基于空时自适应处理的GPS宽带干扰抑制技术

分析研究了GPS宽带干扰抑制的各种空时自适应处理算法,提出了期望信号协方差矩阵的计算方法,该方法使得最大信干噪比方法和最小均方误差方法可以实现。仿真结果表明：和无干扰的GPS接收机相比,基于空时自适应的干扰抑制技术不会引起很大的定位误差。     

激光脉冲法背温信号中噪声信号的滤除方法

针对激光脉冲法背温信号中存在的噪声干扰,通过频谱分析确定了噪声信号的频率范围,设计了相应的FIR数字滤波器,并对滤波后存在的残留噪声进行了递推平滑滤波.滤波前后的数据对比表明,该方法能有效滤波背温信号中的噪声.     

脉冲干扰对软件接收机的影响分析

卫星信号经过长距离传播,信号能量损耗严重,到达地面的功率很弱,容易受到各种干扰的影响。脉冲干扰为常见的干扰类型,所以针对不同功率、不同周期,以及不同占空比的脉冲干扰信号,通过接收前端采集受脉冲干扰的GPS L1信号,利用软件接收机及多相关器生成技术,详细分析了脉冲干扰对接收机信号捕获与跟踪性能的影响。分析结果表明,周期为1ms的脉冲干扰信号,能对接收机产生强烈的干扰效果,捕获图中的噪声明显增大;跟踪过程中,载噪比和相关值突发性减小,造成跟踪数据异常。而长周期的脉冲信号仅在脉冲到达时影响接收机的捕获和跟踪,但由于信号跟踪不能连续进行,导致伪距观测量的不连续与导航数据不能正常解码,从而干扰接收机。     

非均匀环境下利用单个数据集的STAP算法

对非均匀环境下利用单个数据集的机载雷达空时自适应处理(SDS-STAP)算法进行了研究。针对现有单个数据集类空时自适应算法存在的问题,提出了一种基于对待测数据集进行空时二维滑窗处理的二维幅度相位估计(2DAPES)算法。该算法利用回波的统计信息,不仅放宽了算法处理器对于雷达系统阵元数与脉冲数选取的约束,而且能够有效抑制杂波及待测单元内的干扰,同时降低了处理器的计算复杂度。最后,仿真数据及实测数据实验验证了所提算法的正确性与有效性。     

宽带信号去斜脉冲压缩处理方法的研究

 宽带信号广泛应用于雷达、导航和卫星通讯等领域。宽带信号的传统接收处理方法主要是采用匹配滤波或子带分割技术。本文用去斜脉冲压缩处理方法处理宽带信号,给出了具体的实现结构和改进措施,分析了如何选择系统的信号采样频率,同时还给出了脉压波形的仿真结果及性能分析。实验表明:对中心频率为9.5GHz、带宽1.3GHz、脉冲宽度30μs的宽带线性调频信号,采用该方法处理只需90MHz采样数据率,大大降低了数据采集的难度。     

基于数字包络检波的RNSS通道时延标定方法

提出一种采用数字处理的时延测试方法,用于对导航卫星导航信号发射通道分数码片时延的精确测量。该方法是通过高速A/D（模/数）转换器,对导航卫星下行的BPSK（二进制相移键控）信号和卫星导航秒脉冲进行双通道采样,读取采样数据并进行数据处理。根据秒脉冲信号触发门限上升沿确定时延测量起点,对BPSK采样数据进行平方律检波,获取码片换相点,计算换相点和秒脉冲之间的分数码片时延,并进行滤波器时延校准,从而得到导航卫星发射链路的分数码片时延,该方法不需要进行伪随机信号的捕获和跟踪,测量精度主要取决于采样器采样率。通过在测试中使用一根校准电缆对该方法进行验证,验证结果表明,采用本文提出测试方法的测量误差优于0.3ns。     

双通道恒模干扰抑制VHF接收机设计与实现

为了解决民航地空通信受到无线电干扰的问题,提出了双通道恒模干扰抑制自适应抑制接收机的设计实现方案。接收机中的信号处理平台在FPGA中实现自适应干扰抑制,使用DSP判断恒模算法输入信号是否存在干扰,以及对恒模算法输出信号进行属性判断以防止误捕获。再配以具有VHF信号接收功能的射频前端电路,实现接收、干扰抑制、解调地空通信信号的功能。系统测试表明,双通道恒模干扰抑制接收机能够自适应地抑制恒模干扰。     

基于QR分解最小二乘算法消除ECG中工频干扰

研究了一种基于QR分解最小二乘(QRD-LS)算法消除ECG中工频干扰的方法,此方法采用基于数据域处理的QRD-LS算法进行权值的训练和更新,其直接针对输入数据矩阵进行递推,且可用并行Systolic处理结构高效地实现。并且能自适应消除工频干扰,提高信干比,仿真结果验证了理论。     

线性随机系统Kalman滤波的野值修正

本文讨论原始输入采样数据含有野值时Kalman滤波的修正问题,提出了一组易于应用的处理对策与计算方法。     

线性随机系统Kalman滤波的野值修正

本文讨论原始输入采样数据会有野值时Kalman滤波的修正问题，提出了一组易于应用的处理对策与计算方法。     

Median cascaded canceller for robust adaptive array processing

A median cascaded canceller (MCC) is introduced as a robust multichannel adaptive array processor. Compared with sample matrix inversion (SMI) methods, it is shown to significantly reduce the deleterious effects of impulsive noise spikes (outliers) on convergence performance of metrics; such as (normalized) output residue power and signal to interference-plus-noise ratio (SINR). For the case of no outliers, the MCC convergence performance remains commensurate with SMI methods for several practical interference scenarios. It is shown that the MCC offers natural protection against desired signal (target) cancellation when weight training data contains strong target components. In addition, results are shown for a high-fidelity, simulated, barrage jamming and nonhomogenous clutter environment. Here the MCC is used in a space-time adaptive processing (STAP) configuration for airborne radar interference mitigation. Results indicate the MCC produces a marked SINR performance improvement over SMI methods.     

Wideband cancellation of interference in a GPS receive array

We have demonstrated that by using an adaptive space-time array the interference from multiple, strong interferers plus multipath can be canceled down close to the noise floor without producing serious loss or distortion of a GPS signal. Design criteria are presented and limitations are examined. We also compare space-time processing with suboptimum space-frequency processing, and demonstrate by simulation that for equal computational complexity space-time processing slightly outperforms suboptimum space-frequency processing     

Space-time autoregressive filtering for matched subspace STAP

Practical space-time adaptive processing (STAP) implementations rely on reduced-dimension processing, using techniques such as principle components or partially adaptive filters. The dimension reduction not only decreases the computational load, it also reduces the sample support required for estimating the interference statistics. This results because the clutter covariance is implicitly assumed to possess a certain (nonparametric) structure. We demonstrate how imposing a parametric structure on the clutter and jamming can lead to a further reduction in both computation and secondary sample support. Our approach, referred to as space-time autoregressive (STAR) filtering, is applied in two steps: first, a structured subspace orthogonal to that in which the clutter and interference reside is found, and second, a detector matched to this subspace is used to determine whether or not a target is present. Using a realistic simulated data set for circular array STAP, we demonstrate that this approach achieves significantly lower signal-to-interference plus noise ratio (SINR) loss with a computational load that is less than that required by other popular approaches. The STAR algorithm also yields excellent performance with very small secondary sample support, a feature that is particularly attractive for applications involving nonstationary clutter.     

基于Kalman滤波的GPS/INS接收机自适应干扰抑制方法

 考虑到惯导信息辅助GPS(GPS/INS)接收机对干扰抑制实时性的要求,提出一种基于Kalman滤波的GPS/INS接收机自适应干扰抑制方法。自适应广义旁瓣相消(GSC)多采用低复杂度最小均方(LMS)算法更新权矢量,收敛速率较低,严重时会导致接收机定位中断。首先利用Householder变换构建GSC下支路的阻塞矩阵,用于阻塞任意二维阵型阵列接收的期望信号;再用Kalman滤波自适应更新下支路权矢量,从而有效提高阵列输出信干噪比(SINR)。理论分析和仿真结果说明本文方法可有效抑制干扰对接收机的影响,且具有实时性高的特点。     

Efficient robust AMF using the FRACTA algorithm

The FRACTA algorithm has been shown to be an effective space-time adaptive processing (STAP) methodology for the airborne radar configuration in which there exists nonhomogeneous clutter, jamming, and dense target clusters. Further developments of the FRACTA algorithm are presented here in which the focus is on the robust, efficient implementation of the FRACTA algorithm. Enhancements to the FRACTA algorithm include a censoring stopping mechanism, an alternative data blocking approach for adaptive power residue (APR) censoring, and a fast reiterative censoring (RC) procedure. Furthermore, a coherent processing interval (CPI) segmentation scheme for computing the adaptive weights is presented as an alternative approach to computing the adaptive matched filter (AMF) weight vector that allows for lower sample support and reduced computational complexity. The enhanced FRACTA algorithm, denoted as FRACTA.E, is applied to the KASSPER I challenge datacube which possesses dense ground target clusters that are known to have a significant deleterious effect on standard adaptive matched filtering (AMF) processors. It is shown that the FRACTA.E algorithm outperforms and is considerably more computationally efficient than both the original FRACTA algorithm and the standard sliding window processing (SWP) approach. Furthermore, using the KASSPER I datacube, the FRACTA.E algorithm is shown to have the same detection performance as the clairvoyant algorithm where the exact range-dependent clutter covariance matrices are known.     

On adaptive spatial-temporal processing for airborne surveillanceradar systems

Implementing the optimum spatial-temporal (angle-Doppler) processor involves two crucial issues: the selection of processing configurations, and the development of adaptive algorithms which can efficiently approach the performance potential of the selected configuration. Among the three available configurations, the joint-domain, the cascade space-time, and the cascade time-space, this work shows that, in contrast to a popular belief, the detection performance potentials of both cascade configurations can fall far below that of the joint-domain optimum. In addition, this work presents a new adaptive algorithm, called the Joint-Domain Localized Generalized Likelihood Ratio detection (JDL-GLR), which is data efficient i.e., with fast convergence to the joint-domain optimum, as well as computationally efficient, together with such desirable features as the embedded constant false-alarm rate (CFAR) and robustness in non-Gaussian interference     

Reiterative median cascaded canceler for robust adaptive array processing

A new robust adaptive processor based on reiterative application of the median cascaded canceler (MCC) is presented and called the reiterative median cascaded canceler (RMCC). It is shown that the RMCC processor is a robust replacement for the sample matrix inversion (SMI) adaptive processor and for its equivalent implementations. The MCC, though a robust adaptive processor, has a convergence rate that is dependent on the rank of the input interference-plus-noise covariance matrix for a given number of adaptive degrees of freedom (DOF), N. In contrast, the RMCC, using identical training data as the MCC, exhibits the highly desirable combination of: 1) convergence-robustness to outliers/targets in adaptive weight training data, like the MCC, and 2) fast convergence performance that is independent of the input interference-plus-noise covariance matrix, unlike the MCC. For a number of representative examples, the RMCC is shown to converge using ~ 2.8N samples for any interference rank value as compared with ~ 2N samples for the SMI algorithm. However, the SMI algorithm requires considerably more samples to converge in the presence of outliers/targets, whereas the RMCC does not. Both simulated data as well as measured airborne radar data from the multichannel airborne radar measurements (MCARM) space-time adaptive processing (STAP) database are used to illustrate performance improvements over SMI methods.     

Multistage partially adaptive STAP CFAR detection algorithm

A new method of partially adaptive constant false-alarm rate (CFAR) detection is introduced. The processor implements a novel sequence of orthogonal subspace projections to decompose the Wiener solution in terms of the cross-correlation observed at each stage. The performance is evaluated using the general framework of space-time adaptive processing (STAP) for the cases of both known and unknown covariance. It is demonstrated that this new approach to partially adaptive STAP outperforms the more complex eigen-analysis approaches using both simulated DARPA Mountain Top data and true pulse-Doppler radar data collected by the MCARM radar     

Reduced-rank STAP performance analysis

The space-time radar problem is well suited to the application of techniques that take advantage of the low-rank property of the space-time covariance matrix. It is shown that reduced-rank (RR) methods outperform full-rank space-time adaptive processing (STAP) when the space-time covariance matrix is estimated from a data set with limited support. The utility of RR methods is demonstrated by theoretical analysis, simulations and analysis of real data. It is shown that RR processing has two opposite effects on the performance: increased statistical stability which tends to improve performance, and introduction of a bias which lowers the signal-to-noise ratio (SNR). A method for evaluating the theoretical conditioned SNR for fixed RR transforms is also presented. It Is shown that while best performance is obtained using data-dependent transforms, the loss incurred by the application of fixed transforms (such as the discrete cosine transform) may be relatively small. The main advantage of fixed transforms is the availability of efficient computational procedures for their implementation. These findings suggest that RR methods could facilitate the development of practical, real-time STAP technology