• Title, Summary, Keyword: pursuit algorithms

Search Result 26, Processing Time 0.031 seconds

Computational performance and accuracy of compressive sensing algorithms for range-Doppler estimation (거리-도플러 추정을 위한 압축 센싱 알고리즘의 계산 성능과 정확도)

  • Lee, Hyunkyu;Lee, Keunhwa;Hong, Wooyoung;Lim, Jun-Seok;Cheong, Myoung-Jun
    • The Journal of the Acoustical Society of Korea
    • /
    • v.38 no.5
    • /
    • pp.534-542
    • /
    • 2019
  • In active SONAR, several different methods are used to detect range-Doppler information of the target. Compressive sensing based method is more accurate than conventional methods and shows superior performance. There are several compressive sensing algorithms for range-Doppler estimation of active sonar. The ability of each algorithm depends on algorithm type, mutual coherence of sensing matrix, and signal to noise ratio. In this paper, we compared and analyzed computational performance and accuracy of various compressive sensing algorithms for range-Doppler estimation of active sonar. The performance of OMP (Orthogonal Matching Pursuit), CoSaMP (Compressive Sampling Matching Pursuit), BPDN (CVX) (Basis Pursuit Denoising), LARS (Least Angle Regression) algorithms is respectively estimated for varying SNR (Signal to Noise Ratio), and mutual coherence. The optimal compressive sensing algorithm is presented according to the situation.

Novel Compressed Sensing Techniques for Realistic Image (실감 영상을 위한 압축 센싱 기법)

  • Lee, Sun Yui;Jung, Kuk Hyun;Kim, Jin Young;Park, Gooman
    • Journal of Satellite, Information and Communications
    • /
    • v.9 no.3
    • /
    • pp.59-63
    • /
    • 2014
  • This paper describes the basic principles of 3D broadcast system and proposes new 3D broadcast technology that reduces the amount of data by applying CS(Compressed Sensing). Differences between Sampling theory and the CS technology concept were described. Recently proposed CS algorithm AMP(Approximate Message Passing) and CoSaMP(Compressive Sampling Matched Pursuit) were described. This paper compared an accuracy between two algorithms and a calculation time that image data compressed and restored by these algorithms. As result determines a low complexity algorithm for 3D broadcast system.

Probabilistic Exclusion Based Orthogonal Matching Pursuit Algorithm for Sparse Signal Reconstruction (희소 신호의 복원을 위한 확률적 배제 기반의 직교 정합 추구 알고리듬)

  • Kim, Seehyun
    • Journal of IKEEE
    • /
    • v.17 no.3
    • /
    • pp.339-345
    • /
    • 2013
  • In this paper, the probabilistic exclusion based orthogonal matching pursuit (PEOMP) algorithm for the sparse signal reconstruction is proposed. Some of recent greedy algorithms such as CoSaMP, gOMP, BAOMP improved the reconstruction performance by deleting unsuitable atoms at each iteration. They still often fail to converge to the solution because the support set could not escape from a local minimum. PEOMP helps to escape by excluding a random atom in the support set according to a well-chosen probability function. Experimental results show that PEOMP outperforms several OMP based algorithms and the $l_1$ optimization method in terms of exact reconstruction probability.

Genetic Algorithm based Orthogonal Matching Pursuit for Sparse Signal Recovery (희소 신호 복원을 위한 유전 알고리듬 기반 직교 정합 추구)

  • Kim, Seehyun
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.18 no.9
    • /
    • pp.2087-2093
    • /
    • 2014
  • In this paper, an orthogonal matching pursuit (OMP) method combined with genetic algorithm (GA), named GAOMP, is proposed for sparse signal recovery. Some recent greedy algorithms such as SP, CoSaMP, and gOMP improved the reconstruction performance by deleting unsuitable atoms at each iteration. However they still often fail to converge to the solution because the support set could not avoid the local minimum during the iterations. Mutating the candidate support set chosen by the OMP algorithm, GAOMP is able to escape from the local minimum and hence recovers the sparse signal. Experimental results show that GAOMP outperforms several OMP based algorithms and the $l_1$ optimization method in terms of exact reconstruction probability.

Joint Estimation of TOA and DOA in IR-UWB System Using Sparse Representation Framework

  • Wang, Fangqiu;Zhang, Xiaofei
    • ETRI Journal
    • /
    • v.36 no.3
    • /
    • pp.460-468
    • /
    • 2014
  • This paper addresses the problem of joint time of arrival (TOA) and direction of arrival (DOA) estimation in impulse radio ultra-wideband systems with a two-antenna receiver and links the joint estimation of TOA and DOA to the sparse representation framework. Exploiting this link, an orthogonal matching pursuit algorithm is used for TOA estimation in the two antennas, and then the DOA parameters are estimated via the difference in the TOAs between the two antennas. The proposed algorithm can work well with a single measurement vector and can pair TOA and DOA parameters. Furthermore, it has better parameter-estimation performance than traditional propagator methods, such as, estimation of signal parameters via rotational invariance techniques algorithms matrix pencil algorithms, and other new joint-estimation schemes, with one single snapshot. The simulation results verify the usefulness of the proposed algorithm.

Improved Algorithm for Fully-automated Neural Spike Sorting based on Projection Pursuit and Gaussian Mixture Model

  • Kim, Kyung-Hwan
    • International Journal of Control, Automation, and Systems
    • /
    • v.4 no.6
    • /
    • pp.705-713
    • /
    • 2006
  • For the analysis of multiunit extracellular neural signals as multiple spike trains, neural spike sorting is essential. Existing algorithms for the spike sorting have been unsatisfactory when the signal-to-noise ratio(SNR) is low, especially for implementation of fully-automated systems. We present a novel method that shows satisfactory performance even under low SNR, and compare its performance with a recent method based on principal component analysis(PCA) and fuzzy c-means(FCM) clustering algorithm. Our system consists of a spike detector that shows high performance under low SNR, a feature extractor that utilizes projection pursuit based on negentropy maximization, and an unsupervised classifier based on Gaussian mixture model. It is shown that the proposed feature extractor gives better performance compared to the PCA, and the proposed combination of spike detector, feature extraction, and unsupervised classification yields much better performance than the PCA-FCM, in that the realization of fully-automated unsupervised spike sorting becomes more feasible.

Sparse Signal Recovery with Parallel Orthogonal Matching Pursuit and Its Performances (병렬OMP 기법을 통한 성긴신호 복원과 그 성능)

  • Park, Jeonghong;Jung, Bang Chul;Kim, Jong Min;Ban, Tae Won
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.17 no.8
    • /
    • pp.1784-1789
    • /
    • 2013
  • In this paper, parallel orthogonal matching pursuit (POMP) is proposed to supplement the orthogonal matching pursuit (OMP) which has been widely used as a greedy algorithm for sparse signal recovery. The process of POMP is simple but effective: (1) multiple indexes maximally correlated with the observation vector are chosen at the firest iteration, (2) the conventional OMP process is carried out in parallel for each selected index, (3) the index set which yields the minimum residual is selected for reconstructing the original sparse signal. Empirical simulations show that POMP outperforms than the existing sparse signal recovery algorithms in terms of exact recovery ratio (ERR) for sparse pattern and mean-squared error (MSE) between the estimated signal and the original signal.

Data Fusion and Pursuit-Evasion Simulations for Position Evaluation of Tactical Objects (전술객체 위치 모의를 위한 데이터 융합 및 추적 회피 시뮬레이션)

  • Jin, Seung-Ri;Kim, Seok-Kwon;Son, Jae-Won;Park, Dong-Jo
    • Journal of the Korea Society for Simulation
    • /
    • v.19 no.4
    • /
    • pp.209-218
    • /
    • 2010
  • The aim of the study on the tactical object representation techniques in synthetic environment is on acquiring fundamental techniques for detection and tracking of tactical objects, and evaluating the strategic situation in the virtual ground. In order to acquire these techniques, there need the tactical objects' position tracking and evaluation, and an inter-sharing technique between tactical models. In this paper, we study the algorithms on the sensor data fusion and coordinate conversion, proportional navigation guidance(PNG), and pursuit-evasion technique for engineering and higher level models. Additionally, we simulate the position evaluation of tractical objects using the pursuit and evasion maneuvers between a submarine and a torpedo.

On Convergence of Stratification Algorithms for Skewed Populations

  • Park, In-Ho
    • The Korean Journal of Applied Statistics
    • /
    • v.22 no.6
    • /
    • pp.1277-1287
    • /
    • 2009
  • For stratifying skewed populations, the Lavall$\acute{e}$e-Hidiroglou(LH) algorithm is often considered to have a take-all stratum with the largest units and some take-some strata with the middle-size and small units. Related to its iterative nature have been reported some numerical difficulties such as the dependency of the ultimate stratum boundaries to a choice of initial boundaries and the slow convergence to locally-optimum boundaries. The geometric stratification has been recently proposed to provide initial boundaries that can avoid such numerical difficulties in implementing the LH algorithm. Since the geometric stratification does not pursuit the optimization but the equalization of the stratum CVs, the corresponding stratum boundaries may not be (near) optimal. This paper revisits these issues concerning convergence and near-optimality of optimal stratification algorithms using artificial numerical examples. We also discuss the formation of the strata and the sample allocation under the optimization process and some aspects related to discontinuity arisen from the finiteness of both population and sample as well.

Implementation of active mufflers for automobiles using recursive LMS algorithms (순환 LMS 알고리즘을 이용한 자동차 능동소음기 구현)

  • Bang, Kyung-Uk;Seo, Sung-Dae;Nam, Hyun-Do
    • Proceedings of the KIEE Conference
    • /
    • /
    • pp.334-336
    • /
    • 2005
  • According as quality of life improves, pursuit of agreeable iife became realistic problem. Specially, noise had been appraised to element that infiuence in human life directly and indirectly Therefore, necessity of study about noise control is increased for better labor conditions and agreeable habitat. In this paper, implementation of active mufflers using recursive LMS algorithms is presented. Analyze exhaust pipe noise of a gasoline and Diesel car and use adaptation IIR filter algorithm that stability is solidified and controled exhaust pipe noise of a car. computer simulation is performed to show the effectiveness of a proposed algorithm.

  • PDF