Radar Network Research Articles

First Detection of Midlatitude Plasma Bubble by SuperDARN During a Geomagnetic Storm on May 27 and 28, 2017

AbstractWe used the global navigation satellite system‐total electron content (TEC) and Super Dual Auroral Radar Network (SuperDARN) radar to elucidate the characteristics of the plasma bubble extending to midlatitudes over North America during a geomagnetic storm on 27 and 28 May 2017. To identify plasma bubbles, we analyzed the rate of the TEC index (ROTI), which is a good indicator of the occurrence of plasma bubbles. The enhanced ROTI region expanded up to 50°N (geomagnetic latitude), and the upper limit of this region coincided with the equatorward wall of the midlatitude trough. Two‐dimensional ROTI maps show that the enhanced midlatitude ROTI region moved westward at a bulk speed of approximately 310 m/s. The Fort Hays East SuperDARN radar also detected the radar echo, showing the existence of plasma density irregularities at ∼10‐m scales within the midlatitude plasma bubble. The radar echo had a westward velocity of ∼300 m/s, which is almost consistent with the westward motion of the enhanced midlatitude ROTI region. We deduced that the westward propagation of the plasma bubble could be caused by a poleward sub‐auroral polarization stream electric field. The observed radar echo overlapping with the enhanced ROTI region had a narrower spectral width (<50 m/s) than that of the auroral activity. This feature resembled that of the type 1 echo, although the Doppler velocity was smaller than the ion acoustic speed at the F‐region height. This is the first case in which plasma density irregularities within plasma bubbles were captured by the SuperDARN radar.

Bulk Microphysical Characteristics of a Heavy-Rain Complex Thunderstorm System in the Taipei Basin

Abstract Severe rainfall has become increasingly frequent and intense in the Taipei metropolitan area. A complex thunderstorm in the Taipei Basin on 14 June 2015 produced an extreme rain rate (>130 mm h−1), leading to an urban flash flood. This paper presents storms’ microphysical and dynamic features during the organizing and heavy rain stages, mainly based on observed polarimetric variables in a Doppler radar network and ground-based raindrop size distribution. Shallower isolated cells in the early afternoon characterized by big raindrops produced a rain rate > 10 mm h−1, but the rain showers persisted for a short time. The storm’s evolution highlighted the behavior of merged convective cells before the heaviest rainfall (exceeding 60 mm within 20 min). The columnar features of differential reflectivity (ZDR) and specific differential phase (KDP) became more evident in merged cells, which correlated with the broad distribution of upward motion and mixed-phase hydrometeors. The KDP below the environmental 0°C level increased toward the ground associated with the melted graupel and resulted in subsequent intense rain rates, showing the contribution of the ice-phase process. Due to the collision–breakup process, the highest concentrations of almost all drop sizes and smaller mass-weighted mean diameter occurred during the maximum rainfall stage.

Thermodynamic Contribution to Vortex Alignment and Rapid Intensification of Hurricane Sally (2020)

Abstract As a part of the Tropical Cyclone Rapid Intensification Project (TCRI), observations were made of the rapid intensification of Hurricane Sally (2020) as it passed over the Gulf of Mexico. High-altitude dropsondes and radar observations from NOAA’s Gulfstream IV, radar observations from WP-3D aircraft, the WSR-88D ground radar network, satellite images, and satellite-detected lightning strikes are used to apply recently developed theoretical knowledge about tropical cyclone intensification. As observed in many other tropical cyclones, strong, bottom-heavy vertical mass flux profiles are correlated with low (but positive) values of low- to midlevel moist convective instability along with high column relative humidity. Such mass flux profiles produce rapid spinup at low levels and the environmental conditions giving rise to them are associated with an intense midlevel vortex. This low-level spinup underneath the midlevel vortex results in the vertical alignment of the vortex column, which is a key step in the rapid intensification process. In the case of Sally, the spinup of the low-level vortex resulted from vorticity stretching, while the spinup of the midlevel vortex at 6 km resulted from vorticity tilting produced by the interaction of convective ascent with moderate vertical shear. Significance Statement The purpose of this study is to investigate the rapid intensification of Hurricane Sally as it was approaching the Florida Panhandle. We do that by analyzing an unprecedented dataset from the NOAA WP-3D and Gulfstream-IV aircraft, together with ground-based radar and satellite data. We find that both the dynamics (vorticity structure and evolution) and thermodynamics (instability index, saturation fraction, heating/mass flux profiles) need to be considered in diagnosing intensification processes. Further field projects with continuous high-altitude dropsondes and research are needed to see if these are applicable to other reformation events as well as genesis.

Range Deception Jamming Performance Evaluation for Moving Targets in a Ground-Based Radar Network

With the rapid development of electronic information technology, the forms and technologies of electronic warfare have become more complicated, and electronic countermeasures (ECMs) and electronic counter-countermeasures (ECCMs) have become fierce in recent years. Networked radars have become an important means of ECMs due to their “four anti-resistance performance” against electronic jamming, anti-stealth, anti-radiation missiles, and low-altitude penetration. Based on this, this paper evaluates the performance of range deception jamming on an air-based jammer in a ground-based radar network. In this paper, the ground-based radar coordinate system conversion relationship is first established. Then, the statistical variance data fusion criterion for the radar network is constructed. Hence, based on the data fusion criterion, the jamming range delay boundary and the radar position information are recorded. Finally, the jamming performance evaluation can be achieved by analyzing the relationship between the jamming range delay and the radar position. The results of the simulated experiments reveal that when the jamming range delay is sufficiently small, the radar network system can be interfered with successfully by the range false target.

Deep metric learning for robust radar signal recognition

Signal recognition technology is a currently active area in both civilian and military applications. Recently, deep learning has aroused extensive attempts in radar signal recognition due to its remarkable capability of automatic feature extraction. However, existing radar signal recognition networks overly depend on the probability-based decision model, resulting in poor robustness. This paper develops a novel deep metric learning frame to enhance the robustness of the recognition system. First, a multiscale atrous pyramid network (MAPNet) is proposed to efficiently learn high-resolution and distinct feature representation. Second, a variance loss is designed to constrain the intra-class feature distribution in metric space. Third, according to the distribution of training signals in metric space, recognition results are recalibrated to provide explicit rejection probabilities for unknowns. Extensive experiments and evaluations demonstrate that the proposed model can accurately classify known signals while robustly identifying unknown signals. The signal database and model can be freely accessed at https://github.com/bryantky/MAPNet.

Hailstorms and rainstorms versus supercells—a regional analysis of convective storm types in the Alpine region

The behaviour of severe thunderstorms, particularly supercells, in complex terrain is still poorly understood. Utilising 6 years of radar-, lightning- and radiosounding-based thunderstorm data in the domain of the Swiss radar network, we study different thunderstorm types in separate topographical regions. We classify the storms as ordinary thunderstorms, intense and severe rainstorms, hail and severe hailstorms and supercellular storms. After identifying the overlaps between the storm categories of rainstorms, hailstorms and supercells, the life cycles of several intensity metrics are investigated. This analysis allows the identification of predictors for intensification within severe storm life cycles. One of the most important predictors is the detection of a mesocyclone in a supercell before the onset or intensification of hail. We then divide the radar domain into sub-regions ranging from the Northwestern Po Valley, the Southern Prealps, main Alpine ridge, Northern Prealps, Swiss Plateau and Jura. This regional split separates storms in different terrain complexities. An investigation of the intensity distribution of storms in each region shows a clear intensity decrease over the main Alpine ridge, intermediate values over the moderately complex Prealpine regions and peaks for the flat Po Valley and Swiss Plateau. In contrast, the highest frequency of storms is found in the Prealpine regions on each side, with a lower frequency in the flat areas and a minimum in convective activity over the main Alpine ridge.

Embedded radar networks for damage detection in wind turbine blades: validation in a full-scale fatigue test

This paper presents the design and experimental realization of a cooperative radar network for structural health monitoring (SHM) of wind turbine blades. For this purpose, 40 FMCW (frequency-modulated continuous wave) radar sensors operating from 58 to 63.5 GHz have been installed in a 31-m-long blade during manufacturing. A subset of 10 sensors is material-embedded in the core material of the blade, and the remaining thirty sensors are placed inside the blade on an inner rotor blade surface. The sensors are distributed over the entire blade based on previous high-frequency electromagnetic simulations. A full-scale fatigue test has been performed under controlled laboratory conditions. In addition, holes have been drilled into the blade by hand to represent a well-defined and relatively small damage. During the experimental campaign, measurements from the complete radar network have been transferred to a base station through a wireless communication link. Finally, it was demonstrated that fatigue as well as artificial damage could be detected accurately using the proposed damage indicator (DI) approach.

Joint target assignment and power allocation in the netted C-MIMO radar when tracking multi-targets in the presence of self-defense blanket jamming

The netted radar system (NRS) has been proved to possess unique advantages in anti-jamming and improving target tracking performance. Effective resource management can greatly ensure the combat capability of the NRS. In this paper, based on the netted collocated multiple input multiple output (C-MIMO) radar, an effective joint target assignment and power allocation (JTAPA) strategy for tracking multi-targets under self-defense blanket jamming is proposed. An architecture based on the distributed fusion is used in the radar network to estimate target state parameters. By deriving the predicted conditional Cramer-Rao lower bound (PC-CRLB) based on the obtained state estimation information, the objective function is formulated. To maximize the worst case tracking accuracy, the proposed JTAPA strategy implements an online target assignment and power allocation of all active nodes, subject to some resource constraints. Since the formulated JTAPA is non-convex, we propose an efficient two-step solution strategy. In terms of the simulation results, the proposed algorithm can effectively improve tracking performance in the worst case.

Horizontal Correlation Functions of Wind Fluctuations in the Mesosphere and Lower Thermosphere

AbstractMeasurements of kinetic energy in vortical and divergent fluctuations in the mesosphere and lower thermosphere can be used to study stratified turbulence (ST) and gravity waves. This can be done using horizontal correlation functions of the fluctuating component of velocity. This study introduces a novel method for estimating these correlation functions using radars that observe Doppler shifts of ionized specular meteor trails. The technique solves the correlation functions directly on a longitudinal‐transverse‐up coordinate system, assuming axial symmetry. This procedure is more efficient and leads to smaller uncertainties than a previous approach. The new technique is applied to a year‐long data set from a multistatic specular meteor radar network in Germany, to study the annual variability of kinetic energy within turbulent fluctuations at 87–93 km of altitude. In monthly averages, the kinetic energy is found to be nearly equipartitioned between vortical and divergent modes. Turbulent fluctuations maximize during the winter months with approximately 25% more energy in these months than at other times. The horizontal correlation functions are in agreement with the inertial subrange of ST, exhibiting a 2/3 power law in the horizontal lag direction, with an outermost scale of ST to be about 380 km. This suggests that horizontal correlation functions could be used to estimate turbulent energy transfer rates.

Evaluating Simulated Microphysics of Stratiform and Convective Precipitation in a Squall Line Event Using Polarimetric Radar Observations

Recent upgrades to China’s radar network now allow for polarimetric measurements of convective systems in central China, providing an effective data set with which to evaluate the microphysics schemes employed in local squall line simulations. We compared polarimetric radar variables derived by Weather Research and Forecasting (WRF) and radar forward models and the corresponding hydrometeor species with radar observations and retrievals for a severe squall line observed over central China on 16 March 2022. Two microphysics schemes were tested and were able to accurately depict the contrast between convective and stratiform regions in terms of the drop size distribution (DSD) and reproduce the classical polarimetric signatures of the observed differential reflectivity (ZDR) and specific differential phase (KDP) columns. However, for the convective region, the simulated DSDs in both schemes exhibited lower proportions of large drops and lower liquid water content; by contrast, for the stratiform region, the proportion of large drops was found to be too high in the Morrison (MORR) scheme. The underprediction of ice-phase processes in the convective region, particularly the riming processes associated with graupel and hail, was likely responsible for the bias toward large raindrops at low levels. In the stratiform region, raindrop evaporation in the WRF Double-Moment 6-Class (WDM6) scheme, which partially offsets the overestimation of ice-phase processes, produced ground DSDs that more closely matched the observational data, and did not exhibit the overly strong warm-rain collisional growth processes of MORR.

Mainlobe Interference Suppression for Radar Network via RPCA-Based Covariance Matrix Reconstruction

Radar networks can provide spatial diversity by observing targets from different perspectives with adequately separated receiving stations. This article proposes a mainlobe interference suppression approach for radar networks based on covariance matrix reconstruction. First, we formulate the mainlobe interference suppression under multiradar configuration as a multichannel signal enhancement problem and perform range and Doppler equalization to improve interferences’ spatial correlation. Then, we estimate the signal and interference covariance matrix from the sampling covariance matrix by solving a robust principal component analysis (RPCA) model. After that, we devise a time-domain-constrained (TDC) linear estimator dependent on the reconstructed covariance matrices to enhance target echo. To remove those duplicate false targets caused by signal enhancement and estimate the state of the physical targets, we establish and solve a generalized multiple measurement vector (GMMV) model based on the fact that targets have unique locations in the state space. Finally, we confirm the effectiveness of the proposed approach with numerical experiments and demonstrate better performance in comparison with the subspace projection method.

Radar Communication Jamming Signal Recognition Based on the Attention Mechanism

In modern battlefields, radar radiation sources detect targets by transmitting high-power modulated signals. In order to protect the operational effectiveness of the radar, there is a higher requirement for radar communication anti-jamming signal recognition capability. The accurate and fast identification of radar anti-jamming signals is the prerequisite for taking effective anti-jamming measures, which will directly affect the operational effectiveness of radar. At present, the recognition method based on the neural network can automatically extract signal features and obtain good recognition results. Because radar emitter signals have a strong correlation in time sequence, the LSTM network is used in this paper. For better feature extraction and classification of signals, this paper constructs the LSTM attention network, combines the attention mechanism with the LSTM recognition network, and proves the effectiveness of radar communication jamming signal recognition network based on the attention mechanism through experiments.

Transient Response of Polar‐Cusp Ionosphere to an Interplanetary Shock

AbstractInterplanetary (IP) shock‐driven sudden compression of the Earth's magnetosphere produces electromagnetic disturbances in the polar ionosphere. Several studies have examined the effects of IP shock on magnetosphere‐ionosphere coupling systems using all‐sky cameras and radars. In this study, we examine responses and drivers of the polar ionosphere following an IP shock compression on 16 June 2012. We observe the vertical drift and concurrent horizontal motion of the plasma. Observations from digisonde located at Antarctic Zhongshan station (ZHO) showed an ionospheric thick E region ionization and associated vertical downward plasma motion at F region. In addition, horizontal ionospheric convection reversals were observed on the Super Dual Auroral Radar Network ZHO and McMurdo radar observations. Findings suggest that the transient convective reversal breaks the original shear equilibrium, it is expected that the IP shock‐induced electric field triggers an enhanced velocity shear mapping to the E region. The horizontal motion of the plasma was attributed to only the dusk‐to‐dawn electric field that existed during the preliminary phase of sudden impulse. We also found that ionospheric convection reversals were driven by a downward field‐aligned current. The results of these observations reveal, for the first time, the immediate and direct cusp ionosphere response to the IP shock, which is critical for understanding the global response of the magnetosphere following an abrupt change in Interplanetory Magnetic Field (IMF) and solar wind conditions.

Storm Time Electrified MSTIDs Observed Over Mid‐Latitude North America

AbstractMedium‐scale Traveling Ionospheric Disturbances (MSTIDs) are prominent and ubiquitous features of the mid‐latitude ionosphere, and are observed in Super Dual Auroral Radar Network (SuperDARN) and high‐resolution Global Navigational Satellite Service (GNSS) Total Electron Content (TEC) data. The mechanisms driving these MSTIDs are an open area of research, especially during geomagnetic storms. Previous studies have demonstrated that nightside MSTIDs are associated with an electrodynamic instability mechanism like Perkins, especially during geomagnetically quiet conditions. However, dayside MSTIDs are often associated with atmospheric gravity waves. Very few studies have analyzed the mechanisms driving MSTIDs during strong geomagnetic storms at mid‐latitudes. In this study, we present mid‐latitude MSTIDs observed in de‐trended GNSS TEC data and SuperDARN radars over the North American sector, during a geomagnetic storm (peak Kp reaching 9) on 7–8 September 2017. In SuperDARN, MSTIDs were observed in ionospheric backscatter with Line of Sight (LOS) velocities exceeding 800 m/s. Additionally, radar LOS velocities oscillated with amplitudes reaching ±500 m/s as the MSTIDs passed through the fields‐of‐view. In detrended TEC, these MSTIDs produced perturbations reaching ∼50 percent of background TEC magnitude. The MSTIDs were observed to propagate in the westward/south‐westward direction with a time period of ∼15 min. Projecting de‐trended GNSS TEC data along SuperDARN beams showed that enhancements in TEC were correlated with enhancements in SuperDARN SNR and positive LOS velocities. Finally, SuperDARN LOS velocities systematically switched polarities between the crests and the troughs of the MSTIDs, indicating the presence of polarization electric fields and an electrodynamic instability process during these MSTIDs.

The Tornado Probability Algorithm: A Probabilistic Machine Learning Tornadic Circulation Detection Algorithm

Abstract A new probabilistic tornado detection algorithm was developed to potentially replace the operational tornado detection algorithm (TDA) for the WSR-88D radar network. The tornado probability algorithm (TORP) uses a random forest machine learning technique to estimate a probability of tornado occurrence based on single-radar data, and is trained on 166 145 data points derived from 0.5°-tilt radar data and storm reports from 2011 to 2016, of which 10.4% are tornadic. A variety of performance evaluation metrics show a generally good model performance for discriminating between tornadic and nontornadic points. When using a 50% probability threshold to decide whether the model is predicting a tornado or not, the probability of detection and false alarm ratio are 57% and 50%, respectively, showing high skill by several metrics and vastly outperforming the TDA. The model weaknesses include false alarms associated with poor-quality radial velocity data and greatly reduced performance when used in the western United States. Overall, TORP can provide real-time guidance for tornado warning decisions, which can increase forecaster confidence and encourage swift decision-making. It has the ability to condense a multitude of radar data into a concise object-based information readout that can be displayed in visualization software used by the National Weather Service, core partners, and researchers. Significance Statement This study describes the tornado probability algorithm (TORP) and its performance. Operational forecasters can use TORP as real-time guidance when issuing tornado warnings, causing increased confidence in warning decisions, which in turn can extend tornado warning lead times.

SkyWatch: A Passive Multistatic Radar Network for the Measurement of Object Position and Velocity

Quantitative three-dimensional (3D) position and velocity estimates obtained by passive radar will assist the Galileo Project in the detection and classification of aerial objects by providing critical measurements of range, location, and kinematics. These parameters will be combined with those derived from the Project’s suite of electromagnetic sensors and used to separate known aerial objects from those exhibiting anomalous kinematics. SkyWatch, a passive multistatic radar system based on commercial broadcast FM radio transmitters of opportunity, is a network of receivers spaced at geographical scales that enables estimation of the 3D position and velocity time series of objects at altitudes up to 80[Formula: see text]km, horizontal distances up to 150[Formula: see text]km, and at velocities to [Formula: see text][Formula: see text]km/s ([Formula: see text][Formula: see text]Mach). The receivers are designed to collect useful data in a variety of environments varying by terrain, transmitter power, relative transmitter distance, adjacent channel strength, etc. In some cases, the direct signal from the transmitter may be large enough to be used as the reference with which the echoes are correlated. In other cases, the direct signal may be weak or absent, in which case a reference is communicated to the receiver from another network node via the internet for echo correlation. Various techniques are discussed specific to the two modes of operation and a hybrid mode. Delay and Doppler data are sent via internet to a central server where triangulation is used to deduce time series of 3D positions and velocities. A multiple receiver (multistatic) radar experiment is undergoing Phase 1 testing, with several receivers placed at various distances around the Harvard–Smithsonian Center for Astrophysics (CfA), to validate full 3D position and velocity recovery. The experimental multistatic system intermittently records raw data for later processing to aid development. The results of the multistatic experiment will inform the design of a compact, economical receiver intended for deployment in a large-scale, mass-deployed mesh network. Such a network would greatly increase the probability of detecting and recording the movements of aerial objects with anomalous kinematics suggestive of Unidentified Aerial Phenomena (UAP).

Detecting and tracking hypersonic glide vehicles: A cybersecurity-engineering analysis of academic literature

Hypersonic vehicles are vehicles travelling faster than Mach 5 (five times the speed of sound). Hypersonic technologies have existed since the end of the 1950s, but recent developments of defence applications have led to their resurgence. Hypersonic weapons can be hypersonic (powered) cruise missiles or hypersonic glide vehicles (HGVs). The near-space trajectories of HGV, combined with their superior manoeuvrability, enable HGVs to evade existing space and terrestrial sensors used to track ballistic missiles, posing an immediate threat to today’s radar networks and making HGVs well-suited for intercontinental (> 5500 km) targets. Securing HGV detection and tracking systems is of great interest to at-risk nations and cybersecurity researchers alike.
 
 However, like hypersonic flight technologies, HGV defence technologies are heavily guarded secrets. The shortage of public-domain information did not stop academia from proposing various detection and tracking schemes, but a reasonable question is: “How credible and useful is current public-domain information, including academic publications, on HGV detection and tracking for academic researchers to base their cybersecurity research on?” To answer this question, we scanned and critically reviewed public-domain literature on HGV detection and tracking. We then identified ambiguities and knowledge gaps in the literature. In this paper, we provide a concise version of our multivocal literature review and an analysis of the identified ambiguities and knowledge gaps in our attempt to answer our earlier question.

Borealis: An Advanced Digital Hardware and Software Design for SuperDARN Radar Systems

AbstractThe Borealis radar system is a hardware and software upgrade to the conventional Super Dual Auroral Radar Network radar system, which has been used since the early 1990s. The conventional system has hardware and software that is aging, and many components are no longer supported. Limitations of the conventional system limit radar and data techniques for scientific discovery. Using software defined radios, Borealis has improved the flexibility, capabilities, and security of the radar system. Borealis has improved system monitoring and diagnostics and enables more complex experiments. Borealis provides improvements in spatial and temporal resolution. The system can perform full field‐of‐view imaging, pulse phase encoding and simultaneous multi‐frequency operations. With Borealis, data quality and system reliability has been improved. New radar and signal processing techniques are in development to further improve the capabilities of the system and of the data quality.

Study on Attenuation Correction for the Reflectivity of X-Band Dual-Polarization Phased-Array Weather Radar Based on a Network with S-Band Weather Radar

X-band dual-polarization phased-array weather radars (X-PARs) have been used in South China extensively. Eliminating the attenuation and system bias of X-band radar data is the key to utilizing the advantage of X-PAR networks. In this paper, the disdrometer raindrop-size distribution (DSD) measurements are used to calculate the radar polarimetric variables and analyze the characteristics of precipitation attenuation. Furthermore, based on the network of S-band dual-polarization Doppler weather radar (S-POL) and X-PARs, an attenuation-correction method for X-PAR reflectivity is proposed with S-POL constraints in view of the radar-mosaic requirements of a multi-radar network. Linear programming is used to calculate the attenuation-correction parameters of different rainfall areas, which realizes the attenuation correction for X-PAR. The results show that the attenuation-correction parameters simulated based on the disdrometer DSD vary with different precipitation classification; the attenuation-corrected reflectivity of X-PARs is consistent with S-POL and can realize a more precise observation of the evolution of the convective system. Compared with previous attenuation-correction methods with constant correction parameters, the improved method can reduce the deviation between X-PAR reflectivity and that of S-POL in heavy rainfall areas and areas of strong attenuation. The method proposed in this paper is stable and effective. After effective quality control, it is found that the X-PAR network deployed in South China observes data accurately and is consistent with S-POL; thus, it is expected to achieve high temporal–spatial resolution within a radar mosaic.

Joint Aperture and Power Allocation Strategy for a Radar Network Localization System Based on Low Probability of Interception Optimization

In the process of using the Distributed Radar Network Localization System (DRNLS) further to improve the survivability of a carrier platform, the random characteristics of the system's Aperture Resource Allocation (ARA) and Radar Cross Section (RCS) are often not fully considered. However, the random characteristics of the system's ARA and RCS will affect the power resource allocation of the DRNLS to a certain extent, and the allocation result is an essential factor determining the performance of the DRNLS's Low Probability of Intercept (LPI). Therefore, a DRNLS still has some limitations in practical application. In order to solve this problem, a joint allocation scheme of aperture and power for the DRNLS based on LPI optimization (JA scheme) is proposed. In the JA scheme, the fuzzy random Chance Constrained Programmin model for radar antenna aperture resource management (RAARM-FRCCP model) can minimize the number of elements under the given pattern parameters. The random Chance Constrained Programmin model for minimizing Schleher Intercept Factor (MSIF-RCCP model) built on this basis can be used to achieve DRNLS optimal control of LPI performance on the premise of ensuring system tracking performance requirements. The results show that when RCS has some randomness, its corresponding uniform power distribution result is not necessarily the optimal scheme. Under the condition of meeting the same tracking performance, the required number of elements and power will be reduced to a certain extent compared with the number of elements in the whole array and the power corresponding to the uniform distribution. The lower the confidence level is, the more times the threshold is allowed to pass, and the lower the power is, so that the DRNLS can have better LPI performance.