Radar target recognition by fuzzy logic software

Introduction pattern recognition pr deals with the problem of classifying set of patterns or objects obtained from the measurements of physical or mental processes into. Radar target s hrrp always has some information redundancy, and is easily to be affected by noise or lack of separability. We develop a dual doppler radar system for fall detection. Small scale fading is considered in our fuzzy logic system fls design fls with threeantecedents, f3 to compute the likelihood to be a ch for each radar sensor rs at the first stage. A novel bayesian compressive sensing bcs based method for multiview radar automatic target recognition ratr is presented.

Real time image segmentation for face detection based on. Charles norsworthy, kristen nock, elizabeth gilmour, u. Expert users of the wsr88d data provided the truth data sets used to optimize the algorithm performances. An algorithm for automatic target recognition using passive radar and an ekf for. A fuzzylogic based non cooperative target recognition. In my code first i am trying to detect edge and then to remove noise. Edge detection based on fuzzy logic sagar samant, mitali salvi, mohammed husein sabuwala dcvx abstract edge detection is an essential feature of digital image processing. Fuzzy classification logic was employed to detect terrain features. Arabic voice recognition using fuzzy logic and neural network. Radar automatic target recognition based on sequential. For the spol data sets, the polarimetric variables are input into a fuzzy logic polarimetric identification pid algorithm to determine the type of radar echo return that is present. Mlp neural network, arabic voice recognition, wavelet transform and fuzzy logic. Target classification and pattern recognition using microdoppler. In this paper, a novel fuzzy optimal transformation fot method for radar target recognition using highresolution range profile hrrp is proposed.

The fuzzy logic edge detection can performed by using fis. Fuzzy logic has proved to be an efficient tool in problems that also have to deal with decisionmaking for classification problems 2830, hence it. Jul 14, 2015 fuzzy logic has been widely applied to quality control algorithms for radar data to remove ground clutter and anomalous propagation ap e. The objective of this paper is to present a method of target recognition based on the fuzzy logic principles applied to conventional and multifunction radars. Instead of employing traditional twostate logic, it is suggested that the radar signal should be allocated in terms of threshold levels into fuzzy sets with its membership functions being related to the information extracted and the environment.

It is reported that more than one third of seniors 65 and older fall each year in the united states. Processing directed towards the above application areas includes advances in waveform design, adaptive. This report considers a study of radar target modelling based on inverse synthetic aperture radar isar measurements of generic aircraft. Objects targets within a search volume will reflect portions of this energy radar returns or echoes back to the radar. Data fusion approach for tracking systems based on fuzzy logic. Pdf fuzzy fusion system for radar target recognition. Fall detection using doppler radar and classifier fusion.

Mahmood2, computer department, college of electronic. Neurofuzzy logic for partsbased reasoning about complex scenes in remotely sensed data paper 1142316 authors. This paper proposes a novel radar target recognition method using hrrp, namely fuzzy optimal transformation, which maximizes the betweenclass distance by selecting a set of optimal congregating centers in subprofile space while preserving the local structure using fuzzy optimal transformation. A fuzzy logic approach to target tracking 311 the following parts of this section give the details in the design of this fuzzy controller from a fuzzy partition of the universe of discourse of variables 6k,8odjk to defuzzification. Fuzzy sets in pattern recognition and machine intelligence. This is our project design entitled target tracking and shooting using fuzzy logic. The product test areas include aircraft systems manned and unmanned, airborne technology, propulsion, flight test and engineering, avionics design and. O abstract in this paper fuzzy based edge detection algorithm is developed. Motivated by the unique character of fuzzy logic system, simultaneously handling numerical data and linguistic knowledge, and the promising knowledgebased approach, we propose an flsbased approach to sar atr. Algorithms for pattern recognition based on artificial intelligence techniques are also used, neural nets and fuzzy logic 46. Fuzzy set theory has been extensively used in clustering problems where the task is to provide class. In the proposed algorithm, edginess at each pixel of a digital image is calculated using three 3 linear spatial filters i. Automatic target recognition atr generally refers to the autonomous or aided target detection and recognition by computer processing of data from a variety of sensors such as forward looking infrared flir, synthetic aperture radar sar, inverse synthetic aperture radar isar, laser radar ladar, millimeter wave mmw radar, multispectral.

Edge detection using fuzzy logic matlab answers matlab. Modelbased object recognition using laser radar range. The sensor system for path finding consists of machine vision and laser radar. The universal software radio peripheral usrp ni2920, a software defined transceiver so far mainly used in software defined radio applications, is adopted in this work to design a high resolution lband software defined radar system. The correct classification rate was about 93% for used target subjects. Edge detection part is working,but noise removal part have not worked.

Fuzzy fusion system for radar target recognition imen jdey, abdelmalek toumi, ali. The cmd algorithm uses a fuzzy logic approach to combine the information from feature fields into a single decisionmaking field. Automatic target recognition in laser radar imagery. The experiments also show that the type2 fuzzy logic based svms fusion model is better than the type1 based svm fusion model in general. The first step is to calculate feature fields from the reflectivity, velocity, and spectrum width fields of the radar data. Automatic target recognition for sar images based on fuzzy. Fuzzy recognition method for radar target based on kpca. In addition to this some of the soft computing tools are used for detection of radar target automatically based on some algorithms named as ann, rnn, neurofuzzy and genetic algorithms. Fuzzy logic has been widely applied to quality control algorithms for radar data to remove ground clutter and anomalous propagation ap e. An inertial measurement unit imu is used for detecting the tilt of the vehicle, and a speed sensor is used to find the travel speed. Fuzzy clustering in radar sensor networks for target detection. Detection and tracking for radar simulation using matlab. Arabic voice recognition using fuzzy logic and neural network 1 lubna eljawad, 1 rami aljamaeen, 2 mutasem k. Target tracking using fuzzy logic with shape recognition.

In case of singlehop routing, fuzzy cmeans with singular value decompositionqr fcmsvdqr approach is proposed to decide the final ch. It is an approach used most frequently in image segmentation based on abrupt changes in intensity. Multimode radar target detection and recognition using neural. The radar system generates a specific doppler signature for each human activity which is then categorized by a set of classifiers as fall or nonfall. Neural networks, fuzzy logic, case based reasoning. Fuzzy logic based digital image edge detection aborisade, d. Steps of proposed system to accomplish the task of edge detection using fuzzy logic. Radar target recognition based on svca to deal with the targetaspect sensitivity of hrrp, a large number of templates must be stored as library data in training phase, and searching procedure is executed along the aspect axis for an optimal matching in test phase. In addition, the highdimensional hrrps may result in computational. In this paper, using the advantage of kernel methods for solving nonlinear forms, we propose a radar target s hrrp feature extraction method based on kernel principal component analysis kpca and a radar target fuzzy recognition method based on support vector data. High range resolution profiles as motioninvariant features for moving ground targets identification in sarbased automatic target recognition. A closedloop active target recognition radar system is extended to the case of a centralized cognitive radar network, in which a generalized likelihood ratio glr based sequential hypothesis testing sht framework is employed. Radar target recognition based on high resolution range pro les hrrp has received much attention in recent years. Automatic target recognition systems mostly employ fusion strategies for this aim.

Automatic target recognition atr is the ability for an algorithm or device to recognize targets or other objects based on data obtained from sensors target recognition was initially done by using an audible representation of the received signal, where a trained operator who would decipher that sound to classify the target illuminated by the radar. The importance of radar target identification is widely recognized and it has become one of the major concerns in radar surveillance and homeland security applications. Radar hrrp modeling using dynamic system for radar. Time resource management of oar based on fuzzy logic priority for. Naval air warfare center aircraft division patuxent river, md naval air warfare center aircraft division.

The goal of this method is to maximize the betweenclass distance, while preserving the withinclass structure. Automated target recognition software has been designed to perform image segmentation and scene analysis. Input fuzzy sets for 2x2 output fuzzy sets for 2x2 black 0 0 60 90 edge0 0 80 white80 180 255 255 nonedge82 255 255 iv. Radar system use modulated waveforms and directive antennas to transmit electromagnetic energy into a specific volume in space to search for targets. Alsmadi, 2 ibrahim almarashdeh, 3 hayam abouelmagd, 3 sanaa alsmadi, 4 firas haddad, 4 raed a.

Finally a software package was also produced which would simulate the operation of a ppi. Radar hrrp modeling using dynamic system for radar target. Extended target recognition in cognitive radar networks. Target classification based on a combination of possibility and. Fuzzy partition and membersidp functions first, the universe of discourse of the variables 8k and. Mar 02, 2010 this is our project design entitled target tracking and shooting using fuzzy logic. Algorithms for pattern recognition based on artificial intelligence techniques. After a presentation of the parameters which can be delivered by signal and data processing, the paper gives a description of an. Algorithms for pattern recognition based on artificial intelligence techniques are also used, neural nets and fuzzy logic 10, 11, 12. The empirical evidence of the effectiveness of this approach makes it of the main current directions in target recognition research. The objective of the project is to simulate the real time radar detection and tracking operations using matlab software. Moving target detection for frequency agility radar by sparse reconstruction.

Intelligent target recognition based on wavelet adaptive. In particular, we discuss our ongoing development of algorithms and software that. Radar target modelling based on rcs measurements when simulating target seekers, there is a great need for computationally efficient, target models. Target detection is an important issue for all radar systems. High resolution range profile hrrp is being known as one of the most powerful tools for radar target recognition 14. Generally, techniques based on fast fourier transform fft are used in pulsedoppler radars, radars for tracking mobile targets, vibration analysis on laser radar systems 4. On last generation radars, pattern recognition is used to classify known echos in different categories. The performance of the developed system has been evaluated in noisy radar target echo signals. The problem with a fuzzy system is it is difficult to deal w ith too many features, membership functions, andor rules. It has a feature of shape recognition using objects boundary signatures. Target classification and pattern recognition using microdoppler radar signatures. Simulation and modeling, domain analysis and software reuse, artificial neural networks, fuzzy logic, software testing and reliability. A fuzzy fusion system is constructed to combine multiple classifiers in.

Naval air warfare center aircraft division patuxent river. Fuzzy overlap refers to how fuzzy the boundaries between clusters are, that is the number of data points that have significant membership in more than one cluster. When cloud cover or other optical obstructions prevent camera imaging over a target area, sar continue reading. A fuzzy logic enhanced kalman filter was developed to fuse the information from machine vision, laser radar, imu, and speed sensor. Fuzzy set theory is the oldest and most widely reported component ofpresentdaysoft computing or computational intelligence, which deals with the design of. Neural networks, are highly suited for large amounts of features and classes. Therefore some range cfar constant false alarm rate procedures will be discussed which can be applied, especially in multiple target situations, to avoid masking. This report considers a study of radar target modelling based on inverse synthetic aperture radar isar measurements of generic. I am trying to detect edge of gray scale image using fuzzy logic. The feature fields cmd uses for the singlepolarization case are 1 texture of reflectivity, 2 the spinchange of the reflectivity variable as defined by steiner and smith 2002, hereafter spin, and 3 cpa, with the addition of 4 texture of differential. Fuzzy cmeans clustering matlab fcm mathworks united.

The approach presented here is based on implementation of genetic algorithms and fuzzy logic in training the proposed hybrid architecture. Introduction a coherent laser radar can produce range, intensity or doppler images in 3d pulsed imager mode. For coping with the multiple target tracking in the pre sence of complex. Multiview radar target recognition based on bayesian. We address the problem of adaptive waveform design for extended target recognition in cognitive radar networks. Pal fuzzy sets and systems 156 2005 3886 383 suggested by zadeh. Pdf fuzzy fusion system for radar target recognition i. High resolution software defined radar system for target.

This fuzzy logic plays a basic role in various aspects of the human thought process. Representative arts include electronic displays, circuits, semiconductors, computer hardware and software, fuzzy logic decision trees, avionics, military hardware, infrared sensors, pacemakers, pattern recognition systems, optical systems, operating systems, automated biological analysis systems, laserbased measuring systems, ring laser gyros. The enhanced available bandwidth, due to the gigabit ethernet interface, is exploited to obtain a higher slantrange resolution with respect to the existing. Additionally some new results are discussed for target recognition systems which have been developed for.

To meet this challenge, a fuzzy logic based algorithm has been developed. Fuzzy inference systems a fuzzy inference system fis is a system that uses fuzzy set theory to map inputs features in the case of fuzzy cla. Pdf automatic target recognition in laser radar imagery. The test results showed that this system was effective in detecting real radar target echo signals. Automatic target recognition, laser radar, modelbased object recognition. Fuzzy logic has been used in numerous applications such as facial pattern recognition, air conditioners, washing machines, vacuum cleaners, antiskid braking systems, transmission systems, control of subway systems and unmanned helicopters, knowledgebased systems for multiobjective optimization of power systems, weather forecasting systems. The problem of target classification is addressed in the bayesian framework as an. Dawwd1, computer department, college of engineering university of mosul dr. Synthetic aperture radar sar systems generate two dimensional images of a target area using rf energy as opposed to light waves used by cameras. For tactical radars it is important to consider target tracking. The overcomplete dictionary is constructed by all the training sets. Colinradar target recognition by fuzzy logic, ieee aerospace.

In this paper, the recognition combination will be presented using fuzzy fusion based on three classifiers. Blake ruprecht, charlie veal, al cannaday, derek anderson, univ. Device and circuits development for high power solid state transmitters and active aperture phasedarray radars combining quasioptical power. Signal processing, sensorinformation fusion, and target. Ramirez and cluckie, 2008, for hydrometeor classification using polarimetric measurements e. Ieee transactions on aerospace and electronic systems, vol. In addition to this some of the soft computing tools are used for detection of radar target automatically based on some algorithms named as ann, rnn, neuro fuzzy and genetic algorithms. Neuro fuzzy logic for partsbased reasoning about complex scenes in remotely sensed data paper 1142316 authors. Seventh acis international conference on software engineering, artificial intelligence, networking. Mirador is an onoff road, remote control, multisensor system. Fuzzy inference system based edge detection in images. Data fusion approach for tracking systems based on fuzzy.

An automated target recognition technique for image. Automatic radar target recognition system at thz frequency. Theory, algorithms and software, wiley interscience, 2001. Solid state conformal radar, sea clutter, noncooperative target recognition, mmic, cadcimcam and adaptive antennas and processing.