Beamspace maximum likelihood algorithm based on sum and difference beams for elevation estimation

Beamspace super-resolution methods for elevation estimation in multipath environment has attracted significant attention, especially the beamspace maximum likelihood(BML)algorithm. However, the difference beam is rarely used in superresolution methods, especially in low elevation estimation. The target airspace information in the difference beam is different from the target airspace information in the sum beam. And the use of difference beams does not significantly increase the complexity of the system and algorithms. Thus, this paper applies the difference beam to the beamformer to improve the elevation estimation performance of BML algorithm. And the direction and number of beams can be adjusted according to the actual needs. The theoretical target elevation angle root means square error(RMSE) and the computational complexity of the proposed algorithms are analyzed. Finally, computer simulations and real data processing results demonstrate the effectiveness of the proposed algorithms.

High Resolution Multipath Time Delay Estimation Based on FLOCCS-ESPRIT

The non-Gaussian α-stable distribution is introduced to model impulsive noise. Based on the theory of fractional lower order statistics (FLOS), the fractional lower order cross-covariance (FLOCC) sequence for two received signals is obtained and the fractional lower order cross-covariance spectrum (FLOCCS) can be approached by taking a Fourier transform for the FLOCC sequence. When the FLOCCS is treated as a sequence in the time domain, the problem of multipath time delay estimation (TDE) may be converted into one on multi-frequencies estimation or directions of arrival estimation. Accordingly, the high resolution multipath TDE can be realized with the ESPRIT technology. This idea on multipath TDE is referred to as FLOCCS-ESPRIT in this paper. Computer simulations show that this method has good performance both in a Gaussian noise and in an impulsive noise environment.

Human Respiration Rate Estimation Using Ultra-wideband Distributed Cognitive Radar System

It has been shown that remote monitoring of pulmonary activity can be achieved using ultra-wideband (UWB) systems, which shows promise in home healthcare,rescue,and security applications.In this paper,we first present a multi-ray propagation model for UWB signal,which is traveling through the human thorax and is reflected on the air/dry-skin/fat/muscle interfaces,A geometry-based statistical channel model is then developed for simulating the reception of UWB signals in the indoor propagation environment.This model enables replication of time-varying multipath profiles due to the displacement of a human chest.Subsequently, a UWB distributed cognitive radar system (UWB-DCRS) is developed for the robust detection of chest cavity motion and the accurate estimation of respiration rate.The analytical framework can serve as a basis in the planning and evaluation of future rheasurement programs.We also provide a case study on how the antenna beamwidth affects the estimation of respiration rate based on the proposed propagation models and system architecture.

Building and application of multipath model for meter-wave radar in elevation estimation

A research is done for multipath effects of low-angle tracking in meter-wave radar, and the theory of multi-scattering centers of complex target is discussed as well as the character of reflected echoes. This points out that the distribution and scattering properties of scattering centers are the prime reasons which cause the variation of muhipath effects, and all the changes of position, motion and attitude of the target can influence the muhipath effects. By building of muhipath model for multi-scattering centers for target, the analysis above is verified and a new method of elevation estimation for low-angle target is presented. The new method uses canceling vectors obtained by searching to cancel reflected waves in echoes and reduce the influence of reflected com- ponents, which can improve the accuracy of elevation estimation of low-angle target and the performance of low- angle tracking in meter-wave radar. Experimental results verify the availability of the method.

EFFECT OF MULTIPATH CHANNEL MODELS TO THE RECOVERY ALGORITHMS ON COMPRESSED SENSING IN UWB CHANNEL ESTIMATION

Multipath arrivals in an Ultra-WideBand (UWB) channel have a long time intervals between clusters and rays where the signal takes on zero or negligible values. It is precisely the signal sparsity of the impulse response of the UWB channel that is exploited in this work aiming at UWB channel estimation based on Compressed Sensing (CS). However, these multipath arrivals mainly depend on the channel environments that generate different sparse levels (low-sparse or high-sparse) of the UWB channels. According to this basis, we have analyzed the two most basic recovery algorithms, one based on linear programming Basis Pursuit (BP), another using greedy method Orthogonal Matching Pursuit (OMP), and chosen the best recovery algorithm which are suitable to the sparse level for each type of channel environment. Besides, the results of this work is an open topic for further research aimed at creating a optimal algorithm specially for application of CS based UWB systems.

PERFORMANCE ANALYSIS OF CHANNEL ESTIMATION FOR LDPC-CODED OFDM SYSTEM IN MULTIPATH FADING CHANNEL

In this paper, the channel estimation techniques for Orthogonal Frequency Division Multiplexing （OFDM） systems based on pilot arrangement are studied and we apply Low Density Parity Check （LDPC） codes to the system of IEEE 802.16a with OFDM modulation. First investigated is the influence of channel cstimation schemes on LDPC-code based OFDM system in static and multipath fading channels. According to the different propagation environments in 802.16a system, a dynamic channel estimation scheme is proposed. A good irregular LDPC code is designed with code rate of 1/2 and code length of 1200. Simulation results show that the performance of LDPC coded OFDM system proposed in this paper is better than that of the convolution Turbo coded OFDM system proposed in IEEE standard 802.16a.

AN IMPROVED DOA ESTIMATION ALGORITHM FOR ASYNCHRONOUS MULTIPATH CDMA SYSTEM

This paper proposes an improved Direction Of Arrival（DOA） estimation algorithm for asynchronous multipath Code Division Multiple Access（CDMA） system. The algorithm is based on the correlation matrices of outputs of decorrelator, which is a Multi-User Detection（MUD） approach, one of the key techniques for CDMA system. Through decorrelating processing, the desired user＇s mulipath signals can be resolved and all the other resolved multipath signal interference is eliminated. So the proposed algorithm is expected to perforln much better than algorithm such as that based directly on the Matched Filter（MF） bank outputs. Simulation results confirm this. While the improved algorithm performs better and better as Signal-to-Noise Ratio（SNR） increases, the performance of algorithm based directly on the MF bank outputs can not be improved.

A MATCHED FILTER BANK BASED DOA ESTIMATION FOR ASYNCHRONOUS MULTIPATH CDMA CHANNELS

In this paper, a Direction Of Arrival (DOA) estimation algorithm is proposed for multiuser signals through uplink asynchronous multipath Code Division Multiple Access (CDMA) channels. The algorithm is based directly on the correlation matrices of matched filter bank outputs of desired user’s multipath signals and it does not require that the elements of base station antenna array outnumber the multipath signals, which is necessary for the conventional sub-space based direction-finding algorithm. Simulation results show that the proposed algorithm estimates the DOA of multipath signals effectively and acceptably. The proposed algorithm has the prominent advantages of low complexity, simpleness and practicality, which make it much more suitable for practical application.

Multipath Time Delay Estimation Based on MUSIC Algorithm Under Small Sample Conditions

On account of the traditional multiple signal classification(MUSIC)algorithm has poor performance in time delay estimation under the condition of small sampling data and low SNR.In this paper,the traditional MUSIC algorithm is improved.The algorithm combines the idea of spatial smoothing,constructs a new covariance matrix using the covariance information of the measurement data,and constructs a weighted value using the modified noise eigenvalues to weight the traditional estimation spectrum.Simulation results show that the improved algorithm has steeper spectral peaks and better time delay resolution under the condition of inaccurate path number estimation.The time delay estimation accuracy of this algorithm is higher than that of the traditional MUSIC algorithm and the improved SSMUSIC algorithm under the conditions of small sampling data and low SNR.

An enhanced iterative joint channel estimation and symbol detection algorithm for OFDM systems

For orthogonal frequency division multiplexing (OFDM) wireless communication, the system throughput and data rate are usually limited by pilots, especially in a high mobility environment. In this paper, an enhanced iterative joint channel estimation and symbol detection algorithm is proposed to enhance the system throughput and data rate. With lower pilot power, the proposed scheme increases system throughput firstly, and then the channel estimation and symbol detection proceed iteratively within one OFDM symbol to improve the BER performance. In the proposed algorithm, the original channel estimate of each OFDM symbol is based on the channel estimate of the previous OFDM symbol, thus the variation of the mobile channel is traced efficiently, so the number of pilots in the time domain can be reduced greatly. Besides reducing the system overhead, the proposed algorithm is also shown by simulation to give much better BER performance than the conventional iterative algorithm does.

DOA estimation of coexisted noncoherent and coherent signals via sparse representation of cleaned array covariance matrix

A new direction finding method is presented to deal with coexisted noncoherent and co- herent signals without smoothing operation. First the direction-of-arrival （DOA） estimation task is herein reformulated as a sparse reconstruction problem of the cleaned array covariance matrix, which is processed to eliminate the affection of the noise. Then by using the block of matrices, the information of DOAs which we pursuit are implied in the sparse coefficient matrix. Finally, the sparse reconstruction problem is solved by the improved M-FOCUSS method, which is applied to the situation of block of matrices. This method outperforms its data domain counterpart in terms of noise suppression, and has a better performance in DOA estimation than the customary spatial smoothing technique. Simulation results verify the efficacy of the proposed method.

Real-domain GMUSIC algorithm based on unitary-transform for low-angle estimation

In order to realize the elevation angle estimation for low-altitude targets at a low computational cost, a generalized multiple signal classification （GMUSIC） algorithm based on unitary transform is proposed, i.e., the DU-GMUSlC algorithm. Firstly, the covariance matrix of received data is used to construct the Centro- Hermitian matrix. Then, the real-domain GMUSIC algorithm is used to implement the initial angle estimation, and the multipath attenuation coefficient is calculated in succession. Finally, the attenuation coefficient is taken into account in the GMUSIC method to carry out the secondary angle estimation which is beneficial to further improvement of the angle estimation accuracy. This method can meet requirements of low-angle accuracy as well as lower computational burden. Simulation results prove the correctness and effectiveness of the proposed algorithm. Moreover, field experiment data are used to further validate the effectiveness of this method.

Low-angle estimation using frequency-agile refined maximum likelihood algorithm based on optimal fusion

Low elevation estimation,which has attracted wide attention due to the presence of specular multipath,is essential for tracking radars.Frequency agility not only has the advantage of strong anti-interference ability,but also can enhance the performance of tracking radars.A frequency-agile refined maximum likelihood(RML)algorithm based on optimal fusion is proposed.The algorithm constructs an optimization problem,which minimizes the mean square error(MSE)of angle estimation.Thereby,the optimal weight at different frequency points is obtained for fusing the angle estimation.Through theoretical analysis and simulation,the frequency-agile RML algorithm based on optimal fusion can improve the accuracy of angle estimation effectively.

A HIGH RESOLUTION TIME DELAY ESTIMATION BASED ON THE MAXIMUM ENTROPY POWER SPECTRUM ESTIMATON

Multipath time delay estimation is constrained by the width of the signal correlation function when using correlation based methods. This paper obtains a high resolution time delay estimation by introducing Burg algorithm and Marple algorithm of the maximum entropy power spectral estimation to non-resolvable multipath time delay estimatoin. The principles, the performaces and the results of computer simulation are given.

A method of Robust low-angle target height and compound reflection coefficient joint estimation

It is always a challenging issue for radar systems to estimate the height of a low-angle target in the multipath propagation environment.The highly deterministic maximum likelihood estimator has a high accuracy,but the errors of the ground reflection coefficient and the reflecting surface height have serious influence on the method.In this paper,a robust es-timation method with less computation burden is proposed based on the compound reflection coefficient multipath model for low-angle targets.The compound reflection coefficient is es-timated from the received data of the array and then a one-di-mension generalized steering vector is constructed to estimate the target height.The algorithm is robust to the reflecting sur-face height error and the ground reflection coefficient error.Fi-nally,the experiment and simulation results demonstrate the validity of the proposed method.

A Machine Learning Method for Prediction of Multipath Channels

A machine learning method for predicting the evolution of a mobile communication channel based on a specific type of convolutional neural network is developed and evaluated in a simulated multipath transmission scenario.The simulation and channel estimation are designed to replicate real-world scenarios and common measurements supported by reference signals in modern cellular networks.The capability of the predictor meets the requirements that a deployment of the developed method in a radio resource scheduler of a base station pos es.Possible applications of the method are discussed.

Optimal Spacing Design for Pilots in OFDM Systems over Multipath Fading Channels

In wireless orthogonal frequency division multiplexing (OFDM) systems, the time-varying channel is often estimated by algorithms based on pilot symbols. Such an estimator, however, requires statistical prior knowledge that is not easily obtained. Therefore, the pilot tones have to be close enough to fulfill the sampling theorem. In this case the statistical knowledge of the channel is not required to reconstruct correctly the channel impulse response (CIR). This paper explores the optimal placement and number of pilot symbols, we investigate optimal training sequences in OFDM systems and we analyze the number of pilot symbols required to fulfill the sampling theorem. Using a general model for a multipath slowly fading channel, the approach is based on the LS as a criterion of channel estimation while the channel interpolation is done using the piecewise-constant interpolation compromising between complexity and performance. Simulation results demonstrate the good performance of our approach.

LDPC编码辅助的判决反馈信道估计与均衡方法

信道频率选择性衰落较强的环境下,传统正交频分复用非盲信道估计与均衡方法的系统误码性能较差,且需要在信号中增加导频符号的插入,降低系统吞吐量。针对上述问题,提出一种低密度奇偶校验(Low-Density Parity-Check,LDPC)编码辅助的判决反馈信道估计与均衡方法。将LDPC编码增益引入判决反馈回路中,以提升收敛性,在插入极少导频符号的条件下达到渐进非盲信道估计与均衡方法的性能。另外提出一种基于条件最大似然原则的信道估计改进算法,以恒虚警原则检测有效抽头信号以提高信道估计精度,并且能够与判决反馈信道估计与均衡方法相结合,进一步提升算法性能。仿真结果表明,在仿真所选用的信道环境下,LDPC编码辅助的判决反馈信道估计与均衡方法相比传统判决反馈方法具有11~13 dB的误码性能提升,具有优秀的系统误码性能和鲁棒性。

利用海底反射时延结构的深海海底反射区目标距离深度估计方法

针对收发深度关系未知时利用时延差进行距离深度估计可能会出现伪源的问题,提出了一种利用4个时延差直接进行距离深度估计的方法。该方法通过分析4条多径间4个时延差的关系,利用伪源与真实位置间同种时延差的差异,构造模糊平面进行定位,无需判断收发深度关系即可排除伪源,且在单水听器上即可实现。仿真实验与误差分析验证了该方法的有效性,海试结果表明,该方法在两个不同深度的单水听器上均成功实现了对11.0~16.5 km目标的距离深度估计,距离和深度的均方根误差分别小于1.1 km和54 m。

复杂多径下IR-UWB自适应频偏估计算法研究

在脉冲超宽带定位系统中,时钟频率偏移是影响测距和测角精度的重要因素;针对复杂多径下频偏估计精度低的问题,设计了一种适应复杂多径环境的脉冲超宽带自适应频偏估计算法;首先,借助脉冲超宽带信号的抗多径优势,将实时估计的信道脉冲响应作为自适应匹配滤波模板,以保证复杂多径下相关峰的准确提取;利用相关峰辐角信息得到前导符号的相位序列;再使用贝叶斯估计算法提高频偏估计精度;在IEEE 802.15.4a标准CM1、CM2多径信道下进行仿真,仿真结果表明,低信噪比情况下,频偏估计精度保证在0.2 ppm以内;在巷道环境实验结果表明,50 m范围内频偏估计标准差在0.06 ppm以内;实现了复杂多径环境下高精度频偏估计,具有较强的实用性。