Hongbing Ji

Bio: Hongbing Ji is an academic researcher from Xidian University. The author has contributed to research in topics: Filter (video) & Gaussian. The author has an hindex of 20, co-authored 174 publications receiving 1216 citations.

An Adaptive Range-Angle-Doppler Processing Approach for FDA-MIMO Radar Using Three-Dimensional Localization

Abstract: High pulse repetition frequency incurs range ambiguity in radar systems, which in turn results in clutter suppression performance degradation and parameter estimation ambiguities. To tackle this issue, this paper proposes an adaptive range-angle-Doppler processing approach with airborne frequency diverse array (FDA) for multiple-input multiple-output (MIMO) radar. The FDA employs a small frequency increment across array elements and introduces additional controllable degrees-of-freedom (DOFs) in range dimension in the transmit antenna. Thus, it is able to perform range-angle-Doppler processing by exploiting the DOFs in transmit, receive, and pulse dimensions in the FDA-MIMO radar. By properly designing the frequency increment of the FDA, the clutter spectra of different ambiguous range regions can be discriminable in the transmit-receive spatial domains. As a result, multiple beams are formed in the transmit spatial, receive spatial, and Doppler domains and clutters from different range regions can be suppressed. An enhanced three-dimensional localization technique is proposed for the case with severe range ambiguity problem, which evidently reduces the dimensions of the processor and efficiently suppresses clutter in practical applications. Several numerical examples are presented to verify the effectiveness of the proposed approach.

Fuzzy extreme learning machine for classification

Abstract: Compared to traditional classifiers, such as SVM, the extreme learning machine (ELM) achieves similar performance for classification and runs at a much faster learning speed. However, in many real applications, the different input points may not be exactly assigned to one of the classes, such as the imbalance problems and the weighted classification problems. The traditional ELM lacks the ability to solve those problems. Proposed is a fuzzy ELM, which introduces a fuzzy membership to the traditional ELM method. Then, the inputs with different fuzzy matrix can make different contributions to the learning of the output weights. For the weighted classification problems, FELM can provide a more logical result than that of ELM.

A 3D wavelet based spatial-temporal approach for video watermarking

Abstract: This paper presents a watermarking procedure for digital video with spatial-temporal algorithm. Our watermarking procedure is based on the 3D wavelet transform and video scene segmentation. First, the video stream is parsed into video shots and some of shots are randomly selected to embed watermarks. By direct spread spectrum technique, a sequence of watermarks is generated from a watermark image and is adaptively added into the 3D wavelet coefficients of the video shot. The experimental result demonstrates the robustness of our video watermarking procedure to several video degradations and watermarking attacks.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

A Local Contrast Method for Small Infrared Target Detection

Abstract: Robust small target detection of low signal-to-noise ratio (SNR) is very important in infrared search and track applications for self-defense or attacks. Consequently, an effective small target detection algorithm inspired by the contrast mechanism of human vision system and derived kernel model is presented in this paper. At the first stage, the local contrast map of the input image is obtained using the proposed local contrast measure which measures the dissimilarity between the current location and its neighborhoods. In this way, target signal enhancement and background clutter suppression are achieved simultaneously. At the second stage, an adaptive threshold is adopted to segment the target. The experiments on two sequences have validated the detection capability of the proposed target detection method. Experimental evaluation results show that our method is simple and effective with respect to detection accuracy. In particular, the proposed method can improve the SNR of the image significantly.

The Labeled Multi-Bernoulli Filter

Abstract: This paper proposes a generalization of the multi- Bernoulli filter called the labeled multi-Bernoulli filter that outputs target tracks. Moreover, the labeled multi-Bernoulli filter does not exhibit a cardinality bias due to a more accurate update approximation compared to the multi-Bernoulli filter by exploiting the conjugate prior form for labeled Random Finite Sets. The proposed filter can be interpreted as an efficient approximation of the $\delta$ -Generalized Labeled Multi-Bernoulli filter. It inherits the advantages of the multi-Bernoulli filter in regards to particle implementation and state estimation. It also inherits advantages of the $\delta$ -Generalized Labeled Multi-Bernoulli filter in that it outputs (labeled) target tracks and achieves better performance.