Peng Yingning

New image enhancement algorithm for night vision

In order to improve the clearness of images captured at night, a new enhancement algorithm is proposed in this paper. Its characters are: (1) enhancing the dark image with contrast enhancement and histogram equalization in series, so both the local and global information can be used; (2) a contrast transforming function which is suited to deal with dark image is proposed; (3) it can be realized in real time. The new method shows good performance in dealing with night vision images.

Distributed CFAR detector based on local test statistic

Abstract Distributed CFAR detector based on unquantized local test statistic (LTS) is considered. LTSs are provided by local processors (LP) and transmitted to the fusion center (FC) where they are fused to generate global decision. Two types of schemes called S and P types are proposed. For local OS-CFAR processing, three schemes are analyzed, i.e. OS-S-CA, MOS and mOS. Samples in different sensors are assumed to be independent and identically distributed. For a two-sensor network, some explicit expressions for probabilities of false alarm and detection of a Swerling II target in Gaussian noise are derived. With the consideration of both detection performance and the amount of data transmitted between LP and FC, OS-S-CA is the best, and the MOS little inferior.

Ground moving target indication for MIMO-SAR

The multi-input multi-output synthetic aperture radar (MIMO-SAR) is an innovative concept, which can bring high degrees of freedom for SAR system with limited transmitting/ receiving (T/R) elements. In this paper, ground moving target indication (GMTI) for MIMO-SAR is studied based on reasonable usage of the space-time equivalent samplings. Furthermore, this paper gives a deep insight of the azimuth “spurious peaks” effect, caused by the periodically modulated error signal due to the radial velocity of moving target. Also, the compensation method is given to suppress the unwanted “spurious peaks”. Finally, the numerical experiments are also provided to demonstrate the effectiveness of the MIMO-SAR GMTI.

Doppler distributed clutter model of airborne radar and its parameters estimation

To characterize the clutter spectrum center-shift and spread of airborne radar caused by the platform motion, a novel Doppler Distributed Clutter (DDC) model is proposed to describe the clutter covariance matrix in temporal domain. Based on this parametric model, maximum likelihood, subspace based method and other super-resolution methods are introduced into the Doppler parameters estimation, and more excellent performance is obtained than with the conventional approaches in frequency domain. The theoretical derivation and real experimental results are also provided to validate this novel model and methods of parameter estimating.

An approach to radar netting

A novel approach to radar netting is presented. Based on the measured radar cross section (RCS) of interested targets and the coverage diagrams of the different radar in a radar network, the approach can be used to solve the problem of how optimally to locate the radar to achieve a satisfactory surveillance area and some highly concerned areas (i.e., core areas) inside. Several factors have been taken into account in this approach, such as local terrain around the radar, man-made interference, flying direction of target, etc. Simple examples are discussed for illustration. The radar netting optimization is of great importance in the networking technologies, since it is related directly to the optimal distribution of the detection and tracking performance in the surveillance area, and determines where data fusion may become feasible and where it is imperative.

The optimality in Neyman-Pearson sense in the distributed CFAR detection with multisensor

The optimality in Neyman-Pearson (NP) sense in distributed CFAR detection with multisensor is discussed. Most of the existing analysis of optimization of distributed CFAR detection in the NP sense is done under the limitation of binary local decision and no communication among local processors. We find that the real optimization in the NP sense can not be realized under this limitation. If local test statistics (LTS) are used and fused, the real optimal NP test could be implemented by likelihood ratio test (LRT).

Adaptive space-time processing for non-side-looking airborne radar

We discuss the space-time adaptive processing (STAP) for non-side-looking airborne radar (SLAR). The clutter specific property of the airborne radar when the phased array antenna is placed on the aircraft in an arbitrary direction is discussed. Then the STAP of the non-SLAR is studied. A scheme has been proposed, which is processed by way of adaptive space-time processing combined with using multiple pulse repetition frequencies (PRFs). We analyze the system performance by using an approach, which is effective to the SLAR and can be realized in real-time processing. We also study the selection of the PRFs. Computer simulation results show this scheme is feasible.

A new ISAR range alignment method based on particle swarm optimizer

The particle swarm optimizer algorithm (PSO) is a recently developed evolutionary search technique, In order to improve the precision of range alignment in ISAR data processing, a new method is presented based on particle swarm optimizer. The range profile distance is used as fitness function in PSO searching motion parameters, which evaluate each individual based on target range profiles, in such case the computational cost is strongly reduced. The experiment analysis on simulation data and the real ship data verify the effectiveness of the novel algorithm.

A new approach to dual-band polarimetric radar remote sensing image classification

It is very important to efficiently represent the target scattering characteristics in applications of polarimetric radar remote sensing. Three probability mass functions are introduced in this paper for target representation: using similarity parameters to describe target average scattering mechanism, using the eigenvalues of a target coherency matrix to describe target scattering randomness, and using radar received power to describe target scattering intensity. The concept of cross-entropy is employed to measure the difference between two scatterers based on the probability mass functions. Three parts of difference between scatterers are measured separately as the difference of average scattering mechanism, the difference of scattering randomness and the difference of scattering intensity, so that the usage of polarimetric data can be highly efficient and flexible. The supervised/unsupervised image classification schemes and their simplified versions are established based on the minimum cross-entropy principle. They are demonstrated to have better classification performance than the maximum likelihood classifier based on the Wishart distribution assumption, both in supervised and in unsupervised classification.

Study of centralized CFAR detection with multisensor

The centralized constant false alarm rate (CFAR) detection with multiple non-like sensors is studied in nonhomogeneous background. Results show that the centralized processing for CFAR detection with multiple non-like sensors is heavily affected by the variation of the relative ratio of local clutter power levels. The remedies are given.