BOC信号自适应门限判决捕获方法

BOC信号是一种分离频谱的调制方式,与传统的导航扩频信号相比,具有更强的抗干扰能力和更高的测量精度。但BOC信号自相关函数具有多峰值的特点,如何从接收端找出最大峰值并正确恢复BOC信号是技术难点。本文给出了一种BOC信号的捕获方案和具体的实现算法。     

Data Quantization for Narrowband Signal Detection

In automatic radar detection, digital integration of the envelope detector outputs is often used as a good approximation to the optimum. This requires quantizing the envelope detector outputs. In this paper, quantizer structures for narrowband signal detection are considered. Quantizer characteristics are derived to optimize performance as measured by the detector efficacy?an asymptotic performance measure. Asymptotic and finite sample performance results are presented. The results obtained are not limited in their application to Gaussian noise only, although this important case is given specific consideration.     

Adaptive Detection Algorithms for Multiple-Target Situations

The performance of a mean-level detector is considered for the case where one or more interfering target returns are present in the set of cells used in estimating the clutter-plus-noise level. A serious degradation of detection probability is demonstrated for all of the single-pulse Swerling target fluctuation models (i. e., cases 0, 2, and 4). Indeed, for fixed mean radar cross sections of the primary and interfering targets, the probability of detecting the primary target is asymptotic to values significantly less than unity as the signal-to-noise ratios of the returns approach infinity. A class of alternative adaptive detection procedures is proposed and analyzed. These procedures, based on ranking and censoring techniques, maintain acceptable performance in the presence of interfering targets, and require only a minor addition in hardware to a conventional mean-level detector.     

Calculating Detection Probabilities for Adaptive Thresholds

In calculating detection probabiities for radar and sonar systems it is usually assumed that the threshold required to yield a certain probability of false alarm is known. This is often not the case for real systems and therefore the threshold must be estimated using some measure related to the test statistic. This paper presents a calculation technique that handles estimated (adaptive) thresholds in a general framework that can be applied easily to many detection problems. False alarm and detection probabilities are calculated from the characteristic function of the noise or signal plus noise variate and the characteristic function of the threshold estimate. To illustrate the method the detection performance of overlapped discrete Fourier transforms (DFTs) is calculated for a narrowband Gaussian target signal.     

An Adaptive Detection Algorithm

A general problem of signal detection in a background of unknown Gaussian noise is addressed, using the techniques of statistical hypothesis testing. Signal presence is sought in one data vector, and another independent set of signal-free data vectors is available which share the unknown covariance matrix of the noise in the former vector. A likelihood ratio decision rule is derived and its performance evaluated in both the noise-only and signal-plus-noise cases.     

Signal Detection Using Bit-Slice Microprocessors

A bit-slice microprocessor configuration is presented which is applicable to implementation of real-time signal detection algorithms. The general hardware configuration and appropriate software functions are described. General algorithms which are of wide utility in signal detection applications are discussed in the context of the defined hardware configuration. To facilitate the presentation and indicate the practicality of this system configuration, the application of the hardware configuration and software techniques to target detection for passively received signals using multiple receive beams is discussed.     

An Lms Adaptive Array Using a Pilot Signal

A least mean square (LMS) adaptive array requires a reference signal. When the desired signal contains a pilot signal, it may be used as the reference signal. In this paper the steady-state performance of an LMS adaptive array in which the pilot signal is used as the reference signal is examined. It is shown that the LMS adaptive array occasionally suppresses the desired signal. The loop gain, which is an important parameter, is also considered.     

Dynamic Effects of Signal Normalizing on Adaptive Algorithms

A method is shown for suppressing the adaptive switching transient that arises in complex adaptive (LMS and stochastic approximation) algorithms due to signal normalization with controlled switched gains. The method involves rescaling of the adaptive weights with the normalizing gains just prior to the first adaptation with the new gains. In addition, an effective method of rescaling the adaptive gain factor based on the maximum eigenvalues of the respective covariance matrices is shown.     

Bandpass Signal Sampling and Coherent Detection

Coherent detectors in radar and communications receivers are generally implemented in the form of two parallel baseband channels which form in-phase (I) and quadrature (Q) components of a received RF/IF signal. Phase errors of several degrees due to imperfect matching of these separate channels limit the performance achievable from signal processors such as moving target indicators (MTI), coherent integrators, Doppler filters, antenna array processors, and coherent sidelobe cancellers. Thus methods in which a single analog to digital (A/D) converter samples and digitizes the IF signal directly, eliminating the need for IF to baseband conversion, have been of recent interest and are the subject of this paper. To obtain accurate coherent detection from IF samples taken near the Nyquist rate requires interpolation based upon a number of stored samples. An algorithm derived from sampling theory is defined and used to demonstrate accurate reconstruction of the original IF signal from digitized samples. In-phase and quadrature components of the signal are shown to be available from processed samples with demonstrated phase errors less than 0.2°.     

Data-Adaptive Detection of a Weak Signal

Motivated by a form of the likelihood-ratio-tesf statistic for detection of a rank-one Gaussian signal in colored Gaussian noise, we apply our earlier technique for estimation of a low-rank signal to the problem of estimating and subtracting the waveform of a strong sinusoidal interference prior to detection of a weak sinusoidal signal. We consider the difficult case in which samples of data are taken over a short interval of time or space and the frequencies of the sinusoidal signal and sinusoidal interference are more closely spaced than the reciprocal of the extent of the aperture. The method can be applied to cases of nonsinusoidal and/or random signals and interference. The most important assumption is that when the samples of the interference are arranged in matrix form the matrix is approximately of low rank in the sense that, with high probability, the interference-only matrix can be well approximated by a matrix of low rank.     

A Nominator-Selector Two-Stage Signal Detection Scheme

A two-stage approach to computationally efficient signal detection is to nominate candidate signal vectors using a suboptimal, but computationally cheap, detection scheme, and subsequently perform optimal detection on the candidates. Bounds on the detection performance of this approach are presented along with sufficient conditions for optimality. Examples are presented of an optimal and nearly optimal two-stage detection scheme where computational costs are drastically reduced relative to the singli-stage optimal detector.     

The Generation of Correlated Weibull Clutter for Signal Detection Problems

A method is discussed for generating Weibull vectors with a desired correlation matrix and specified parameters. Such vectors may represent samples of a correlated clutter signal. The presented method makes use of a suitable nonlinear transformation of random Gaussian vectors with correlated components. Computational aspects of the method are also discussed.     

飞机极限信号系统检测仪的设计与实现

首先介绍了飞机极限信号系统的功能和特点以及研制其检测设备的重要性。然后阐述了系统检测仪的总体设计思想,并对其具体的软硬件的实现方法进行了详细的介绍。     

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.     

Generation of Non-Gaussian Random Vibration Excitation Signal for Reliability Enhancement Test

During environment testing, the time histories of some dynamic environments follow non-Gaussian distribution. It is always assumed that the random vibration simulated follows Gaussian distribution, because the traditional digital random vibration control system can only supply the random vibration excitation signal of Gaussian. Yo simulate the real environment of product, a method is developed in this paper that can generate non-Gaussian random signal with specified power spectrum density （PSD）, skewness and kurtosis by shot noise. In this way, non-Gaussian random vibration can be produced on traditional electrodynamic shaker. It solves the problems of spectral valley and energy shortage in low frequency on omni-axis shaker. At last, the wavelet is used to analyze the non-Gaussian signal     

On the Limitations of Random Sensor Placement for Distributed Signal Detection

We consider the design of a sensor network for detecting an emitter which if present is known to be located in an interval but whose exact position is unknown. We seek to minimize the total system power consumption subject to detection performance constrains by carefully choosing the thresholds and positions of the sensors. Toward this goal, we propose an iterative algorithm for the optimization problem. Numerical results are given to provide insights into the design of such networks. We show that random sensor placement can perform poorly, in contrast to what many currently believe.     

时变微弱扩频信号检测

提出了一种解决微弱高动态扩频信号捕获的新方法,利用分数阶域滤波设计了伪码—时频联合匹配滤波的框架,最后进行了相关计算并给出了应用实例。     

自适应神经元在信号检测中的应用

本文对自适应神经元构成的自适应应网进行了分析,对该网络在信号检测中的应用进行了讨论产给出计算机仿真结果。