A low-complexity method for angle estimation in Multiple-input multiple-output radar (MIMO) radar is presented. In this approach, the signal subspace can be spanned by the orthogonal vectors which are obtained by Multi-stage Wiener Filter (MSWF), then the ESPRIT method can be used to estimate direction of departures (DODs) and direction of arrivals (DOAs). Compared with the conventional ESPRIT algorithm, the proposed method does not involve estimation of the covariance matrix and its eigen-decomposition, which alleviates remarkably the computational complexity. Moreover, the proposed method achieves the similar angle estimation performance. Simulation results are presented to verify the efficiency of the proposed method.

In this paper, the Getis statistic is applied to ALOS- PALSAR (Advanced Land Ovserving Satellite-Phased Array L-band Synthetic Aperture Radar) images for assessing the building damage caused by the Wenchuan earthquake in 2008. As a proposed image analysis, a simulated building image using mapping and projection algorithm is first presented for analysis of the Getis statistic. The results show the high accuracy of the assessment of the proposed approach. The Getis statistic is then applied to two ALOS-PALSAR images acquired before and after the Wenchuan earthquake to assess the level of building damage. Results of the Getis statistic show that the damage level is approximately 81%.

In spectrum-sharing systems where the secondary user (SU) opportunistically accesses the primary user (PU)'s licensed channel, the SU should satisfy both the transmit power constraint of the SU transmitter and the received power constraint at the PU receiver. This letter studies the ergodic capacity of spectrum-sharing systems in fading channels. The ergodic capacity expression along with the optimal power allocation scheme is derived considering both the average transmit and received power constraints. The capacity function in terms of the two power constraints is found to be divided into transmit power limited region, received power limited region and dual limited region. Numerical results in Rayleigh fading channels are presented to verify our analysis.

In this paper, a novel approach for central angle estimation of coherently distributed targets that utilizes electric vector sensors in bistatic MIMO radar is proposed. First, the coherently distributed targets signal model in bistatic MIMO radar that equipped with electric vector sensors is reconstructed. The Hadamard product rotation invariance property of the coherently distributed targets' steering vectors is found to get the initial estimation of direction of departure (DOD). 1-D MUSIC is then used to estimate the accurate central angles of direction of arrival (DOA) and DOD. The proposed method can estimate the central angles of DOA and DOD efficiently and accurately without pairing even in the situation where the angular signal distribution functions are unknown. Our method has better performance than Guo's algorithm. Numerical results verify the improvement and performance of the proposed algorithm.

In this paper, the statistical characteristic of the Error Detection Delay (EDD) of Finite Precision Binary Arithmetic Codes (FPBAC) is discussed. It is observed that, apart from the probability of the Forbidden Symbol (FS) inserted into the list of the source symbols, the probability of the source sequence and the operation precision as well as the position of the FS in the coding interval can affect the statistical characteristic of the EDD. Experiments demonstrate that the actual distribution of the EDD of FPBAC is quite different from the geometric distribution of infinite precision arithmetic codes. This phenomenon is researched deeply, and a new statistical model (gamma distribution) of the actual distribution of the EDD is proposed, which can make a more precise prediction of the EDD. Finally, the relation expressions between the parameters of gamma distribution and the related factors affecting the distribution are given.

The conventional covariance matrix technique based subspace methods, such as the 2-D Capon algorithm and computationally efficient ESPRIT-type algorithms, are invalid with a single snapshot in a bistatic MIMO radar. A novel matrix pencil method is proposed for the direction of departures (DODs) and direction of arrivals estimation (DOAs) estimation. The proposed method constructs an enhanced matrix from the direct sampled data, and then utilizes the matrix pencil approach to estimate DOAs and DODs, which are paired automatically. The proposed method is able to provide favorable and unambiguous angle estimation performance with a single snapshot. Simulation results are presented to verify the effectiveness of the proposed method.

This article presents the analysis of the Bragg scattering phenomenon which has been observed in the images of machine-planted rice paddies acquired by the JERS-1 L-band synthetic aperture radar (SAR). The simultaneous measurements of rice plants were made at the SAR data acquisition times. Large differences of 20-25 dB in image intensity between the transplanting and ripening stages are found to be dependent on the planting direction and bunch separation. This selective image enhancement is a result of the Bragg resonance backscatter due to the double-bounce of incident L-band microwave between the flooded water surface and periodically planted bunches of rice plants. Support for the idea of double-bounce scattering is provided by the decomposition analysis of L-band and X-band polarimetric Pi-SAR data; and a simple numerical simulation based on the physical optics model shows fairly good agreement with the JERS-1 SAR data. The results presented in this paper is mainly of academic interest, but a suggestion can be made on the selection of suitable microwave band for monitoring rice fields.

Accurate traffic flow prediction is the precondition for many applications in Intelligent Transportation Systems, such as traffic control and route guidance. Traditional data driven traffic flow prediction models tend to ignore traffic self-features (e.g., periodicities), and commonly suffer from the shifts brought by various complex factors (e.g., weather and holidays). These would reduce the precision and robustness of the prediction models. To tackle this problem, in this paper, we propose a CNN-based multi-feature predictive model (MF-CNN) that collectively predicts network-scale traffic flow with multiple spatiotemporal features and external factors (weather and holidays). Specifically, we classify traffic self-features into temporal continuity as short-term feature, daily periodicity and weekly periodicity as long-term features, then map them to three two-dimensional spaces, which each one is composed of time and space, represented by two-dimensional matrices. The high-level spatiotemporal features learned by CNNs from the matrices with different time lags are further fused with external factors by a logistic regression layer to derive the final prediction. Experimental results indicate that the MF-CNN model considering multi-features improves the predictive performance compared to five baseline models, and achieves the trade-off between accuracy and efficiency.

Codebooks with small maximal cross-correlation amplitudes have important applications in code division multiple access (CDMA) communication, coding theory and compressed sensing. In this letter, we design a new codebook based on a construction of Ramanujan graphs over finite abelian groups. We prove that the new codebook with length K=q+1 and size N=q2+2q+2 is asymptotically optimal with nearly achieving the Levenshtein bound when n=3, where q is a prime power. The parameters of the new codebook are new.

The quality of the Side-information frame (S frame) influences significantly the rate-distortion performance in the Distributed Video Coding (DVC). In this letter, we propose an efficient Side-Information Frame Generator (SIFG). It considers smoothness constraints of both the motion vector field and spatial adjacent pixels. Simulation results show that the proposed techniques provide potential rate-distortion performance advantages. Besides, the fine visual quality of the S frame is obtained.

The objective of this letter is to present a family of q-ary codes with parameters $[rac{q^m-1}{q-1},rac{q^m-1}{q-1}-2m,d]$, where m is a positive integer, q is a power of an odd prime and 4≤d≤5. The parameters are proved to be optimal or almost optimal with respect to an upper bound on linear codes.

An inversion method of bridge height over water by polarimetric synthetic aperture radar (SAR) is developed. A geometric ray description to illustrate scattering mechanism of a bridge over water surface is identified by polarimetric image analysis. Using the mapping and projecting algorithm, a polarimetric SAR image of a bridge model is first simulated and shows that scattering from a bridge over water can be identified by three strip lines corresponding to single-, double-, and triple-order scattering, respectively. A set of polarimetric parameters based on the de-orientation theory is applied to analysis of three types scattering, and the thinning-clustering algorithm and Hough transform are then employed to locate the image positions of these strip lines. These lines are used to invert the bridge height. Fully polarimetric image data of airborne Pi-SAR at X-band are applied to inversion of the height and width of the Naruto Bridge in Japan. Based on the same principle, this approach is also applicable to spaceborne ALOSPALSAR single-polarization data of the Eastern Ocean Bridge in China. The results show good feasibility to realize the bridge height inversion.

A method that jointly estimates the carrier frequency offset (CFO), channel and symbol timing for orthogonal frequency division multiplexing (OFDM) is proposed in this letter. Based on the characteristic of cyclic training symbols in the frequency domain, the joint estimation is divided into three separate estimations. The CFO and equivalent channel impulse response (CIR) are first estimated by an iterative joint maximum likelihood estimation (JMLE), then the symbol timing offset (STO) is obtained by the assistance of equivalent CIR, finally the CIR is calculated based on the equivalent CIR after known STO and CFO. In our proposed method, the effect of imperfect CIR is considered in the CFO estimator. Moveover, a procedure, which eliminates the inverse operation of a covariance matrix at each iterative process, was adopted to reduce the complexity of our proposed method. Simulations show that the proposed method is capable of retaining the same bit error rate as joint CFO and CIR maximum likelihood estimation without symbol timing error.

Under the background of non-homogenous and dynamic time-varying clutter, the processing ability of the traditional constant false alarm rate (CFAR) detection algorithm is significantly reduced, as well as the detection performance. This paper proposes a CFAR detection algorithm based on clutter knowledge (CK-CFAR), as a new CFAR, to improve the detection performance adaptability of the radar in complex clutter background. With the acquired clutter prior knowledge, the algorithm can dynamically select parameters according to the change of background clutter and calculate the threshold. Compared with the detection algorithms such as CA-CFAR, GO-CFAR, SO-CFAR, and OS-CFAR, the simulation results show that CK-CFAR has excellent detection performance in the background of homogenous clutter and edge clutter. This algorithm can help radar adapt to the clutter with different distribution characteristics, effectively enhance radar detection in a complex environment. It is more in line with the development direction of the cognitive radar.

Multimodal named entity recognition (MNER) is the task of recognizing named entities in multimodal context. Existing methods focus on utilizing co-attention mechanism to discover the relationships between multiple modalities. However, they still have two deficiencies: First, current methods fail to fuse the multimodal representations in a fine-grained way, which may bring noise of visual modalities. Second, current methods ignore bridging the semantic gap between heterogeneous modalities. To solve the above issues, we propose a novel MNER method with bottleneck fusion and contrastive learning (BFCL). Specifically, we first incorporate the transformer-based bottleneck fusion mechanism, subsequently, information between different modalities can only be exchanged through several bottleneck tokens, thus reducing the noise propagation. Then we propose two decoupled image-text contrastive losses to align the unimodal representations, making the representations of semantically similar modalities closer, while the representations of semantically different modalities farther away. Experimental results demonstrate that our method is competitive to the state-of-the-art models, and achieves 74.54% and 85.70% F1-scores on Twitter-2015 and Twitter-2017 datasets, respectively.

Question answering (QA) systems are designed to answer questions based on given information or with the help of external information. Recent advances in QA systems are overwhelmingly contributed by deep learning techniques, which have been employed in a wide range of fields such as finance, sports and biomedicine. For generative QA in open-domain QA, although deep learning can leverage massive data to learn meaningful feature representations and generate free text as answers, there are still problems to limit the length and content of answers. To alleviate this problem, we focus on the variant YNQA of generative QA and propose a model CasATT (cascade prompt learning framework with the sentence-level attention mechanism). In the CasATT, we excavate text semantic information from document level to sentence level and mine evidence accurately from large-scale documents by retrieval and ranking, and answer questions with ranked candidates by discriminative question answering. Our experiments on several datasets demonstrate the superior performance of the CasATT over state-of-the-art baselines, whose accuracy score can achieve 93.1% on IR&QA Competition dataset and 90.5% on BoolQ dataset.