Millimeter wave radar with clutter measurements

A portable millimeter wave test radar system, also suitable for battery operation, gives interesting possibilities for clutter recordings at hard-to-reach sites. The designed system covers all common radar frequencies from the Ka- to V-bands and enables spatial detection of targets or clutter elements within an adjustable time gate, whereby spatial clutter profiles of rain can be analyzed. The construction allows full operation with non-scanning antennas as well. This is advantageous when measuring temporal RCS variations of selected targets or surface clutter from snow dunes.     

A tracking approach for targets with measurements containingjamming or clutter signals

The concept of maneuvering target tracking which is presented by K. Demirbas, (see ibid., vol.AES-23, p.757-66, 1987) is used to track maneuvering targets whose observations contain interference representing jamming or clutter signals. The resulting tracking approach produces state estimates that closely follow the actual state values, as in target tracking in a clear environment     

Growler detection in sea clutter with coherent radars

Ship navigation through ice-infested waters is a problem of deep concern to the Canadian shipping and exploration industry. Conventional marine radars have difficulty detecting small pieces of glacial ice called growlers which are very hazardous to vessels if struck. In an effort to improve detection performance, X-band radar measurements were collected and analyzed to determine the characteristics of clutter and growler returns that could lead to their separability. These analyses suggested that coherent medium dwell-time processing (i.e., integration times of a fraction of a second) could provide improvement In growler detectability over conventional methods; and long dwell-time processing (i.e., integration times on the order of seconds) could provide even further improvement. We report on the performance of two new coherent, medium dwell-time detectors. A third detector which is representative of conventional marine radar serves as a basis for comparison Although significant improvement in growler detectability is achieved, the two coherent detectors still fall short of operational requirements. This leads to the development of a long dwell-time detector which provides considerably better performance. Empirical results indicate that this new detector could well satisfy stringent operational requirements     

CFAR detection in clutter with unknown correlation properties

We develop a constant false-alarm rate (CFAR) approach for detecting a random N-dimensional complex vector in the presence of clutter or interference modeled as a zero mean complex Gaussian vector whose correlation properties are not known to the receiver. It is assumed that estimates of the correlation properties of the clutter/interference may be obtained independently by processing the received vectors from a set of reference cells. We characterize the detection performance of this algorithm when the signal to be detected is modeled as a zero-mean complex Gaussian random vector with unknown correlation matrix. Results show that for a prescribed false alarm probability and a given signal-to-clutter ratio (to be defined in the text), the detectability of Gaussian random signals depends on the eigenvalues of the matrix R_c^-1R_s. The nonsingular matrix R_c and the matrix R_s are the correlation matrices of clutter-plus-noise and signal vectors respectively. It is shown that the “effective” fluctuation statistics of the signal to be detected is determined completely by the eigenvalues of the matrix R_c^-1R_s. For example the signal to be detected has an effective Swerling II fluctuation statistics when all eigenvalues of the above matrix are equal. Swerling I fluctuation statistics results effectively when all eigenvalues except one are equal to zero. Eigenvalue distributions between these two limiting cases correspond to fluctuation statistics that lie between Swerling I and II models     

PDAF with multiple clutter regions and target models

This paper presents the theory of a new multiple model probabilistic data association filter (PDAF). The analysis is generalized for the case of multiple nonuniform clutter regions within the measurement data that updates each model of the filter. To reduce the possibility of clutter measurements forming established tracks, the solution includes a model for a visible target. That is, a target that gives sensor measurements that satisfy one of the target models. Other features included in the algorithm are the selection of a fixed number of nearest measurements and the addition of signal amplitude to the target state vector. The nonuniform clutter model developed here is applicable to tracking signal amplitude. Performance of this algorithm is illustrated using experimentally recorded over-the-horizon radar (OTHR) data.     

Radar detection in clutter

Clutter is defined as any unwanted radar return. The presence of clutter in a range/Doppler cell complicates the detection of a target return signal in that cell. In order to quantify the effect of clutter on the probability of detection, we must first specify sets of models suitable for representing the clutter and target. The simplest and most common model for clutter is based on the gamma density. We include two additional models, the NCG and NCGG clutter models for low grazing angles. They are motivated by physical arguments, the latter of which can accommodate the well-known phenomenon of speckle. Using one of these models for clutter together with one of several models for targets, we determine, in a range/Doppler cell, expressions for probabilities of detection of a target in the presence of clutter. It is important to control the probability of false alarms. The presence of clutter in a cell necessitates an increase in the detection threshold setting in order to control false alarms, thus lowering the probability of detection. If the clutter level is unknown, then we need to take measurements of the clutter and use it to adjust the threshold. The more clutter samples we take, the better the estimate of the clutter level and the less is the resulting detection loss. Using the expressions for the probability of detection in clutter, we can quantify the detection loss for a pair of commonly used constant false-alarm rate (CFAR) techniques and investigate how the loss varies with different parameter values, especially with regard to the number of clutter samples taken to estimate the clutter level.     

STAP for clutter suppression with sum and difference beams

A unique approach for airborne radar clutter rejection is developed and evaluated. This spatial and temporal adaptive approach employs the sum and difference beams of an antenna, which has significant practical advantages because it can be implemented with no/little change to the front-end electronics of airborne systems where sum and difference beams already exist for other reasons. The low sidelobe implementation of many sum and difference beam systems and the low gain of the difference beam in the direction of the target gives this approach the potential in many radars for a more predictable response pattern. The impact of these factors is shown in an airborne clutter rejection demonstration where the performance of this approach is compared with that of the factored approach (FA) using additional spatial channels and that of conventional pulse-Doppler (PD) processing. Reliable detection of an injected target is only achieved by this approach     

Three-dimensional target motion analysis using angle and frequency measurements

It is well known that passive target tracking by a single observer, commonly referred to as target motion analysis (TMA), can be done using angle and/or frequency measurements. Depending on the measurement set, different passive tracking procedures result: angle-only tracking (AOT), frequency-only tracking (FOT), and angle/frequency tracking (AFT). Whereas the two-dimensional passive tracking problem has been solved for a multitude of scenarios, thus giving a good insight into the parametric dependences, the three-dimensional problem has been discussed only in a few specific cases. To get a deeper insight into the parametric dependences of three-dimensional TMA, this work analyzes AOT and AFT in typical three-dimensional Airborne Warning and Control System (AWACS) scenarios. A Cramer Rao (CR) analysis of the problem reveals the parametric dependences of both methods and gives a clear idea of the increase in estimation accuracy by using AFT instead of AOT. The results obtained in this way are well confirmed by Monte Carlo simulations taking maximum likelihood (ML) as estimation procedure.     

Tracking in clutter with nearest neighbor filters: analysis andperformance

The measurement that is “closest” to the predicted target measurement is known as the “nearest neighbor” (NN) measurement in tracking. A common method currently in wide use for tracking in clutter is the so-called NN filter, which uses only the NN measurement as if it were the true one. The purpose of this work is two fold. First, the following theoretical results are derived: the a priori probabilities of all three data association events (updates with correct measurement, with incorrect measurement, and no update), the probability density functions (pdfs) of the NN measurement conditioned on the association events, and the one-step-ahead prediction of the matrix mean square error (MSE) conditioned on the association events. Secondly, a technique for prediction without recourse to expensive Monte Carlo simulations of the performance of tracking in clutter with the NN filter is presented. It can quantify the dynamic process of tracking divergence as well as the steady-state performance. The technique is a new development along the line of the recently developed general approach to the performance prediction of algorithm with both continuous and discrete uncertainties     

Adaptive detection schemes in compound-Gaussian clutter

Radar detection of coherent pulse trains embedded in compound-Gaussian disturbance with partially known statistics is discussed. We first give a thorough derivation of two recently proposed adaptive detection structures. Next, we derive a different detection scheme exploiting the assumption that the clutter is wide-sense stationary. Resorting to the theory of circulant matrices, in fact, we demonstrate that the estimation of the structure of the clutter covariance matrix can be reduced to the estimation of its eigenvalues, which in turn can be (efficiently) done via fast Fourier transform codes. After a thorough performance assessment, mostly carried on via computer simulations, the results show that the newly proposed detector achieves better performance than the two previously introduced adaptive detectors. Moreover, a sensitivity analysis shows that, even though this detector does not strictly guarantee the constant false alarm rate property with respect to the clutter covariance matrix, it is robust, in the sense that its performance is only slightly affected by variations in the clutter temporal correlation     

Polarization diversity detection in compound-Gaussian clutter

We present the problem of polarization diversity detection in compound-Gaussian clutter with unknown covariance matrix. To this end we assume that a set of secondary data, free of signal components and with the same covariance structure of the cell under test, is available. Due to the lack of a uniformly most powerful (UMP) detector we resort to a design procedure based upon the Rao and the Wald tests. Specifically we first derive the Rao and the Wald tests assuming that the covariance matrix is known, and then we substitute into the derived decision rules a suitable estimate of the clutter covariance. Interestingly, the newly proposed detectors share the constant false alarm rate (CFAR) property with respect to the texture statistical characterization. Moreover simulation results have shown that the Wald test based detector ensures a performance level higher than the Rao test. We have also conducted a further performance analysis, in the presence of real clutter data and in comparison with the previously proposed generalized likelihood ratio test (GLRT) based receivers, which highlights that, in general, the Wald test receiver outperforms its counterparts. Finally, since the newly proposed decision rules as well as the previously designed GLRTs do not ensure the CFAR property with respect to the clutter covariance matrix, we have developed a sensitivity analysis on the probability of false alarm (P/sub fa/), based on simulated clutter with covariance matrix estimated from real radar data. The results have shown that (P/sub fa/) is only slightly affected by variations in the clutter correlation properties and hence the CFARness is substantially achieved.     

Optimal CFAR detection in Weibull clutter

Optimal, in the maximum likelihood sense, constant false-alarm rate (CFAR) detection for Weibull clutter statistics, is investigated. The proposed OW (optimal Weibull) estimator is proved to be an asymptotically efficient estimator of the mean power of the Weibull clutter. Theoretical analysis of the OW-CFAR detector is provided, while detection performance analysis is carried out using the Monte Carlo simulation method. The operation of the median and morphological (MEMO)-CFAR detector in Weibull clutter statistics is also explained. It performs almost optimally in uniform clutter and, simultaneously, it is robust in multitarget situations. The performance of the proposed OW-CFAR detector in uniformal Weibull clutter is used as a yardstick in the analysis of the MEMO cell-averager (CA) and ordered statistic (OS) CFAR detectors. Nonfluctuating and fluctuating (Swerling II) targets are considered in detection analysis. The performance of the detectors is also examined at clutter edges     

Asymptotically optimum radar detection in compound-Gaussian clutter

An asymptotically optimum receiver designed for detecting coherent pulse trains in compound-Gaussian clutter is introduced and assessed. The proposed receiver assumes knowledge of the structure of the clutter covariance matrix, but does not require that of its amplitude probability density function (apdf). Performance is analytically evaluated, showing that the loss, as measured with respect to the corresponding optimum structure, is kept within a few dBs even for a relatively small number of integrated pulses and that it largely outperforms the matched-filter detector under all instances of practical interest. Interestingly, the proposed detector achieves constant false alarm rate (CFAR), regardless of the clutter envelope distribution and, consequently, its power     

Diffusion analysis of track loss in clutter

In tracking a target through clutter, the selection of incorrect measurements for track updating causes track divergence and eventual loss of track. The plot-to-track association algorithm is modeled as a Markov process and the tracking error is modeled as a diffusion process in order to study the mechanism of track loss analytically, without recourse to Monte Carlo simulations, for nearest-neighbor association in two space dimensions. The time evolution of the error distribution is examined, and the connection of the approach with diffusion theory is discussed. Explicit results showing the dependence of various performance parameters, such as mean time to lose track and track half-life, on the clutter spatial density are presented. The results indicate the existence of a critical density region in which the tracking performance degrades rapidly with increasing clutter density. An optimal gain adaptation procedure that significantly improves the tracking performance in the critical region is proposed     

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     

Structures for radar detection in compound Gaussian clutter

The problem of coherent radar target detection in a background of non-Gaussian clutter modeled by a compound Gaussian distribution is studied here. We show how the likelihood ratio may be recast into an estimator-correlator form that shows that an essential feature of the optimal detector is to compute an optimum estimate of the reciprocal of the unknown random local power level. We then proceed to show that the optimal detector may be recast into yet another form, namely a matched filter compared with a data-dependent threshold. With these reformulations of the optimal detector, the problem of obtaining suboptimal detectors may be systematically studied by either approximating the likelihood ratio directly, utilizing a suboptimal estimate in the estimator-correlator structure or utilizing a suboptimal function to model the data-dependent threshold in the matched filter interpretation. Each of these approaches is studied to obtain suboptimal detectors. The results indicate that for processing small numbers of pulses, a suboptimal detector that utilizes information about the nature of the non-Gaussian clutter can be implemented to obtain quasi-optimal performance. As the number of pulses to be processed increases, a suboptimal detector that does not require information about the specific nature of the non-Gaussian clutter may be implemented to obtain quasi-optimal performance     

Space-time adaptive radar performance in heterogeneous clutter

Traditional analysis of space-time adaptive radar generally assumes the ideal condition of statistically independent and identically distributed (IID) secondary data. To the contrary, measured data suggests realistic clutter environments appear heterogeneous and so the secondary data is no longer IID. Heterogeneity leads to mismatch between actual and estimated covariance matrices, thereby magnifying the loss between the adaptive implementation and optimum condition. Concerns regarding the impact of clutter heterogeneity on space-time adaptive processing (STAP) warrant further study. To this end, we propose space-time models of amplitude and spectral clutter heterogeneity, with operational airborne radar in mind, and then characterize expected STAP performance loss under such heterogeneous scenarios. Simulation results reveal loss in signal-to-interference plus noise ratio (SINR) ranging between a few tenths of a decibel to greater than 16 dB for specific cases     

Nonlinear clutter cancellation and detection using a memory-basedpredictor

In this paper, a nonlinear prediction (NLP) method is proposed as an alternative to the conventional linear prediction (LP) method for clutter cancellation. Because of the nonlinearity and non-Gaussianity of a clutter process, a nonlinear predictor is therefore needed to suppress clutter optimally. A memory-based predictor which uses a table look-up strategy to perform NLP is used in this work. The advantages of the memory-based approach are fast learning, algorithmic simplicity, robustness and suitability for parallel implementation. The memory-based predictor is then used as an adaptive detector for small surface target detection embedded in clutter. The effectiveness of the new method is demonstrated using real sea clutter data, and the results show improvement when compared with the conventional LP techniques     

Uniform versus nonuniform sampling when tracking in clutter

Many target tracking subsystems have the ability to schedule their own data rates; essentially they can "order" new information whenever they need it, and the cost is in terms of the sensor resource. But among the unmanaged schemes, uniform sampling, in which a new measurement is requested periodically and regularly, is the most commonly-used sampling scheme; deliberately nonuniform schemes are seldom given serious consideration. In this paper, however, we show that such schemes may have been discarded prematurely: a nonuniform sampling can have its benefits. Specifically, the nonuniform and uniform sampling schemes are compared for two kind of trackers: the probabilistic data association filter (PDAF), which updates its track based on a single scan of information at a time; and N-D assignment (an optimization-based implementation of the multi-hypothesis tracker (MHT)), in which the sliding window involves many scans of observations. We find that given the ground rule of maintenance of the same overall scan rate (i.e., the same sensor effort) uniform sampling is always optimal for the single-scan tracker in the sense of track life. However, nonuniform sampling can outperform uniform sampling if a more sophisticated multi-scan tracker is used, particularly when 1) the target has a high process noise, and/or 2) the false alarm density is high, and/or 3) the probability of detection is high.     

Dynamic Cramer-Rao bound for target tracking in clutter

Recently, there have been several new results for an old topic, the Cramer-Rao lower bound (CRLB). Specifically, it has been shown that for a wide class of parameter estimation problems (e.g. for objects with deterministic dynamics) the matrix CRLB, with both measurement origin uncertainty (i.e., in the presence of false alarms or random clutter) and measurement noise, is simply that without measurement origin uncertainty times a scalar information reduction factor (IRF). Conversely, there has arisen a neat expression for the CRLB for state estimation of a stochastic dynamic nonlinear system (i.e., objects with a stochastic motion); but this is only valid without measurement origin uncertainty. The present paper can be considered a marriage of the two topics: the clever Riccati-like form from the latter is preserved, but it includes the IRF from the former. The effects of plant and observation dynamics on the CRLB are explored. Further, the CRLB is compared via simulation to two common target tracking algorithms, the probabilistic data association filter (PDAF) and the multiframe (N-D) assignment algorithm.