量化对GPS接收机捕获性能影响的研究

由于接收机输入端的信噪比对接收机的捕获性能具有很重要的意义,所以要尽量减少接收机输入端信噪比的损失。本文首先给出了含高斯白噪声的GPS信号经过不同量化位数和量化电平的量化器后的输出信噪比计算公式;然后,在此基础上分析其对接收机捕获性能的影响;最后,仿真实验表明,接收机可以在不影响捕获性能的情况下,尽量选择合适的量化位数和量化门限。实验结果也为量化器的设计提供了重要的技术参考。     

高斯噪声中非高斯弱信号的双谱检测

传统的能量检测在接收信号为高斯分布下最优,其检测性能最终取决于信噪比。当信噪比下降时,检测性能必然恶化,而且这种恶化是不可避免的和难以改善的。为了改善弱信号的检测性能,本文同时利用了信号的三阶和二阶统计量构造一种双通道信号检测器,在强高斯噪声的背景下,只要弱信号蕴含有足够多的双谱信息,其检测性能将远远超过基于传统的能量检测器的性能。     

连续相位调制解调技术研究

连续相位调制（CPM）是一种相位连续的恒包络调制方式,以其较高的功率有效性和带宽利用率,在未来军用遥测系统中具有广阔的应用前景。研究适用于多进制部分响应CPM信号的相干解调技术,阐述了如何在信号状态网格图的基础上应用维特比算法对信号进行解调,并给出了最大似然序列检测（MLSD）接收机的结构。在高斯白噪声信道下对该算法进行性能仿真,结果表明,在载波恢复准确的情况下,该算法能够得到最佳的解调性能。     

基于导航雷达实测数据的CFAR检测算法分析

为提高导航雷达在复杂环境中的目标检测能力,研究了修正中值(MMD)检测器在导航雷达中的应用,并与经典非参量广义符号(GS)检测器和参量最小选择(SO)检测器的检测结果进行对比。仿真结果表明:GS检测器对海上单一目标有较好的检测性能,但是在多目标环境下的检测性能严重下降;SO检测器虽然对上述环境有较好的检测性能,但是由于杂波包络分布类型难以准确已知,杂波抑制能力较差;MMD检测器在多目标环境下有较好的检测性能和杂波抑制能力。     

噪声调制连续波雷达信号波形射频隐身特性

雷达信号波形的射频(RF)隐身性能是雷达系统能否适应现代战场环境的重要因素,雷达射频隐身信号波形设计是现代雷达系统设计中的重要课题.首先,在介绍随机噪声信号雷达原理的基础上,基于Schleher截获因子阐述了噪声调制连续波雷达信号波形的射频隐身特性.然后,分析了高斯噪声相位和频率调制连续波雷达输出自相关函数和高斯噪声相...     

先验失配条件下的知识辅助检测器稳健性分析

充分利用探测环境的先验信息是提高雷达探测能力的有效途径之一。先验信息必须在雷达检测算法设计阶段确定下来,因此先验信息与当前探测环境之间可能存在不一致性。以复合高斯杂波中的、利用纹理分量先验信息的知识辅助(KA)检测器作为研究对象,首先建立了该检测器检测性能与先验分布参数失配之间的量化关系,然后根据给定的杂波探测环境模型参数,分析了先验模型失配对检测性能的影响。分析结果表明:知识辅助检测器的稳健性与当前探测环境模型参数有关。进一步给出了先验模型失配的容许区间,当先验模型参数在这个区间内,知识辅助检测器性能优于不使用先验信息的检测器性能。     

一种提高脉冲雷达多普勒相位精度的方法

针对低信噪比条件下脉冲雷达模糊多普勒相位精度较低,可能导致相位测距时不能正确解相位模糊问题,基于EMD（Empirical Mode Decomposition,经验模式分解）区间阈值去噪方法,提出了一种新的提高多普勒相位精度的方法：利用EMD分解后各层信号的频率特性和能量特性,选取合适的阈值,并对各层信号进行区间阈值化处理,在提高信号信噪比的同时保持了信号的连续性.分别在回波信号噪声为高斯白噪声和AR（2）相关噪声的情况下,以及不同信噪比条件下,对该方法进行验证.仿真结果表明：在低信噪比条件下,当回波信号噪声为白噪声和相关噪声时,EMD区间阈值去噪方法能将回波信号信噪比提高5 dB,去噪性能优于小波阈值去噪方法,其对应多普勒相位精度能提高1倍以上.     

航空发动机气路故障诊断的SANNWA-PF算法

针对航空发动机非线性、非高斯的特点,提出一种用于航空发动机气路故障诊断的自适应神经网络权值调整粒子滤波（SANNWA PF）算法。该算法根据粒子分布情况确定分裂和调整的粒子数目,进而根据粒子权重采用正态分布的方式进行分裂,采用反向传插(BP)神经网络进行权值调整,缓解了粒子的退化和贫化,具有更强的自适应性能和跟踪能力。通过一维非线性跟踪模型和航空发动机气路故障诊断仿真研究表明:SANNWA PF算法具有良好的非高斯性能,相对粒子滤波一维非线性追踪模型估计精度提高约21%,航空发动机气路故障诊断在高斯噪声和非高斯噪声下分别提高约30%和26%,诊断速度分别提高约7倍和10倍。      

基于正交小波变换的目标检测方法

理论分析表明,独立高斯噪声经过正交小波变换后保持方差和独立性不变。基于Mallat的小波多分辨分析,通过对小波系数进行平方律处理,建立了基于正交小波变换的恒虚警率检测器模型,推导了相应的虚警和检测概率公式,分析了噪声未知情况下小波系数序列长度对检测性能的影响,并给出了合适的长度值。实验结果表明,所提出的检测器能满足不同虚警概率和杂波背景的要求,具有较好自适应性。     

拉普拉斯白噪声下的分组Turbo码

目前,传统航空测控通信所采用的纠错码大多是建立在高斯信道基础上的。然而,航空测控环境中不可避免存在着多种尖锐的噪声,测控通信纠错码的可靠性能在非高斯信道中尚未得到充分的研究。分析了一类国际航空遥测的分组Turbo码（BTC）在拉普拉斯白噪声信道下的译码和性能。将传统Chase迭代译码算法引入到拉普拉斯白噪声信道中,建立相应的数学模型,同时,基于该数学模型设计了3种不同的译码接收器下的BTC译码方案。仿真结果验证了该数学模型的正确性与可行性,在误码率为10^-4时最佳译码方案相比于硬限幅接收机有3.7 dB的增益,相比原有的高斯信道下的接收机仅有0.6 dB的性能损失。     

A Suboptimum Rank Test for Nonparametric Radar Detection

The optimum rank detector structure, in the Neyman-Pearson sense and under Gaussian noise conditions, is approximated by a suboptimum structure that depends on an adjustable parameter. This new rank detector, which operates on radar video signal, includes other well-known detectors as particular cases. The asymptotic relative efficiency (ARE) of the proposed rank detector is computed, with its maximum value the ARE of the locally optimum rank detector (LORD). The detection probability versus signal-to-noise ratio, and the effects of interfering targets are also calculated by Monte-Carlo simulations for different parameter values.     

Weak Signal Detection in Non-Gaussian Noise of Unknown Level

An adaptive threshold detector to test for the presence of a weak signal in additive non-Gaussian noise of unknown level is discussed. The detector consists of a locally optimum detector, a noise level estimator, and a decision device. The detection threshold is made adaptive according to the information provided by the noise level estimator in order to keep a fixed false-alarm probability. Asymptotic performance characteristics are obtained indicating relationships among the basic system parameters such as the reference noise sample size and the underlying noise statistics. It is shown that, as the reference noise sample size is made sufficiently large, the adaptive threshold detector attains the performance of a corresponding locally optimum detector for detecting the weak signal were the noise level known.     

Data quantization effects in CFAR signal detection

In this paper, we investigate data quantization effects in constant false alarm rate (CFAR) signal detection. Exponential distribution for the input data and uniform quantization are assumed for the CFAR detector analysis. Such assumptions are valid in the case of radar for a Swerling I target in Gaussian clutter plus noise and a receiver with analog square-law detection followed by analog-to-digital (A/D) conversion. False alarm and detection probabilities of the cell averaging (CA) and order statistic (OS) CFAR detectors operating on quantized observations are analytically determined. In homogeneous backgrounds with 15 dB clutter power fluctuations, we show analytically that a 12-bit uniform quantizer is sufficient to achieve false alarm rate invariance. Detector performance characteristics in nonhomogeneous backgrounds, due to regions of clutter power transitions and multiple interfering targets, are also presented and detailed comparisons are given     

Nonparametric rank detectors on quantized radar video signals

Uniform randomization of ties is required for defining distribution-free ranks of independent and identically distributed quantized samples. Formulas of rank probabilities are given and applied to radar detection under quantized video samples. For some detectors, and assuming Gaussian noise, the asymptotic loss L_∞(q) is calculated versus the normalized quantization step q, and the loss L(q) is estimated by Monte Carlo simulations. Both of these resulted in monotonic functions of q (0<q<1.1) that are independent of the other parameters. Furthermore, L(q)≈L_∞(q )⩽0.45 dB, as q<0.8. The quantization step q is normalized with respect to the noise standard deviation     

Nonparametric Radar Extraction Using a Generalized Sign Test

A nonparametric procedure used in a constant false alarm rate (CFAR) radar extractor for detecting targets in a background of noise with unknown statistical properties is described. The detector is based on a generalization of the well-known two-sample sign test and thus requires a set of reference noise observations in addition to the set of observations being tested for signal presence. The detection performance against Gaussian noise is determined for a finite number of observations and asymptotically, for both nonfluctuating and pulse-to-pulse Rayleigh fluctuating target statistics. It is noted that the performance loss, as compared to the optimum parametric detector, depends critically on the number of reference noise observations available when the number of hits per target is not large. In the same case a much larger loss is also found for a pulse-to-pulse fluctuating target even though the asymptotic loss is the same as for a nonfluctuating target. A comparison is finally made with a detector based on the Mann-Whitney test, which usually is considered to be one of the better nonparametric procedures for the two-sample case.     

Optimum Detection of an Optical Image on a Photoelectric Surface

The detection of an optical image in the presence of uniform background light is based on a likelihood ratio formed of the numbers of photoelectrons emitted from small elements of a photoelectric surface onto which the image is focused. When diffraction is negligible and the surface has unit quantum efficiency, this detector is equipollent with the optimum detector of the image-forming light. Its performance is compared with that of the threshold detector and that of a detector basing its decisions on the total number of photoelectrons from a finite area of the image. The illuminance of the image is postulated to have a Gaussian spatial distribution. All three detectors exhibit nearly the same reliability.     

Optimum and mismatched detection against K-distributed plusGaussian clutter

We derive the optimum radar receiver to detect fluctuating and non-fluctuating targets against a disturbance which is modeled as a mixture of coherent K-distributed and Gaussian-distributed clutter. In addition, thermal noise, which is always present in the radar receiver, is considered. We discuss the implementation of the optimum coherent detector, which derives from the likelihood ratio test under the assumption of perfectly known disturbance statistics, and evaluate its performance via a numerical procedure, when possible, and via Monte Carlo simulation otherwise. Moreover, we compare the performance of the optimum detector with those of two detectors which are optimum for totally Gaussian and totally K-distributed clutter respectively, when they are fed with such a mixed disturbance. We conclude that, though the optimum detector has a larger computational cost, it provides sensibly better detection performance than the mismatched detectors in a number of operational situations. Thus, there is a need to derive suboptimum target detectors against the mixture of disturbances which trade-off the detection performance and the implementation complexity     

Modified Savage and Modified Rank Squared Nonparametric Detectors

A modified form of the basic Savage statistic is considered and the performance of a modified Savage (MS) nonparametric detector using this modified statistic is derived. Also, a detector using a modified rank squared statistic (MRS) is introduced. The asymptotic relative efficiency (ARE) of the detectors is determined for chisquare, Rician, and log-normal signal fluctuations when the background noise is assumed Gaussian. The ARE performance of the generalized sign (GS) and Mann-Whitney (MW) detectors is also determined for these families of fluctuations. The ARE performance of the various detectors is then compared, and the results of a computer simulation are presented in which, for a finite number of samples, the performance of the modified detectors is compared with the performance of the GS and MW detectors. It is shown that when using a large number of reference noise samples, the ARE of the GS and MW detectors, the MRS and RS detectors, and the MS and Savage detectors are 0.75, 0.868, and 1, respectively. It is also shown that when using a finite number of reference noise samples the MS and MRS detectors can give a superior performance to that obtained with the MW detector, and that this is particularly true in the cases in which the degree of signal fluctuation is high.