Narrowband elliptic bandpass filter using dual-mode microstrip square loop resonator for WiMax application

In this paper, a narrowband bandpass filter using dual-mode microstrip square loop resonator is proposed. This structure has a 5.1% fractional bandwidth at 2.3GHz. By using some simple techniques, the optimum results will be achieved. The dual-mode resonator will be produced by adding a square patch inside the loop resonator. The simulation and measurement results are also presented. The filter is fabricated on RT/Duroid 6010 substrate having a relative dielectric constant of 10.2 and 0.635 mm thickness. The final dimension is measured at 19.65 mm 19.65 mm. The minimum measured insertion loss is 1.68 dB and return loss obtained is better than -20 dB, where experimental results and simulated values are in good agreement

Low side lobe level multilayer antenna for wireless applications

A low cost and easy fabrication multilayer antenna for wireless applications was presented to cover the industrial, scientific, and medical ISM band of (5.725-5.875) GHz with a gain of 11.7 dB. The antenna was composed of a feeding patch fabricated on a Rogers RT/Duroid 5880 substrate, and three superstrate layers of Rogers RO3006 were located above the feeding patch at a specific height for each layer. The superstrate layers were added to enhance the bandwidth and gain of the antenna and reduce its side-lobe level and return loss. The simulated and measured results of the operating frequency, return loss, bandwidth, and gain for the antenna were presented. CST Microwave Studio was used in this design's simulation

Artificial neural network approach in radar target classification

Problem statement: This study unveils the potential and utilization of Neural Network (NN) in radar applications for target classification. The radar system under test is a special of it kinds and known as Forward Scattering Radar (FSR). In this study the target is a ground vehicle which is represented by typical public road transport. The features from raw radar signal were extracted manually prior to classification process using Neural Network (NN). Features given to the proposed network model are identified through radar theoretical analysis. Multi-Layer Perceptron (MLP) back-propagation neural network trained with three back-propagation algorithm was implemented and analyzed. In NN classifier, the unknown target is sent to the network trained by the known targets to attain the accurate output. Approach: Two types of classifications were analyzed. The first one is to classify the exact type of vehicle, four vehicle types were selected. The second objective is to grouped vehicle into their categories. The proposed NN architecture is compared to the K Nearest Neighbor classifier and the performance is evaluated. Results: Based on the results, the proposed NN provides a higher percentage of successful classification than the KNN classifier. Conclusion/Recommendation: The result presented here show that NN can be effectively employed in radar classification applications

A ground based circular synthetic aperture radar

Detecting on-the-ground objects is a subject of interest for some applications. Typical example is foreign object detection on the airport runway. In response to this demand, a ground-based Circular Synthetic Aperture Radar (CSAR) system is proposed and explained in the paper. In the proposed CSAR, the antennas represent a circular movement trajectory. Wideband Linear Frequency (LFM) chirps were used for transmission. A simulation model for CSAR, based on the Doppler Effect between the radar and object is developed in this paper. In addition, a processing method for object detection using correlation between image data produced by simulation and experimental data is developed. The resultant of the simulated model at each point, which represents the object's behavior in an ideal and clutter-free environment, is used as a template for object detection. Simulation and experimental results demonstrate that the proposed method is well suited in detecting small objects at different positions

RCS classification on ground moving target using LTE passive bistatic radar

Detection and location on the ground moving target are a function of dependent bistatic Radar Cross Section (RCS) and radar design parameters which in this experimental study used LTE signal as a source for passive bistatic radar (PBR). Ground moving target also can be classified in dimensions using conventional processing approaches which we performed a simulation using Computer Simulation Technology (CST) Microwave studio. The target bistatic radar cross-section will give a realistic calculation on PBR performance with the requirement of complete treatment. Three models of ground moving target are designed using Autodesk software which the models are classified as compact car, saloon car and sport utility vehicle (SUV) for size of small and medium and large respectively. The designs are for observation on the performance of RCS using a bistatic area between transmitter and receiver with the frequency transmit signal from long-term evolution (LTE) based station is 2.6 GHz and with far-field conditions. The simulation results show that largest area of ground moving target, SUV had better outcome compared to other ground moving target which reliable with Babinet’s principle, which declares a target of physical cross-sectional area is proportionate to RCS. Different cross-sectional area of transmitting signal from other ground moving target give smaller RCS which cause from the reduction area of reflected signal such as compact car according to small size and saloon car according to medium size. This might improve the sensitivity of LTE passive bistatic radar if using greater size of ground moving target for a better RCS performance

A passive forward scattering radar for detecting humans and characterizing human behaviours

Background: The capability to detect human and identify their movement is progressively important in military and security applications. Usually, most of the radar systems are active systems which it is easily to be detected by the opponent. In consequence, passive radar is set to become alternative to conventional active radar which it offers a decisive operational advantage, it could not be located. Passive radar does not emit any signals of its own which it could not be jammed. Therefore, passive radar use many different transmission sources that are sent out from various location to detect ground moving target especially human. Objective: The integrating of passive forward scattering radar that provide a lot of benefits and capable to detect human and characterize human behaviors which the radar system analysis and signal processing are using MATLAB software. Results: The radar system able to detect human and characterize the behaviors which divided into two movements, walking and running. Conclusion: This is the evolving area of research provide a more useful outcomes in detecting and characterizing the human movements specifically used the passive forward scattering radar concept of unseen by others

A multistatic circular synthethic aperture radar for small object detection

This paper introduces a ground-based Multistatic Circular Synthetic Aperture Radar (MuCSAR) used to detect small objects on the ground. The received signals have been modelled and the system prototype has been developed. The proposed signal processing is also described. An experimental investigation for Foreign Object Detection application has been analysed

A Linear Frequency Modulated bistatic radar for on-the-ground object detection

A radar system for detecting and localizing small targets on the ground is proposed in this paper. The system transmits wideband Linear Frequency Modulated pulses from ground-based transmitter. The reflected pulses will be collected simultaneously by two different ground-based receivers installed in different bistatic positions. Accurate range processing in this bistatic configuration will lead us to detect small objects like N-type connectors in several meters distances