Further results on radar pointing error reduction using discreteextended Kalman filtering

A recently proposed method of reducing target glint errors in radar systems using extended Kalman filtering is further extended with the inclusion of and compensation for clutter effects. A discrete target model and discrete Kalman filter (DKF) are used. Simulation results demonstrating the DKF are presented, and the limits on the effectiveness of the method are investigated. The major advantage of the DKF is that it can be implemented in software in the digital processor of the radar, offering flexibility over continuous time filters. The ability of the filter to reduce clutter effects further demonstrates the usefulness of this technique for radar pointing error reduction     

Frequency-Agility Processing to Reduce Radar Glint Pointing Error

A technique to reduce radar pointing errors due to glint using frequency agility and amplitude weighting is presented. The reduction in rms tracking error is developed into an equation dependent upon the original glint tracking error, ?g, and the number of returns weighted, N. The rms tracking error is thereby reduced approximately by a factor of N. Finally, the equation formulated allows one to evaluate the reduction in glint error versus the number of frequencies chosen for frequency agility.     

A Real-Time Statistical Radar Target Model

Radar glint arises from the spatial phase perturbations of the radar signal echoed from a complex target. The glint phenomenon is closely related to the target radar cross section (RCS). This relationship plays a significant part in modern missile seeker signal processing. We present a statistical glint/RCS target model for realtime simulation of target signatures. Particular emphasis is placed upon the modeling and simulation of the appropriate glint/RCS statistical dependency. The fundamental approximation of locating uniformly distributed scatterers around the instantaneous radar centroid employed in the Delano-Gubonin [1, 2, 3] model is removed. A key result which follows from this representation is that the mean glint estimator is unbiased. This enables the estimation of model parameters from the first-order glint and RCS statistics which can easily be computed from measured data. A method of estimating model parameters is presented, and the results are applied to data from a typical combat aircraft target. It is shown that the Delano-Gubonin results are a special case of the results presented here. The 14.6 percent probability of glint falling beyond the target extent as derived by Delano [1] is not true in general. It is further shown that glint and RCS are uncorrelated but are statistically dependent. A Monte-Carlo simulation is performed to verify the assumptions made and to demonstrate the feasibility of the working models.     

Discrete extended Kalman filter for radar point error reduction

A recently proposed method of reducing target glint errors in radar systems is further enhanced with the development of a discrete target model and discrete Kalman filter (DKF). Simulation results demonstrating the DKF are presented, and the limits of the usefulness of this method are investigated. The major advantage of the DKF is that it can be implemented in software in the digital processor of the radar offering flexibility over continuous-time filters     

Target racking with glint noise

In tracking targets, there can be an uncertainty associated with the measurements in addition to their inaccuracy, which is usually modeled by aDditive Gaussian noise. However, the Gaussian modeling of the noise may not be true. Noise can be non-Gaussian. The non-Gaussian noise arising in a radar system is known as glint noise. The distribution of glint noise is long tailed and will seriously affect the tracking performance. An algorithm is developed which can significantly improve the tracking performance when glint noise is present     

A nonlinear IMM algorithm for maneuvering target tracking

In target tracking, the measurement noise is usually assumed to be Gaussian. However, the Gaussian modeling of the noise may not be true. Noise can be non-Gaussian. The non-Gaussian noise arising in a radar system is known as glint noise. The distribution of glint noise is long tailed and will seriously affect the tracking performance. We develop a new algorithm that can effectively track a maneuvering target in the glint environment The algorithm incorporates the nonlinear Masreliez filter into the interactive multiple model (IMM) method. Simulations demonstrate the superiority of the new algorithm     

Angular Glint and the Moving, Rotating, Complex Radar Target

Angular glint produces errors in radar-indicated target direction and in the Doppler frequency. Glint arises from phase perturbations of the radar signal echoed from a complex target, as compared to those from a point target. The phase gradient V? represents these glint effects very well. The direction of this vector is that of radar angle sensing. The Doppler shift is obtined from the dot product of the gradient and the target velocity. A procedure that isolates and measures glint phase variations alone, for the inaccessible target, is described.     

Analysis of CFAR performance in Weibull clutter

Recent interest has focused on order statistic-based (OS-based) algorithms for calculating radar detection thresholds. Previous analyses of these algorithms are extended, to determine closed-form approximations for the signal-to-clutter ratio required to achieve a particular probability of detection in clutter environments whose amplitude statistics are modeled by the Weibull distribution, and where the clutter dominates receiver noise. Performance is evaluated in both homogeneous and inhomogenous clutter. The analysis shows that the OS-based algorithm is quite robust against both interference and clutter edges. A method is suggested for improving performance at clutter inhomogeneities for short-range targets     

Volterra Series Synthesis of Nonlinear Stochastic Tracking Systems

One of the most important objectives of a radar angle-tracking loop is to keep the target within the beamwidth of the radar antenna. Thus, the behavior of the antenna pointing error is of vital interest in determination of tracking performance. For a tracker with a general polynomial linearity (representing nonlinear receiver characteristics), subjected to constant line-of-sight rate inputs, random initial antenna pointing errors, and white Gaussian receiver noise, a method to obtain approximations to the transient mean and variance of the antenna pointing error as explicit functions of time is presented.     

Phased array of large reflectors for deep-space communication

In this paper the problem of uplink array calibration for deep-space communication is considered. A phased array of many modest-size reflectors antennas is used to drastically improve the uplink effective isotropic radiated power of a ground station. A radar calibration procedure for the array phase distribution is presented using a number of in-orbit targets. Design of optimal orbit and the number of calibration targets is investigated for providing frequent calibration opportunities needed for compensating array elements phase center movements as the array tracks a spacecraft. Array far-field focusing based on the near-filed in-orbit (low Earth orbit (LEO)) calibration targets is also presented and array gain degradation analysis based on the position error of the array elements and in-orbit targets has been carried out. It is shown that errors in the in-orbit targets positions significantly degrade the far-field array gain while the errors in array elements positions are not very important. Analysis of phase errors caused by thermal noise, system instability, and atmospheric effects show insignificant array gain degradation by these factors     

Radar Partial Coherence Theory: An Introduction

The nature of physical phenomena is such that scattering from portions of an object, a number of objects, or clutter, is not completely unrelated; the underlying environment causes some degree of order in the phenomenon. Radar partial coherence theory describes a structure for the general target, or clutter, and its relationship to radar cross section, waveform coding, and the radar output signal. The clutter ambiguity function is introduced for extended bodies and embraces the (Woodward) ambiguity function for a point target. Due to nonlinear effects caused by partial coherence within the general target, radar signals and targets are formulated in terms of mutual coherence functions. The basic quantities describing the radar output are 1) the radar mutual coherence function (formulated in terms of the radar waveform) and 2) the target mutual coherence function which depends upon target properties, physical environment, and viewing aspect. Random noise (independent point scatterers) and partially coherent portions of reflecting bodies are made accountable in the theory. Partial coherence effects are treated as patches of reflected energy: self-coherent energy patches plus mutually coherent energy among the patches.     

A novel target detection approach based on adaptive radar waveform design

To resolve problems of complicated clutter, fast-varying scenes, and low signal-clutterratio (SCR) in application of target detection on sea for space-based radar (SBR), a target detection approach based on adaptive waveform design is proposed in this paper. Firstly, complicated sea clutter is modeled as compound Gaussian process, and a target is modeled as some scatterers with Gaussian reflectivity. Secondly, every dwell duration of radar is divided into several sub-dwells. Regular linear frequency modulated pulses are transmitted at Sub-dwell 1, and the received signal at this sub-dwell is used to estimate clutter covariance matrices and pre-detection. Estimated matrices are updated at every following sub-dwell by multiple particle filtering to cope with fast-varying clutter scenes of SBR. Furthermore, waveform of every following sub-dwell is designed adaptively according to mean square optimization technique. Finally, principal component analysis and generalized likelihood ratio test is used for mitigation of colored interference and property of constant false alarm rate, respectively. Simulation results show that, considering configuration of SBR and condition of complicated clutter, 9 dB is reduced for SCR which reliable detection requires by this target detection approach. Therefore, the work in this paper can markedly improve radar detection performance for weak targets.     

Radar Detection Prediction in K-Distributed Sea Clutter and Thermal Noise

The compound K-distribution model for high resolution seaclutter is extended to cover the addition of thermal noise. Thismodel is not only a good match to real data but also allows thepulse-to-pulse correlation of the clutter returns to be modeled. Thematching of real data and the analysis required for target detectionprediction are described, together with some typical detectionresults.     

Waveform Design for Multistatic Radar Detection

We derive the optimal Neyman-Pearson (NP) detector and its performance, and then present a methodology for the design of the transmit signal for a multistatic radar receiver. The detector assumes a Swerling I extended target model as well as signal-dependent noise, i.e., clutter. It is shown that the NP detection performance does not immediately lead to an obvious signal design criterion so that as an alternative, a divergence criterion is proposed for signal design. A simple method for maximizing the divergence, termed the maximum marginal allocation algorithm, is presented and is guaranteed to find the global maximum. The overall approach is a generalization of previous work that determined the optimal detector and transmit signal for a monostatic radar.     

Adaptive Radar Detection in Doubly Nonstationary Autoregressive Doppler Spread Clutter

The problem of adaptive radar detection in clutter which is nonstationary both in slow and fast time is addressed. Nonstationarity within a coherent processing interval (CPI) often precludes target detection because of the masking induced by Doppler spreading of the clutter. Across range bins (i.e., fast time), nonstationarity severely limits the amount of training data available to estimate the noise covariance matrix required for adaptive detection. Such difficult clutter conditions are not uncommon in complex multipath propagation conditions where path lengths can change abruptly in dynamic scenarios. To mitigate nonstationary Doppler spread clutter, an approximation to the generalized likelihood ratio test (GLRT) detector is presented wherein the CPI from the hypothesized target range is used for both clutter estimation and target detection. To overcome the lack of training data, a modified time-varying autoregressive (TVAR) model is assumed for the clutter return. In particular, maximum likelihood (ML) estimates of the TVAR parameters, computed from a single snapshot of data, are used in a GLRT for detecting stationary targets in possibly abruptly nonstationary clutter. The GLRT is compared with three alternative methods including a conceptually simpler ad hoc approach based on extrapolation of quasi-stationary data segments. Detection performance is assessed using simulated targets in both synthetically-generated and real radar clutter. Results suggest the proposed GLRT with TVAR clutter modeling can provide between 5–8 dB improvement in signal-to-clutter plus noise ratio (SCNR) when compared with the conventional methods.     

False alarm effects on estimation in multitarget trackers

An analysis of false alarm effects on tracking filter performance in multitarget track-while-scan radars, using variable correlation gates, is presented. The false alarms considered originate from noise, clutter, and crossing targets. The dimensions of the correlation gates are determined by filter prediction and measurement error variances. Track association is implanted either by means of a distance weighted average of the observations or by the nearest neighbor rule. State estimation is performed by means of a second-order discrete Kalman filter, taking into consideration random target maneuvers. Measurements are made in polar coordinates, while target dynamics are estimated in Cartesian coordinates, resulting in coupled linear filter equations. the effect of false alarms on the observation noise covariance matrix, and hence on state estimation errors, is analyzed. A computer simulation example, implementing radar target tracking with a variable correlation gate in the presence of false alarms, is discussed     

Maximum likelihood identification of glint noise

If the non-Gaussian distribution function of radar glint noise is known, the Masreliez filter can be applied to improve target tracking performance. We investigate the glint identification problem using the maximum likelihood (ML) method. Two models for the glint distribution are used, a mixture of two Gaussian distributions and a mixture of a Gaussian and a Laplacian distribution. An efficient initial estimate method based on the QQ-plot is also proposed. Simulations show that the ML estimates converge to truths     

比相单脉冲雷达测角与角闪烁研究

比相单脉冲雷达采用和差通道接收信号比相的方法测角。一般认为,雷达事实上并不是在测量目标的方向,而是在测量目标的角闪烁方向。从特殊的两点目标出发,分析了单点目标和复杂目标下比相单脉冲雷达和差通道方式测角的原理与内在异同,探讨了比相单脉冲雷达测角与角闪烁的关系,提出有关雷达测角的新的观点。     

Analysis of array errors and a short-time processor in airbornephased array radars

Array errors are inherent in a realistic phased array radar system. The influence of array errors on the clutter degrees of freedom and the clutter subspace in an airborne phased array radar is analyzed. Based on the presented theoretic results, a method of short-time processing followed by coherent integration is proposed for clutter suppression in airborne phased array radars. It can approximate the two-dimensional optimal processor well even in the presence of array errors, clutter fluctuations and aircraft drift, with a considerable saving in computations     

An interacting multiple model approach for target tracking withglint noise

The application of the interacting multiple model (IMM) estimation approach to the problem of target tracking when the measurements are perturbed by glint noise is considered. The IMM is a very effective approach when the system has discrete uncertainties in the dynamic or measurement model as well as continuous uncertainties. It is shown that this method performs better than the “score function” method. It is also shown that the IMM method performs robustly when the exact prior information of the glint noise is not available