Airborne Radar Research Articles

Autoencoder Neural Network-Based STAP Algorithm for Airborne Radar with Inadequate Training Samples

Clutter suppression is a key problem for airborne radar, and space-time adaptive processing (STAP) is a core technology for clutter suppression and moving target detection. However, in practical applications, the non-uniform time-varying environments including clutter range dependence for non-side-looking radar lead to the training samples being unable to satisfy the sample requirements of STAP that they should be independent identical distributed (IID) and that their number should be greater than twice the system’s degree of freedom (DOF). The lack of sufficient IID training samples causes difficulty in the convergence of STAP and further results in a serious degeneration of performance. To overcome this problem, this paper proposes a novel autoencoder neural network for clutter suppression with a unique matrix designed to be decoded and encoded. The main challenges are improving the accuracy of the estimation of the clutter-plus-noise covariance matrix (CNCM) for STAP convergence, designing the form of the data input to the network, and making the network successfully explored to the improvement of CNCM. For these challenges, the main proposed solutions include designing a unique matrix with a certain dimension and a series of covariance data selections and matrix transformations. Consequently, the proposed method compresses and retains the characteristics of the covariances, and abandons the deviations caused by the non-uniformity and the deficiency of training samples. Specifically, the proposed method firstly develops a unique matrix whose dimension is less than half of the DOF, meanwhile, it is based on a processing of the selected clutter-plus-noise covariances. Then, an autoencoder neural network with l2 regularization and the sparsity regularization is proposed for the unique matrix to be decoded and encoded. The training of the proposed autoencoder can be achieved by reducing the total loss function with the gradient descent iterations. Finally, an inverted processing for the autoencoder output is designed for the reconstruct ion of the clutter-plus-noise covariances. Simulation results are used to verify the effectiveness and advantages of the proposed method. It performs obviously superior clutter suppression for both side-looking and non-side-looking radars with strong clutter, and can deal with the insufficient and the non-uniform training samples. For these conditions, the proposed method provides the relatively narrowest and deepest IF notch. Furthermore, on average it improves the improvement factor (IF) by 10 dB more than the ADC, DW, JDL, and original STAP methods.

Genetic algorithm-based mathematical morphology for clutter removal in airborne radars

This paper presents a novel approach for clutter removal in airborne radars using a genetic algorithm and mathematical morphology. The clutter returns are detected when constant alarm rate processing is applied on range-Doppler images. In the proposed method, mathematical morphological operations are performed on range-Doppler images to obtain clutter images. The clutter image is then applied as a mask to remove false detections due to clutter. Also, the targets embedded in clutter are detected using gray-scale morphological operations. The morphological filter and the sequence of operations are designed by a genetic algorithm. The advantage of the proposed method is that it does not require the computation of statistical measures from clutter data and filters are optimally designed using a genetic algorithm. The proposed method has shown an increase in clutter leak reduction when compared to that of a deep morphological network.

Clutter suppression and target energy focussing for airborne rotating antenna radar using improved Space‐time adaptive processing

AbstractRotating antenna radar is widely used on the airborne platform to extend the range of the radar detection airspace. However, the echo signal received by an airborne rotating radar is spread in the Doppler domain due to the platform motion and the antenna rotation. In the process of a long‐time integration, the antenna rotation harms the signal‐to‐noise ratio and reduces the performance of target detection when using conventional processing methods. Although conventional space‐time adaptive processing (STAP) has been used successfully to suppress clutter on an airborne radar, it does not take into account the antenna rotation. It decreases the performance of clutter suppression on the airborne rotating antenna radar. The signal model of airborne rotating antenna radar is analysed and the STAP technique to the rotating antenna scenario is improved to effectively suppress clutter and focus on target energy. Firstly, the echo of the airborne rotating antenna radar is modelled. Secondly, the factors affecting the Doppler difference of the array elements are investigated. Thirdly, an improved STAP method is proposed. Finally, the effect of antenna rotation on clutter Doppler characteristics and the clutter suppression and target energy focussing on the effects of the improved STAP technique are investigated. Experimental results demonstrate the effectiveness of the proposed method.

Use of Airborne Radar Images and Machine Learning Algorithms to Map Soil Clay, Silt, and Sand Contents in Remote Areas under the Amazon Rainforest

Soil texture has a great influence on the physical–hydric and chemical behavior of soils. In the Amazon regions, due to the presence of dense forest cover and limited access to roads, carrying out surveys and mapping of soils is challenging. When data exist, they are relatively sparse and the distribution is quite uneven. In this context, machine learning algorithms (ML) associated with remote sensor covariates offer a framework to derive digital maps of soil attributes. The objective of this study was to produce maps of surface and subsurface soil clay, silt, and sand contents in a 13.440 km2 area in the Amazon. The specific objectives were to (a) evaluate the gain in prediction accuracy when using the P-band of airborne radar as a covariate; (b) evaluate two sampling approaches (Reference Area—RA and Total Area—TA); and (c) evaluate the transferability and performance of three ML algorithms: regression tree (RT), random forest (RF), and support vector machine (SVM). The study site was divided into three blocks, called Urucu, Araracanga, and Juruá, respectively. The soil dataset consisted of 151 surface and subsurface sand, silt, and clay observations and 21 covariates (20 relief variables and the backscattering coefficient from the P-band). Both the RA and TA sampling approach used 114 observations for training the prediction models (75%) and 37 for validation (25%). The RA approach was better for the development of sand and silt models. Overall, RF derived the most accurate predictions for all variables. The effect of introducing the P-band backscattering coefficient improved the sand prediction accuracy at the surface and subsurface in Araracanga, which had the highest sand content, with relative improvements (RI) of the R2, root mean square error (RMSE), and mean absolute error (MAE) of 46%, 3%, and 4% at the surface, respectively, and 66.7%, 4.4%, and 5.2% at the subsurface, respectively. For silt, the P-band improved the predictions at the surface in Araracanga, which had the lowest silt contents among the blocks. For clay, adding the P-band improved the RF predictions at the subsurface, with RI of the R2, RMSE, and MAE of 29%, 5%, and 5%, respectively. Despite the low observation density, inherently hindered by the low accessibility of the area and high costs of sampling thereof, the results showed the potential of ML algorithms boosted by airborne radar P-band to map soil clay, silt, and sand contents in the Amazon.

Airborne Radar Anti-Jamming Waveform Design Based on Deep Reinforcement Learning.

Airborne radars are susceptible to a large number of clutter, noise and variable jamming signals in the real environment, especially when faced with active main lobe jamming, as the waveform shortcut technology in the traditional regime can no longer meet the actual battlefield radar anti-jamming requirements. Therefore, it is necessary to study anti-main-lobe jamming techniques for airborne radars in complex environments to improve their battlefield survivability. In this paper, we propose an airborne radar waveform design method based on a deep reinforcement learning (DRL) algorithm under clutter and jamming conditions, after previous research on reinforcement-learning (RL)-based airborne radar anti-jamming waveform design methods that have improved the anti-jamming performance of airborne radars. The method uses a Markov decision process (MDP) to describe the complex operating environment of airborne radars, calculates the value of the radar anti-jamming waveform strategy under various jamming states using deep neural networks and designs the optimal anti-jamming waveform strategy for airborne radars based on the duelling double deep Q network (D3QN) algorithm. In addition, the method uses an iterative transformation method (ITM) to generate the time domain signals of the optimal waveform strategy. Simulation results show that the airborne radar waveform designed based on the deep reinforcement learning algorithm proposed in this paper improves the signal-to-jamming plus noise ratio (SJNR) by 2.08 dB and 3.03 dB, and target detection probability by 26.79% and 44.25%, respectively, compared with the waveform designed based on the reinforcement learning algorithm and the conventional linear frequency modulation (LFM) signal at a radar transmit power of 5 W. The airborne radar waveform design method proposed in this paper helps airborne radars to enhance anti-jamming performance in complex environments while further improving target detection performance.

Characterization of Southern Ocean Boundary Layer Clouds Using Airborne Radar, Lidar, and In Situ Cloud Data: Results From SOCRATES

AbstractClimate and numerical weather prediction models struggle to accurately predict radiative forcing over the Southern Ocean (SO), as the amount of clouds and their phases are poorly represented in such models due to a lack of observations upon which to base parameterizations. To address this, a novel particle identification (PID) scheme, based upon airborne radar and lidar data, was applied to data collected during the Southern Ocean Clouds, Aerosol, Radiation Transport Experimental Study (SOCRATES) to assess the vertical structure of SO boundary layer clouds. A comparison between the PID scheme and in situ phase data from SOCRATES showed relatively good agreement between the two data types. The convectivity of the clouds sampled during SOCRATES was determined using the novel Echo Classification from COnvectivity for Vertically pointing radars product. The PID and in situ data were then used synergistically to identify the following features of cloud vertical structure: (a) Supercooled liquid water was very common (Probability, P ∼ 80%) at cloud top for convective and stratiform clouds; (b) Supercooled large drops with maximum dimensions >95 μm frequently appear within a hundred meters below cloud top, particularly within convective clouds (Max P 35%–45%), but also within stratiform clouds (Max P 20%–30%); (c) Ice production was associated with convective activity, with P ∼ 20% at cloud top, increasing to 50%–70% 200 m below top, compared to P < 30% everywhere in stratiform clouds; (d) Convective clouds were found to be more vertically heterogeneous than stratiform clouds.

Sizing ice hydrometeor populations using the dual-wavelength radar ratio

Abstract. Dual-wavelength (3.2 and 0.32 cm, i.e., X- and W-radar bands) radar ratio (DWR) measurements in ice clouds and precipitation using Canada's National Research Council Institute for Aerospace Research airborne radar are compared to closely collocated particle microphysical in situ sampling data in order to develop relations between DWR and characteristic hydrometeor size. This study uses the radar and in situ data sets collected during the In-Cloud ICing and Large-drop Experiment (ICICLE) campaign in midlatitude frontal clouds. Since atmospheric particle scattering at X band is predominantly in the Rayleigh regime and the W-band frequency is the highest frequency usually used for hydrometeor remote sensing, the X–W-band combination provides a relatively strong dual-wavelength reflectivity difference. This study considers radar and in situ measurements conducted in relatively homogeneous cloud and precipitation conditions. Measurements show that under these conditions, the difference between the X-band radar reflectivities observed with vertical and horizontal pointing of the radar beam are generally small and often negligible. However, W-band reflectivities at vertical beam pointing are, on average, larger than those for horizontal beam pointing by about 4 dB, which is a non-Rayleigh scattering effect from preferentially oriented non-spherical particles. A horizontal radar beam DWR–mean volume particle size relation, Dv, provides robust estimates of this characteristic size for populations of particles with different habits. Uncertainties of Dv retrievals using DWR are around 0.6 mm when Dv is greater than approximately 1 mm. Size estimates using vertical radar beam DWRs have larger uncertainties due to smaller dual-wavelength signals and stronger influences of hydrometeor habits and orientations at this geometry of beam pointing. Mean relations among different characteristic sizes, which describe the entire particle size distribution (PSD), such as Dv, and other sizes used in various applications (e.g., the mean, effective, and median sizes) are derived, so the results of this study can be used for estimating different PSD characteristic sizes.

End-to-End Moving Target Indication for Airborne Radar Using Deep Learning

Moving target indication (MTI) based on space–time adaptive processing (STAP) has been widely used in airborne radar due to its ability for clutter suppression performance. However, the existing MTI methods suffer from the problems of insufficient training samples and low detection probability in a non-homogeneous clutter environment. To address these issues, this paper proposes a novel deep learning framework to improve target indication capability. First, combined with the problems of target indication caused by the non-homogeneous clutter, the clutter-plus-target training dataset was modeled by simulation, where various non-ideal factors, such as aircraft crabbing, array errors and internal clutter motion (ICM), were considered. The dataset considers various realistic situations, making the proposed method more robust. Then, a five-layer two-dimensional convolutional neural network (D2CNN) was designed and applied to learn the clutter and target characteristics distribution. The proposed D2CNN can predict the target with a high resolution to implement an end-to-end moving target indication (ETE-MTI) with a higher detection accuracy. In this D2CNN, the input was obtained by the clutter-plus-target angle-Doppler spectrum with a low-resolution estimated only by a few samples. The label was given by the target angle-Doppler spectrum with a high-resolution obtained by the target’s exact angle and Doppler. Thirdly, the proposed method used a few samples to improve the target indication and detection probability, which solved the problem of insufficient samples in the non-homogeneous clutter environments. To elaborate, the proposed method directly implements ETE-MTI without the support of the conventional STAP algorithm to suppress the clutter. The results verify the validity and the robustness of the proposed ETE-MTI with a few samples in the non-homogeneous and low signal-to-clutter ratio (SCR) environments.

The influence analysis of antenna radiation pattern aberration of airborne radar on clutter feature

In order to quantitatively evaluate the influence of the aberration of the antenna radiation pattern of airborne radar on clutter feature, the range-azimuth two dimensional (2D) model of the airborne radar clutter is established. Then the theoretic modelling and simulation analysis are proposed in three aspects, including the Doppler, power spectrum and space-time 2D characteristics of the clutter. Finally, the processing of moving target indication, pulse Doppler and space-time adaptive processing are used to suppress the clutter of the echo signals before and after the aberration. The results of the simulations indicate the aberration would elevate the sidelobe level, extend the power spectrum and bend the space-time trajectory of the clutter Doppler spectra. And the residual clutter would be elevated nearly 5 dB after STAP processing. Then the pattern preserving and fast time cancel combined space-time adaptive processing method is proposed, which can obtain narrow clutter notch and large improvement factor, and effectively suppress clutter. The modelling and quantitative analysis study have important theoretical and engineering significance for the detection capability of the airborne radar installed on the fuselage of the aircraft platform, the tactical and technical performance index, and improving the signal processing ability of airborne radar.

Multichannel Sea Clutter Measurement and Space-Time Characteristics Analysis with L-Band Shore-Based Radar

In order to study the space-time characteristics of sea clutter, the sea clutter is always measured by the airborne multichannel radar; however, the sea clutter shows the heterogeneity between range gates, which means the space-time covariance matrix’s correspondence to the single range gate cannot be estimated accurately. Meanwhile, the measurement of the sea clutter data by the airborne radar is usually affected by the motion of the platform, which makes the analysis results unrepresentative of the space-time characteristics of the pure sea clutter. In this paper, a sea clutter measurement method based on L-band shore-based multichannel radar is proposed, where the transmit sub-array periodically moves with the pulse repetition period to obtain multiple sets of coherent processing interval pulses for each range gate. This measurement method can exclude the influences of the moving platform. Moreover, a sea clutter space-time signal model of the single range gate is proposed, and the model is used to simulate three-dimensional sea clutter data with space-time coupling characteristics. With verification of the measured and simulated data, it can be seen that the data composed of single range gate and multiple coherent processing interval pulses can accurately estimate the space-time covariance matrix corresponding to this single range gate. Furthermore, the space-time characteristics are analyzed based on the measured data. The results show that the eigenvalue spectrum and the spread width of space-time power spectrum are influenced by the backscattering coefficient of sea clutter and the speed of sea surface motion. In comparison, the decorrelation effect caused by the backscattering coefficient of sea clutter is stronger than that caused by the speed of the surface motion. The proposed method is helpful for guiding multichannel sea clutter measurement and the analysis results are of great significance to the clutter suppression algorithms of the marine multichannel radar.

Investigation of Microphysical Properties within Regions of Enhanced Dual-Frequency Ratio during the IMPACTS Field Campaign

Abstract Multifrequency airborne radars have become instrumental in evaluating the performance of satellite retrievals and furthering our understanding of ice microphysical properties. The dual-frequency ratio (DFR) is influenced by the size, density, and shape of ice particles, with higher values associated with the presence of larger ice particles that may have implications regarding snowfall at the surface. The Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign involves the coordination of remote sensing measurements above winter midlatitude cyclones from an ER-2 aircraft to document the fine-scale precipitation structure spanning four radar (X-, Ku-, Ka-, and W-band) frequencies and in situ microphysical measurements from a P-3 aircraft that provide additional insight into the particle size distribution (PSD) behavior and habits of the hydrometeors related to the DFR. A novel approach to identify regions of prominently higher Ku- and Ka-band DFR at the P-3 location for five coordinated flights is presented. The solid-phase mass-weighted mean diameter (Dm) was 58% larger, the effective density (ρe) 37% smaller, and the liquid-equivalent normalized intercept parameter (Nw) 74% lower in regions of prominently higher DFR. Microphysical properties within a triple-frequency framework suggest signatures consistent with aggregation and riming as in previous studies. Last, a pretrained neural network radar retrieval is used to investigate the vertical structure of microphysical properties associated with the larger DFR signatures and provides the spatial context for inferring certain microphysical processes. Significance Statement The purpose of this study is to better understand what radar measurements from multiple frequencies can tell us about the sizes, shapes, and concentrations of ice particles within winter snowstorms, and how these observations are related to banded precipitation structures since they can have implications for snowfall at the surface. Our results show that ice particles are on average larger and less dense when the reflectivity difference between two radars operating at different wavelengths is larger and supports the process by which crystals aggregate to form larger particles. These findings aim to improve how satellites and forecasting models represent precipitation in the cloud and at the surface.

The Study of the Possibility of Applying Parallel Programming to the Algorithms of Space-Time Adaptive Processing

The article presents the description, assumptions and subsequent steps of the space-time adaptive processing (STAP) algorithms used as a signal processing tool in radars. The possibilities of object detection using the Sample Matrix Inversion (SMI) and Data Domain Least Squares (DDLS) algorithms were compared and showned. The article shows the impact of the use of parallel programming on the computation time of both algorithms. The main aim of this study was to propose an efficient method for the real-time implementation of the STAP algorithm in airborne radar systems. The idea of using parallel programming in STAP, supported only by the preliminary research results presented above, gives a real chance for the casual implementation of the STAP algorithm in a radar operating in close to real time mode.

Study on airborne radar sea clutter suppression using deterministic space time model in post-Doppler domain

When the airborne radar observes the land or the sea scene, the echoes will contain a large of clutter. Therefore, the target detection will be influenced and the clutter suppression is necessary. The time varying sea clutter makes the detection of sea targets more difficult. However, the airborne radar sea clutter suppression is rarely studied. In this paper, the sea clutter suppression for single channel and multi-channel are studied, respectively. The Doppler domain localization (DDL) method is used to suppress the real sea clutter data measured with a single channel. The results show that we can choose proper range cell number in the post Doppler domain to get better sea clutter suppression performance. For the multi-channel sea clutter, the clutter suppression is executed using sample matrix inverse (SMI), cross spectral metric (CSM) methods, subspace projection method and the proposed inverse sample covariance matrix (SCM) reconstruction method in the post-Doppler domain with joint three Doppler channels. It can be found that the sea clutter can be cancelled effectively by CSM and inverse SCM reconstruction methods. The residual sea clutter is relatively more for the subspace projection method due to the inaccuracy of the projection matrix. The moving targets can be detected with SMI, CSM and inverse SCM reconstruction methods, while they are submerged in the sea clutter for the subspace projection method. Meanwhile, the minimal detectable velocity (MDV) curves under different sea states show that the detection performance of the slow moving targets deteriorates with the increase of the sea state. The results can provide effective support for airborne sea clutter suppression and sea target detection.

An Analysis of Tropical Cyclone Vortex and Convective Characteristics in Relation to Storm Intensity Using a Novel Airborne Doppler Radar Database

Abstract This analysis introduces a novel airborne Doppler radar database, referred to as the Tropical Cyclone Radar Archive of Doppler Analyses with Re-centering (TC-RADAR). TC-RADAR comprises over 900 analyses from 273 flights into TCs in the North Atlantic, eastern North Pacific, and central North Pacific basins between 1997 and 2020. This database contains abundant sampling across a wide range of TC intensities, which facilitated a comprehensive observational analysis on how the three-dimensional, kinematic TC inner-core structure is related to TC intensity. To examine the storm-relative TC structure, we implemented a novel TC center-finding algorithm. Here, we show that TCs below hurricane intensity tend to have monopolar radial profiles of vorticity and a wide range of vortex tilt magnitudes. As TC intensity increases, vorticity becomes maximized within an annulus inward of the peak wind, the vortex decays more slowly with height, and the vortex tends to be more aligned in the vertical. The TC secondary circulation is also strongly linked to TC intensity, as more intense storms have shallower and stronger lower-tropospheric inflow as well as larger azimuthally averaged ascent. The distribution of vertical velocity is found to vary with TC intensity, height, and radial domain. These results—and the capabilities of TC-RADAR—motivate multiple avenues for future work, which are discussed. Significance Statement Acquiring observations of the inner core of tropical cyclones (TCs) is a challenge due to the hazardous conditions inherent to the storm. A proven method of sampling the TC core region is the use of airborne radar. This study presents a novel database comprising over 900 airborne radar analyses collected in storms between 1997 and 2020, which is freely available to the research community. Here we demonstrate the utility of the database by examining how the three-dimensional structure of the TC core region changes depending upon the intensity of the storm. By identifying how the baseline TC vortex structure varies with TC intensity, this work provides the foundation for multiple future research avenues and model evaluation efforts.

Joint Online Route Planning and Resource Optimization for Multitarget Tracking in Airborne Radar Systems

Reasonable route planning and resource allocation strategy in the airborne radar systems (ARS), can sufficiently utilize the limited resources and promote the multitarget tracking (MTT) performance. However, using separately the route planning and resource optimization method cannot take full advantage of the airborne platform. Considering this issue, we propose a joint online route planning and resource optimization strategy in the ARS to improve the system capability for MTT. First, a kinematic model of the ARS, including the mathematical expression between the radar states and the system control parameters, is introduced, which integrates the route planning to the radar scheduling scheme. Next, the posterior Cramér–Rao lower bound about route planning and resource optimization variables for the tracking targets is derived. Then, a scaled-based utility function is established to quantify the MTT performance. Hereafter, a nonconvex problem is formulated by minimizing the utility function with route and resource constraints, and then a efficient three-stage partition-based solution is proposed. Finally, simulation experiments demonstrate the effectiveness of the proposed algorithm. Furthermore, over the traditional benchmark algorithm, the tracking performance of the proposed approach improves 30.44%.

ЩОДО ОЦІНЮВАННЯ ЕКОНОМІЧНОГО ЕФЕКТУ ПРОТИДІЇ РАДІОЕЛЕКТРОННИМ ЗАВАДАМ ПРОТИВНИКА У ПОВІТРЯНОМУ БОЮ МЕТОДОМ ВІДВЕРНЕНОГО ЗБИТКУ

The Russian-Ukrainian war once again showed the importance of using aviation in a war, which can decisively influence the final result of military operations as a whole. The effective use of aviation determines the final result of a military confrontation by 60-70%, but aviation without means of electronic countermeasures and protection today has a rather low survivability, which contributes to the active development of individual electronic interference stations. The authors propose a mathematical model for describing the processes of functioning of airborne weapon control radar stations in conditions of interference and simultaneous active counteraction to enemy jamming stations in order to avoid early full-scale modeling of these processes, since full-scale models have a significant cost. The article also defines a system of indicators and criteria that characterize the effectiveness of the functioning of airborne weapons control radar stations when using known methods of actively counteracting enemy electronic interference in air combat, which, as a result of the uncertainty of the air situation, use the features of fuzzy set theory. The authors carried out a military-economic assessment of the use of methods of actively counteracting enemy radio-electronic interference stations, as well as using the method of prevented damage, the need to use the developed model for early evaluation of the effectiveness of the use of new methods of counteraction or their integrated application was assessed. The model will make it possible to justify the description of the functioning of airborne weapon control radar stations under conditions of interference and simultaneous active counteraction to them, and will also allow evaluating the economic effect of the use of new methods. Keywords: airborne radar stations, military-economic feasibility, mathematical modeling, probability of destruction, interference, active interference stations for personal protection, fuzzy estimates.

Random Matrix Theory-Based Reduced-Dimension Space-Time Adaptive Processing under Finite Training Samples

Space-time adaptive processing (STAP) is a fundamental topic in airborne radar applications due to its clutter suppression ability. Reduced-dimension (RD)-STAP can release the requirement of the number of training samples and reduce the computational load from traditional STAP, which attracts much attention. However, under the situation that training samples are severely deficient, RD-STAP will become poor like the traditional STAP. To enhance RD-STAP performance in such cases, this paper develops a novel RD-STAP algorithm using random matrix theory (RMT), RMT-RD-STAP. By minimizing the output clutter-plus-noise power, the estimate of the inversion of clutter plus noise covariance matrix (CNCM) can be obtained through optimally manipulating its eigenvalues, thus producing the optimal STAP weight vector. Specifically, the clutter-related eigenvalues are estimated according to the clutter-related sample eigenvalues via RMT, and the noise-related eigenvalue is optimally selected from the noise-related sample eigenvalues. It is found that RMT-RD-STAP significantly outperforms the RD-STAP algorithm when the RMB rule cannot be satisfied. Theoretical analyses and numerical results demonstrate the effectiveness and the performance advantages of the proposed RMT-RD-STAP algorithm.

A fast sparse Bayesian learning method with adaptive Laplace prior for space‐time adaptive processing

Space-time adaptive processing with finite samples is supposed to be a crucial technique for airborne radar systems. Inspired by the application of Gaussian prior in sparse Bayesian learning algorithm and the adaptive least absolute shrinkage and selection operator algorithm, a hierarchical Bayesian framework with adaptive Laplace priors is proposed. In this paper, a novel method is applied to avoid the high-dimension matrix inverse operation in the proposed algorithm. Moreover, in order to apply the method in the complex-valued domain, the complex-valued signal is split into two independent variables. Then, the sparse recovery problem in the complex-valued domain can be transformed into the real-value domain. Simulation experiments show that the proposed algorithm can achieve great clutter suppression performance and also ensure high computational efficiency.

기상 클러터 효과를 고려한 표면 클러터 반사신호 모노펄스 레이다 공대지 신호 생성

An airborne radar echo signal includes signals returned from clutter and a target; thus, simulating the signal from the clutter may be crucial for predicting and analyzing accurate radar performance. Because it can be extremely difficult and expensive to obtain real measured signal data for various scenarios, an accurate signal generation method is required. Existing weather clutter and ground clutter simulators for an X-band airborne radar are combined to simulate the echo signal when two clutters exist simultaneously. The weather clutter effect becomes more significant with increasing frequencies, such as in the X-band. The proposed simulator can generate time-domain signals by considering the random characteristics of the clutter signal and the Doppler effect caused by the movement of aircraft and clutter. In addition, a simple scheme is proposed for generating signals from artificial structures. Finally, the finite bandwidth of the transmitted chirp signal is calculated to improve the accuracy of the generated signal.

Multichannel Clutter Modeling, Analysis, and Suppression for Missile-Borne Radar Systems

When a missile-borne radar system works in downward-looking surveillance mode, the broadened ground clutter signal in virtue of platform high-speed motion will be received by the radar receiver, which will cause difficulty in moving target detection and attacking. Unlike airborne and spaceborne platforms, a missile-borne platform exhibits some unique motion characteristics, such as diving, spinning, and coning, causing the clutter space–time distribution property significantly different from those of airborne and spaceborne radar platforms. In addition, the forward target striking requirements make the missile-borne clutter space–time spectrum further exhibit the severe range-dependent property. To deal with these issues, accurate motion modeling of a missile-borne radar platform is first carried out in this article, where the complex platform motions including forward-looking diving, spinning, and coning are considered. Then, the autocorrelation processing combined with iterative adaptive approach is applied to estimate the clutter angle-Doppler center frequencies, so as to effectively realize the clutter nonstationary compensation along spatial and temporal directions. Finally, a time-domain sliding window-based subspace projection method is proposed to achieve the robust clutter suppression. Both simulation and real-measured radar data processing results are presented to validate the effectiveness and feasibility of the proposed algorithm.