International Journal of Aerospace Engineering

International Journal of Aerospace Engineering / 2020 / Article

Review Article | Open Access

Volume 2020 |Article ID 4673763 | https://doi.org/10.1155/2020/4673763

Jianping Ou, Jun Zhang, Ronghui Zhan, "Processing Technology Based on Radar Signal Design and Classification", International Journal of Aerospace Engineering, vol. 2020, Article ID 4673763, 19 pages, 2020. https://doi.org/10.1155/2020/4673763

Processing Technology Based on Radar Signal Design and Classification

Academic Editor: Angelo Cervone
Received28 Jul 2019
Revised05 Sep 2019
Accepted30 Dec 2019
Published17 Jan 2020

Abstract

It is well known that the application of radar is becoming more and more popular with the development of the signal technology progress. This paper lists the current radar signal research, the technical progress achieved, and the existing limitations. According to radar signal respective characteristics, the design and classification of the radar signal are introduced to reflect signal’s differences and advantages. The multidisciplinary processing technology of the radar signal is classified and compared in details referring to adaptive radar signal process, pulse signal management, digital filtering signal mode, and Doppler method. The transmission process of radar signal is summarized, including the transmission steps of radar signal, the factors affecting radar signal transmission, and radar information screening. The design method of radar signal and the corresponding signal characteristics are compared in terms of performance improvement. Radar signal classification method and related influencing factors are also contrasted and narrated. Radar signal processing technology is described in detail including multidisciplinary technology synthesis. Adaptive radar signal process, pulse compression management, and digital filtering Doppler method are very effective technical means, which has its own unique advantages. At last, the future research trends and challenges of technologies of the radar signals are proposed. The conclusions obtained are beneficial to promote the further promotion applications both in theory and practice. The study work of this paper will be useful for choosing more reasonable radar signal processing technology methods.

1. Introduction

Radar is an electronic system with the advantages of low cost, low-power consumption, and high precision [1], which can be significantly applied in space shuttle topographic missions [2, 3], optics [4], geotechnical mapping [5], meteorological detection [6], and railway ballast evaluation [7]. With the continuous progress of technology [8, 9] and the demand of utilization [10, 11], radar has gradually changed from obtaining the distance [12], azimuth [13], and altitude information [14, 15] from the target to the launching point of electromagnetic wave to gaining more expanded information [16, 17], such as hand-gesture recognition [18], displacement field of the Landers earthquake mapped, and detecting pedestrians with multiple-input multiple-output (MIMO) [19, 20].

All advances and utilities of radar technology are based on reliable and stable radar signal (RS) [21] which affects the detection result of radar directly [22]. RS researches are extensive and professional involving signal-to-noise ratio (SNR) [23], polarization properties [24], micromotion characteristics [25], time-domain convolution [26], and so on. Effects of nonuniform beam filling on the propagation of RS at X-band frequencies were conducted to verify signal attenuation in vertical and horizontal directions. Based on the power law relationship, Gosset and Zawadzki [27] took two mechanisms to investigate the modified action of nonuniform beam filling (NUBF) of the radar beam, which indicates that the apparent two method attenuations often compensate each other by distance owing to overestimating or underestimating a particular attenuation. Furthermore, phase measurement analyzed by examination of differential phase shift and weighted by reflectivity and attenuation in sampling volume will cause negative values in the retrieved specific differential phase shifts except for beam widths of less than 1°, which points out some of the practical problems that may be encountered using rain measurement algorithms at X-band [28].

The RS change is not only related to frequency but also to the propagation process of radar signal, especially in the ionosphere. In order to explore the relationship between the E region ionosphere and the velocity of the electron drift and ion acoustic, Nielsen et al. [29] applied the double-pulse technique to determine the systematic variation trend of Doppler frequency shift with drift velocity and flow angle. The research results show that the ratio of the maximum line-of-sight velocity to the ion acoustic velocity is decreasing from 1.2 to 1.05 when the electron drift speed increases from 600 to 1600 m s-1 which can be exploited this new capability in a new joint campaign. Grima et al. [30] valued the influence factors of ionospheric confinement on radio wave propagation with control the evolution of the European ionosphere configuration, which revealed dispersive phase shift and Faraday rotation are the main impacts on RS propagation with a function of the total electron content (up to ) and the Jovian magnetic field strength at Europa (~420 nT). The scattering or absorption of radar signals by ionization in the atmosphere has been extended to the upper atmosphere of the Mars which was put into effect by Espley et al. [31]. Though the designed instrument MARSIS transmitted a continuous-wave pulse of ~91 μsec duration in 160 frequency steps between 100 kHz and 5.6 MHz, no persistent ionospheric meteorology produced from the solar energetic particles and the daily ionization cycle was found. Namely, part of the high-frequency RS can be scattered or absorbed by the Martian ionosphere which indicates that the radar device can be used in Mars exploration in the future. In order to maintain the time stability of RS for a long time, the deep penetration method is more suitable for velocity mapping which was obtained by the experiment results of Rignot et al. [32]. The conclusion of the study is that the application of long-wave radar in glaciology has important advantages, which was drawn from that C-band penetration is small (1-2 m) on exposed ice, but up to 10 m on cold firn. Close et al. [33] examined the strength relationship among frequency, azimuth of nonspecial meteor trajectories, and RS. With the increase of the angle between the radar beam and the background magnetic field, the signal intensity decreases by 3 to 4 decibels per degree at 160 MHz. The research that focus on aiming to strengthen the radar signals and reduce the attenuation, the penetration depth, wall dispersive [34] and resolution of ultra-wideband, and so on are studied successively [35, 36].

As shown in Figure 1, according to radar signal respective characteristics, the design and classification of the RS are introduced by this paper primarily to reflect signal’s differences and advantages. And then the multidisciplinary processing technology of the RS is classified and compared in details referring to adaptive radar signal process, pulse signal management, digital filtering signal mode, Doppler method, and high frequency. In this review, the design and classification of the radar signal are introduced by this paper primarily to reflect signal’s differences and advantages according to radar signal respective characteristics. And the multidisciplinary processing technology of the radar signal is classified and compared in details referring to adaptive radar signal process, pulse signal management, digital filtering signal mode, Doppler method, and high frequency. The work done in this paper can effectively help promote the stability of radar signal which is essential to image resolution of coherent imaging, data transmission, and radar receive. Based on the study work of this paper, the researchers can choose more reasonable radar signal processing technology and methods.

2. Radar Signal Transmission

2.1. Transmission Process

Radar technology has been extensively used in practical applications owing to its signal has the characteristics of large bandwidth and time-width for the complex modulation to enhance the antijamming ability of the signal [37, 38], whose signal transmission processes are displayed in Figure 2. The stability and reliability of signals can be optimized and improved from the multidisciplinary aspects of signal generation, amplification, reception, processing, and detection.

As a special equipment, Radar’s daily work is carried out with the help of a certain amount of information [39]. Signal reflection can play a role in recognizing objects, which is also the focus of the development of radar technology in the current era [40]. Normalized signal application will exert tremendous influence in practice. Especially in complex combat environment, once the problem of nonstandardized signal recognition is encountered, it will directly restrict radar synthesis ability. The quality of radar signal depends on the signal transmission loss, the interference degree of other signals, and the influence of transmission mode [41, 42].

2.2. Information Screening

Information screening is important function of RS recognition. In the information age, different information sources bring different values, which determine that in the corresponding information application process, information value system is ensured in an active way [41]. The construction and formation of this value system is based on information recognition. Different radars have certain differences in the working band which will directly affect the recognition and feedback of radar signals. Therefore, it should optimize the signal application mode to ensure the validity and authenticity of radar information application in a more active way [42]. Cooper et al. [43] presented the THz imaging radar technology of the Jet Propulsion Laboratory (JPL) with a portable laboratory prototype radar system operating frequency-modulated continuous-wave (FMCW) mode over a 28.8 GHz bandwidth, presently centered at 676.7 GHz. The radar information screening research facilities are showed in Figure 3, and the study finds that the signal to noise ratio of human or clothed mannequin targets typically falls in the range 20-40 dB for a single 100 s FMCW waveform.

In Figure 3, panel (a) is the beam path optic schematic which contains the feed reflector, rotating mirror, subreflector, and the main aperture to transform beam in the range of 660~690 GHz; panel (b) is the radar optic photograph; panel (c) is the time-delay two-beam multiplexing implemented principle; panel (d) is the additional optical components, panel (e) is the concealed object detection in half the time due to parallel acquisition of the right and left image halves; (f) is the range spectrum of a single radar waveform showing simultaneous radar detection of two different locations on a target. Modelling and computer simulation method were adopted to discuss the radar screening by Blackledge [44] through the impulse response functions (IRFs) associated with radar microwave backscattering from a strong and weakly ionized plasma screen. The established model for the RS generated with and without a plasma screen and the screening of the scattered by the plasma is characterized by a simple negative exponential whose decay rate is determined by the conductivity which in turn is proportional to the electron number density. Through the analysis of Bierwagen et al. [45], it can be found that the role of radar signal recognition in the current environment is very important which is one of the effective ways to show the application of mine equipment under the new technology [46, 47]. Exploring the application of radar signal recognition system in an active way is helpful to construct the related recognition application system which becomes an inevitable development [48]. In case of emergencies, we can make better information identification judgment by making corresponding innovations and improving the existing identification application system [49].

3. Radar Signal Design and Classification

3.1. Signal Design of RS

In the field of modern radar signal utilization, the requirements of signal ability and function compatibility are increasingly stringent, which demands not only a large amount of data information [39, 43] but also a good system compatibility timely and punctual [40, 50]. The comprehensively used multidisciplinary radar signal design methods are shown in Figure 4. The design targets include the frequency division multiplexing, stealth targets, polyphase, analog, GSM-based passive, and waveform design. The corresponding objectives are different, and different design methods can be adopted with their respective design focus and highlights.

RS should be based on high range and speed of the resolution and then be designed with multidisciplinary algorithms [49, 51]. The radar signal characteristics and performance of various design methods are synthesized as shown in Table 1. Overall, high-performance radar signals processed by multidisciplinary hybrid algorithm are available.


RS design modeCharacteristicPerformance

Orthogonal frequency multiplexing [51](1) Optimised ambiguity function
(2) Low peak-to-average power ratio
(1) ACF SLL↓ from -15 to -20 dB
(2)

Nonsinusoidal RS design [52](1) Stealth targets
(2) Pulse compression with Barker codes
(1) Sidelobe free interval↑
(2) with time

Heisenberg nilpotent Lie group [53](1) Phase discontinuity of Fourier
(2) Chirp waveforms of microoptics
(1) FEB recaptured
(2) Transmitted at the rate of 2 W/s

Ambiguity function design [50](1) Discrete frequency-coded
(2) Synthesised polyphase
(1) increases from 3 to 6
(2) Transmitted at the rate of 2 W/s

Multidisciplinary algorithms [51, 52] have been innovatively applied to radar signal design, such as thumbtack range-velocity resolution functions and Hamming scan algorithm [53] which have been achieved significant results. In order to reduce the computational requirements, Nohara [54] completed the design of a space-based radar signal processor through the radar signal processor (RSP) function definitions, sampling correction, frequency alignment, pulse Doppler and compression, monopulse ratio, and noncoherent integration and detection. It leads to a reduction of 25% of the PD function, 20% fewer pulse compression operation of the PC function, and 20% decrease of the peak computational processing requirements after optimizations. Singh and Rao [50] employed the discrete frequency-coded (DFC) to design the RS which achieved very outstanding performance improvement results with the autocorrelation sidelobe peaks (ASP) and cross-correlation peaks (CP) as showed in Figure 5. Figure 5 shows that the value of ASP is much lower than that of CP but their trends are consistent. Compared with polyphase coding sequence set, DFC sequence set with the thumbtack ambiguity function has better correlation and the corresponding values of DFC sequences are much smaller than those of 32-phase sequences.

In order to achieve different technical specifications, the RS design method is also developed towards the trend of multidisciplinary and multimethod combination. To achieve high imaging quality, Song et al. [55] focused on the cognitive waveform optimization design for radar imaging and designed a waveform optimization method maximum the receiving signal-to-clutter ratio under dual constraints including transmitting energy constraint and range profile constraint. The comparison of the different optimization waveforms of the RS is displayed in Figure 6. Among the three waveform power spectra, the TISLR value increases, and more power is concentrated on the frequencies with high signal-to-clutter ratio values. The performance of the optimized design is obviously improved and the solve values derived from binary searching are 0.3349, 0.4864, and 0.7984 of , , and , respectively, which are just 0.62, 0.66, and 0.70 times the range profile ISLR of the maximized mutual information- (MMI-) based optimization waveform. When , the waveform has the best performance compared with and .

In order to improve the application effect of integrated identification, it will be more complex to do a comprehensive design of new radar. Prediction and management are needed under various conditions to make sure the effect is more real and effective [56]. Setting up new radar types requires comprehensive consideration of various problems, especially the technical means and application methods of competitors. New mode of radar application should be constructed to obtain more information resources in the process of new radar design and used to promote information awareness [57, 58]. Other functions are constantly strengthened and developed to form a more complete database and analytical response methods.

3.2. Radar Signal Type

In the process of signal application, an effective method is to set up new radar classes. In the face of increasingly complex information resources, single signal recognition has become very simple and the actual use effect is not very good [59, 60]. It needs comprehensive technological innovation and optimization to drive a new application of modern radar signals. Hobson et al. [61] used the combination watershed segmentation and -means clustering algorithm partitioned merged radar reflectivity into multiscale storm clusters which are able to distinguish between smaller-scale features embedded within a larger storm. The comparisons of data acquisition for different radar types at 200 km and 2000 km contingency are listed in Table 2, which show that there are obvious differences between the two kinds of radar types and more in-depth research is needed to distinguish type 1 or 2 is more in line with reality. Observed storm types of RS1 make up of supercell, ordinary, and short-lived which are different from each other. Based on the data of Table 2, the consistency of storm RS2 type prediction is better than RS1.


200 km contingencyObserved storm types of RS 1
SupercellOrdinaryShort-lived convective
Forecasted storm types of RS 2Supercell50515
Ordinary3131452
Short-lived139147
2000 km contingencyObserved storm types RS 1
Convective lineUnorganized
Convective line6623
Unorganized33200

The Radar Library is relatively perfect from a theoretical view point, but modern warfare is affected by various factors especially the application of information in complex electromagnetic environment, which will become the main direction of development [62]. In practice, whether certain signals to be identified can be accurately judged will be a great test. A phase-modulated surface radar type is presented by Chambers et al. [63] with the comparison of nominal switching frequency displayed in Figure 7. They demonstrate wideband spectral component suppression levels of about 20 dB which is equivalent to reducing the detection range of an electronic support measures receiver by a factor of 10. The code length bits decrease with the nominal switching frequency increasement and the zero-bit sum peak sidelobe level value increases gradually.

Mohamed [64] investigated a high-resolution with no sinusoidal radar whose range resolution is smaller than or equal to target length. The extended target is illuminated by a sequence of short rectangular pulses. The received signal consists of a number of identical target signatures where each provides information about target shape, size, and orientation. As a result, target classification and recognition can be performed at any aspect angle. The radar type based on the feature space trajectory concept was identified by Kim and Jeong [65]. They proposed a systematic approach with the central moments of a range profile and a Bayes classifier to yielding very small dimensional feature vectors, which is an available technique skill to diversity in radar signal processing [66].

3.3. Classification of Signal

Potential applications requiring classification of unknown radar signals include maritime barrier operations aimed at preventing illegal immigration [3, 10], arms and drug smuggling [11, 18], illegal fishing, and piracy [6, 14]. RS classification method is based on diverse radar signals which has been extensively studied and applied in the field of signal classification. Radar signal classification mode is illustrated in Figure 8 which includes the methods of the neural network [67], cluster method [68], similitude entropy [69], support vector machine [70], time-scale characteristics [71], modulation domain [72], basis function neural networks [73], Rihaczek distribution and Hough transform [74], frequency estimation [75], pulse repetition interval [76], bispectrum two dimensions [77], and so on. These classification methods synthesize the research methods of many disciplines, such as system control [67, 73], heat transfer [69], and mathematics [68, 77]. These radar classification methods also reflect the integration of multidisciplines which is helpful to improve the performance of radar signals.

To enable reliable functioning in complex signal environments with multiple radar emitters, modern signal classification must be capable of processing unknown, corrupted, and ambiguous measurements in a robust and reliable manner. For vehicle-type radar classification and speed determination in a computationally cost-effective manner, Cho and Tseng [70] developed optimization algorithm which will benefit for real-time intelligent transportation systems with 8 categories of radar signal classification mode setting. The recognition rate comparisons of frequency under different linear discriminant analysis (LDA) and support vector machine (SVM) are listed in Figure 9 which suggested support vector machine approach is an effective radar signal classification method with high recognition and correct rate. SVM has the maximum failure rate (≤97%) and the LDA has a lower failure rate (≤94%) which have the same change trend.

It is extremely necessary to classify the modulation type of the intercepted RS for an electronic intelligence receiver in a noncooperative environment (fall detection) with the experimental and simulation methods [71, 72]. The classification of radar signals mainly depends on the improvement of related algorithms [73]. The simulations of the classification algorithm proposed by Zeng et al. [74] showed that the probabilities of successful radar recognition can reach 90% when the SNR is above -4 dB. On the theoretical research of radar signal classification, Gini et al. [75] completed the derivation of the joint maximum likelihood estimator of complex amplitude and Doppler frequency, which used the method of a radar target signal embedding in correlated non-Gaussian clutter modelled as a compound-Gaussian process. It is different from the previous direct classification of radar signals in the past that Kauppi et al. [76] classify the received pulse train with the use of sliding windows to clearly detect submodes. The accuracy, robustness, and reliability of the technology are proved by a large number of static and dynamic simulations of pulse repetition interval modulation modes. With the application and development of large data and related databases [77], a reference library with the single and two aspects library correlations for radar signals is established by Smith et al. [78] to improve the efficiency of radar signal classification. The single and two aspects of library correlations for wheeled and tracked vehicles radar signals are displayed in Figure 10 under the angle of 0°, 30°, 60°, 90°, 120°,150°, and 180°. Although there are great errors in the two correction methods, it does promote the establishment and development of radar signal classification database [79, 80].

4. Radar Signal Processing Technology

The characteristics of radar signal include carrier frequency [81], its variation characteristics and pulse repetition frequency [82] and its variation characteristics, pulse width and its variation characteristics [83], antenna scanning type, antenna scanning period, signal spectrum, and signal azimuth, and there are a large number of intravenous characteristics of signals [84, 85]. The extraction of features from radar signals is influenced by many factors, which leads to the existence of subjectivity, speculation, and a certain degree of disordered distribution of the extracted features [86]. There are many methods for radar signal processing including the application of single method [87, 88] and the combination of multidisciplinary and multimethod (as shown in Figure 11). In order to eliminate the subjectivity of feature extraction and improve the accuracy of radar signal sorting, recognition, and processing, effective multidisciplinary and multimethods are needed to perform signal processing work [8991].

The performance of processed radar signals has been greatly improved in all aspects. Different radar signals adopt varied processing methods in order to improve their special performance requirement. Reconfigurable computing application can significantly improve the working efficiency of high-performance front-end radar signal processor [92]. Frequency subband processing and feature analysis of forward-looking ground-penetrating can remarkably enhance the diagnostic judgment rate of the landmine detection [93]. Radar signal processing for vehicle speed measurements promotes the development of driverless and intelligent transportation [94], and the RS performance improvement aspect through processing is listed in Table 3.


Processing methodsPerformance improvement aspectDetails

Model-based frequency algorithms [166]Resolution enhancementFourier transformation
Coherent processing [167]Integrate target energyLinear transformations
Coprime sampling [168]Power spectrum densityAmbiguity function of matched filter
Coherent fusion scheme [169]Range resolution with low sidelobesEstimate the phase difference

The allocation requirement radar resources must be executed efficiently by a process method to optimize the performance of the overall radar system [95], which forced the research methods of signal processing to develop toward multidisciplinary and multiplan.

4.1. Adaptive Radar Signal Process

Adaptive radar signal process is a self-adaptation method which can adjust the sequence, parameters, boundary conditions, or constraints according to the characteristics of the processing RS data [96], which can make the signal adapt to the statistical distribution and structural characteristics of the processed data to achieve the best processing effect [97]. Bhattacharya et al. [98] are concerned with the Wiener solution of partially adaptive radar arrays using the cross-spectral subspace selection technique. Compared with the performance of principal component techniques, the adaptive radar arrays have better partially adaptive performance and its output signal-to-interference plus noise ratio for the eigen-subspace Wiener filter is 7.65 dB with a loss of 15.63 dB. Owing to the advantages of time-varying null steering in both the temporal and spatial frequency domains, adaptive beamforming methods of the RS are studied by Griffiths [99] which can reduce the noise floor in addition to the suppression of strong interference lines such especially at Doppler frequencies -21 Hz and -9 Hz. Raju and Reddy [100] proposed a nonparametric and hyperparameter-free iterative adaptive approach to estimate the power spectral density for getting the accurate amplitude and frequency of the simulated data with less computational time.

Adaptive computing time is related to the structure of the algorithm. Sometimes, in order to get the desired results, it is worth sacrificing some computing time. In order to search the optimum tap-length of the RS combined with target motions, Hu et al. [101] developed an adaptive algorithm under a tap-length gradient control scheme and selected the parameter, which is checked by the experiments on human target with different motions behind wall. Among the approaches of two-order moving target indication (TOMTI), accumulative average background subtraction (AABS), moving average background subtraction (MABS), exponential average background subtraction (EABS), and the proposed adaptive algorithm methods, the comparison of the consumed time and amplitude of processing 200 pulses in the 3 experiments is listed in Figure 12. Proposed adaptive algorithm method needs more computing time (<0.6 s; this time consumption is completely acceptable.) to search optimum tap-length but it is still acceptable for real-time implementation. Compared with the conventional background subtraction methods, the proposed adaptive algorithm magically solved the tailings problem with overcoming the target information loss in the two-order moving target indication method, which can retain more motion details with a good adaptive ability to indicate different motions.

Liu et al. [102] designed the multichannel adaptive filters to achieve effective clutter suppression and target signal preservation. Robust adaptive signal processing methods for heterogeneous radar clutter scenarios were measured by Rangaswamy et al. [103] with the self-adaptive method [104]. The adaptive method can be used in many aspects of radar signal processing including radar waveform optimization [105], signal synthesis adaptive antenna design [106], and 2-D signal adaptive processing QR and IQR algorithm optimization [107, 108].

4.2. Pulse Compression Management

The process of generating narrow time pulses by compression filter processing is called pulse compression. The condition of pulse compression is that the transmitting pulse signal has a large time-bandwidth product and a compression network to eliminate the phase dispersion of the input echo signal. Pulse compression signals include linear frequency modulation signal, nonlinear frequency modulation signal and phase-coded signal, and so on. Time-domain processing and frequency-domain processing are two common processing methods. Pulse compression management (PCM) is one of the most necessary and effective means to improve signal performance [109].

Some radar signals employ very narrow pulses which make little jitter inaccuracy large enough to destroy the signal correlation property and then degrade clutter suppression performance. Zhu et al. [110] proposed the PCM method to remove the clutter through impulse plus exponential mean background subtraction algorithm which can improve through-wall human indication performance during the experiments. Levanon and Mozeson [111] created a complementary set between the frequency and pulse of the sequences of N pulses is sequenced to reduce the sidelobe level around the main autocorrelation lobe. The conclusion was obtained that a coherent pulse train can provide an alternative to single-frequency signals with good immunity against mutual interference or jamming. Levanon et al. [112] obtained the frequency weighting to get desired weighting law with reducing the desired sidelobe which can be stretched based on the single-size frequency step. Zhang et al. [113] established a monopulse theory radar model to receive signals returned from extended objects and the targets can be localized by a maximum likelihood estimator to detect and localize multiple unresolved extended targets. Based on the desideratum of the merit factor, bialphabetic pulse compression radar signal algorithm was designed by the research team of Pasha et al. [114] to solve the pulse compression problem by Hamming scan optimization. The results showed that the backtracking algorithm for bialphabetic sequence can improve the merit factor values compared with the Hamming scan as displayed in Figure 13. The changes in the two modes are roughly the same, but the backtracking Hamming scan value is higher than Hamming scan with the max value of 19.8 and the min value of 14.

The optimization is not only limited to the improvement of algorithm but also embodied in the design and upgrade of digital signal processor. Real-time parallel implementation of pulse Doppler radar signal processing chain was designed by Klilou et al. [115] to improve the parallel system efficiency about 90% by experiment verification. The interprocessor communication can be reduced by the proposed optimization with eliminating the Doppler filtering and the postprocessing. The parallel machine executes 94% of communication with the pulse compression. The pulsed high-efficiency power amplifier can improve the spectral purity with the increasements of the power amplifier and peak-to-average ratio with 78% and 90%, respectively [116].

4.3. Digital Filtering

Filtering is an effective technique to eliminate clutter and enhance RS performance which is an important measure to suppress and prevent interference by filtering the frequency of a specific band in the signal. Radar interferogram filtering for geophysical applications was developed by Goldstein and Werner [117] to improve both measurement accuracy and phase. Compared with 64% of the unfiltered segment, it achieves 92% unwrapping the test interferogram segment. The digital filtering function of the Potin et al. [118] is to remove clutter with the established clutter geometrical model of a frequency analysis. The (the first criterion is the percentage measurement of the clutter power) comparisons of the adapted filter and the filter coefficients depend the filter order , 10, and 15 are listed in Figure 14 which illustrated that decreases with the increase of filter order. The adapted filter has significant advantages with the . Adapted filtering has stronger adaptability and better filtering performance with research object of uncertain system or information process. It is widely used in many fields, such as system identification, echo cancellation, adaptive spectrum enhancement, adaptive channel equalization, speech linear prediction, adaptive antenna array, and so on. Moreover, the implementation of adapted filter is simple with low computational cost with high false alarm detection probability.

How the pulses are attenuated and distorted and the frequency-dependent properties were analyzed by Shaari et al. [119] through the ground-penetrating radar. The pulse shape and amplitude relationship between the different moisture contents and of propagation distances were obtained which expanded the research depth and scope of filtering. Based on uniform filter banks, the spectrum and variance estimation of atmospheric radar signal were studied by Reddy and Reddy [120] to find wind speed at an altitude of 18 kilometers which made the SNR improvement of about 6 dB at low SNR regions. The effect of single filtering application is obvious, and the performance of filtering function combined with other methods is improved, such as the optimization of wavelet in radar signal Brown noise [121], deinterlacing, and pseudorandom filtering recognition [122].

4.4. Doppler Method

The phenomenon when the source and the observer have relative motion and the frequency of the wave received by the observer is different from the frequency of the source is called Doppler effect. Pulse Doppler radar can search at the same time of tracking and can change or increase the working state of radar which makes radar have the ability to deal with various jamming and recognize targets beyond visual range. Radar can work at different pulse repetition frequencies and has the ability of adaptive waveform. It can select low, medium, or high pulse repetition frequencies waveforms according to different tactical states and can obtain the best performance of various working states [123]. Using Doppler beam sharpening technology to obtain high resolution, high-resolution map mapping and high-resolution local magnification mapping can be provided in air-to-ground applications [124, 125] and dense formation targets can be distinguished by judging the state of air-to-air enemy. Within the framework of the Kirchhoff approximation, different mD responses for various air targets were experimental tested by Meshkov and Karaev [126] using collecting the radar signal with the digital television broadcast signals. The shift and width of the Doppler spectrum of a microwave signal were derived which can stand for the reflected from a rough water surface in the case of a small incidence angle. Doppler spectrum of a microwave radar signal with a fundamental modulation period of 0.83 ms reflected from a water surface with the 24 fan blades and the specify nominal operation 55%~60% of the max value, resulting in an expected fan speed of approximately 3000 r/min [127].

The progress and development of radar technology cannot be separated from the promotion of multidisciplinary basic research, such as optics [128, 129], measuring means [130132], imaging [133], experimental observation [134], algorithm improvement [135], and model optimization [136]. Multidisciplinary radar design and optimization [137, 138] not only considers the coupling design between disciplines but also is more appropriate to the essence of the problem, so that the radar signal can be high quality and fidelity. Most multidisciplinary optimizations [139] consider the multiobjective mechanism to balance the interdisciplinary influence and explore the overall optimal solution, which can effectively avoid the waste of manpower, physics, financial resources, and time caused by repeated design [140]. Some radar multidisciplinary optimization can adopt collaborative design and concurrent design, which can shorten the cycle as much as possible.

5.1. Future Research Trends
5.1.1. Deeply Expand the Basic Content

With the scientific progress in microwave, computer, semiconductor, large-scale integrated circuit, and other fields, radar technology is developing continuously and its connotation and research content are constantly expanding [141]. Radar function has gradually evolved from a single function to a multitask and multifunction radar system. Radar engineering theory is not confined to Shannon theorem whose working frequency, bandwidth, and resolution are improving with the multifunctional architecture [142]. The implementation and analysis of the path planning and wavelength are also applied [143, 144].

5.1.2. Diversification of Signal Processing Technology

In addition to conventional processing methods such as correlated/uncorrelated processing, signal processing technology includes space-time adaptive (STAP), multiple-input multiple-output (MIMO), synthetic aperture (SAR/ISAR/CSAR), synthetic pulse and aperture (SIAR), and adaptive/cognitive radar signal processing technology based on artificial intelligence [145].

5.1.3. Classification of Detection Techniques

The corresponding detection means are also varied for differentiated waveforms of radar signals [146]. Multiple technology methods of wavelet-based transform, clutter detection, algorithmic improvement, time-frequency, and phase-coded are applied to detect the radar signal, which can reduce signal divergence and attenuation dramatically [147, 148]. The work done can effectively help promote the stability of radar signal which is essential to image resolution of coherent imaging, data transmission, and radar receive.

5.2. Research Challenges

Significant changes have taken place in the targets observed by radar, and the electromagnetic environment of radar work has deteriorated seriously, which has a tremendous impact on the development of radar.

5.2.1. New Challenges in the Used Environment

Ground radars are difficult to detect and early warn from a long distance because of the observation dead angle, strong ground clutter background, and much higher flight speed than ground vehicles. The harsh electromagnetic environment of strong electronic jamming in the future, as well as the discovery, recognition, and confirmation of high-speed, invisible targets (cruise missiles) and camouflage, concealment and deception CCD targets in the background of severe ground and sea clutter, makes it difficult for the original centralized launching mechanical scanning radar to meet these new requirements [149].

5.2.2. Active Phased Array Radar Technical Requirement

Active phased array radar needs a large number of T/R components whose performance, weight, size, and cost of T/R components are important considerations for the whole AESA system. The phase shifter, attenuator, amplifier, preamplifier driver stage, switch, and control circuit are all integrated in a single circuit with only about 4~5mm2 chip of the multifunctional core which is limited by chip development technology.

5.2.3. Heat Dissipation of Radar System

Radar system is a complex and multifunctional integrated system. Data processing is carried out at all times. This way of work will generate a lot of heat. The problem of heat dissipation of multifunctional radar system needs to be solved urgently. Some heat dissipation techniques can be tried and applied, such as heat pipe heat dissipation [150, 151] and establishment and development of heat management system [152, 153].

6. Conclusion

With the increasing energy shortage [154158], pollutant generation [159163], and the rapid development of large-scale integrated circuits, the application of radar has gradually changed from military to civilian [164, 165]. It has been found that the development of radar [166170] in the field of industrial products is unprecedented in our daily life, covering transportation, search and tracking, navigation, and so on. Direct or indirect acquisition of high-quality and stable radar signals is the main research focus of researchers [171173]. Radar signal, as a special signal, has opened up new research fields and methods for meteorology, exploration, flight, and autopilot and even opened up new horizons [174176]. In this review, the design and classification of the radar signal are introduced by this paper primarily to reflect signal’s differences and advantages according to radar signal respective characteristics. And then the multidisciplinary processing technology of the radar signal is classified and compared in details referring to adaptive radar signal process, pulse signal management, digital filtering signal mode, Doppler method, and high frequency. It can be concluded that radar signals will become more common and stable in future applications. (1)The transmission process of radar signal is summarized including the transmission steps of radar signal and the factors affecting radar signal transmission and radar information screening(2)The design method of radar signal and the corresponding signal characteristics are compared in terms of performance improvement. Radar signal classification method and related influencing factors are also contrasted and narrated. Different radar signal forms have different applications and effects(3)Radar signal processing technology is described in detail including multidisciplinary technology synthesis. Adaptive radar signal process, pulse compression management, digital filtering, and Doppler method are very effective technical means, which have its own unique advantages(4)The current trends of radar signal research, the technical progress achieved, and the existing limitations are listed. The future research trends and challenges of technologies of the radar signals are proposed

Nomenclature

RS:Radar signal
NUBF:Nonuniform beam filling
UWB:Ultra-wideband
MIMO:Multiple-input multiple-output
ACF SLL:Autocorrelation function sidelobe level
AF:Ambiguity function
BCL:Barker code length
t:Main lobe centered at time
FEB:Finite energy and bandwidth
:Number of sequences
RSP:Radar signal processor
DFC:Discrete frequency-coded
ASP:Autocorrelation sidelobe peaks
CP:Cross-correlation peaks
JPL:Jet Propulsion Laboratory
FMCW:Frequency-modulated continuous-wave
IRFs:Impulse response functions
MMI:Maximizing mutual information
PMS:A phase-modulated surface
LDA:Linear discriminant analysis
SVM:Support vector machine
:Range frequency
TOMTI:Two-order moving target indication
AABS:Accumulative average background subtraction
MABS:Moving average background subtraction
EABS:Exponential average background subtraction
PCM:Pulse compression management
:The first criterion is the percentage measurement of the clutter power
:The filter coefficients depend only on the filter order
SNR:Signal to noise ratio.

Conflicts of Interest

The authors declare that they have no conflict of interests regarding the publication of this paper.

Acknowledgments

This work is supported by the National Natural Science Foundation of China under the research grant of 61471370.

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