scholarly journals A New Efficient Ordering Scheme for Sphere Detection

2011 ◽  
Vol 2011 ◽  
pp. 1-4
Author(s):  
Cao Haiyan ◽  
Li Jun
Keyword(s):  
Complexity Reduction ◽  
Qr Decomposition ◽  
Absolute Difference ◽  
Zero Forcing ◽  
Deep Impact ◽  
Low Snr ◽  
Sphere Detection ◽  
Gradient Based ◽  
Simulation Results ◽  
Forcing Solution

The decoding order has a deep impact in the complexity of sphere detection. A new ordering scheme for sphere detection is presented in this paper, which is based on SIC (serial interference canceling) and the gradient defined by the accumulated probability of the absolute difference between symbol element and the zero-forcing solution. The simulation results show that the proposed ordering scheme can achieve a significant complexity reduction, especially for high numbers of antennas and large constellation sizes in the low SNR region. Compared with sphere detection complexity under BSQR (balanced sorted QR) decomposition and GB (gradient-based) orderings atSNR=5 dB, the average number of visited nodes under our proposed ordering is reduced by almost 30% and 33% in4×416QAM system and by almost 30% and 50% reduction in6×616QAM system, respectively. For4×464QAM system, almost 75% and 80% reduction atSNR=0 dBand more than 40% and 50% reduction atSNR=5 dBcan be achieved, respectively.

FEMLIB: An Object-Oriented Framework for Optimization and Simulation

1995 ◽  
Author(s):  
Michael M. Tiller ◽  
Jonathan A. Dantzig
Keyword(s):  
Material Properties ◽  
Object Oriented ◽  
The Finite Element Method ◽  
Simulation And Optimization ◽  
Gradient Based ◽  
Level Problem ◽  
Simulation Results ◽  
Optimization And Simulation ◽  
High Level ◽  
Simulation Parameters

Abstract In this paper we discuss the design of an object-oriented framework for simulation and optimization. Although oriented around high-level problem solving, the framework defines several classes of problems and includes concrete implementations of common algorithms for solving these problems. Simulations are run by combining these algorithms, as needed, for a particular problem. Included in this framework is the capability to compute the sensitivity of simulation results to the different simulation parameters (e.g. material properties, boundary conditions, etc). This sensitivity information is valuable in performing optimization because it allows the use of gradient-based optimization algorithms. Also included in the system are many useful abstractions and implementations related to the finite element method.

Multi-Sensor N-P Criterion Fusion Detection Based on Weighting by SNR

2014 ◽  
Vol 1044-1045 ◽  
pp. 818-824
Author(s):  
Bo Fan Yang ◽  
Rui Wang ◽  
Gang Wang ◽  
Li Zhao
Keyword(s):  
Signal Detection ◽  
False Alarm ◽  
Sensor Fusion ◽  
Detection Probability ◽  
Detection System ◽  
False Alarm Probability ◽  
Radar Target ◽  
Low Snr ◽  
Simulation Results ◽  
Fusion Detection

Aiming at signal detection of radar target, concerning about on the basis of the influence of SNR on detection probability when false alarm probability is given based on N-P criterion, a kind of multi-sensor fusion detection based on SNR is put forward. It can improve system’s detection probability under the condition of required false alarm probability in the detection of low SNR signal. The simulation results show that the detection performance is significantly increased, no matter fusion detection system is composed of same sensors working in the same working point or different sensors.

INFRARED SMALL TARGET DETECTION ALGORITHM BASED ON FEATURE SALIENCE AND MULTI-FEATURES FUSION

2011 ◽  
Vol 25 (02) ◽  
pp. 299-308 ◽  
Author(s):  
ZHEN-XUE CHEN ◽  
CHENG-YUN LIU ◽  
FA-LIANG CHANG
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Absolute Difference ◽  
Small Target ◽  
Probability Of Error ◽  
Small Target Detection ◽  
Low Snr ◽  
Complex Background ◽  
Small Targets

It is an important and challenging problem to detect small targets in clutter scene and low SNR (Signal Noise Ratio) in infrared (IR) images. In order to solve this problem, a method based on feature salience is proposed for automatic detection of targets in complex background. Firstly, in this paper, the method utilizes the average absolute difference maximum (AADM) as the dissimilarity measurement between targets and background region to enhance targets. Secondly, minimum probability of error was used to build the model of feature salience. Finally, by computing the realistic degree of features, this method solves the problem of multi-feather fusion. Experimental results show that the algorithm proposed shows better performance with respect to the probability of detection. It is an effective and valuable small target detection algorithm under a complex background.

An Improved Signal Detection Technique for OFDM System

2013 ◽  
Vol 347-350 ◽  
pp. 3527-3531
Author(s):  
Xiao Hong Wang ◽  
Feng Ming Li
Keyword(s):  
Computational Complexity ◽  
Channel Estimation ◽  
Signal Detection ◽  
Detection Probability ◽  
Roc Curves ◽  
Detection Technique ◽  
Ofdm System ◽  
Low Snr ◽  
Awgn Channel ◽  
Simulation Results

In this paper a signal detection technique based on pilots which are transmitted for channel estimation in OFDM system is proposed in AWGN channel. We analyse the algorithm based on pilots and derive an improved signal detection technique. The performance is compared in terms of detection probability and ROC curves are given. The simulation results show that the improved detection technique whose computational complexity is not high can increase the precision of the detection probability at low SNR.

A Method for Tracking Dim Target Using Particle Filter

2014 ◽  
Vol 602-605 ◽  
pp. 3127-3130
Author(s):  
Hong Wei Quan ◽  
Jun Hua Li ◽  
Da Yu Huang
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Random Sampling ◽  
Unscented Kalman Filter ◽  
Numerical Technique ◽  
System Model ◽  
Low Snr ◽  
Practical Engineering ◽  
Simulation Results ◽  
Optimal Evaluation

Traditional methods encountered two serious problems in tracking dim targets. One is the nonlinearity of the system model, and other is the low SNR of measurement signals. The two problems are hardly solved simultaneously in practical engineering applications. The particle filter is a recursive numerical technique which uses random sampling to approximate the optimal evaluation to target tracking problems. In this paper, we developed a method for tracking dim target using particle filter. Simulation results showed that the tracking performance of this method has greatly improved compared with classical extended Kalman filter and unscented Kalman filter.

Improved AODV Routing Protocol Based on SNR

2014 ◽  
Vol 610 ◽  
pp. 910-914
Author(s):  
Bin Wang ◽  
Hao Meng ◽  
Shen Yong Li
Keyword(s):  
Routing Protocol ◽  
Data Transmission ◽  
Transmission Rate ◽  
Transmission Efficiency ◽  
Network Delay ◽  
Paper Briefly ◽  
Low Snr ◽  
Data Throughput ◽  
Simulation Results ◽  
Aodv Routing Protocol

This paper briefly analyses the routing establishing process of AODV and points out its imperfection, proposes the AODV routing establishing principle based on SNR. The Network delay and data throughput of the improved AODV have all been improved as simulation results proves. Finally the conclusion was given, illustrating that the improvement avoids the bad influence to the transmission rate of the whole link, which brings by low SNR node, and increases the data transmission efficiency.

scholarly journals A Compact Gradient Based Neural Network for Capon Spectral Estimation

2020 ◽  
Vol 7 ◽  
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Spectral Estimation ◽  
Original Method ◽  
Vector Product ◽  
Neural Network Architecture ◽  
Computational Burden ◽  
The Neural Network ◽  
Gradient Based ◽  
Simulation Results

This paper describes the use of a novel gradient based recurrent neural network to perform Capon spectral estimation. Nowadays, in the fastest algorithm proposed by Marple et al., the computational burden still remains significant in the calculation of the autoregressive (AR) Parameters. In this paper we propose to use a gradient based neural network to compute the AR parameters by solving the Yule-Walker equations. Furthermore, to reduce the complexity of the neural network architecture, the weights matrixinputs vector product is performed efficiently using the fast Fourier transform. Simulation results show that proposed neural network and its simplified architecture lead to the same results as the original method which prove the correctness of the proposed scheme.

scholarly journals Real power loss diminution by predestination of particles wavering search algorithm

2020 ◽  
Vol 9 (2) ◽  
pp. 92
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Real Power ◽  
Gradient Based ◽  
Local Optimal Solution ◽  
Simulation Results ◽  
Power Problem ◽  
Steady Velocity

In this work Predestination of Particles Wavering Search (PPS) algorithm has been applied to solve optimal reactive power problem. PPS algorithm has been modeled based on the motion of the particles in the exploration space. Normally the movement of the particle is based on gradient and swarming motion.  Particles are permitted to progress in steady velocity in gradient-based progress, but when the outcome is poor when compared to previous upshot, immediately particle rapidity will be upturned with semi of the magnitude and it will help to reach local optimal solution and it is expressed as wavering movement. In standard IEEE 14, 30, 57,118,300 bus systems Proposed Predestination of Particles Wavering Search (PPS) algorithm is evaluated and simulation results show the PPS reduced the power loss efficiently.

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