4 research outputs found
Estimation of Autoregressive Parameters from Noisy Observations Using Iterated Covariance Updates
Estimating the parameters of the autoregressive (AR) random process is a problem that has been well-studied. In many applications, only noisy measurements of AR process are available. The effect of the additive noise is that the system can be modeled as an AR model with colored noise, even when the measurement noise is white, where the correlation matrix depends on the AR parameters. Because of the correlation, it is expedient to compute using multiple stacked observations. Performing a weighted least-squares estimation of the AR parameters using an inverse covariance weighting can provide significantly better parameter estimates, with improvement increasing with the stack depth. The estimation algorithm is essentially a vector RLS adaptive filter, with time-varying covariance matrix. Different ways of estimating the unknown covariance are presented, as well as a method to estimate the variances of the AR and observation noise. The notation is extended to vector autoregressive (VAR) processes. Simulation results demonstrate performance improvements in coefficient error and in spectrum estimation
Novel implementation technique for a wavelet-based broadband signal detection system
This thesis reports on the design, simulation and implementation of a novel
Implementation for a Wavelet-based Broadband Signal Detection System.
There is a strong interest in methods of increasing the resolution of sonar systems for
the detection of targets at sea. A novel implementation of a wideband active sonar
signal detection system is proposed in this project. In the system the Continuous
Wavelet Transform is used for target motion estimation and an
Adaptive-Network-based Fuzzy inference System (ANFIS) is adopted to minimize the
noise effect on target detection. A local optimum search algorithm is introduced in this
project to reduce the computation load of the Continuous Wavelet Transform and make
it suitable for practical applications.
The proposed system is realized on a Xilinx University Program Virtex-II Pro
Development System which contains a Virtex II pro XC2VP30 FPGA chip with 2
powerPC 405 cores. Testing for single target detection and multiple target detection
shows the proposed system is able to accurately locate targets under
reverberation-limited underwater environment with a Signal-Noise-Ratio of up to -30db,
with location error less than 10 meters and velocity estimation error less than 1 knot.
In the proposed system the combination of CWT and local optimum search algorithm
significantly saves the computation time for CWT and make it more practical to real
applications. Also the implementation of ANFIS on the FPGA board indicates in the
future a real-time ANFIS operation with VLSI implementation would be possible
Novel implementation technique for a wavelet-based broadband signal detection system
This thesis reports on the design, simulation and implementation of a novel Implementation for a Wavelet-based Broadband Signal Detection System. There is a strong interest in methods of increasing the resolution of sonar systems for the detection of targets at sea. A novel implementation of a wideband active sonar signal detection system is proposed in this project. In the system the Continuous Wavelet Transform is used for target motion estimation and an Adaptive-Network-based Fuzzy inference System (ANFIS) is adopted to minimize the noise effect on target detection. A local optimum search algorithm is introduced in this project to reduce the computation load of the Continuous Wavelet Transform and make it suitable for practical applications. The proposed system is realized on a Xilinx University Program Virtex-II Pro Development System which contains a Virtex II pro XC2VP30 FPGA chip with 2 powerPC 405 cores. Testing for single target detection and multiple target detection shows the proposed system is able to accurately locate targets under reverberation-limited underwater environment with a Signal-Noise-Ratio of up to -30db, with location error less than 10 meters and velocity estimation error less than 1 knot. In the proposed system the combination of CWT and local optimum search algorithm significantly saves the computation time for CWT and make it more practical to real applications. Also the implementation of ANFIS on the FPGA board indicates in the future a real-time ANFIS operation with VLSI implementation would be possible.EThOS - Electronic Theses Online ServiceGBUnited Kingdo