An Efficient Algorithm for the Radar Recognition of Ships on the Sea Surface

In this paper, the radar target recognition is given by projected features of frequency-diversity RCS (radar cross section). The frequency diversity means signals are collected by sweeping the frequency of the incident illumination. Initially, the frequency- diversity RCS data from targets are collected and projected onto the PCA (principal components analysis) space. The elementary recognition of targets is efficiently performed on the PCA space. To achieve well separate recognition of targets, the features of the PCA space are further projected onto the LDA (linear discriminant algorithm) space. Simulation results show that accurate results of radar target recognition can be obtained by the proposed frequency- diversity scheme. In addition, the proposed frequency-diversity scheme has good ability to tolerate noise effects in radar target recognition.

An essential characteristic of any radar target is the measure of its ability to reflect energy to the receiving antenna. The parameter used to describe this ability is the radar cross section (RCS) of the target. Stealth techniques and technologies, which affect the efficiency of early warning radar systems currently in operation, are described in ref [1]. The problem is to find countermeasures that fully integrate new requirement capabilities into the current long-range radar net and infrastructure, keeping research & development, and maintenance costs, low. The netcentric approach recently proposed in ref [2] using VHF-based multi-band radar systems is a new concept for Recognized Air Picture production based on long-range radars, which offers a powerful solution for the surveillance of large areas at a reasonable cost. Target RCS behaviour, fluctuations due to aspect angle and frequency are of particular interest in these systems, because the antenna beam positioning of the "VHF", "L" (or "S") and "X" band radars should be synchronised in azimuth and elevation. Targets could change orientation, course and kinematical characteristics during the time required for beam positioning. Advance knowledge and accurate prediction of target RCS is therefore desirable in order to design and develop efficient (robust) discrimination algorithms for the optimisation of target detection, tracking and non-cooperative target recognition. This paper reviews the target RCS aspects in current radar systems of interest, and examines RCS behaviour for use in future multi-band radar system applications.

A measurement method and system based on conventional radars has been proposed to address the problem of radar cross-section (RCS) measurement of ship targets in open seas. Calibration errors for RCS measurement on ground plane range (GPR) are analysed, and the error elimination method and accurate RCS calculation formula are given. An RCS measurement system is designed by improving conventional radars. These systems measure two Luneburg lens reflectors of different sizes on the lake. The average RCS of the big Luneburg lens reflector is obtained, and the error is calculated to be 2.73% compared with its actual value, which verifies the rationality of the proposed measurement method and system. The system measures the RCS of a ship in the open sea and obtains its circumferential RCS distribution. The effect of the error in the ship's position calculation on the RCS measurement results is analysed, and the uncertainty range of the error is obtained.

A new and simple method of interpreting the of the backscatter radar cross section (RCS) from ocean waves of individual wavelength is presented. Using the Kirchhoff scattering (Physical Optics) theory, the RCS from the ambient waveheight spectrum is first computed as a function of the wavenumber. Differentiating this cumulative RCS yields the distribution function of the RCS from ocean waves of different wavenumbers. The present study shows, as a general feature, that the maximum RCS contribution tends to come from the ocean waves of wavenumbers closer to the Bragg wavenumber as the radar incidence angle increases. Under low wind speeds (/spl sim/2 m/s) and at incidence angles between 200 and 45/spl deg/, the dominant RCS contribution at both C-band and L-band comes from ocean waves of wavenumbers close to the Bragg wavenumber. Under intermediate to high wind speeds (/spl sim/10-15 m/s) and at the small incidence angle of 200, the radar backscatter is dominated by long ocean waves with little contribution from the Bragg waves at both bands. At the intermediate incidence angles (/spl sim/35/spl deg/ to 45/spl deg/), the RCS distribution is centered at wavenumbers near the Bragg wavenumber at all wind speeds from 2 m/s to 15 m/s. These features are more pronounced at C-band than at L-band. The present theory is based on the Kirchhoff scattering model, and as such, its validity may also be limited to a range of small to intermediate incidence angles where the polarization effect is not very significant. The RCS distribution is evaluated for the ambient sea surface and the surface perturbed by varying currents induced by the interaction between the current and bottom topography.

A straightforward and accurate method of calibrating field measurements of radar crosssection (RCS) is presented. Using a hand-held, square trihedral comer reflector camed onboard a helicopter it is possible to calibrate field measurements of RCS .against the known freespace value of the comer reflector to an accuracy of about fldB. INTRODUCTlON Radar cross-section (RCS) measurements of targets at aea are frequently subject to variations arising from interference between the radio frequency (RF) waves propagated over direct paths from the transmitter to the target and then back to the receiver, and those returning to the receiver after reflection(s) from the sea surface. Such multipath effects are most prominent under low sea state conditions and hamper the measurement of the free-space RCS needed for system analyses, simulation and mathematical modelling. Figure 1 illustrates the variations in a target's radar echo that can occur when the illuminating RF waves are propagated over a smooth sea surface.

This paper realizes the Radar Cross Section (RCS) of an anti-ship missile at different frequencies and heights on sea surface. The sea surface is considered as a large conducting plate. All the simulations are done by using LE-PO method available in FEKO 14.0 simulation software.

Aiming at the problems of traditional sparse representation algorithms that has a large amount of calculation and low recognition rate under the condition of small samples, a sparse representation classification algorithm (PCA-SRC) combined with principal component feature extraction is proposed. The algorithm can maintain the manifold structure of the finger vein space, solve the singularity problem of the characteristic equation matrix in small sample image recognition. Principal component analysis (PCA) is used to reduce the dimension of the feature matrix to eliminate redundancy. Construct an over-complete dictionary of sparse representation, and design a classifier based on sparse representation to get the classification results. The finger vein feature matching experiment is carried out using the self-built finger vein data set, and the algorithm proposed in this paper is compared with the traditional algorithm. The results show that the recognition rate of the four algorithms can reach more than 94%. The fusion of PCA and SRC speeds up the recognition speed. The recognition rate is higher than the traditional algorithm under different numbers of training samples, and the best recognition rate is 100%. The algorithm has high finger vein recognition rate and low dependence on the training sample set, which provides a new idea for the realization of finger vein recognition.

Relevant with some important subjects like target recognition, sensor fusion systems can be considered as one of the main issues highlighting here. Environmental condition, target characteristic and sensor efficiency are three parameters which can impress on sensor value in target recognition so for recognizing targets, a group of sensors which have more recognition rates, must be selected intelligently. Utilizing many sensors to acquire the highest object recognition rate would have extra cost and decrease energy of mobile sensors rapidly. Therefore make a tradeoff between sensor numbers and object recognition rate would be imperatively. This paper attempts to design a multi objective optimization service by using optimization algorithm and neural network. This service specifies highest recognition rate for each distinct sensor numbers. We propose multi objective optimization algorithm to help accessing the best sensory configuration for a definite environment regarding to the environmental conditions, sensors performance, and object features. Our multi objective optimization algorithm has two functions. Genetic algorithm is used to perform as one of functions to specify object recognition rates of each sensor group. Neural network is used to perform as fitness function of each genetic algorithm chromosomes. Another function is sensor numbers determinant. Highest recognition rate and lowest sensor numbers are two objects which multi objective optimization algorithm wants to make a balance between them. We define 500 different scenarios for 6 different sensors in different conditions. Object recognition rate of each sensor is collected. These rates are used for neural networks training process. By defining new scenario and run multi objective optimization algorithm in this scenario, this algorithm makes a Pareto front between sensor numbers and object recognition rate. Finally this algorithm finds, by distinct numbers of sensor, which sensors by which recognition ability must be used to reach the highest recognition rate.

This paper presents the characteristics of radio wave scattering from rough sea surfaces in Ka-band. The physical optics method, which is one of the high frequency approximation algorithm, is employed to calculate radar cross section of electromagnetic simulation models of rough sea surfaces with Ship-EDF software. Rough sea surfaces are divided into six levels according to the wind speed over them. The simulation results show the effection that wind speed, the grazinng angle and frequency of radio wave have on the monostatic radar cross section of rough sea surfaces under the conditions of both horizontal and vertical polarization. With the increase of the roughness of sea surfaces and grazing angle of radio wave, the radar cross section value of rough sea surfaces increase. The frequencies of Ka-band have little contribution to the radar cross section value.

In this paper, Physical Optics (PO) is used to calculate the electromagnetic scattering of electrically large complex target and background. Two dimensional surface is simulated by using spectral method based on PM spectrum. Then, the target and rough surface model are combined together to be a very accurate composite model, after which surface mesh is generated with the information of incidence frequency. Shading judgment and the radar cross section (RCS) of the composite model is calculated. The results of the validation model show good agreement with the FEKO (MoM) method. Finally, the bistatic RCS of the actual large rough sea surface with missile are given.

Image recognition is a feature extraction and pattern matching technique to classify various objectives in computer science. In theory, it mainly relies on the images with distinguishable features as a good starting point. Each image has its own unique characteristics, and image features can be categorized into color features, texture features, and shape features etc. Therefore, using modern recognition techniques, we extract the image features through various algorithms to search for images with high similarities. There are many image recognition algorithms, and some of them are with high recognition rate while others are robust. But not all algorithms can be unconditionally implemented without adjusting to the real situation. In this paper, we introduce the classification algorithm based on Support Vector Machine (SVM) and the feature extraction method based on Principal Components Analysis (PCA). We employ the feature extraction algorithm to characterize facial features and recognize faces by comparing them to those stored in the training data set. Finally, we show the applications of feature extraction and classification algorithms in character recognition. The real characters printed on a cord are preprocessed by PCA, and then classified and identified by SVM with a good recognition rate if properly processed.

Metabolic footprinting has been applied as a non-invasive approach to study the behaviour and responses of cultured cells to a range of genetic and environmental perturbations. Gas chromatography interfaced with time-of-flight mass spectrometry (GC-ToF-MS) has become a powerful tool for the analysis of metabolome-derived samples. Generally, two data analysis strategies are used to interrogate and understand the biological patterns within the multi-dimensional data. The first strategy, a commoner one, uses multivariate analysis after chromatographic and mass spectral deconvolution, and the second strategy directly employs multivariate analysis of non-deconvoluted data. Here, two strategies have been assessed for the separation and classification of metabolic footprints (exometabolomes) of two strains of Candida albicans grown on three different carbon sources (glycerol, glucose and galactose). We describe a semi-automated approach that simultaneously processes all samples using the chromatographic dimension data with principal components analysis (PCA), which can include data pre-processing before PCA analysis. The preprocessed and non-deconvoluted total ion chromatogram (TIC) data showed good separation of classes defined by growth on different carbon sources and when comparing the two strains grown on the same carbon source separation was achieved for strains grown on glucose and glycerol after preprocessing. The discrimination observed is greater for preprocessed and non-deconvoluted TIC data than for that of preprocessed and non-deconvoluted single ion chromatogram data. The results from the proposed approach with those produced by MZmine were compared. The results from MZmine data depicted separations in PCA space according to carbon source, but no separation was seen when studying strains grown on the same carbon source. Our research showed that the non-deconvoluted strategy is suitable for fast comparison of large sets of GC-MS data although it will not directly provide biological information. The non-deconvoluted strategy can avoid problems of analyzing complex samples using deconvolution software.

The application of species distribution models (SDMs) to areas outside of where a model was created allows informed decisions across large spatial scales, yet transferability remains a challenge in ecological modeling. We examined how regional variation in animal‐environment relationships influenced model transferability for Canada lynx (Lynx canadensis), with an additional conservation aim of modeling lynx habitat across the northwestern United States. Simultaneously, we explored the effect of sample size from GPS data on SDM model performance and transferability. We used data from three geographically distinct Canada lynx populations in Washington (n = 17 individuals), Montana (n = 66), and Wyoming (n = 10) from 1996 to 2015. We assessed regional variation in lynx‐environment relationships between these three populations using principal components analysis (PCA). We used ensemble modeling to develop SDMs for each population and all populations combined and assessed model prediction and transferability for each model scenario using withheld data and an extensive independent dataset (n = 650). Finally, we examined GPS data efficiency by testing models created with sample sizes of 5%–100% of the original datasets. PCA results indicated some differences in environmental characteristics between populations; models created from individual populations showed differential transferability based on the populations' similarity in PCA space. Despite population differences, a single model created from all populations performed as well, or better, than each individual population. Model performance was mostly insensitive to GPS sample size, with a plateau in predictive ability reached at ~30% of the total GPS dataset when initial sample size was large. Based on these results, we generated well‐validated spatial predictions of Canada lynx distribution across a large portion of the species' southern range, with precipitation and temperature the primary environmental predictors in the model. We also demonstrated substantial redundancy in our large GPS dataset, with predictive performance insensitive to sample sizes above 30% of the original.

In this paper a simple and efficient numerical procedure for treating problems of radar cross section (RCS) prediction of arbitrarily shaped Perfect Electric Conductor (PEC) objects is developed using Micro-ElectroMechanicalSystems(MEMS). Surface Acoustic Wave (SAW) devices perform a radar measurement using impulse response of the SAW transponder via a high frequency electromagnetic radio link. By analyzing the time delay between the backscattered pulses with different time delays the RCS can be estimated from the SAW transponder. It is found from the simulation that the complexity of prediction can be reduced using proposed algorithm.

Toxic plants have been a major threat to public health in China. However, identification and tracing of poisoned species with traditional methods are unreliable due to the destruction of plant morphology by cooking and chewing. DNA barcoding is independent of environmental factors and morphological limitations, making it a powerful tool to accurately identify species. In our study, a total of 83 materials from 26 genera and 31 species of 13 families were collected and 13 plant materials were subjected to simulated gastric fluid digestion. Four markers (rbcL, trnH-psbA, matK, and ITS) were amplified and sequenced for all untreated and mock-digested samples. The effectiveness of DNA barcoding for the identification of toxic plants was assessed using Basic Local Alignment Search Tool (BLAST) method, PWG-Distance method, and Tree-Building (NJ) method. Except for the matK region, the amplification success rate of the remaining three regions was high, but the sequencing of trnH-psbA and ITS was less satisfactory. Meanwhile, matK was prone to be more difficult to amplify and sequence because of simulated gastric fluid. Among the three methods applied, BLAST method showed lower recognition rates, while PWG-Distance and Tree-Building methods showed little difference in recognition rates. Overall, ITS had the highest recognition rate among individual loci. Among the combined loci, rbcL + ITS had the highest species recognition rate. However, the ITS region may not be suitable for DNA analysis of gastric contents and the combination of loci does not significantly improve species resolution. In addition, identification of species to the genus level is sufficient to aid in the clinical management of most poisoning events. Considering primer versatility, DNA sequence quality, species identification ability, experimental cost and speed of analysis, we recommend rbcL as the best single marker for clinical identification and also suggest the BLAST method for analysis. Our current results suggest that DNA barcoding can rapidly identify and trace toxic species and has great potential for clinical applications. In addition, we suggest the creation of a proprietary database containing morphological, toxicological and molecular information to better apply DNA barcoding technology in clinical diagnostics.