A New Algorithm for Tracking a Maneuvering Target

A new algorithm for tracking a maneuvering target in presence of clutter or false measurements is addressed. Due to the availability of feature or attribute information in measurement vector, a joint probability density function description of the target state and target class is given. Using the joint state-class description the predictive measurement pdf can be proven to be a Gaussian mixture distribution. A Gaussian mixture Kalman filter is used for state estimation, where maneuver detection can also be avoided. In simulation the results with three tracking algorithms are compared, which have shown that proposed method here is more effective.

Joint Blind Symbol Rate Estimation and Data Symbol Detection for Linearly Modulated Signals

This paper focuses on non-data aided estimation of the symbol rate and detecting the data symbols in linearlymodulated signals. A blind oversampling-based signal detector under the circumstance of unknown symbol period is proposed. First, the symbol rate is estimated using the Expectation Maximization (EM) algorithm. However, within the framework of EM algorithm, it is difficult to obtain a closed form for the loglikelihood function and the density function. Therefore, these two functions are approximated in this paper by using the Particle Filter (PF) technique. In addition, a symbol rate estimator that exploits the cyclic correlation information is proposed as an initialization estimator for the EM algorithm. Second, the blind data symbol detector based on the PF algorithm is designed.Since the signal is oversampled at the receiver side, a delayed multi-sampling PF detector is proposed to manage the intersymbol interference caused by oversampling, and to improve the demodulation performance of the data symbols. In the PF algorithm, the hybrid importance function is used to generate both data samples and channel model coefficients, and the Mixture Kalman Filter (MKF) algorithm is used to marginalize out the fading channel coefficients.