針對雙基陣提供的有偏方位角量測信息,對雙基陣純方位目標(biāo)可觀測性的必要條件及其CRAMER-Rao下限 進(jìn)行了理論推導(dǎo).在此基礎(chǔ)上,采用一種新的輔助變量方法對雙基陣純方位跟蹤性能進(jìn)行改進(jìn),并在可觀測條件下對 目標(biāo)進(jìn)行了蒙特卡洛仿真實驗.實驗結(jié)果表明,新的輔助變量方法可以使參數(shù)估計精度大大提高,并且上述理論對制 定實際的跟蹤策略或算法具有一定的參考價值
標(biāo)簽： CRAMER-Rao 雙基 方位角 變量
上傳時間： 2016-05-24
上傳用戶：changeboy
This paper studies the problem of tracking a ballistic object in the reentry phase by processing radar measurements. A suitable (highly nonlinear) model of target motion is developed and the theoretical Cramer—Rao lower bounds (CRLB) of estimation error are derived. The estimation performance (error mean and
標(biāo)簽： processing ballistic the tracking
上傳時間： 2014-10-31
上傳用戶：yyyyyyyyyy
This paper studies the problem of tracking a ballistic object in the reentry phase by processing radar measurements. A suitable (highly nonlinear) model of target motion is developed and the theoretical Cramer—Rao lower bounds (CRLB) of estimation error are derived. The estimation performance (error mean and
標(biāo)簽： processing ballistic the tracking
上傳時間： 2014-01-14
上傳用戶：奇奇奔奔
This paper studies the problem of tracking a ballistic object in the reentry phase by processing radar measurements. A suitable (highly nonlinear) model of target motion is developed and the theoretical Cramer—Rao lower bounds (CRLB) of estimation error are derived. The estimation performance (error mean and
標(biāo)簽： processing ballistic the tracking
上傳時間： 2013-12-22
上傳用戶：asddsd
We consider the problem of target localization by a network of passive sensors. When an unknown target emits an acoustic or a radio signal, its position can be localized with multiple sensors using the time difference of arrival (TDOA) information. In this paper, we consider the maximum likelihood formulation of this target localization problem and provide efficient convex relaxations for this nonconvex optimization problem.We also propose a formulation for robust target localization in the presence of sensor location errors. Two CRAMER-Rao bounds are derived corresponding to situations with and without sensor node location errors. Simulation results confirm the efficiency and superior performance of the convex relaxation approach as compared to the existing least squares based approach when large sensor node location errors are present.
標(biāo)簽： 傳感器網(wǎng)絡(luò)
上傳時間： 2016-11-27
上傳用戶：xxmluo
The software implements particle filtering and Rao Blackwellised particle filtering for conditionally Gaussian Models. The RB algorithm can be interpreted as an efficient stochastic mixture of Kalman filters. The software also includes efficient state-of-the-art resampling routines. These are generic and suitable for any application.
標(biāo)簽： filtering particle Blackwellised conditionall
上傳時間： 2013-12-17
上傳用戶：zsjzc
Rao Blackwellised Particle Filtering for Dynamic Conditionally Gaussian Models基于高斯模型的rbpf（粒子濾波器）的matlab程序
標(biāo)簽： Blackwellised Conditionally Filtering Particle
上傳時間： 2015-10-13
上傳用戶：lizhizheng88
Rao-Blackwellised Particle Filters (RBPFs) are a class of Particle Filters (PFs) that exploit conditional dependencies between parts of the state to estimate. By doing so, RBPFs can improve the estimation quality while also reducing the overall computational load in comparison to original PFs. However, the computational complexity is still too high for many real-time applications. In this paper, we propose a modified RBPF that requires a single Kalman Filter (KF) iteration per input sample. Comparative experiments show that while good convergence can still be obtained, computational efficiency is always drastically increased, making this algorithm an option to consider for real-time implementations.
標(biāo)簽： Particle Filters Rao-Blackwellised exploit
上傳時間： 2016-01-02
上傳用戶：refent
n this demo, we show how to use Rao-Blackwellised particle filtering to exploit the conditional independence structure of a simple DBN. The derivation and details are presented in A Simple Tutorial on Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. This detailed discussion of the ABC network should complement the UAI2000 paper by Arnaud Doucet, Nando de Freitas, Kevin Murphy and Stuart Russell. After downloading the file, type "tar -xf demorbpfdbn.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab5 and type "dbnrbpf" for the demo.
標(biāo)簽： Rao-Blackwellised conditional filtering particle
上傳時間： 2013-12-17
上傳用戶：zhaiyanzhong
The software implements particle filtering and Rao Blackwellised particle filtering for conditionally Gaussian Models. The RB algorithm can be interpreted as an efficient stochastic mixture of Kalman filters. The software also includes efficient state-of-the-art resampling routines. These are generic and suitable for any application. For details, please refer to Rao-Blackwellised Particle Filtering for Fault Diagnosis and On Sequential Simulation-Based Methods for Bayesian Filtering After downloading the file, type "tar -xf demo_rbpf_gauss.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this directory, load matlab and run the demo.
標(biāo)簽： filtering particle Blackwellised conditionall
上傳時間： 2014-12-05
上傳用戶：410805624
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