Estimating matched filter amplitude probability functions byfailure rate analysis

Performance analysis of echolocation systems requires knowledge of the probability density function (pdf) or cumulative distribution function (cdf) of a matched filter output. A method is presented to estimate these and other probability functions from data by estimating the failure rate function, a function employed in reliability theory. The method can also be used to derive approximations to closed-form probability functions. The method is demonstrated using experimental sonar and radar clutter data and a closed-form radar clutter model     

Detection of chi-square fluctuating targets in arbitrary clutter

In a recent paper, general expressions were derived for the density and cumulative probability functions of the amplitude of a linear matched-filter output given a nonfluctuating target in a clutter-limited environment. These expressions were based on the clutter amplitude density function. The results are extended to calculate the cumulative probability function of the output of a linear matched filter used to detect a chi-square fluctuating target in a clutter-limited environment. The resulting method is applied to a common radar clutter model, and experimental sonar data.     

Direct Evaluation of Radar Detection Probabilities

A simple and effective procedure for evaluating detectionperformances in radar and sonar detection problems is derived forboth fixed-threshold and adaptive-threshold detection. Using theprocedure, the cumulative probabilities of the test statistic can bedirectly evaluated from the moment generating functions bycalculating residues. The exact formulae for computing the detectionperformances for the chi-square family of fluctuating targets withan integer fluctuation parameter are given in a finite sum formwithout any special functions for both fixed threshold and cellaverageconstant false-alarm rate detection by using the methoddeveloped here.     

Planetary characteristics from radar observations

Conclusions Long wavelength radar observations of Venus yield a surface reflectivity of about 15%. Total power measurements at 12.5 cm and 3.6 cm strongly suggest that significant atmospheric absorption is operative in this wavelength region. If the observed low value of reflectivity at 3.6 cm is attributed to atmospheric absorption alone an opacity of = 1.14 is implied at this wavelength rather independently from assumptions concerning the surface scattering characteristics of Venus. An inverse ² opacity law for the atmosphere is consistent with the reflectivity measurements over the complete range of observations wavelengths.The mathematical characteristics of the Venusian backscatter law are the same as for the moon but wavelength-dependent mean effective slopes indicate that Venus appears smoother than the moon at all radar wavelengths.Considerable progress has been made toward obtaining a precise value for the Venusian axial rotation vector which is found to be oriented to within 10 degrees of the planet's orbital plane. The period of (retrograde) rotation lies within the range 242–250 days with the lower value favored by the statistics of the data. Regions of enhanced radar return fixed to the surface have been found and verified at a later conjunction. Measurements of the surface radar depolarization support the hypothesis that the prominences are due to increased surface roughness as opposed to regional increases of dielectric constant.Observations of Mercury strongly suggest that the rotation period of the planet is about 59 days, a conclusion which has been supported, a posteriori, by theoretical tidal calculations and rediscussions of optical observations of surface markings. Mercury has radar backscatter characteristics more similar to the moon than Venus and exhibits a reflectivity of about 5%.Mars has demonstrated strong variations of radar backscatter characteristics which appear correlated with the Martian longitude and, in turn, with the dark surface markings in its north equatorial zone. Particularly reliable correlations have been discovered with the positions of Trivium Charontis and Syrtis Major. The observed variations appear to be primarily manifested in terms of the Martian radar backscatter law or surface roughness as opposed to variations in the intrinsic surface material reflectivities although the observations are not sufficiently precise to resolve this question. Variations in surface materials are apparently also present but their degree is currently unassayable. The reflectivity of the average surface has been crudely determined to be about 7% which suggests that the surface of Mars is composed of underdense materials. The 7% value is consistent with the values of 7.5% and 5% for the moon and Mercury, respectively, and is significantly different from the 15% value for Venus,No unequivocal radar detection of Jupiter has been made although a statistically weak detection has been reported for a single opposition which could not be verified in succeeding attempts.     

Environment and Radar Operation Simulator

The environment and radar operation simulator (EROS) is a hardware system whose function is to produce realistic synthetic radar backscatter, incorporating both target and clutter. The simulator is electrically connected to a subject radar and responds in real time to the radar's antenna scan angle by producing the correct composite video signal.     

Loss from Approximations to Square-Law Detectors in Quadrature Systems with Postdetection Integration

The trend toward digital processing of radar and sonar signals has ledto the use of ?baseband? or bipolar video signals. The processingof both the in-phase (I) and quadrature (Q) channels generally allows an exact representation of the signals, and no loss of sensitivity need occur. However, for reasons of economy or weight only one channel is often implemented. This correspondence shows that the sensitivity loss for this case often deviates considerably from the 3-dB rule of thumb.     

Status of CR-like lower bounds for nonlinear filtering

The motivation and mechanics of utilizing Cramer-Rao (CR) -type lower bounds to gauge the performance of filters being evaluated in nonlinear estimation applications such as in sonar, sonobuoy, and radar target tracking are reviewed. The status of several similar alternative CR-type lower bounds that have been considered or used for this purpose and certain limitations and/or caveats associated with their use are offered. These results should be of interest to sonar, sonobuoy, and radar practitioners and Kalman-filtering or nonlinear-filtering theorists     

Detection of small objects in clutter using a GA-RBF neural network

Detection of small objects in a radar or satellite image is an important problem with many applications. Due to a recent discovery that sea clutter, the electromagnetic wave backscatter from a sea surface, is chaotic rather than purely random, computational intelligence techniques such as neural networks have been applied to reconstruct the chaotic dynamic of sea clutter. The reconstructed sea clutter dynamical system which usually takes the form of a nonlinear predictor does not only provide a model of the sea scattering phenomenon, but it can also be used to detect the existence of small targets such as fishing boats and small fragments of icebergs by observing abrupt changes in the prediction error. We applied a genetic algorithm (GA) to obtain an optimal reconstruction of sea clutter dynamic based on a radial basis function (RBF) neural network. This GA-RBF uses a hybrid approach that employes a GA to search for the optimum values of the following RBF parameters: centers, variance, and number of hidden nodes, and uses the least square method to determine the weights. It is shown here that if the functional form of an unknown nonlinear dynamical system can be represented exactly using an RBF net (i.e., no approximation error), this GA-RBF approach can reconstruct the exact dynamic from its time series measurements. In addition to the improved accuracy in modeling sea clutter dynamic, the GA-RBF is also shown to enhance the detectability of small objects embedded in the sea. Using real-life radar data that are collected in the east coast of Canada by two different radar systems: a ground-based radar and a satellite equipped with synthetic aperture radar (SAR), we show that the GA-RBF network is a reliable detector for small surface targets in various sea conditions and is practical for real-life search and rescue, navigation, and surveillance applications     

Synchronization improvements using traceability in spread spectrumsignal design

Synchronization improvements using traceability information found in the narrowband auto-ambiguity function (NB AAF) are demonstrated. This new auto-ambiguity function property, traceability, is the key to defining a signal design approach for solving the synchronization problems in spread spectrum system such as communication systems, radar networks, and sonar systems. This work defines the property and introduces a new tracing synchronization scheme that significantly reduces initial synchronization time and the number of false synchronization events     

Graphical Derivations of Radar, Sonar, and Communication Signals

The designer of a communication system often has knowledge concerning the changes in distance between transmitter and receiver as a function of time. This information can be exploited to reduce multipath interference via proper signal design. A radar or sonar may also have good a priori information about possible target trajectories. Such knowledge can again be used to reduce the receiver's response to clutter (MTI), to enhance signal-to-noise ratio, or to simplify receiver design. There are also situations in which prior knowledge about trajectories is lacking. The system should then utilize a single-filter pair which is insensitive to the effects induced by relative motion between transmitter, receiver, and reflectors. For waveforms with large time-bandwidth products, such as long pulse trains, it is possible to graphically derive signal formats for both situations (trajectory known and unknown). Although the exact form of the signal is sometimes not specified by the graphical procedure, the problem in such cases is reduced to one which has already been solved, i. e., the generation of an impulse equivalent code.     

Spectral Properties of Homogeneous and Nonhomogeneous Radar Images

On the basis of a two-dimensional, nonstationary white noisemodel for the complex radar backscatter, the spectral properties ofa one-look synthetic-aperture radar (SAR) system is derived. It isshown that the power spectrum of the complex SAR image is sceneindependent. It is also shown that the spectrum of the intensityimage is in general related to the radar scene spectrum by a linearintegral equation, a Fredholm's integral equation of the third kind.Under simplifying assumptions, a closed-form equation giving theradar scene spectrum as a function of the SAR image spectrum canbe derived.     

Dynamics and radar cross section density of chaff clouds

A new chaff cloud model (CCM) is described which is based on fundamental principles with modifications based on laboratory observations. Excellent approximations to the exact physical model have been developed which can rapidly predict the chaff fiber density and orientation as a function of location, time and fiber characteristics. Using this information, the time varying radar cross section (RCS) density is determined for any frequency and polarization anywhere within the chaff cloud. The results are consistent with full scale observations, and the computational speed allows the model to be integrated into existing real time radar simulations.     

Composite Bound on Arrival Time Estimation Errors

Time of arrival (TOA) estimation of narrowband signals is a problem of considerable practical interest in radar and sonar applications. A new technique is presented to analyze the mean square error (MSE) performance of TOA estimation schemes, based on recently developed lower bound. We obtain a complete characterization of the MSE as a function of the signal and noise parameters. The results are given in a simple closed-form analytical expression.     

飞机座舱复合玻璃电磁性能和透光性能研究

 研究了电磁性能 ,如电磁波后向散射功率、吸收功率以及反射功率与透明导电膜的内在关系 ;研究透光性能 ,如光学膜系的优化设计等。提出了透明导电膜与多层玻璃合理组合以提高电磁性能 ;薄金属膜与多层光学膜合理匹配以提高透光率。采用溅射和蒸发镀膜方法分别实现了上述优化设计的光学膜系结构 ,并与多层玻璃合理层合后 ,斜入射电磁后向散射功率下降 1 0 d B,透光率可达到 77%。     

A General Steepest Descent Algorithm

The following paper derives a general gradient process for extremization ion by imposing an " exponential decay" condition on the controlled variable. The general process is applied to two adaptive linear filter problems; a sonar application by Widrow [3] and a radar problem of Brennan & Reed [4]. The results are discussed in sections 11 and Ill, respectively. A central feature of the Fletcher-Powell process [1] is a matrix modification algorithm due to Davidon [2]. It is shown in Appendix A that this algorithm may be derived using the same exponential condition imposed on the proper error term.     

Space-time adaptive processing and adaptive arrays: specialcollection of papers

Space-time adaptive processing (STAP) and related adaptive array techniques hold tremendous potential for improving sensor performance by exploiting signal diversity. Such methods have important application in radar, sonar, and communication systems. Recent advances in digital signal processing technology now provide the computational means to field STAP-based systems. The objective of this special collection of papers is to examine the current state-of-the art in STAP technology and explore the remaining obstacles, practical issues and novel techniques required to implement STAP-based radar, sonar or communication systems     

Bounds on Time-Delay Estimation for Monochromatic Signals

Time-delay estimation (TDE) of monochromatic signals,observed by spatially separated sensors, is widely used for sourcelocalization in radar/sonar applications. We use lower bounds tostudy the accuracy of TDE as a function of signal to noise ratio(SNR), frequency, and sensor separation. We show that the Cramer-Rao bound, which is frequently used as a standard ofreference, yields optimistic predictions in many cases.     

Calculating Detection Probabilities for Systems Employing Noncoherent Integration

Cumulative probability distributions that occur in radar and sonar detection problems are calculated directly from the characteristic function by using a Fourier series. The error in the result is controlled by two parameters which can be adjusted to suit the application. The technique is applied to the problem of determining the detection performance of consecutive discrete Fourier transforms (DFTs) for a narrowband Gaussian signal with a rectangular spectrum. Since the characteristic function is used directly in its product form this technique does not suffer from the numerical problems associated with the partial fraction approach. The technique can handle many different problems in a single computational structure making it a valuable tool in system performance studies.     

Foliage attenuation and backscatter analysis of SAR imagery

The ability of a synthetic aperture radar (SAR) to detect targets under foliage is in part determined by the attenuation suffered by radiation propagating through the foliage and the backscatter from the foliage. MIT Lincoln Laboratory made measurements of foliage attenuation and backscatter using the NASA/JPL-UHF, L-, C-band fully-polarimetric SAR in July 1990. In this experiment, a 48 km² forested area near Portage, Maine was imaged. Twenty-seven 8 ft trihedral corner reflectors were arrayed throughout the imaged area in order to measure foliage attenuation. Ground truth was recorded at the time of the experiment in order to correlate the attenuation and backscatter results with foliage biophysical properties. The probability densities for foliage attenuation and for backscatter are determined as functions of frequency, polarization, and depression angle     

Scanning, Tracking, and Mapping State Variable Estimation Algorithms

Recursive state equation estimation algorithms are derived to determine optimal estimation error covariance and state estimate for a linear dynamic system, driven by time-varying and positionverying (or angle-varying) functions whose a priori covariance are described. Retracing the same trajectory with the system measuring device causes the position varying function to repeat and can significantly reduce estimation errors. Applications for these algorithms include improving accuracy of a position dependent quantity to be mapped, or recursively processing radar or sonar data from repeating scans over the same area. Three types of return path patterns are considered: 1) multiple independent returns, 2) reverse returns, and 3) cyclical returns.