Wan Jian-wei

Modified unscented particle filter for nonlinear Bayesian tracking

Abstract A modified unscented particle filtering scheme for nonlinear tracking is proposed, in view of the potential drawbacks (such as, particle impoverishment and numerical sensitivity in calculating the prior) of the conventional unscented particle filter (UPF) confronted in practice. Specifically, a different derivation of the importance weight is presented in detail. The proposed method can avoid the calculation of the prior and reduce the effects of the impoverishment problem caused by sampling from the proposal distribution. Simulations have been performed using two illustrative examples and results have been provided to demonstrate the validity of the modified UPF as well as its improved performance over the conventional one.

ISAR based on micro-doppler analysis and Chirplet parameter separation

When target or its parts exist micro-motion, ISAR is severely contaminated by the micro-Doppler effect caused by the micro-motion. To solve the problem, the micro-motion models of translation, rotation and vibration are analyzed, and then the time-frequency distribution of these micro-motions are discussed. After echoes are decomposed with Chirplet transformation, an algorithm, which separates signals using micro Doppler and micro Doppler rate in Chirplet parameter domain, is investigated. The algorithm is verified by simulation data of point-scatterer and real data of an airliner, nice ISAR images are obtained.

Passive target tracking using marginalized particle filter

Abstract A marginalized particle filtering (MPF) approach is proposed for target tracking under the background of passive measurement. Essentially, the MPF is a combination of particle filtering technique and Kalman filter. By making full use of marginalization, the distributions of the tractable linear part of the total state variables are updated analytically using Kalman filter, and only the lower-dimensional nonlinear state variable needs to be dealt with using particle filter. Simulation studies are performed on an illustrative example, and the results show that the MPF method leads to a significant reduction of the tracking errors when compared with the direct particle implementation. Real data test results also validate the effectiveness of the presented method.

Block compressive sensing of hyperspectral images based on prediction error

The paper proposed the new hyperspectral image compression algorithm by using block compressive sensing and inter-band prediction error. Because of making the best use of the intense spectral correlation between adjacent bands, we can obtain the higher reconstruction quality and lower complexity. In this method, we firstly estimate the prediction parameters between adjacent bands by using least squares method. Then each band is measured by the same random projection matrix independently. Then the prediction parameters and random measurements are transmitted to the decoder. At last, we exploit a new restore process and prediction information to reconstruct the hyperspectral images. The prediction information is computed based on the previous restore adjacent band and the prediction parameters. Several experimental results demonstrate that the proposed algorithm is obviously better than the traditional compressive sensing, and has a very low complexity. In addition, our algorithm is easy to realize in practice.

Scale transform algorithm used in FMCW SAR data processing

Abstract The frequency-modulated continuous-wave (FMCW) synthetic aperture radar (SAR) is a light-weight, cost-effective, high-resolution imaging radar, which is suitable for a small flight platform. The signal model is derived for FMCW SAR used in unmanned aerial vehicles (UAV) reconnaissance and remote sensing. An appropriate algorithm is proposed. The algorithm performs the range cell migration correction (RCMC) for continuous non-chirped raw data using the energy invariance of the scaling of a signal in the scale domain. The azimuth processing is based on step transform without geometric resampling operation. The complete derivation of the algorithm is presented. The algorithm performance is shown by simulation results.

In Time Passive Localization using Multi Base-Line Phase comparison Receivers

A new in time passive localization system based on multi base-line phase comparison receivers is proposed. The new system uses short base-line to avoid the long base-line phase illegibility then uses the phase differences without illegibility to get the signal arriving angle and to detect the emitters range. The GDOP and simulation performance have also been discussed in this paper

An algorithm embedding bit series in image spatial domain

An algorithm embedding bit series in the image spatial domain is studied. The algorithm determines the embedding strength using characteristics of the human visual system, so the resulting image quality is guaranteed. The algorithm utilizes the fact that natural image data is locally correlated, and uses a precede method to reduce the original images energy, and at the same preserves the watermark information. The algorithm is discussed in detail in theory, and experimental results show consistency with theory.

The research on new direction-finding algorithm based on spectral-correlation technique

This paper introduces two new types of direction-finding algorithms. They effectively combine time and spatial processing, through using the theorem of spectrum correlation. The algorithms have signal selection and overcome the shortcomings of general spatial spectral evaluation algorithms. They improve the properties of direction-finding spectral evaluation. This paper develops known spectral-correlation features for the desired signal. In cases of multipath and arbitrary numbers, arbitrary disturbances can elevate the direction of arrival of the desired signal. Computer simulations are shown as illustration.