As illustrated in Fig. 2, a pulse transmitted by Radar-1 is reflected by weather targets and returns to Radar-1. Whenever the pulse is reflected by a weather target, its amplitude and phase change according to the reflectiv- ity and velocity of the target. As a weather target actually consists of a tremendous number of weather scatter- ers, it is extremely difficult to individually consider and model the impact of each scatterer on the reflection of the radar signal. Thus, we employ a simple approach that has been adopted and validated in many previous studies [8, 11]. In the approach, the radar signal’s path is divided into multiple resolution volumes (a.k.a. range bins) and it is assumed that there exists a single virtual point target in each range bin. Then, the channel impulse response of a target is defined as the aggregated impacts of all scat- terers in the corresponding range bin on the reflection. This approach is illustrated in Fig. 2, in which a series of black circles indicates the virtual (point) targets. For further simplification, we integrate the effect of signal propagation into the channel responses of the targets.
DOI: 10.15662/ijareeie.2014.0307055 Different applications require windows with different characteristics. Windows with low maximum sidelobe level is chosen for applications with strong interfering signals near the frequency of interest. If two signal components are near to each other then the window with high resolution (narrow mainlobe width) is chosen to identify the signal components. For good accuracy of amplitude measurement windows with wider main lobe is chosen. From the above parameter estimation of different windows, it’s evident that flattop window has good amplitude accuracy, so has poor resolution and the poor amplitude accuracy is observed in Bartlett window. Because of Flattop window’s poor resolution it’s harder for the window function to determine the frequency content of the signal exactly. Bartlett window has better resolution since it has narrow mainlobe width and has the ability to detect the signals with frequency content closely spaced. The hamming window has lower PSL than that of Hanning window but the distant sidelobes in hanning window is lower than that of hamming window. The Blackman window has wider mainlobe width next to Flattop window and has more sidelobe attenuation than hamming window and hanning window.
The NLFM processing algorithm has the advantage to improve the shape of the compression- weighting (Hamming) filter response for low values of the signal base (i.e., less than 100) and to assure a significant sidelobesuppression (i.e., more than −40 dB) similar to the one achieved in the case of signals with high-values of the base (i.e., more than 100), or other piecewise linear/nonlinear techniques [4, 8, 9, 16]. In addition, using the proposed optimization procedures of the parameters assigned to the nonlinear predistortioning laws, an additional decreasing of the sidelobe level more than 6 dB is also acquired. Generally, NLFM signals generated by predistortioning (frequency/temporal) techniques have some major drawbacks, namely: the mainlobe width and signal processing losses are increased, and the range resolution can be sometimes significantly reduced. However, in our study case and according to special literature [1, 4], the worsening of the range resolution (which is a very important tactical characteristic of a (military) radar) can be considered one acceptable.
1057 | P a g e In the pulsecompression NLFM pulse resolving closely placed multiple targets known as range resolution. Another advantage it reduces sidelobe to many possible preferable values. The mathematical expressions is discussed to achieve the desired value. The NLFM signal is given tothe matched filter. The output of the matched filter is given to the sidelobesuppression filter.
From the simulation results in Section 3, we can find that both phase predistortion and spectrum modification have the ability of sidelobesuppression. But phase predistortion can bring hunches far away from the mainlobe and spectrum modification has good effect of suppression to the sidelobes in the distance. So, we predict that the combination of these two techniques will bring better pulsecompression performance. The
Although X-band radar has recently been hailed as an effective candidate for gap-filling or mobile operation within weatherradar networks, we selected the Ka- instead of the X-band for system compactness in our feasibility study. Millimeter wavelength(MMW) radars, usually implemented at Ka- or W-band complement centimeter-wavelength radars due to the possession of more sensitive characteristics. Conversely, such radars are disadvantaged as a result of high attenuation. Several examples of research areas in MMW weatherradar are the general cloud physics associated with climate-affecting cloud-radiation interaction, the weather modification activities, etc. [20, 21]. In Ka-band, 35.2 ∼ 36 GHz of frequency band was allotted for meteorological aids service with other applications such as radiolocation service. We applied 12.5 MHz of bandwidth to one channel — including the some guard bands — as well as four channels in one band and with a total of 10 bands being available between 35.5 GHz and 36 GHz.
The work offers an insight into the development of a RNN based system for prediction of target from radar return signal. The work improves the performance of ATR with signal processing. The system tackles well clutter mixed signals with range variations. The most striking feature of the work is the use of RNN which enables the system to capture time variation in the inputs which generates a CRR of above 82%. It makes the system suitable for ATR applications usingpulseradar returns. The difficulties associated with RNNs includes dimensionality and generalization problem. For learning many dimensional data such as radar return, either a huge number of samples is required, or the data must first be reduced by the extraction of a relatively small numbers of features for the RNN to learn. If the number of data samples available is too small, then the network will over fit the training data, resulting in poor generalization on test data. Hence, designing a RNN requires experimentation to determine the best architecture for a given problem and associated data set. The time required to train the system with the acquired data is found to be very high. This can be further reduced with high performance computational framework, although the system is required to be trained only once. The system in modular or distributed design shall provide improve results that the present ones.
It is well known that wireless sensor networks (WSN) are a fast growing class of systems. In , the authors pre- sented a new method that makes use of the properties of data of multiple sensors to enable reliable data collec- tion. In , the authors adopted a mutual-information- based sensor selection (MISS) algorithm to help sensing devices collaborate among themselves to improve the tar- get localization and tracking accuracies. Alike WSN, RSN has been recently considered to overcome the perfor- mance degradation of a single radar. In , the authors design a network of distributed radar sensors that work in an ad hoc fashion and the simulation results showed that proposed RSN can provide much better detection performance than that of single radar sensor. However, RSN is quite diﬀerent from WSN. The waveform of each radar sensor has to be properly designed, other- wise, these radar sensors are likely to badly interfere with each other in the RSN. As a result, the design of radar
simulated results show a surface clutter rejection of about 20 dB in case of 2-sigma separation (i.e., the centers of the two spectra are separated by more than twice the sum of their widths), decreasing to about 4 dB for 1-sigma separation. The most challenging scenarios are obviously those where rain and surface spectra are entirely overlapped; in this case useful performance seems achievable only for moderately broad rain spectra (i.e., 2 m/s or more). The main limitation of the method thus occurs when the rain spectrum is both narrow and centered at zero Doppler. Fortunately, this case requires light rain and a wind component of around 5 m/s in the radar look direction; this is an unlikely situation in the severe weather and hurricanes that would be targeted by NIS. Our results indicate that while clutter presents a signiﬁcant challenge for NIS, Doppler ﬁltering can signiﬁcantly reduce its eﬀects, even when using a staggered PRF.
It is found from the results that it has been possible to have low side lobe radiation pattern without major increase in beam widths using the above typical tapers. The results of an array of Isotropic radiators are found to be identical with those of continuous line source. For discrete array calculations, the spacing suggested by Ishimaru  is used. In the case of discrete radiators, the resultant pattern is the product of array factor and element pattern. The resultant low side lobe (-59.8 dB) radiation patterns are very useful for point to point communications systems.
The first step of the analysis is to identify pixel groups within the radar image that are obviously affected by the same type of clutter. Three different types of clutter were classified: the “city clutter” of Munich caused by obstacles within a distance of 40 km to the south of the radar site, “mountain clutter” of the Alps in the South at distances of over 70 km from the radar site and “spokes” originating from obstacles near the radar. Basically, each type of clutter or dis- turbance can be identified as long as its appearance is statis- tically significant. Figure 2 gives an overview of corrupted and uncorrupted radar pixels for reflectivity level 1. The red colour represents pixels which are affected by clutter or clut- ter correction and the orange colour indicates spokes. The city clutter and the mountain clutter can easily be separated because of the underlying landscape. For these two clutter types, a certain area including corrupted pixels of the same source is visually defined, where uncorrupted pixels form the majority of pixels. The blue colour in Fig. 2 indicates the area of mountain clutter. Pixels in this area should have compara- ble beam heights and distances from the radar. Corrupted and uncorrupted pixels within these areas have to be separated. This separation has only been made once and includes man- ual work. For each area of correction, thresholds of frequen- cies of occurrence are used to separate those pixels which are obviously corrupted from the rest. Additionally, a buffer of 2 km is established around the corrupted pixels to mark those pixels which are likely to be influenced by clutter. For the residual pixels a histogram of frequencies of occurrence is established. The uncorrupted pixels (comparison group) show comparable frequencies of occurrence and, therefore, form a distinctive peak in the histogram. Pixels which differ from this distribution can be separated manually if the pre- selection of uncorrupted pixels was difficult. As a last step, the final separation is realised by the analysis of an empiri- cal distribution of frequencies of occurrence, where its 95 % interval marks the range of uncorrupted pixels. As reflectiv- ity level 1 shows the highest amount of corrupted pixels, the classification for all levels is based on level 1.
In this paper the RRBF is proposed for radarpulsecompression. The simulation results reveal that the performance of RRBF based pulsecompression is much better than MLP, RNN and RBF based pulsecompression techniques. The convergence rate of RRBF is higher than that of all other networks and it has low training error. The RRBF approach provides better PSR values in different adverse conditions such as noise and Doppler shift conditions. The range resolution ability of RRBF network is much superior than MLP, RNN and RBF networks. Although the algorithms are applied for 13-bit and 35-bit Barker codes, they can also be used for any other biphase codes.
Apart from that, in technique , only non-zero value pixels and a zone of zero-value pixels around the non-zero value pixels are used in the training process. Considering the zone of zero-value pixels is important because the Gaussian envelopes may assume some arbitrary values beyond the rainfall event boundaries. This selection during training of the network ensures the Gaussian function represented by the hidden nodes is restricted to the boundaries determined by the zero-value pixels zone. At the same time, the majority of zeros in the weather image are not used in the training process.
The radar signals simulators became an essential requirement for the development and the evaluation of the performance of the radar systems. Many types of the radar signals simulators were implemented using different techniques. Some of them use digital electronics boards and digital electronics cards as help access to real-world signals and instrumentation for test different types of radar systems, but these types of simulators did not has the ability to generate the radar signals in the intermediate frequency (IF) stage. In this paper, the radar signals simulator in video stage and (IF) stage using PC, arbitrary waveform generator card (DA4300) and National Instruments Digital Electronic Field programmable Gate Array (NI-FPGA) board was proposed. In addition of the hardware requirements, LabVEIW program was used with the FPGA board to generate some of the radar signals such as the synchronization signal (SYNC), Antenna Location signals (ACP1, ACP2 (Angle Clock pulse) and NP (North Pulse)), and others, while Microsoft Visual C++ software was used with the (DA4300) card to generate a transmitted signal, target signal, and other signals in (IF) and video stages. The proposed simulator system has the ability to generate the signals for different types of radars; one of these types is the pulsecompressionradar. The generation of linear FM pulsecompressionradar signals is compared with the MATLAB Simulink results for this type of radar.
A neuron is an information processing unit for the operation of a neural network. The operation in a single neuron involves the computation of the weighted sum of inputs and threshold . The resultant signal is then passed through activation function. The activation functions can be defined as a limiting the amplitude of the output of the neuron and it is also called a squashing function in that it squashes (limits) the permissible amplitude range of the output signal to the some finite value. The neuronal model also includes an externally applied bias, expressed by bi, the bias bi has the effect of increasing or lowering the net input of the activation function, depending on whether it is positive or negative, respectively. The basic structure of a single neuron is shown in Figure 2.6.
With the EDC method, we will deploy the extended data carriers over the whole used OFDM spectrum and also over the Guard Band. When EDC’s interval is equal to OFDM system’s interval, it’s possible to cause a severe interference to the original data carriers. On account of this, we will use the EDC with different intervals. Then there will be a relatively large number of cancellation carriers and they can change their own weighted factors to minimize the sidelobe more precisely so as to get a better suppression effect.
Multipath environments and electromagnetic interference are very common in RADAR measurement set-up and give rise to severe problem in accuracy of measurement . The challenges for conventional outdoor target detection in the low frequency region of RADAR operation the problems of clutter, multipath & interference are more severe . A targets RCS represents the amount of energy reﬂected from the target toward the receiver as a function of the target aspect with respect to the transmitter receiver pair. It is well known that this function is rapidly changing as a function of the target aspect . Some latest technological methods like DSSS, OFDM (Orthogonal Frequency Division Multiplexing), MIMO (multiple-input multiple- output) can be utilized in the open range measurement set-up [7, 8].
Several waveform design options for a rainfall measurement mode for the RA-2 Radar Altimeter due to fly on E SA ’s EN VIS AT mission (ESTEC Contract No. 10882/94/NL) have been investigated. The basic task was to develop a feasible operational rain mode with the least impact to the existing original altimeter design. The range sidelobe level requirement for a full deramp mode o f operation is o f approximately -50 dB, but only for the surface return whose time position is accurately known through the altimeter tracking. A full deramp technique simulation is described, which predicts the rain and Earth's surface power levels taking into account the waveform and range sidelobe reduction processing technique employed. The results o f these simulations show that a combination o f amplitude and phase weighting schemes can meet the sidelobe level specification at the expense of a signal-to-noise ratio loss and signal distortion.
A matched ﬁlter is designed to maximise the response of a linear system to particular known signal. Fig. 2.9 shows the basic block diagram of a matched ﬁlter radarsystem. The transmitted waveform is generated by a signal generator designated as s(t). The signal output from s(t) is ampliﬁed, fed to antenna, radiated, reﬂected from a target and return to receiver. The output of receiver is fed into the matched ﬁlter after suitable ampliﬁcation. The matched ﬁlter impulse response, h(t), is simply a scaled, time reversed and delayed form of the input signal. The shape of the impulse response is related to the signal and therefore matched to the input. The matched ﬁlter has the property of being able to detect the signal even in the presence of noise. It yields a higher output peaksignal to mean noise power ratio for the input than for any other signal shape with the same energy content.
Networks of weatherradar stations offer unique possibili- ties to analyse animal movement at large spatial scales and at a flyway-wide level, enhancing our ability to understand general movement patterns and make predictions (Shamoun- Baranes et al. 2014, Kelly and Horton 2016, Bauer et al. 2017), especially in light of global environmental changes that are affecting migrating birds (Cox 2010, Møller et al. 2010, Tomotani et al. 2018). Despite these possibilities, the use of biological information gathered by continental net- works of weatherradar stations remains limited in Europe due to technical challenges in accessing and processing the large volume of data produced by radars, restrictions on data usage by national weather services, and a lack of international scientific cooperation and standardization of available data across Europe. Recent advances in algorithms to process the data (Dokter et al. 2011, Sheldon et al. 2013), as well as computational power, has, for the first time, made it fea- sible to explore the biological information that is registered by weather radars on continental scales. Through several initiatives the data has also become more accessible. In the US the long-term national archive of NEXRAD weather data