2016/1

Browse

Now showing 1 - 5 of 28

An Iterative Design with Variable Step Prototype Filter for Cosine Modulated Filter Bank
(Společnost pro radioelektronické inženýrství, 2016-04) Chris Prema, Samuel; Dasgupta, Shubra Kankar
A systematic and self controlled prototype filter design approach for multichannel Cosine Modulated Near Perfect Reconstruction (NPR) filter bank is proposed in this paper. The primary goal is to design a prototype filter with enhanced performance i.e., minimum amplitude distortion and aliasing error. This algorithm approximates 3dB cutoff frequency very close to π/2M. This is achieved by selecting suitable step size which is a function of transition width. If the selection of step size is too fine, the objective function oscillates. Whereas, if step size is coarse, 3dB cutoff frequency will not be close to π/2M. This will degrade the overall performance of the prototype filter. Thus by choosing the step size as a function of transition width and varying the step size from coarser to finer level, the minimum amplitude distortion and aliasing error can be definitely achieved. The proposed filter is designed using two input parameters: number of subbands M and attenuation A and all other system parameters are derived from it to avoid heuristic inputs. Simulation results indicate better performance with reference to algorithms existing in literature. In addition, the design approach is systematic and self controlled.
A Novel Data Association Method for Frequency Based MIMO Systems
(Společnost pro radioelektronické inženýrství, 2016-04) Kalkan, Yilmaz
Whenever more than one target exist, the most important problem is associating the received signals to the correct targets. This problem appears for all multiple target applications such as multiple target tracking and it is known as "Data Association". For frequency-based systems, Multiple-Input Multiple-Output (MIMO) configuration together with the frequency diversity of the system enable us to determine the number of moving targets by using the Doppler frequencies. These frequencies include all relevant information about the location, velocity and direction of the targets and hence, they can be used efficiently to estimate the other unknown target parameters.
Single Tree Vegetation Depth Estimation Tool for Satellite Services Link Design
(Společnost pro radioelektronické inženýrství, 2016-04) Hasirci, Zeynep; Cavdar, I. Hakki; Ozturk, Mehmet
Attenuation caused by tree shadowing is an important factor for describing the propagation channel of satellite services. Thus, vegetation effects should be determined by experimental studies or empirical formulations. In this study, tree types in the Black Sea Region of Turkey are classified based on their geometrical shapes into four groups such as conic, ellipsoid, spherical and hemispherical. The variations of the vegetation depth according to different tree shapes are calculated with ray tracing method. It is showed that different geometrical shapes have different vegetation depths even if they have same foliage volume for different elevation angles. The proposed method is validated with the related literature in terms of average single tree attenuation. On the other hand, due to decrease system requirements (speed, memory usage etc.) of ray tracing method, an artificial neural network is proposed as an alternative. A graphical user interface is created for the above processes in MATLAB environment named vegetation depth estimation tool (VdET).
Box-Particle Implementation and Comparison of Cardinalized Probability Hypothesis Density Filter
(Společnost pro radioelektronické inženýrství, 2016-04) Song, Li-ping; Liang, Meng; Ji, Hong-bing
This paper develops a box-particle implementation of cardinalized probability hypothesis density filter to track multiple targets and estimate the unknown number of targets. A box particle is a random sample that occupies a small and controllable rectangular region of nonzero volume in the target state space. In box-particle filter the huge number of traditional point observations is instead by a remarkably reduced number of interval measurements. It decreases the number of particles significantly and reduces the runtime considerably. The proposed algorithm based on box-particle is able to reach a similar accuracy to a Sequential Monte Carlo cardinalized probability hypothesis density (SMC-CPHD) filter with much less computational costs. Not only does it propagates the PHD, but also propagates the cardinality distribution of target number. Therefore, it generates more accurate and stable instantaneous estimates of target number as well as target state than the box-particle probability hypothesis density (BP-PHD) filter does especially in dense clutter environment. Comparison and analysis based on the simulations in different probability of detection and different clutter rate have been done. The effectiveness and reliability of the proposed algorithm are verified by the simulation results.
A Novel System for Non-Invasive Method of Animal Tracking and Classification in Designated Area Using Intelligent Camera System
(Společnost pro radioelektronické inženýrství, 2016-04) Matuska, Slavomir; Hudec, Robert; Kamencay, Patrik; Benco, Miroslav; Radilova, Martina
This paper proposed a novel system for non-invasive method of animal tracking and classification in designated area. The system is based on intelligent devices with cameras, which are situated in a designated area and a main computing unit (MCU) acting as a system master. Intelligent devices track animals and then send data to MCU to evaluation. The main purpose of this system is detection and classification of moving animals in a designated area and then creation of migration corridors of wild animals. In the intelligent devices, background subtraction method and CAMShift algorithm are used to detect and track animals in the scene. Then, visual descriptors are used to create representation of unknown objects. In order to achieve the best accuracy in classification, key frame extraction method is used to filtrate an object from detection module. Afterwards, Support Vector Machine is used to classify unknown moving animals.

Browse

Recent Submissions