Search

This paper focuses on the design of a frequency modulated continuous wave (FMCW) dual-functional radar-communication (DFRC) system with low-loss radar detection performance, aiming at achieving a balance among communication rate, symbol error rate (SER), and out-of-band leakage. For what the communication functionality concerns, the constrained frequency hopping (C–FH) sequence mapping and up/down chirp are used to embed data, limiting the out-of-band leakage. We provide explicit rules for traversal-FH sequence to adjust the number of configurable frequency points within each chip, and limit the SER at the receiver. For the radar signal processing, after coherent processing and sampling, we proceed time-domain extension and range-bin phase compensation (TE-RBPC) according to the FH value, achieving the same range profile as an unmodulated (FMCW) without any influence from the FH sequences. The simulation results show that the proposed C–FH-chirp waveform reduces the out-of-band power leakage by 10 dB if compared with a phase shift keying scheme, and provides a 5 dB improvement in the signal noise ratio under the same SER if compared with an unconstrained FH-chirp scheme. Moreover, by comparing it with a scheme without compensation, the sidelobe amplitude of radar range profile in TE-RBPC scheme is reduced by about 39 dB. [ABSTRACT FROM AUTHOR]

A chirp cyclostationary (CCS) process is a nonstationary stochastic process, whose statistics have been defined and utilized in radar and communication signal processing. However, spectrum analysis of CCS signals, which provides a spectral view to characterize a signal, has not been performed. Classical spectrum analysis is based on the Fourier transform, which is not suitable for CCS signals because of the multiplicative chirp signals in a CCS model. In this study, we propose a generalized spectral representation of chirp cyclostationary signals associated with a linear canonical transform. The proposed representation shows that a CCS signal can be represented by a linear combination of a series of jointly stationary and equal-bandwidth processes, which bridge the nonstationary and stationary processes. Furthermore, the relationships between the second-order statistics of the CCS process and its stationary representations are introduced. Finally, based on the above observations, a linear time-varying Wiener filter was designed for CCS signal denoising and time-varying channel estimation. Finally, the relationships between the nonstationarity of the input and output and time-varying properties of the systems are discussed, which are helpful for determining the nonstationarity of the outputs or the time-varying property of a system. • A generalized spectral decomposition of chirp cyclostationary processes is proposed. • The CCS process is proven as a linear combination of joint stationary processes. • Statistics of the CCS process are linked with those of the stationary components. • A linear chirp periodically time-varying chirp cyclic Wiener filter is designed. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive detection algorithm with tunable robustness to the mismatched target. We notice that the test statistic of existing algorithms is the function of the test statistic of the generalized likelihood ratio test (GLRT) and the loss factor, which motivates us to propose the tunable robustness test (TRT) via a linear combination of the above two variables. The probability of false alarm (P f a) and the probability of detection (P d) for the mismatched target are presented for the rank one target and the multidimensional subspace target, respectively. Through numerical simulations, we verify that the tuning parameter will influence the ability to distinguish between matched and mismatched targets, and therefore the TRT can achieve robustness that existing algorithms do not have. [ABSTRACT FROM AUTHOR]

Retransmission interference in radar and communication signal processing significantly threatens the received linear frequency modulation signal. With the advancement in the hardware capabilities of interference devices, they can intercept and retransmit signals within the duration of a single pulse. Therefore, we refer to this type of interference as intra-pulse retransmission interference, which includes retransmission modes smeared spectrum, interrupted sampling repeater jamming, and interrupted nonuniform sampling repeater jamming. Although traditional interference cancellation techniques offer optimal solutions under the assumption of fully known interference parameters, practical scenarios often involve unknown parameters, estimation errors, random parameters, and multiple interferences, leading to performance degradation. To address these problems, we propose a Multi-Residual Encoder-Decoder Network (MRDENet), a novel approach for effectively mitigating intra-pulse retransmission interferences and recovering the signal of linear frequency modulation. In contrast to parameter estimation and interference cancellation, MREDNet extracts crucial features of both the target and interference and performs reconstruction. More specifically, MREDNet acquires information about the retransmission pattern of interference and the modulation frequency through its multi-level residual shortcut connections. To address the issue of network parameters being real while the signals are complex, we devised decomplexification—a data preprocessing approach enabling neural networks for direct utilization in front-end complex signal processing. Besides, we evaluate the performance of MREDNet comprehensively. The experimental conditions encompassed single and multiple interferences, incomplete data sets, varying signal-to-noise ratios, and jamming-to-signal ratios. The computational simulations demonstrate that MREDNet successfully solves the problem of recovering linear frequency modulation signals interfered with by intra-pulse retransmission interference. According to the boundary condition of MREDNet, the proposed method exhibits valuable applications in real-world scenarios. • We propose a recovery network named MREDNet, which is employed for recovering LFM signals interfered by intra-pulse retransmission interference. • In contrast to other methods, MREDNet possesses the capability to batch-process multiple pulses and handle multiple interferences. • MREDNet is applicable in scenarios with varying interference types between the test and training data sets. • Simulations demonstrate that MREDNet still performs well even under conditions of high signal-to-interference ratios and low signal-to-noise ratios. [ABSTRACT FROM AUTHOR]

In the present century, digital signal processing (DSP) approaches are considered to be one of the most powerful technologies which may shape the science and technology in coming decades. From 1970 onwards, a drastic revolution took place in a wider domain of DSP which has made it popular in several studies such as radar and sonar signal processing, digital televisions, wireless communication scenarios and other multimedia setup etc. Digital filters form the backbone of this DSP architecture and in point of fact the field of digital filter design has drawn justified recognition from the researchers throughout the world for the last 50 years. In connection to this, thousands of research articles may be found from the literature which had extensively addressed on the design of such filters. In order to meet the requirement of narrow transition-band, finite impulse response (FIR) filters are commonly assumed to be of higher order and accordingly it significantly enhances computational complexity. In regard to this, construction of hardware efficient digital filters had drawn significant consideration which aims to include minimum hardware elements during its application and consequently consumes less power. This review paper illustrates various techniques for the implementation of hardware efficient narrow transition-band FIR filter and investigates a number of favourable attributes which are capable in sustaining the stringent requirements of communication standards. A good number of relevant articles are taken from the literature so as to make a robust and complete review. • Review shows an overall picture of research carried out in the field of low-complexity, narrow transition-band filter design. • Present study has also provided a brief overview on the implementation of sharp cut-off VBW FIR filters. • Comparative analysis has been shown amongst different methods in terms of several performance metrics. • This study illustrates various techniques for the implementation of hardware efficient narrow transition-band FIR filter. • Review investigates different attributes which are capable in sustaining stringent requirements of communication standards. • A good number of relevant articles are taken from the literature so as to make a robust and complete review. [ABSTRACT FROM AUTHOR]

This paper presents an efficient optimization technique for gridless 2-D line spectrum estimation, named decoupled atomic norm minimization (D-ANM). The framework of atomic norm minimization (ANM) is considered, which has been successfully applied in 1-D problems to allow super-resolution frequency estimation for correlated sources even when the number of snapshots is highly limited. The state-of-the-art 2-D ANM approach vectorizes the 2-D measurements to their 1-D equivalence, which incurs huge computational cost and may become too costly for practical applications. We develop a novel decoupled approach of 2-D ANM via semi-definite programming (SDP), which introduces a new matrix-form atom set to naturally decouple the joint observations in both dimensions without loss of optimality. Accordingly, the original large-scale 2-D problem is equivalently reformulated via two decoupled one-level Toeplitz matrices, which can be solved by simple 1-D frequency estimation with pairing. Compared with the conventional vectorized approach, the proposed D-ANM technique reduces the computational complexity by several orders of magnitude with respect to the problem size, at no loss of optimality. It also retains the benefits of ANM in terms of precise signal recovery, small number of required measurements, and robustness to source correlation. The complexity benefits are particularly attractive for large-scale antenna systems such as massive MIMO, radar signal processing and radio astronomy. [ABSTRACT FROM AUTHOR]

Ground based radar is a key system for aerial defence of any country. Current generations of radar use Moving Target Indicator (MTI) filter, Constant False Alarm Rate (CFAR) and Peak Detection (PD) algorithms for detection of target and rejection of non-target (static clutter, dynamic clutter, interference and noise) objects. These conventional Radar Signal Processing (RSP) techniques find their limitation when either targets are not detected or false alarms are generated due to lack of built-in intelligence. In order to eliminate both the issues, a realistic radar environment has been analyzed using Artificial Intelligence (AI). Special emphasis has been made on invalid detections caused by weather clutter, which was never worked upon previously using AI. A novel deep learning based object detection approach has been devised keeping radar environment, high accuracy requirement and real-time constraint of radar in view. The proposed approach has been validated for its efficacy through standalone testing as well as Poof of Concept (PoC) implementation on a real radar. Integration of AI model in radar platform also involves a novel implementation scheme, whereby results of AI model are augmented by conventional RSP. Inference time of AI models is a bottleneck for their use in radar systems, in this context, new strategies have also been analyzed to achieve a real-time performance for radar application. As the objective of this research is to devise scalable and effective solutions for upcoming generations of radar; therefore, the work is of practical value for realistic deployments. [ABSTRACT FROM AUTHOR]

• Adaptive grayscale image correction based on RAW prior knowledge is provided. • Adaptive power correction and compensation is provided. • Adaptive brightness and power correction based on HIS prior knowledge is provided. • An adaptive enhancement algorithm based on prior knowledge and HIS space. • The algorithm can achieve the best performance on various multi-modal data sets. • The best performance in the task of high grayscale infrared image target detection. Gray-scale images are widely used in many fields, including satellite remote sensing, autonomous driving, medical CT, and X-ray detection, but gray-scale images have many shortcomings, such as high bit, low contrast, and high dynamic ranges. The visual analysis of these images is complicated and limited by people's psychology and physiology, and pseudo-color technology is an effective way to solve this problem. To address the weak adaptive capability, poor robustness, and insufficient generalization of existing pseudo-color enhancement methods, an adaptive enhancement algorithm for high grayscale images based on prior knowledge and HIS space, called APKHHIS, was proposed. Firstly, an adaptive gray-scale image correction mapping algorithm based on original prior knowledge was designed, and the quantization of images with different bit depths is realized. Secondly, to ensure the effectiveness of the enhanced data, an adaptive power compensation method was designed to compensate for the brightness of the gray-scale corrected images. Finally, an adaptive brightness and power correction algorithm was innovatively designed for the first time, which can adaptively adjust the color saturation and spatial brightness of the image more reasonably. To verify the effectiveness of the algorithm proposed in this paper, adaptive enhancement experiments were carried out on multi-modal data sets. The subjective and objective experimental results show that the APKHHIS algorithm can achieve the best performance on various data sets, and has strong robustness and universality, which provides an effective solution for the adaptive enhancement of multi-modal low-quality images. [ABSTRACT FROM AUTHOR]

[Display omitted] • Novel algorithm to separate ambient air echoes and precipitation echoes. • Leverages two hitherto unexplored Doppler power spectrum characteristics. • Accurate estimation of horizontal wind profiles during precipitation. • Computationally cheaper and easier to implement than existing methods. Wind profiler radars are powerful research tools for analyzing atmospheric dynamics, and their acquired data quality is vital for accurate wind parameter estimation. The presence of precipitation poses a major challenge to traditional estimation techniques. This paper presents a novel algorithm for efficiently separating ambient air and precipitation echoes from the Doppler power spectrum. The proposed Mirror-Minimum Algorithm leverages mirror image formation in opposite off-zenith beams to separate the echoes. It is a straightforward and practical solution that can be implemented with less computing complexity than existing methods. The wind profiler radars operating worldwide can easily employ this method to improve the accuracy of estimating horizontal wind components from measurements affected by precipitation. The proposed algorithm has been validated on the Doppler power spectrum obtained from the wind profiler radar operating at 205 MHz. The experimental evaluation shows promising results in varying precipitation conditions, even in highly adverse ones. [ABSTRACT FROM AUTHOR]

Timely awareness of jamming situations and classification of jamming categories are vital for radars to suppress jamming, ensure viability, and maintain functions in complex electromagnetic environments. To satisfy strict time requirements for radars on embedded devices, a micro-dynamic convolutional neural network for jamming signal classification is proposed in this paper. The proposed network takes the range-Doppler distribution obtained from built-in radar signal processing as input. The proposed data augmentation algorithm, together with the attention mechanism and the efficient convolutional architecture, improves the generalization capability and reduces the computational complexity. In addition, we propose a dynamic depth mechanism based on a task difficulty evaluator that enables the network to be adjusted automatically and further reduces the average computational complexity of classification. Simulation results verify the advantages of our approach in size, accuracy, and efficiency. The proposed network achieved 98.82% and 85.00% top-1 accuracy in two datasets with only 1.73 M multiply–accumulate operations. [ABSTRACT FROM AUTHOR]

Highlights • Low signal-to-noise ratio degrades performance of radar signal recognition. • Feature extraction method is applicable to normally sampled signals in practice. • Denoising algorithm removes influence of noise effectively by geometric constraint. • Type number estimation method avoids extra storage and computation burden. • The recognition approach has a correct recognition rate of almost 100% when SNR ≥ 0 dB. Abstract Low signal-to-noise ratio (SNR) in wideband radar reconnaissance degrades performance of existing signal type recognition methods. To achieve higher correct recognition rate in low SNR levels, this paper proposes a recognition approach containing feature extraction and automatic estimation for signal type number. First, feature vectors of radar pulses are constructed from maximum energy angle (MEA) and maximum energy slice (MES) of ambiguity function (AF). To improve applicability in practical implementing, AF grids method is developed to obtain MEA and MES. A novel algorithm for denoising MES with geometric shape constraint is also proposed to improve validity of features with low SNR. Second, for realistic reconnaissance lacking prior knowledge, a type number estimation method is developed that costs little extra processing burden. Simulation results indicate that the proposed denoising algorithm brings high stability and robustness to extracted features in low SNR levels. The recognition approach in this paper is applicable to more realistic scenarios and achieves higher correct recognition rate with respect to low SNR compared with existing methods. [ABSTRACT FROM AUTHOR]

Highlights • A general scheme of the SRC based centralized target detection in multistatic radar is proposed. • Different neighboring SRCs of multistatic radar can simultaneously exceed the test threshold. • Geometry of multistatic radar can influence the centralized target detection. Abstract The problem of spatial resolution cell (SRC) based centralized target detection in multistatic radar is considered. Due to the fact that using the observation data from different radar sites can result in a much smaller uncertainty volume, the SRC of multistatic radar is usually an intersection of the overlapping range, azimuth and elevation resolution cells of all spatial diversity channels (SDCs). Then, the target detection problem in multistatic radar can be considered as deciding whether a target is present in each probed SRC. In this paper, a general scheme of the SRC based centralized target detection in multistatic radar is proposed. Considering that different neighboring SRCs of multistatic radar may correspond to the same range, azimuth and elevation resolution cell of one SDC, the desired signals or noises from these neighboring SRCs hence can be mutually dependent. Motivated by this, the symbiosis relationship of these neighboring SRCs in exceeding the test threshold during the process of centralized target detection is specifically studied. Furthermore, the performance of the peak search method to decide which SRC on earth contains the target of interest is accordingly analyzed. [ABSTRACT FROM AUTHOR]

Highlights • We consider the distributed target detection problem in compound Gaussian clutter with Gamma texture. • Three detectors are proposed using exploiting persymmetry. • We prove the constant false alarm rate of the proposed detectors against the covariance matrix structure. Abstract We consider the problem of detecting a distributed target in compound-Gaussian sea clutter with Gamma distributed texture components. We design the Rao, Wald, and generalized likelihood ratio test (GLRT) detectors according to the two-step method: the first step is to obtain the Rao, Wald tests and GLRT on the assumption that the texture or/and covariance matrix structure are known; then the maximum a posteriori probability estimate of the clutter texture and the fixed point estimate of covariance matrix exploiting persymmetry are employed to replace the known texture and covariance matrix in the tests derived in the first step. Remarkably these proposed detectors ensure constant false alarm rate with respect to the covariance matrix structure. The effectiveness of the proposed detectors is verified by using simulated and real sea clutter data. [ABSTRACT FROM AUTHOR]

In this paper we derive a closed-form solution to generate a backscatter power representation for both unfocused synthetic aperture radar altimetry and conventional altimetry signals in the frequency/slow-time domain. This new approach involves fast convolutions and leads to a fast numerical computational model which uses very few approximations. The transform back to the range-time/doppler frequency domain is then made numerically. Two retrackers based on this Signal Model Involving Numerical Convolution (SINC) are constructed for conventional and synthetic aperture radar altimetry and are named SINC2 and SINCS respectively. Output from retracking are the epoch t 0 with respect to the tracking reference point, the significant wave height and the backscattering cross section at normal incidence σ 0 . Main benefits of the SINC model are its flexibility, the possibility to use the real point target response (PTR) or more complex representations of the height probability density function of scattering sea surface elements (PDF), and its fast computation speed to retrack the waveforms. The retrackers are applied to CryoSat-2 reduced SAR (RDSAR) and unfocused SAR waveforms. The RDSAR SINC2 retracker is only ten percent slower than the commonly used MLE3 retracker, which is a minor slowdown compared to the benefits of the SINC model. The SAR processing including the SINCS retracking procedure is three times slower than the RDSAR processing including retracking with SINC2. Model and algorithms are validated on both synthetic data generated by Monte-Carlo simulations and on real datasets. SAR SAMOSA+ results accessible through the ESA G-POD SARvatore service serve as a reference. Cross-validation shows higher agreement between SAR SAMOSA+ and SAR SINCS sea level anomaly and lower agreement between SAR SAMOSA+ and RDSAR SINC2, with accuracy of −0.1 cm for SAR SINCS and 1.7 cm for RDSAR SINC2. Instead, the accuracy of significant wave height, 4.9 cm and 3.9 cm for SAR and RDSAR and of backscatter coefficient, 0.16 and 0.12 dB in SAR and RDSAR, are comparable. Cross-validation results shows for sea level anomaly and significant wave height a higher precision of SAR SINCS and a lower precision of RDSAR SINC2, with precision of 0.92 cm and 6.6 cm for SAR SINCS and 1.96 cm and 12.6 cm for RDSAR SINC2 in sea level anomaly and significant wave height respectively. Instead, the precision of the backscatter coefficient is higher in RDSAR SINC2 than in SAR SINCS, and is 0.014 and 0.016 dB. In summary the new open ocean retrackers are a viable alternative recommended for both reduced and unfocused SAR processing in open ocean. [ABSTRACT FROM AUTHOR]

In this paper, we experimentally demonstrate a radar signal distribution over optical networks. The use of fiber enables us to distribute radar signals to distant sites with a low power loss. Moreover, fiber networks can reduce the radar system cost, by sharing precise and expensive radar signal generation and processing equipment. In order to overcome the bandwidth challenges in electrical switches, a semiconductor optical amplifier (SOA) is used as an all-optical device for wavelength conversion to the desired port (or channel) of a wavelength division multiplexing (WDM) network. Moreover, the effect of chromatic dispersion in double sideband (DSB) signals is combated by generating optical single sideband (OSSB) signals. The optimal values of the SOA device parameters required to generate an OSSB with a high sideband suppression ratio (SSR) are determined. We considered various parameters such as injection current, pump power, and probe power. In addition, the effect of signal wavelength conversion and transmission over fiber are studied in terms of signal dynamic range. [ABSTRACT FROM AUTHOR]

This article describes an algorithm for calculating the relative permittivity from a stepped frequency continuous wave radar (SFCW) signal. The intended application for the developed radar is a quality estimation of the road surface layer, which includes homogeneity and density estimation. Automated fast computations are needed for road surveying purposes, which must be conducted alongside the normal traffic and with typical traffic speeds. For SFCW, we get N sweep waveforms for each sample point, where N equals the number of frequency steps, which poses a computational problem for onboard data acquisition. Radar signal processing steps are at first conducted in the frequency domain, where initial calibration corrections are applied. This data is then transferred to the time domain via inverse Discrete Fourier Transform. The relative permittivity is calculated from the peak produced by the surface reflection. A proof of the concept is given in the form of a pilot measurement on a real road. Results indicate that the automated signal detection algorithm is accurate enough to be used in real life measurements. [ABSTRACT FROM AUTHOR]

We describe a method for processing radar signals, combining the outputs of two – a Weighted average Doppler algorithm and an existing Binominal Design algorithm via a point-wise minimum processor. The method renders a better performance in range sidelobe suppression for the detection of multiple moving targets using Golay complementary waveforms than the existing Binominal Design algorithm. The effectiveness of the method is justified by analyzing the corresponding ambiguity functions. We also show the performance of the new signal processing method by simulations under a realistic scenario. [ABSTRACT FROM AUTHOR]

An important objective of the Global Precipitation Measurement (GPM) mission is the detection of falling snow, since it accounts for a significant fraction of precipitation in the mid-high latitudes. The GPM Core Observatory carries the first spaceborne Dual-frequency Precipitation Radar (DPR), designed with enhanced sensitivity to detect lighter liquid and solid precipitation. The primary goal of this study is to assess the DPR's ability to identify snowfall using near-coincident CloudSat Cloud Profiling Radar (CPR) observations and products as an independent reference dataset. CloudSat near global coverage and high sensitivity of the W-band CPR make it very suitable for snowfall-related research. While DPR/CPR radar sensitivity disparities contribute substantially to snowfall detection differences, this study also analyzes other factors such as precipitation phase discriminators that produce snowfall identification discrepancies. Results show that even if the occurrence of snowfall events correctly detected by DPR products is quite small compared to CPR (around 5–7%), the fraction of snowfall mass is not negligible (29–34%). A direct comparison of CPR and DPR reflectivities illustrates that DPR misdetection is worsened by a noise-reducing DPR algorithm component that corrects the measured reflectivity. This procedure eliminates the receiver noise and side lobe clutter effects, but also removes radar signals related to snowfall events that are associated with relatively low reflectivity values. In an effort to increase DPR signal fidelity associated with snowfall, this paper proposes a simple algorithm using matched DPR Ku/Ka radar reflectivities producing an increase of the fraction of snowfall mass detected by DPR up to 59%. [ABSTRACT FROM AUTHOR]

The thickness of lunar regolith is an important index of evaluating the quantity of lunar resources such as 3 He and relative geologic ages. Lunar penetrating radar (LPR) experiment of Chang’E-3 mission provided an opportunity of in situ lunar subsurface structure measurement in the northern mare imbrium area. However, prior work on analyzing LPR data obtained quite different conclusions of lunar regolith structure mainly because of the missing of clear interface reflectors in radar image. In this paper, we utilized finite-difference time-domain (FDTD) method and three models of regolith structures with different rock density, number of layers, shapes of interfaces, and etc. to simulate the LPR signals for the interpretation of radar image. The simulation results demonstrate that the scattering signals caused by numerous buried rocks in the regolith can mask the horizontal reflectors, and the die-out of radar echo does not indicate the bottom of lunar regolith layer and data processing such as migration method could recover some of the subsurface information but also result in fake signals. Based on analysis of simulation results, we conclude that LPR results uncover the subsurface layered structure containing the rework zone with multiple ejecta blankets of small crater, the ejecta blanket of Chang’E-3 crater, and the transition zone and estimate the thickness of the detected layer is about 3.25 m. [ABSTRACT FROM AUTHOR]

InSAR measurements can be significantly affected by the atmosphere when the radar signal propagates through the atmosphere since it varies with space and time. Great efforts have been made in recent years to better understand the properties of the tropospheric effects and to develop methods for mitigating these effects. By using the basic principles of InSAR, the quantitative analysis of ionospheric delay effects on topography and surface deformation have been introduced for the first time. The measurement errors can be related to the vertical ionospheric total electron content (v TEC ). By using the ionospheric observations, the effects of temporal ionospheric variations on InSAR have been analyzed. The results indicate that the ionospheric variations with time, season, solar cycle and geomagnetic activities can compromise the effectiveness of InSAR for both the measurement of topography and surface determination. The repeat-pass SAR interferometry errors induced by ionosphere should be corrected by actual measurements. [ABSTRACT FROM AUTHOR]

In allusion to the coupling relationship among radial velocity variation, range migration and Doppler spread of a maneuvering target, a dynamic programming (DP) based long time coherent integration (DPCI) detection method is proposed. The energy of the target of high velocity and strong maneuver could be integrated along its trajectory by the proposed DPCI method with high efficiency. The main idea of the DPCI is the joint execution of two operations: the modified DP procedure that could search the current position and velocity of the target, and the improved coherent integration that could calibrate the Doppler shift during the entire process with any large phase spread. The proposed method is applicable to a target with arbitrary motion without estimating its specific movement parameters. Simulation results and performance comparisons show the accuracy and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Civil marine radar, which operates at a low grazing-incidence angle and with horizontal polarization in transmitting and receiving, can be modified and used as a primary tool for surface wave monitoring. The high spatial resolutions of sea-clutter image sequences from X-band radar offer a means of deriving individual waves and wave field at low-cost. However, the performance of X-band radar is impaired under rainy conditions, which are usually accompanied by the severe weather at sea. In the present study, we examine the effectiveness of S-band radar for wave measurements under precipitation. The results of comprehensive comparative studies with sea-truth data show that S-band radar is capable of carrying out wave measurements in rainy conditions. Although the longer wavelength of the S-band leads to a coarser resolution of radar imagery, the S-band radar features at least the equivalent performance of the X-band system in non-rainy conditions, in terms of wave height measurement. The results suggest that the S-band and X-band could be complementary systems. In rainy conditions the S-band is more efficient but in the non-rainy periods the X-band gives more confident results. The relationship of significant wave height with radar signal-to-noise ratio (SNR), and the modulation transfer function (MTF) between radar spectrum and wave spectrum for the used X-band and S-band radars are established and discussed in this paper. [ABSTRACT FROM AUTHOR]

In this paper, the potential use of P-band radar signals for the estimation of soil roughness parameters is analyzed. The numerical moment method backscattering model is used to study the sensitivity to soil roughness parameters of backscattered P-band signals. Two roughness scales related to terrain microtopography and low frequency roughness structures are considered. In the case of microtopography, the rms height is shown to be the dominant influence in the relationship between radar signals and roughness. For low frequency structures, the parameter Zs is strongly correlated with the backscattered signals. An analysis of the behavior of P-band radar signals as a function of multi-scale soil roughness (microtopography and large-scale roughness structures) reveals the complexity of using P-band data for the study of bare surface soil parameters. The Moment method model is then compared with the real data covering a large range of microtopographic roughness values, derived from experimental airborne P-band SAR campaigns made over agricultural fields, at two sites in France. The significant discrepancies observed between measurements and simulations confirm the limitations of an analysis based on microtopographic characterizations only. [ABSTRACT FROM AUTHOR]

The scattering properties of the radar signal at Ka and Ku-band frequencies are investigated using a theoretical model and snow grain observations obtained during previous field campaigns. Our results show that the combination of radar altimeters operating at these two frequencies should allow for the retrieval of snow depth over Arctic sea ice. We estimate uncertainties of the ice surface position in relation to crossover observations over sea ice and show that the accuracy of the crossover methodology with short time gap (3 days or less) is better than 3 cm. Comparison of the CryoSat-2/AltiKa retrieved snow depth with in situ measurements provided by Operation IceBridge shows a good agreement with a Root Mean Square Error (RMSE) of 5 cm. Analysis of the CryoSat-2/AltiKa retrieved snow depths over three winters (2013–2015) reveals a thinner snow cover on both Multi-Year (32%–57%) and First-Year Ice (63%–75%) relative to the 1954–91 Warren climatology, suggesting the need for more contemporary year-round and basin-scale snow depth fields. [ABSTRACT FROM AUTHOR]

The variation of concrete cover thickness on bridge decks has been observed to significantly affect the rebar reflection amplitude of the ground penetrating radar signal. Several depth correction approaches have been previously proposed in which it is assumed that, for any bridge, at least a portion of the deck area is sound concrete. The 90th percentile linear regression is a commonly used procedure to extract the depth-amplitude relationship of the assumed sound concrete. It is recommended herein that normalizing the depth-dependent amplitudes be divided into two components. The first component takes into account the geometric loss due to inverse-square effect and the dielectric loss caused by the dissipation of electromagnetic energy in sound concrete. The second component is the conductive loss as a result of increased free charges associated with concrete deterioration. Whereas the conventional depth correction techniques do not clearly differentiate the two components and tend to incorporate both in the regression line, they are separately addressed in this research. Specifically, while the first component was accounted for based on a library of GPR signals collected from sound areas of twenty four bare concrete bridge decks, the conductive loss caused by an increased conductivity is linearly normalized by the two-way travel time. The implementation of the proposed method in two case studies showed that, while the method significantly improves the accuracy of GPR data analysis, the conventional methods may lead to a loss of information regarding the background attenuation that would indicate the overall deterioration of bridge decks. [ABSTRACT FROM AUTHOR]

Multi-pulse train interferometry is used to measure distance for meter-scale/nanometer-resolution geometric measurements. The core of multi-pulse train interferometry is object positioning accuracy, which is limited by the ability to position the interference fringe envelope peak. The Fourier fringe analysis method, optical Hilbert transform method, and other methods are used in the envelope demodulation of multi-pulse train interferometry. The matched filter, which provides good noise reduction capability in the time domain, is widely used in radar signal processing. In this study, a matched filter is combined with the Fourier fringe analysis method and used to demodulate the envelopes of a fringe in a multi-pulse train interferometry because the proposed combined method performs noise reduction in both the time and frequency domains for fringe envelope signal processing. Compared with two conventional methods, the proposed combined method was proven to be feasible for envelope demodulation of multi-pulse train interferometry. • An approach that can amplitude demodulation the multiple fringe envelope was developed. • This is the first report regarding a matched filter method is combined with the Fourier fringe analysis method. • The proposed method performs noise reduction in both the time and frequency domains. [ABSTRACT FROM AUTHOR]

Pulse Compression technique is extensively used in radar systems. It improves the Signal-to-Noise Ratio (SNR) of received signal without increasing the peak transmitted power. Non-Coherent Pulse Compression (NCPC) has been recently investigated as a new variant of standard (coherent) pulse compression technique. NCPC uses Manchester-coded pulse compression sequences, such as Barker and Ipatov sequences. This paper proposes a novel technique for implementation of NCPC that is based on well-known m-sequences along with a modified coding scheme. Theoretical evaluation shows that new NCPC technique gives perfect periodic-cross-correlation results (i.e., zero side lobes). Analytical and simulation analysis are performed that show that SNR performance of proposed scheme is comparable to the previously reported Manchester-coded NCPC using Ipatov sequences. It is also demonstrated that compared to previously reported techniques, transmitter and receiver implementation is much simpler in the scheme presented in this article. [ABSTRACT FROM AUTHOR]

This paper presents a prediction algorithm for features detection in Ground Penetrating Radar (GPR) based surveys. Based on signal processing and soft-computing techniques, the coupled use of principal-component analysis and neural networks enable a definition of an efficient method for analyzing GPR electromagnetic data. To guarantee a low error rate, a study of the algorithm main numerical parameters was performed by means of electromagnetic synthetic-data models. Results for detecting features of geological layers demonstrate not only the method predictions accuracy but also the simple interpretation of its output through scenarios reconstructed images. [ABSTRACT FROM AUTHOR]

We review the current state of knowledge of the Europan plasma environment, its effects on radio wave propagation, and its impact on the performance and design of future radar sounders for the exploration of Europa׳s ice crust. The Europan ionosphere is produced in two independently-rotating hemispheres by photo-ionization of the neutral exosphere and interaction with the Io plasma torus, respectively. This combination is responsible for temporal and longitudinal ionospheric heterogeneities not well constrained by observations. When Europa׳s ionosphere is active, the maximum cut-off frequency is 1 MHz at the surface. The main impacts on radar signal propagation are dispersive phase shift and Faraday rotation, both a function of the total electron content (up to 4×10 15 m −2 ) and the Jovian magnetic field strength at Europa (~420 nT). The severity of these impacts decrease with increasing center frequency and increase with altitude, latitude, and bandwidth. The 9 MHz channels on the Radar for Icy Moons Exploration (RIME) and proposed Radar for Europa Assessment and Sounding: Ocean to Near-surface (REASON) will be sensitive to the Europan ionosphere. For these or similar radar sounders, the ionospheric signal distortion from dispersive phase shift can be corrected with existing techniques, which would also enable the estimation of the total electron content below the spacecraft. At 9 MHz, the Faraday fading is not expected to exceed 6 dB under the worst conditions. At lower frequencies, any active or passive radio probing of the ice shell exploration would be limited to frequencies above 1–8 MHz (depending on survey configuration) below which Faraday rotation angle would lead to signal fading and detection ambiguity. Radar instruments could be sensitive to neutrals and electrons added in the exosphere from any plume activity if present. [ABSTRACT FROM AUTHOR]

Stepped-frequency radar is a prominent example of the class of continuous-wave radar systems. Since raw data are recorded in frequency-domain direct investigations referring to the frequency content can be done on the raw data. However, a transformation of these data is required in order to obtain a time-domain representation of the targets illuminated by the radar. In this paper we present different ways of arranging the raw data which then are processed by means of the inverse fast Fourier transform. On the basis of the time-domain result we discuss strengths and weaknesses of each of these data structures. Furthermore, we investigate the influence of phase noise on the time-domain signal by means of an appropriate model implemented in our simulation tool. We also demonstrate the effects of commonly known techniques of digital signal processing, such as windowing and zero-padding of frequency-domain data. Finally we present less commonly known methods, such as the processing gain of the (inverse) fast Fourier transform by means of which the signal to noise ratio of the time-domain signal can be increased. [ABSTRACT FROM AUTHOR]

Abstract: We propose efficient target localization methods for a passive radar system using time-of-arrival (TOA) information of the signals received from multiple illuminators, where the position of the receiver is subject to random errors. Since the maximum likelihood (ML) formulation of this target localization problem is a non-convex optimization problem, semi-definite relaxation (SDR)-based optimization methods in general do not provide satisfactory performance. As a result, approximated ML optimization problems are proposed and solved with SDR plus bisection methods. For the case without position error, it is shown that the relaxation guarantees a rank-one solution. The optimization problem for the case with position error involves only a relaxation of a scalar quadratic term. Simulation results show that the proposed algorithms outperform existing methods and provide root mean-square error performance very close to the Cramer–Rao lower bound. [Copyright &y& Elsevier]

With the development of Internet of things (IoT) and critical infrastructure in modern industry, many technologies are facing serious challenges brought by the change of application. For instance, the real-time radar signal processing is based on the IoT and infrastructure. In particular, parameter estimation based on Radar Cross Section (RCS) from narrow-band radar requires sufficient data and effective algorithm. In this paper, the geometric parameters of the target are estimated by the Deep Learning method. Moreover, considering the difference of the information collected by different radars, a data fusion method based on channel attention mechanism is proposed for multi-site RCS time-series, which greatly improves the performance of parameters estimation. [ABSTRACT FROM AUTHOR]

Heavy-tailed random samples, as well as their sum or average, are encountered in a number of signal processing applications in radar, communications, finance, and natural sciences. Modeling such data through the Pareto distribution is particularly attractive due to its simple analytical form, but may lead to infinite variance and/or mean, which is not physically plausible: in fact, samples are always bounded in practice, namely because of clipping during the signal acquisition or deliberate censoring or trimming (truncation) at the processing stage. Based on this motivation, the paper derives and analyzes the distribution of the sum of right-censored Pareto Type-II variables, which generalizes the conventional Pareto (Type-I) and Lomax distributions. The distribution of the sum of truncated Pareto is also obtained, and an analytical connection is drawn with the unbounded case. A numerical analysis illustrates the findings, providing insights on several aspects, including the intimate mixture structure of the obtained expressions. An illustrative application to the analysis of real radar data is also provided. [ABSTRACT FROM AUTHOR]

Abstract: Exploration of subsurface oceans on Jupiter׳s icy moons is a key issue of the icy moons׳ geology. Radar is in fact the only sounding technique which is able to penetrate their icy mantles, which can be many kilometers thick. Surface clutter, i.e. scattering of the radio waves on the rough surface, is known to be one of the most important problems of subsurface radar probing. Adequate numerical modeling of this scattering is required on all stages of subsurface radar experiment, including design of an instrument, operational strategy planning and data interpretation. In the present paper, a computer simulation technique for numerical simulations of radar sounding of rough surfaces is formulated in general form. Subsurface radar location of the ocean beneath Ganymedian ice with chirp radar signals has been simulated. [Copyright &y& Elsevier]

Abstract: In Ground Penetrating Radar it is of great interest to have a pulse width as narrow as possible to achieve best resolution capability. However, ultrawideband antennas may cause distortion to the radar signal due to dispersion, which is dependent on the frequency and bandwidth, as well as the direction of radiation. As dispersion causes an increase of the peak's width, the resolution capability is degraded. When frequency-based radar systems are utilized, such as stepped-frequency radar, a transformation of the recorded frequency-domain data to time domain is often required. There are different means of transformation at hand, the most popular being inverse Fourier Transform. Generally, such a transformation involves certain degrees of freedom in the processing of the data, which affect the appearance or even distort the resulting signal in time domain. In the same way, distortion may also be introduced to signals obtained by pulse radar, which is operating in time domain, when digital signal processing filters are applied to the time-domain data, modifying their frequency content. In contrast to dispersion these effects can be controlled — but not avoided. As an example, the typical sinc-shaped distortion of the time-domain signal after inverse Fourier Transform may be avoided by windowing of the frequency-domain data, a well-known basic technique in the digital signal processing domain. As dispersion, also windowing causes a broadening of the peak's width. However, this leads to the important question, to which degree both, dispersion and windowing, affect the time domain signal and which of them has more severe impact. In this paper we investigate both effects. We compare dispersion of different ultrawideband Vivaldi, Bowtie and Loaded Bowtie antennas with the ideal (theoretical) non-dispersive one. And we compare these results with the distortion effects introduced to the radar signal by windowing frequency-domain data prior to inverse Fourier Transform. [Copyright &y& Elsevier]

Abstract: In this paper some classical concepts regarding scattering of radio waves on turbulent structures in the ionosphere are summarized. Spectral representations according to Batchelor and Driscoll & Kennedy are compared and the role of various potential tracer gradients is elucidated. Systematic similarities and differences in the representation of the impact of these tracers on scatter intensity are investigated. The importance of turbulence and background parameters for radar volume reflectivities is discussed. This study highlights the importance of measuring these parameters as completely and reliably as possible when interpreting the strength of backscattered radar signal in terms of turbulent and atmospheric background parameters. [Copyright &y& Elsevier]

Highlights: [•] The data of target was collected by a bistatic ultra-wideband radar system. [•] The signal measured by the bistatic radar is related to the type of the target. [•] Features that are representative of the target types are extracted. [•] An improved support vector machine (SVM) classifier is developed. [ABSTRACT FROM AUTHOR]

Abstract: In this work, a complementary metal-oxide semiconductor (CMOS) based transceiver with a sensitivity time control antenna is successfully implemented for advanced traffic signal processing. The collected signals from the CMOS radar system are processed with optimization algorithms for vehicle-type classification and speed determination. The high recognition rate optimization algorithms are mainly based upon the information of short setup time and different environmental installation of each sensor. In the course of optimization, a video recognition module is further adopted as a supervisor of support vector machine and support vector regression. Compared with conventional circuit-based detector systems, the developed CMOS radar integrates submicron semiconductor devices and thus not only possesses low stand-by power but also is ready for production. In the meantime, the developed algorithm of this study simultaneously optimizes the vehicle-type classification and speed determination in a computationally cost-effective manner, which benefits real-time intelligent transportation systems. [Copyright &y& Elsevier]

Abstract: The application of SAR interferometry (InSAR) in topographic mapping is usually limited by geometric/temporal decorrelations and atmospheric effect, particularly in repeat-pass mode. In this paper, to improve the accuracy of topographic mapping with high-resolution InSAR, a new approach to estimate and remove atmospheric effect has been developed. Under the assumptions that there was no ground deformation within a short temporal period and insignificant ionosphere interference on high-frequency radar signals, e.g. X-bands, the approach was focused on the removal of two types of atmospheric effects, namely tropospheric stratification and turbulence. Using an available digital elevation model (DEM) of moderate spatial resolution, e.g. Shuttle Radar Topography Mission (SRTM) DEM, a differential interferogram was firstly produced from the high-resolution InSAR data pair. A linear regression model between phase signal and auxiliary elevation was established to estimate the stratified atmospheric effect from the differential interferogram. Afterwards, a combination of a low-pass and an adaptive filter was employed to separate the turbulent atmospheric effect. After the removal of both types of atmospheric effects in the high-resolution interferogram, the interferometric phase information incorporating local topographic details was obtained and further processed to produce a high-resolution DEM. The feasibility and effectiveness of this approach was validated by an experiment with a tandem-mode X-band COSMO-SkyMed InSAR data pair covering a mountainous area in Northwestern China. By using a standard Chinese national DEM of scale 1:50,000 as the reference, we evaluated the vertical accuracy of InSAR DEM with and without atmospheric effects correction, which shows that after atmospheric signal correction the root-mean-squared error (RMSE) has decreased from 13.6m to 5.7m. Overall, from this study a significant improvement to derive topographic maps with high accuracy has been achieved by using the proposed approach. [Copyright &y& Elsevier]

• Development of an EM framework for the solution of multiple-snapshot noisy AR parameter estimation problem. • A computationally efficient implementation for the EM based solution. • Better AR parameter estimation performance in comparison to the state-of-the-art estimators in a wide range of scenarios. Maximum likelihood autoregressive (AR) model parameter estimation problem with independent snapshots observed under white Gaussian measurement noise is studied. In addition to the AR model parameters, the measurement noise variance is also included among the unknowns of the problem to develop a general solution covering several special cases such as the case of known noise variance, noise-free snapshots, the single snapshot operation etc. The presented solution is based on the expectation-maximization method which is formulated by assigning the noise-free snapshots as the missing data. An approximate version of the suggested method, at a significantly reduced computational load with virtually no loss of performance, has also been developed. Numerical results indicate that the suggested solution brings major performance improvements in terms of estimation accuracy and does not suffer from unstable AR filter estimates unlike some other methods in the literature. The suggested method can be especially useful for small-dimensional multiple-snapshot noisy AR modeling applications such as the clutter power spectrum modeling application in radar signal processing. [ABSTRACT FROM AUTHOR]

Digital Pulse-Doppler radar chain consists of signal processing algorithms that require high computing power. Multi-processor and multi-core parallel embedded machines are one of the solutions to meet real-time constraints of many radar applications. In this paper, we proposed efficient and scalable parallelization methods of the Pulse-Doppler radar signal processing chain. First, we evaluated Open Multi Processing (OpenMP) to identify its best scheduling technique in order to exploit efficiently the available computing cores. Then, we have proposed new parallel and scalable approaches based on direct memory access (DMA) and inter-processor communication (IPC) techniques, combined with the best OpenMP scheduling method to accelerate radar signal processing chain. To prove the scalability of our proposed parallel approaches, two radar use cases with different real-time and memory constraints have been experienced. We used the eight cores C6678 digital signal processor (DSP) as a target for all our implementations. The obtained results show an overall parallel efficiency of 95%, which is better than the best state-of-the-art implementations. [ABSTRACT FROM AUTHOR]

Abstract: We study the rapid-fluctuating radar signal detection, where the arrival time of the received signal, i.e., the range cell index of the target, as well as the complex amplitude of the signal and the noise are unknown. We show that even in additive white Gaussian noise (AWGN) environment, uniformly most powerful invariant (UMPI) test does not exist. Instead, the UMPI detector in known SNR is used as an upper performance bound for performance evaluation of any invariant detector performance. In addition, we derive generalized likelihood ratio (GLR) detectors for this signal in AWGN with unknown noise variance and also in clutter with unknown covariance matrix. The GLR test statistic for AWGN represents the ratio of the maximum power over all cells to the total power. The GLRT for a clutter environment is also a power ratio which is the maximum over all range cells of the Euclidean norms of the spatially whitened observed sequence. Simulation results demonstrate that the performance of the proposed GLR test in AWGN is very close to the upper bound performance, i.e., to that of the UMPI test in known SNR. [Copyright &y& Elsevier]

Abstract: The Arecibo 430MHz incoherent scatter radar (ISR) has been used to observe the ionosphere and meteors over 40 years. The meteor signals have traditionally been treated as the interference in ionosphere observation data, while more recently the incoherent scatter signals have been described as background noise in the meteor observation data. In this paper we present signal processing techniques that separate these two different signals into different geophysically interesting data sets. We investigate two techniques for two different data sets. For meteor observation data an adaptive filter is used to locate and separate meteor returns from the total data so that we improve meteor detection rate. For ionosphere observation data we use a filterbank followed by the short time Fourier transform (STFT) analysis to remove the meteor signals from the incoherent scatter results. By doing this the incoherent scatter data will not be contaminated and we can also analyze meteor characteristics from the separated signal. [Copyright &y& Elsevier]

An optical device is described and demonstrated that uses a spatial-spectral holographic material to perform coherent signal processing operations on analog, high-bandwidth optical signals with large time-bandwidth-products. Signal processing is performed as the material records the coherent spectral interference (or cross-power spectrum) of modulated optical signals as a spatial-spectral population grating between electronic transition states. Multiple exposures of processing pulse sequences are integrated with increasing grating strength. The device, coined as the Spatial-Spectral Coherent Holographic Integrating Processor (or S2-CHIP), is described as currently envisioned for a broadband, mid-to-high pulse repetition frequency range-Doppler radar signal processing system. Experiments were performed in Tm:YAG (0.1 at% at 5 K) to demonstrate time delay variation, integration dynamics, and effects of coding as applied to a radar range processor. These demonstrations used baseband modulation with a 1 gigabit per second (GPBS) bit rate and code length of 512 bits (512 ns), where delays up to 1.0 μs were resolved with greater than a 40 dB peak to RMS sidelobe ratio after 800 processing shots. Multi-GHz processing was demonstrated using a bit rate of 2.5 GBPS (baseband modulation) and code length of 2048 bits (819.2 ns). Processing of double-sideband modulated signals on a radio frequency (RF) carrier was demonstrated, where 512 bit, 1.0 GBPS codes were modulated on a 1.75 GHz carrier and then modulated on the optical carrier. [Copyright &y& Elsevier]

• Modified binary floating-point multiplier using Vedic mathematics Urdhva-Tiryakbhyam technique is used for high-speed multiplication. • 32-bit Binary floating-point multiplier using Kogge stone adder is better in terms of power, area and power delay product compared to ripple carry adder. • Floating point Vedic multiplier and Vedic adder is more advantageous over power and power delay product in implementation with various radix 2K single path delay feedback FFT architectures. Digital Signal Processing (DSP) is a very significant and active research area. High throughput is a requirement for most wireless communication systems. The critical bottleneck that affects communication ability is the Fast Fourier Transform (FFT), which is the essence of most modulators. Currently, Floating point FFT processors have been used in Radar signal processing, fast convolution, Spectrum estimation and OFDM based modulators/demodulators. Efficient VLSI based architectures are required for real-time FFT processing. The multiplication limits the performance in terms of throughput of FFT. Consequently, there is a need for high speed and low power multiplier architectures with minor truncation error. The present paper presents a modified binary floating-point multiplier using Vedic mathematics and a modification in the previously published Vedic multiplier circuit has been proposed. The entire design has been implemented in Verilog HDL. Synthesis and simulations are done using Xilinx ISE Design Suite 14.5.The performance evaluation in terms of speed and area occupied is compared with the previously reported Vedic multiplier architectures. Using 90 nm technologies, the Power Delay Product (PDF) of the proposed Vedic multiplier gets reduced through KSA by 86.41% compared to the multipliers. The Vedic adder power is reduced by 45.9% when it is compared with the carry look-ahead adder. The overall power delay product is reduced around 55–60% through the FFT processor by using Vedic mathematics. [ABSTRACT FROM AUTHOR]

Understanding the mesoscale spatial and temporal variability of precipitation (of the order of 1 to 100 km, and of minutes to hours, respectively) is essential for larger-scale studies, especially in highly heterogeneous areas, such as coastal regions, mountains and urban areas. The adequate capturing of the precipitation patterns in both diagnostic and prognostic terms remains an open challenge. On the one hand, commonly used ground measuring devices, such as rain gauges, disdrometers and other point instruments, exhibit an ineffective sampling for the whole range of scales and areas covered with precipitation. On the other hand, weather radars provide a much greater spatial and temporal resolution which, however, must be exploited with care in rain rate estimations, due to complicated and non-unique transfer functions used in the processing procedures. This study aims at bridging, to at least some extent, this scale gap and to demonstrate the role of fine-scale precipitation estimates derived from weather radar data, and to illustrate their impacts on larger scale weather patterns. The focus is on the precipitable water content and its changes during a Mediterranean cyclone evolution. In this paper, the first results with the regional radar signal processing chain that provides the radar data assimilation (RDA) in the Harmonie convection permitting numerical model are described. The numerical experiments exhibit effective simulation of the precipitable water at different stages of the frontal depression which affected Cyprus in the Eastern Mediterranean, during 31 December 2018–2 January 2019. In particular, it was found that the major volume of precipitable water was related to the cyclone frontal zone (which is in turn related to heavier precipitation), followed by a weaker field of precipitable water behind the frontal zone (and weaker precipitation). During the whole severe weather period, the precipitable water minimum was maintained over the island's central mountain range, reflecting the importance of interactions and feedbacks between the air flow and orography. The radar data assimilation influences the results of the simulations, depending on the particular area and cyclone stage. The largest innovations due to RDA occur with the approach of the cyclone's frontal zone. Enhancement of precipitable water due to RDA is observed over most of the whole area, but this is more pronounced in weaker precipitation locations. The impact is associated with a shift of the precipitable water maximum towards higher values in the precipitable water distribution, which is accompanied by enlarged sizes of areas with greater precipitable water. During the cyclone's mature stage and at the rear zone, the integrated RDA impact over the whole domain is almost negligible but takes the form of mesoscale cells of opposite signs; at this stage, radar measurements yield an alteration of both the positions and rates of spatial precipitable water fields, which result in intermittent mesoscale changes. • Radar data assimilation in the Harmonie numerical model is performed. • Radar data from two weather radars in Cyprus are assimilated into the model. • The focus is on simulated precipitable water and its changes. • Simulations with an eastern Mediterranean depression are reported. • The assimilation renders considerable influence on the results of the simulations. [ABSTRACT FROM AUTHOR]

Hardware system based radar signal detection, specific progress and current limitations. As indicated by the radar signal's characteristics, the radar signal's plan and arrangement are introduced with the irregularities and favorable conditions of the mirror mark. The radar signal's multidisciplinary preparatory invention is equipped with a versatile radar signal cycle, beat signal administration, computerized sifting signal mode, and Doppler strategy indicating nuances. An image display algorithm based on the radar signal's transmission cycle is extracted, which includes the transmission stages of the radar signal, the factors affecting the radar signal transmission and the radar data screening. Planning technology for radar signal and related signal characteristics will be evaluated to improve implementation. Radar signal calibration strategy and related impact factors are additionally isolated and described. Radar signal handling is described in detail, including the Innovation Multidisciplinary Innovation compound. Versatile radar signal cycle beat pressure executives and computerized separating Doppler strategy are very effective specialized techniques with interesting features. Finally, the difficulties of future testing methods and the progress of radar signals are proposed. [ABSTRACT FROM AUTHOR]

• Description of the polyphase coded radar system with the link budget analysis. • Presents aspect angle pattern of the standard target in open range condition. • All experimental results are compared with the theoretically simulated results. • We analyze the detectability and environmental effect rejection capabilities. This paper has described the development of the spread spectrum radar system and radar cross-section (RCS) measurements in the frequency range of 1–3 GHz, using a LabVIEW controlled fully digital radar setup. In this paper, the performance of a polyphase coded spread spectrum radar is illustrated and compared with the theoretical calculation for bistatic RCS measurements. For the performance calibration, different (sizes) flat plates are used as reference targets and tested over a wide frequency range. Here in addition to the controlling LabView platform is used for radar signal processing. MATLAB platform is used to compute the theoretical RCS pattern for radar performance validation. [ABSTRACT FROM AUTHOR]