Chunping Hou

Cumulants-Based Toeplitz Matrices Reconstruction Method for 2-D Coherent DOA Estimation

In this paper, a new high-resolution approach called fourth-order cumulants-based Toeplitz matrices reconstruction (FOC-TMR) method, is presented for two-dimensional (2-D) direction-of-arrival (DOA) estimation of incident narrowband coherent signals. The angle estimation problem is addressed by arranging the cumulants elements of received signals from two parallel uniform linear arrays (ULAs) to two Toeplitz matrices. In Gaussian noise cases, it is shown that the ranks of the two Toeplitz matrices equal the number of the incoming waves and are independent of their coherency. Therefore, with eigen decomposition of the Toeplitz-based generalized DOA matrix, the closed-form, automatically paired 2-D angle parameters can be estimated properly from its large eigenvalues and corresponding eigenvectors, respectively. In the condition of two closely spaced coherent signals in both 2-D angles, simulation results show that, in comparison with the fourth-order cumulants-based forward spatial smoothing (FOC-FSS) method, the proposed algorithm has lower computational complexity and yields better estimation performance in terms of maximum probability of success (MPS), maximum root mean square error (MRMSE) of incoming signals in both white noise and color Gaussian noise situations, especially, in low signal-to-noise ratio (SNR) and small number of snapshots conditions.

An Experimental Study of WiMAX-Based Passive Radar

Passive radars using illuminators of opportunity have attracted much attention in recent years. The worldwide inter-operability for microwave access (WiMAX) signal is a new illumination source for passive radar. This paper presents a study to understand and to demonstrate the feasibility of using WiMAX signals for passive radar. The study includes WiMAX signal analysis, the design and implementation of a WiMAX-based passive radar demonstrator, the associated radar signal processing scheme, and the field measurements. Results based on field measurements for various moving targets provide some useful references about the performance and potential capability of using WiMAX signals for passive radar applications.

Efficient Two-Dimensional Direction-of-Arrival Estimation for a Mixture of Circular and Noncircular Sources

In this paper, the two-dimensional (2-D) direction-of-arrival (DOA) estimation problem for a mixture of circular and noncircular sources is considered. In particular, we focus on a 2-D array structure consisting of two parallel uniform linear arrays and build a general array model with mixed circular and noncircular sources. The received array data and its conjugate counterparts are combined together to form a new data vector, based on which a series of 2-D DOA estimators is derived. Compared with existing methods, the proposed one has three main advantages. First, it can give a more accurate estimation in situations, where the number of sources is within the traditional limit of high-resolution methods. Second, it can still work effectively when the number of mixed signals is larger than that of the array elements. Finally, the paired 2-D DOAs of the proposed method can be obtained automatically without the complicated 2-D spectrum peak search and, therefore, has a much lower computational complexity.

Improved Azimuth/Elevation Angle Estimation Algorithm for Three-Parallel Uniform Linear Arrays

Two-dimensional (2-D) direction-of-arrival (DOA) estimation method using three-parallel uniform linear arrays (ULAs) is proposed in this letter. The 2-D DOA estimation problem is addressed by making full use of elements of the three-parallel ULAs. Furthermore, the proposed algorithm has better angle estimation performance in practical mobile elevation angles between 70° and 90° and can automatically pair the estimated azimuth and elevation angles with lower complexity. Simulation results demonstrate the effectiveness of the proposed algorithm.

Direction finding and mutual coupling estimation for uniform rectangular arrays

A novel two-dimensional (2-D) direct-of-arrival (DOA) and mutual coupling coefficients estimation algorithm for uniform rectangular arrays (URAs) is proposed. A general mutual coupling model is first built based on banded symmetric Toeplitz matrices, and then it is proved that the steering vector of a URA in the presence of mutual coupling has a similar form to that of a uniform linear array (ULA). The 2-D DOA estimation problem can be solved using the rank-reduction method. With the obtained DOA information, we can further estimate the mutual coupling coefficients. A better performance is achieved by our proposed algorithm than those auxiliary sensor-based ones, as verified by simulation results. HighlightsWe create a general mutual coupling model for uniform rectangular array (URA).The steering vector of a URA with mutual coupling is similar to that of a ULA.An array calibration algorithm without auxiliary sensors for URAs is proposed.Our algorithm has a better performance than auxiliary sensor based algorithms.

ESPRIT-like two-dimensional direction finding for mixed circular and strictly noncircular sources based on joint diagonalization

In this paper, a two-dimensional (2-D) direction-of-arrival (DOA) estimation method for a mixture of circular and strictly noncircular signals is presented based on a uniform rectangular array (URA). We first formulate a new 2-D array model for such a mixture of signals, and then utilize the observed data coupled with its conjugate counterparts to construct a new data vector and its associated covariance matrix for DOA estimation. By exploiting the second-order non-circularity of incoming signals, a computationally effective ESPRIT-like method is adopted to estimate the 2-D DOAs of mixed sources which are automatically paired by joint diagonalization of two direction matrices. One particular advantage of the proposed method is that it can solve the angle ambiguity problem when multiple incoming signals have the same angle θ or β. Furthermore, the theoretical error performance of the proposed method is analyzed and a closed-form expression for the deterministic Cramer-Rao bound (CRB) for the considered signal scenario is derived. Simulation results are provided to verify the effectiveness of the proposed method.

A New SVM-Based Modeling Method of Cabin Path Loss Prediction

A new modeling method of cabin path loss prediction based on support vector machine (SVM) is proposed in this paper. The method is trained with the path loss values of measured points inside the cabin and can be used to predict the path loss values of the unmeasured points. The experimental results demonstrate that our modeling method is more accurate than the curve fitting method. This SVM-based path loss prediction method makes the prediction much easier and more accurate, which covers performance traditional methods in the channel propagation modeling.

An experimental WiMAX based passive radar study

Singapore has launched the worlds first maritime WiMAX service network which also has introduced a new illuminator of opportunity for passive radar study. This paper presents a new study of passive radar using WiMAX signals. In this paper, the WiMAX standards, WiMAX-based passive radar demonstrator design based on a MIMO-OFDM testbed, and a primary field measurement for detecting ground vehicle are described. Interesting field measurement results may help us to understand the potential capabilities of WiMAX-based passive radar for range and Doppler detection and measurement of moving targets.

WiMAX signal generation based on MIMO-OFDM testbed for passive radar application

In order to analyze the ambiguity function of WiMAX signals and evaluate their suitability for passive radar, the first step should be the generation of WiMAX transmission signal, including random data generation, modulation, Space Time Block Coding (STBC), preamble generation for synchronization and channel estimation, frame combination and Orthogonal Frequency Division Multiplexing (OFDM) modulation. In this paper, WiMAX signal generation based on a Multiple-Input-Multiple-output (MIMO) OFDM testbed as well as detailed baseband signal processing algorithms are presented. Field trials and measurements based on the MIMO-OFDM testbed in different channel conditions have verified that the signal generation method and the adopted baseband signal processing algorithms are effective.

Transferred Deep Learning Based Waveform Recognition for Cognitive Passive Radar

Abstract Passive radar capable of recognizing illumination of opportunities can improve the detection performance on account of its functional properties of environment adaptivity. Waveform recognition approaches based on Deep Learning can outperform traditional methods based on hand-crafted feature as shown in recent studies. In this paper, we propose a novel transferred deep learning waveform recognition method which makes use of multi-scale convolution and temporal dependency characteristics to improve the recognition performance. Firstly, we develop a two-channel convolutional neural networks combining with Bi-directional Long Short-Term Memory (TCNN-BL) architecture to extract features of different scales and merge past and future states. Then in order to solve the transferability problem across various sampling frequencies, we present a parameter transfer approach which initializes target domain classifier using source domain parameters. Based on our experiments on both public datasets and our own datasets, it can be demonstrated that the proposed approach significantly outperforms the state-of-the-art methods.