Showing papers by "Pu Wang published in 2008"

Generalized High-Order Phase Function for Parameter Estimation of Polynomial Phase Signal

Abstract: The high-order phase function (HPF) has been introduced recently to estimate the parameters of a polynomial phase signal (PPS). In this correspondence, we generalize the standard HPF by introducing multiple time instants. Thus, the standard HPF can be treated as a special example of the generalized HPF with identical time instants. We propose a procedure for finding time instants minimizing the mean-square error (MSE). The proposed method achieves better performances than the high-order ambiguity function (HAF) and polynomial Wigner-Ville distribution (PWVD). The theoretical analysis as well as the Monte Carlo simulations verify the advantages such as lower MSE and lower SNR threshold for the PPS.

Performance of Cooperative Relay With Binary Modulation in Nakagami- $m$ Fading Channels

Abstract: We consider a wireless cooperative relay system with one source, one relay, and one destination node. The average bit-error probability (BEP) of several coherent or differential relay schemes with binary modulation is derived for Nakagami-m fading channels and verified by computer simulation. Our analytical results provide a set of mathematical tools for the design and analysis of cooperative communication systems in general fading channels.

Parameter estimation of linear frequency-modulated signals using integrated cubic phase function

Abstract: In this paper, an integrated cubic phase function (ICPF) for parameter estimation of linear frequency-modulated (LFM) signals is introduced. The ICPF extends the standard cubic phase function (CPF) and provides improved estimation performances in cases involving low signal-to-noise ratio (SNR) and multi-component LFM signals, at the cost moderately increased complexity. The asymptotic bias and mean squared error (MSE) of an ICPF-based estimator are derived in closed-form and verified by computer simulation. A comparison with several existing approaches shows that the ICPF serves as a good candidate for LFM signal analysis.

Performance evaluation of parametric Rao and GLRT detectors with KASSPER and Bistatic data

Abstract: The parametric Rao and GLRT detectors, recently developed by exploiting a multichannel autoregressive (AR) model for the spatially and temporally colored disturbance, were shown to perform well with limited or even no range training data for the airborne radar configuration. In previous computer simulation studies of these parametric detectors, the disturbance was generated as a multichannel AR process. However, the disturbance signal in an airborne radar environment do not necessarily follow an exact multichannel AR model. In this paper, we evaluate the detection performance of the parametric Rao and GLRT detectors using more realistic datasets: the KASSPER 2002 dataset that includes many real-world effects such as heterogeneous terrains, antenna errors and leakage, and dense ground targets/discretes, etc., and the Bistatic dataset which contains range-dependent clutter due to bistatic geometry. Experimental results on both datasets show that the parametric detectors can provide good detection performance with limited or no range training in more realistic radar environments.

Distributed non-parametric estimation in a bandwidth-constrained sensor network

Abstract: Non-parametric estimation of an unknown position parameter in a bandwidth-constrained wireless sensor network (WSN) is considered in this paper. Due to bandwidth constraint, each sensor is restricted to send only one bit of information to a fusion center. We propose a non-parametric estimator that employs a recently introduced adaptive quantization (AQ) scheme. Specifically, the position parameter is estimated as the sample mean of the quantization thresholds used in AQ. The proposed non-parametric estimator is based on the fact that the AQ thresholds asymptotically converge (in mean) to the unknown position parameter, under the condition that the position parameter is an integer multiple of the stepsize used in AQ. When the condition is not met, there is a bias which can, however, be made negligible by choosing the stepsize to be small (compared with the position parameter). Numerical results are provided to demonstrate the effectiveness of the proposed non-parametric estimator.