KEYWORDS: Signal to noise ratio, Principal component analysis, Error analysis, Interference (communication), Signal processing, Signal analysis, Signal analyzers, Signal detection, Time-frequency analysis, General packet radio service
In this paper, we apply a time and frequency analysis method based on the complete ensemble empirical mode decomposition (CEEMD) in GPR signal processing. It decomposes the GPR signal into a sum of oscillatory components, with guaranteed positive and smoothly varying instantaneous frequencies. The key idea of this method relies on averaging the modes obtained by EMD applied to several realizations of Gaussian white noise added to the original signal. It can solve the mode mixing problem in empirical mode decomposition (EMD) method and improve the resolution of ensemble empirical mode decomposition (EEMD) when the signal has low signal noise ratio (SNR). First, we analyze the difference between the basic theory of EMD, EEMD and CEEMD. Then, we compare the time and frequency analysis results of different methods. The synthetic and real GPR data demonstrate that CEEMD promises higher spectral-spatial resolution than the other two EMDs method. Its decomposition is complete, with a numerically negligible error.
KEYWORDS: Radar, Signal to noise ratio, Transceivers, Detection and tracking algorithms, Data modeling, Data acquisition, Associative arrays, Optimization (mathematics), Radar imaging, Compressed sensing
Compressive sensing techniques have been widely used to decrease the data acquisition time while generating high-resolution images due to the sparsity of the target space in through-the-wall radar imaging application. The CS-based imaging techniques mainly discretize the continuous target space into grid points and generate a dictionary of model data to form an optimization problem. The choice of the grid for generating the sparsity inducing basis or dictionary is a central point of CS and sparse approximation. However, good sparse recovery performance is based on the assumption that the targets are positioned at the pre-discretized grid locations; otherwise, the performance would significantly degrade. In this paper, the first-order approximation to estimate the targets' off-grid shifts and the joint sparse recovery method are used for reducing the effect of the grid to locate the off-grid target. Numerical examples demonstrate the robust results with lower localization errors using the joint sparse recovery method are obtained for off-grid targets compared to standard sparse reconstruction techniques.
Ultra-wideband (UWB) technology has been widely utilized in radar system because of the advantage of the
ability of high spatial resolution and object-distinction capability. A major challenge in UWB signal processing
is the requirement for very high sampling rate under Shannon-Nyquist sampling theorem which exceeds the
current ADC capacity. Recently, new approaches based on the Finite Rate of Innovation (FRI) allow significant
reduction in the sampling rate. A system for sampling UWB radar echo signal at an ultra-low sampling rate
and the estimation of time-delays is presented in the paper. An ultra-low rate sampling scheme based on FRI
is applied, which often results in sparse parameter extraction for UWB radar signal detection. The parameters
such as time-delays are estimated using the framework of compressed sensing based on total-variation norm
minimization. With this system, the UWB radar signal can be accurately reconstructed and detected with
overwhelming probability at the rate much lower than Nyquist rate. The simulation results show that the
proposed method is effective for sampling and detecting UWB radar signal at an ultra-low sampling rate.