Hungarian algorithm to segment the target, and finally input the data into a neural network for classification. The classification accuracy of the system proposed in this paper reaches 92.2% in multi-person scenarios through experimental tests with the five actions we set.

Interference mitigation and target detection for automotive FMCW radar with range-Doppler sparse regularization

Interference mitigation and target detection for automotive FMCW radar with range-Doppler sparse regularization

Spraying dsRNA with chitosan formulation improves control of the western flower thrips, Frankliniella occidentalis, in a greenhouse.

The western flower thrips, Frankliniella occidentalis, is a serious pest causing both direct feeding damage and indirect harm by transmitting the tomato spotted wilt virus. A spraying double-stranded RNA (dsRNA) targeted at the vacuolar-type ATPase (vATPase) gene was developed and demonstrated high insecticidal activity in the laboratory but less effective in field applications. To improve control efficacy under field conditions, three strategies were explored in this study. First, to identify a more efficient RNA interference (RNAi) target, dsRNA specific to the Snf7 gene was tested alongside dsRNA targeting vATPase, and both were found to be similarly effective in controlling the thrips. Second, to elucidate the factors contributing to dsRNA resistance, dsRNA-degrading enzymes were annotated and their physiological roles in diminishing RNAi efficacy were investigated. Third, to suppress the dsRNA degradation from the dsRNase activities and protect it in field conditions, the dsRNA was encapsulated with chitosan. This formulation enhanced the dsRNA's resistance to environmental stressors such as ultraviolet light and the digestive enzymes in the thrips' gut. Additionally, the chitosan formulation specifically increased the RNAi efficacy, likely by facilitating more efficient entry into the target cells, thus bolstering the insecticidal activity of the dsRNA. The formulated dsRNA was applied on F. occidentalis infesting the hot peppers in a greenhouse at a concentration of 500 ppm, demonstrating an 82.4% control efficacy compared with 59.2% control efficacy observed with the application of naked dsRNA. This study further demonstrated an enhancement in the spectrum of control by combining dsRNAs specific to three distinct thrips species, while the mixture showed no adverse effects on non-target insects, such as the lepidopteran Spodoptera exigua. Collectively, these findings reveal that the chitosan formulation of dsRNA not only improves control efficacy under field conditions but also broadens the control spectrum against three different thrips pests.

Knockdown of KCNQ1OT1 Alleviates the Activation of NLRP3 Inflammasome Through miR-17-5p/TXNIP Axis in Retinal Müller Cells

Purpose Diabetic retinopathy (DR) is one of the most severe and common complications caused by diabetic mellites. Inhibiting NLRP3 inflammasome activation displays a crucial therapeutic value in DR. Studies have shown that KCNQ1OT1 plays a critical role in regulating NLRP3 inflammasome activation and participates in the pathogenesis of diabetic complications. The present study aims to explore the role, and the potential mechanism of KCNQ1OT1 in regulating the activation of NLRP3 inflammasome in DR. Methods qRT-PCR was used to detect the expression of KCNQ1OT1, miR-17-5p, TXNIP, NLRP3, ASC, caspase-1 and IL-1β. Western blot was performed to detect the expression of NLRP3, ASC, caspase-1, IL-1β and TXNIP. Immunohistochemistry and immunostaining were performed to detect the expression of caspase-1. The levels of the inflammatory cytokine IL-1β were determined by ELISA assay. FISH was used to detect the subcellular localisation of KCNQ1OT1. Bioinformatic analysis, luciferase reporter assay and in vitro studies were performed to elucidate the mechanism of KCNQ1OT1-mediated dysfunction. Results The expression of KCNQ1OT1 and the activation of NLRP3 inflammasome were increased in experimental DR models. KCNQ1OT1 knockdown alleviated NLRP3 inflammasome-associated molecules expression. In addition, KCNQ1OT1 was found to be localized mainly in the cytoplasm of Müller cells and facilitated TXNIP expression by acting as a miR-17-5p sponge. KCNQ1OT1 promoted the activation of NLRP3 inflammasome through miR-17-5p/TXNIP axis. Conclusions In conclusion, it was found in this study that KCNQ1OT1 promoted the activation of NLRP3 inflammasome both in vitro and in vivo, which was mediated by miR-17-5p/TXNIP axis. KCNQ1OT1 might be an effective interference target for the prevention and treatment of DR.

Automatic Identification of Sunken Oil in Homogeneous Information Perturbed Environment through Fusion Image Enhancement with Convolutional Neural Network

With the development of the marine oil industry, leakage accidents are one of the most serious problems threatening maritime and national security. The spilt crude oil can float and sink in the water column, posing a serious long-term threat to the marine environment. High-frequency sonar detection is currently the most efficient method for identifying sunken oil. However, due to the complicated environment of the deep seabed and the interference of the sunken oil signals with homogeneous information, sonar detection data are usually difficult to interpret, resulting in low efficiency and a high failure rate. Previous works have focused on features designed by experts according to the detection environments and the identification of sunken oil targets regardless of the feature extraction step. To automatically identify sunken oil targets without a prior knowledge of the complex seabed conditions during the image acquisition process for sonar detection, a systematic framework is contrived for identifying sunken oil targets that combines image enhancement with a convolutional neural network (CNN) classifier for the final decision on sunken oil targets examined in this work. Case studies are conducted using datasets obtained from a sunken oil release experiment in an outdoor water basin. The experimental results show that (i) the method can effectively distinguish between the sunken oil target, the background, and the interference target; (ii) it achieved an identification accuracy of 83.33%, outperforming feature-based recognition systems, including SVM; and (iii) it provides important information about sunken oil such as the location of the leak, which is useful for decision-making in emergency response to oil spills at sea. This line of research offers a more robust and, above all, more objective option for the difficult task of automatically identifying sunken oils under complex seabed conditions.

Goal-directed attention transforms both working and long-term memory representations in the human parietal cortex.

The abundance of distractors in the world poses a major challenge to our brain's limited processing capacity, but little is known about how selective attention modulates stimulus representations in the brain to reduce interference and support durable target memory. Here, we collected functional magnetic resonance imaging (fMRI) data in a selective attention task in which target and distractor pictures of different visual categories were simultaneously presented. Participants were asked to selectively process the target according to the effective cue, either before the encoding period (i.e., perceptual attention) or the maintenance period (i.e., reflective attention). On the next day, participants were asked to perform a memory recognition task in the scanner in which the targets, distractors, and novel items were presented in a pseudorandom order. Behavioral results showed that perceptual attention was better at enhancing target memory and reducing distractor memory than reflective attention, although the overall memory capacity (memory for both target and distractor) was comparable. Using multiple-voxel pattern analysis of the neural data, we found more robust target representation and weaker distractor representation in working memory for perceptual attention than for reflective attention. Interestingly, perceptual attention partially shifted the regions involved in maintaining the target representation from the visual cortex to the parietal cortex. Furthermore, the targets and distractors simultaneously presented in the perceptual attention condition showed reduced pattern similarity in the parietal cortex during retrieval compared to items not presented together. This neural pattern repulsion positively correlated with individuals' recognition of both targets and distractors. These results emphasize the critical role of selective attention in transforming memory representations to reduce interference and improve long-term memory performance.

A Robust Target Detection Algorithm Based on the Fusion of Frequency-Modulated Continuous Wave Radar and a Monocular Camera

Decision-level information fusion methods using radar and vision usually suffer from low target matching success rates and imprecise multi-target detection accuracy. Therefore, a robust target detection algorithm based on the fusion of frequency-modulated continuous wave (FMCW) radar and a monocular camera is proposed to address these issues in this paper. Firstly, a lane detection algorithm is used to process the image to obtain lane information. Then, two-dimensional fast Fourier transform (2D-FFT), constant false alarm rate (CFAR), and density-based spatial clustering of applications with noise (DBSCAN) are used to process the radar data. Furthermore, the YOLOv5 algorithm is used to process the image. In addition, the lane lines are utilized to filter out the interference targets from outside lanes. Finally, multi-sensor information fusion is performed for targets in the same lane. Experiments show that the balanced score of the proposed algorithm can reach 0.98, which indicates that it has low false and missed detections. Additionally, the balanced score is almost unchanged in different environments, proving that the algorithm is robust.

Arpc2 integrates ecdysone and juvenile hormone metabolism to influence metamorphosis and reproduction in Tribolium castaneum.

Actin-related protein 2/3 complex regulates actin polymerization and the formation of branched actin networks. However, the function and evolutionary relationship of this complex subunit 2 (Arpc2) has been poorly understood in insects. To address these issues, we performed comprehensive analysis of Arpc2 in Tribolium castaneum. Phylogenetic analysis revealed that Arpc2 was originated from one ancestral gene in animals but evolved independently between vertebrates and insects after species differentiation. T. castaneum Arpc2 has a 906-bp coding sequence and consists of 4 exons. Arpc2 transcripts were abundantly detected in embryos and pupae but less so in larvae and adults, while it had high expression in the gut, fat body and head but low expression in the epidermis of late-stage larvae. Knockdown of it at the late larval stage inhibited the pupation and resulted in arrested larvae. Silencing it in 1-day pupae impaired eclosion, which caused adult wings to fail to close. Injection of Arpc2 dsRNAs into 5-day pupae made adults have smaller testis and ovary and could not lay eggs. The expression of vitellogenin 1 (Vg1), Vg2 and Vg receptor (VgR) was downregulated after knocking down Arpc2 5 days post-adult emergence. Arpc2 silencing reduced 20-hydroxyecdysone titer by affecting the enzymes of its biosynthesis and catabolism but increased juvenile biosynthesis via upregulating JHAMT3 expression. Our results indicate that Arpc2 is associated with the metamorphosis and reproduction by integrating ecdysone and juvenile hormone metabolism in T. castaneum. This study provides theoretical basis for developing Arpc2 as a potential RNA interference target for pest control. © 2024 Society of Chemical Industry.

Passive localization using horizontal dual-array acoustic intensity striations

The formation of interference striations is closely related to channel structure and target location, which has attracted extensive attention from scholars at home and abroad. In this paper, the passive localizing method using observed interference striations of horizontal dual-arrays will be studied. The relationship between interference phenomena and target motion is discussed firstly, then the basic principle of the passive positioning method by using horizontal dual-array acoustic intensity striations is derived mathematically. Meanwhile, it can be seen that the shape of the interference striations is related to the parameters, which reflects the motion parameters of the target. This provides mathematical theory support for extracting the the target motion state parameters carried out by Hough transformation according to these acoustic interference striations.The simulation results show that the proposed method can effectively obtain the targets’ range. Meanwhile, comparing the ranging results of different groups of parameters, we can see that the more accurate the target motion parameters are, the smaller the estimated target distance error is. Finally, the performance of the algorithm under different noise conditions is discussed. The simulation results show that the error of extracted parameters is larger under the condition of low SNR, which leads to a larger passive positioning error. However, at high SNR, the range estimation performance is good, within 10%.

Infrared Small Dim Target Detection Using Group Regularized Principle Component Pursuit

The detection of an infrared small target faces the problems of background interference and non-obvious target features, which have yet to be efficiently solved. By employing the non-local self-correlation characteristic of the infrared images, the principle component pursuit (PCP)-based methods are demonstrated to be applicable to infrared small target detection in a complex scene. However, existing PCP-based methods heavily depend on the uniform distribution of the background pixels and are prone to generating a high number of false alarms under strong clutter situations. In this paper, we propose a group low-rank regularized principle component pursuit model (GPCP) to solve this problem. First, the local image patches are clustered into several groups that correspond to different grayscale distributions. These patch groups are regularized with a group low-rank constraint, enabling an independent recovery of different background regions. Then, GPCP model integrates the group low-rank components with a global sparse component to extract small targets from the background. Different singular value thresholds can be exploited for image groups corresponding to different brightness and grayscale variance, boosting the recovery of background clutters and also enhancing the detection of small targets. Finally, a customized optimization approach based on alternating direction method of multipliers is proposed to solve this model. We set three representative detection scenes, including the ground background, sea background and sky background for experiment analysis and model comparison. The evaluation results show the proposed model has superiority in background suppression and achieves better adaptability for different scenes compared with various state-of-the-art methods.

An RNA interference (RNAi) target with potential to control Dothistroma needle blight

Spray-induced gene silencing (SIGS) involves exogenous spray applications of double-stranded RNA (dsRNA) molecules targeting specific genes, such as those essential to pathogens. This technique has shown great potential for controlling fungal pathogens of horticultural and agricultural plants, but few studies have evaluated its application to forest pathogens. Here we report the first demonstration of exogenous application of dsRNA molecules targeting a gene required for virulence in the foliar pine pathogen Dothistroma septosporum, the causal agent of Dothistroma needle blight (DNB). Constructs expressing sense and antisense dsRNAs were generated targeting a 509 bp fragment of the dothistromin toxin regulatory gene (DsAflR) in D. septosporum. DsAflR is required for the production of dothistromin, a phytotoxin essential for full pathogen virulence. We found that exogenous dsRNA is taken up and the RNA silencing mechanism is functional in D. septosporum through a series of in vitro and in planta trials, such as monitoring the uptake of fluorescently labelled DsAflR-dsRNA into the cell by confocal microscopy and quantifying the extent of gene silencing by quantitative Polymerase Chain Reaction (qPCR). In vitro applications of DsAflR-dsRNA to D. septosporum reduced the accumulation of DsAflR mRNA compared to controls and, in some cases, were found to be statistically significant, despite a high level of variability in the results. Our study also demonstrated a reduction in DNB symptoms on infected pine needles sprayed with DsAflR-dsRNA, which was further supported by a significant reduction in fungal biomass. This preliminary study highlights the potential for SIGS as a future management scheme for controlling the forest pathogen D. septosporum, as well as other forest pathogens.

Immunomodulatory effects of one novel microRNA miR-63 in pearl oyster Pinctada fucata martensii

Novel microRNA miR-63 (novel-miR-63) from pearl oyster Pinctada fucata martensii (Pm-novel-miR-63) is a species-specific miRNA. Our previous research has shown that the expression of Pm-novel-miR-63 was significantly downregulated at 24 h after nucleus transplantation. In this study, we analyzed the function and regulatory role of Pm-novel-miR-63 in the immune response of pearl oysters. The results showed that Pm-novel-miR-63 expression increased after the stimulation of pathogen associated molecular patterns at 6–12 h, and the activity of immune and antioxidant enzymes in the serum decreased after Pm-novel-miR-63 overexpression. Transcriptome analysis revealed that Pm-novel-miR-63 participated in regulating transplantation immunity through the Notch and mRNA surveillance signaling pathways. Target prediction and dual luciferase analysis revealed that Pm-GDP-FucTP, Pm-CysLTR2, and Pm-RLR were the target genes of Pm-novel-miR-63. These results suggested that Pm-novel-miR-63 participated in regulating the immune response in pearl oysters and can serve as a new interference target to reasonably control excessive immune rejection in pearl culture.

Novel kinetoplastid-specific cAMP binding proteins identified by RNAi screening for cAMP resistance in Trypanosoma brucei.

Cyclic AMP signalling in trypanosomes differs from most eukaryotes due to absence of known cAMP effectors and cAMP independence of PKA. We have previously identified four genes from a genome-wide RNAi screen for resistance to the cAMP phosphodiesterase (PDE) inhibitor NPD-001. The genes were named cAMP Response Protein (CARP) 1 through 4. Here, we report an additional six CARP candidate genes from the original sample, after deep sequencing of the RNA interference target pool retrieved after NPD-001 selection (RIT-seq). The resistance phenotypes were confirmed by individual RNAi knockdown. Highest level of resistance to NPD-001, approximately 17-fold, was seen for knockdown of CARP7 (Tb927.7.4510). CARP1 and CARP11 contain predicted cyclic AMP binding domains and bind cAMP as evidenced by capture and competition on immobilised cAMP. CARP orthologues are strongly enriched in kinetoplastid species, and CARP3 and CARP11 are unique to Trypanosoma. Localization data and/or domain architecture of all CARPs predict association with the T. brucei flagellum. This suggests a crucial role of cAMP in flagellar function, in line with the cell division phenotype caused by high cAMP and the known role of the flagellum for cytokinesis. The CARP collection is a resource for discovery of unusual cAMP pathways and flagellar biology.

Minimum Variance Distortionless Response—Hanbury Brown and Twiss Sound Source Localization

Sound source target localization is an extremely useful technique that is currently utilized in many fields. The Hanbury Brown and Twiss (HBT) interference target localization method based on sound fields is not accurate enough for localization at low signal-to-noise ratios (below 0 dB). To address this problem, this paper introduces Minimum Variance Distortionless Response (MVDR) beamforming and proposes a new MVDR-HBT algorithm. Specifically, for narrowband signals, the inverse of the correlation matrix of the sound signal is calculated, and a guiding vector is constructed to compute the MVDR direction weights. These direction weights are then used to weight the correlation function of the HBT algorithm. Subsequently, the MVDR-HBT algorithm is extended from narrowband signals to broadband signals. As a result, the directivity of the HBT algorithm is optimized for wide- and narrowband signals, resulting in improved localization accuracy. Finally, the target localization accuracy of the MVDR-HBT algorithm is analyzed through simulation and localization experiments. The results show that the MVDR-HBT algorithm can accurately determine the direction of a sound source, with localization errors at different positions that are smaller than those produced by HBT. The localization performance of MVDR-HBT is considerably better than that of HBT, further verifying the simulation results. This study provides a new idea for target localization within an acoustic propagation medium (air).

Automatic segmentation of tunnel lining defects based on multiscale attention and context information enhancement

To address the dual challenges of complex environmental interference and multiscale targets in deep learning-based tunnel lining defect identification, a novel segmentation algorithm, named Tunnel Lining Defect Segmentation with Multiscale attention and Context information enhancement (MC-TLD), is proposed. In MC-TLD, a context-enhanced feature encoder is designed to compensate the deficiency of Convolutional Neural Network to extract global context information fully, thus reducing the misdetection and omission caused by complex environmental interference. In addition, an atrous spatial pyramid pooling module with multiscale attention is designed to improve the feature extraction capability of the network for different scales of defects. Finally, the traditional linear interpolation upsampling method in the feature decoding module is replaced by parameter learnable DUpsampling to output more accurate pixel prediction results. The experimental results show that MC-TLD has higher segmentation accuracy than other comparison models, which is suitable for the task of tunnel lining multiscale defect identification in complex environments.

A secreted salivary effector from Riptortus pedestris impairs soybean defense through modulating phytohormone signaling pathways.

Riptortus pedestris (Fabricius), one of the major piercing-sucking insects in soybeans, causes delayed plant senescence and abnormal pods, known as staygreen syndrome. Recent research has shown that direct feeding of this insect is the major cause of soybean staygreen syndrome. However, it remains unclear whether R. pedestris salivary proteins play vital roles in insect infestation. Here, we found that 4 secretory salivary proteins can induce cell death in Nicotiana benthamiana by transient heterologous expression. The cell death induced by Rp2155 relies on the nucleotide-binding leucine-rich repeat helper, HSP90. Tissue-specificity assays indicated that Rp2155 is specifically expressed in the salivary gland of R. pedestris and is significantly induced during insect feeding. The expression of salicylic acid (SA)-, jasmonic acid (JA)-related genes was increased in soybean when fed by Rp2155-silenced R. pedestris. More importantly, soybean staygreen symptoms caused by R. pedestris were significantly alleviated when Rp2155 was silenced. Together, these results suggest that the salivary effector Rp2155 is involved in promoting insect infestation by suppressing the JA and SA pathways, and it can be considered as a potential RNA interference target for insect control.

Multi-target CFAR detector based on compressed sensing radar system

ABSTRACT In the multi-target scenarios, we consider the problem of constant false alarm rate (CFAR) target detection. For the conventional CFAR algorithms, the multi-target detection performance is mainly limited by the interference target tolerance within the reference window. In this paper, we propose a local ordered statistic CFAR (OS-CFAR) target detector based on compressed sensing (CS) radar system to address the degradation of detection performance in multi-target scenarios. With the analog-information-converter (AIC), the radar intermediate frequency (IF) signal is compressively sampled into discrete linear measurements. At each detection stage, multiple signal components are generated by correlation tests between the sensing matrix and linear measurements. By support set merge and proxy pruning, the signal components with the largest energy are retained. These components are the echoes reflected from the targets. Since target components are output in decreasing order of correlation, the proposed detector only requires a local OS-CFAR decision in the interval where the least correlated target is located, rather than traversing the entire interval. By continuously updating the detection stage, all targets that satisfy the false alarm rate requirement can be screened out. Finally, the performance of the proposed detector in the multi-target scenarios is confirmed by simulation results.