M-correlated sweeps performance analysis of mean-level CFARprocessors in multiple target environments

This paper is devoted to the detection performance evaluation of the mean-level (ML) constant false-alarm rate (CFAR) detectors processing M-correlated sweeps in the presence of interfering targets. The consecutive pulses are assumed to be fluctuating according to the Swerling I model. Exact expressions are derived for the detection probability of the conventional mean-level detector (MLD) and its modified versions under Rayleigh fluctuating target model. Performance for independent sweeps can be easily obtained by setting the sweep-to-sweep correlation coefficient equal to zero. Results are obtained for both homogeneous and nonhomogeneous background environments. It is shown that for fixed M, the relative improvement over the single sweep case increases as the correlation between sweeps decreases. For the same parameter values, the minimum MLD has the best performance in the presence of extraneous target returns among the reference noise samples     

Detection of Slow Fluctuating Targets with Frequency Diversity Channels

In this paper an exact closed-form expression for the radar detection probability is derived and results are plotted for a frequency diversity radar receiver. The receiver model performs post-detection integration on all received pulses in all diversity channels. The target model assumed is the slow fluctuating Rayleigh-distributed (Swerling case I target) scatterer. Each of the M frequency diverse channels receives N amplitude-correlated returns to give a total of NM post square-law detection integrations. The tabulated data falls between the two extreme cases, that for which all the returns are amplitude-correlated and that for which each return is independent. The plotted results fall close to the figures obtained through simple empirical relationships.     

Radar target identification using the bispectrum: a comparativestudy

Radar target identification is performed using time-domain bispectral features. The classification performance is compared with the performance of other classifiers that use either the impulse response or frequency domain response of the unknown target. The classification algorithms developed here are based on the spectral or the bispectral energy of the received backscatter signal. Classification results are obtained using simulated radar returns derived from measured scattering data from real radar targets. The performance of classifiers in the presence of additive Gaussian (colored or white), exponential noise, and Weibull noise are considered, along with cases where the azimuth position of the target is unknown. Finally, the effect on classification performance of responses horn extraneous point scatterers is investigated     

Correlation Function for a 1/f Spectrum

The performance of ground surveillance radars depends on the spatial correlation, or spectrum, of the radar returns. Many radar terrain images are suspected to have a I/f spatial spectrum. This spectrum is thought to be difficult to handle mathematically in modeling and analysis because of its singularity at zero frequency and its infinite energy. A two-dimensional, band-limited I/f spectrum has neither of these problems: this is convenient for modeling and analysis in any practical system because the sensor measuring of an image always introduces band limiting, even if the natural phenomena are not band limited. Also shows the presence of terrain with a 1/f spectrum will not be readily apparent from the correlation function of the terrain image because the shape of the correlation function is largely determined by the sensor band limit and not by the underlying spectrum.     

Performance of a Mean Level Detector Processing M-Correlated Sweeps

The performance of a mean level detector processing M-correlated consecutive sweeps is derived. Performance when sweeps are independent can be obtained simply as a special case. The background noise is assumed stationary Gaussian and pulses are fluctuating according to the Swerling I model. Results are obtained for both finite and infinite reference noise samples. It is shown that for fixed M the relative improvement over the single hit case increases when the correlation between sweeps decreases and as the probability of false alarm is kept at lower rates.     

Precision large scale air traffic surveillance using IMM/assignmentestimators

We present the development and implementation of a multisensor-multitarget tracking algorithm for large scale air traffic surveillance based on interacting multiple model (IMM) state estimation combined with a 2-dimensional assignment for data association. The algorithm can be used to track a large number of targets from measurements obtained with a large number of radars. The use of the algorithm is illustrated on measurements obtained from 5 FAA radars, which are asynchronous, heterogeneous, and geographically distributed over a large area. Both secondary radar data (beacon returns from cooperative targets) as well as primary radar data (skin returns from noncooperative targets) are used. The target IDs from the beacon returns are not used in the data association. The surveillance region includes about 800 targets that exhibit different types of motion. The performance of an IMM estimator with linear motion models is compared with that of the Kalman filter (KF). A number of performance measures that can be used on real data without knowledge of the ground truth are presented for this purpose. It is shown that the IMM estimator performs better than the KF. The advantage of fusing multisensor data is quantified. It is also shown that the computational requirements in the multisensor case are lower than in single sensor case, Finally, an IMM estimator with a nonlinear motion model (coordinated turn) is shown to further improve the performance during the maneuvering periods over the IMM with linear models     

A CFAR adaptive matched filter detector

An adaptive algorithm for radar target detection using an antenna array is proposed. The detector is derived in a manner similar to that of the generalized likelihood-ratio test (GLRT) but contains a simplified test statistic that is a limiting case of the GLRT detector. This simplified detector is analyzed for performance to signals on boresight, as well as when the signal direction is misaligned with the look direction     

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.     

Discrete-time min-max tracking

The problem of optimal state estimation of linear discrete-time systems with measured outputs that are corrupted by additive white noise is addressed. Such estimation is often encountered in problems of target tracking where the target dynamics is driven by finite energy signals, whereas the measurement noise is approximated by white noise. The relevant cost function for such tracking problems is the expected value of the standard H/sub /spl infin// performance index, with respect to the measurement noise statistics. The estimator, serving as a tracking filter, tries to minimize the mean-square estimation error, and the exogenous disturbance, which may represent the target maneuvers, tries to maximize this error while being penalized for its energy. The solution, which is obtained by completing the cost function to squares, is shown to satisfy also the matrix version of the maximum principle. The solution is derived in terms of two coupled Riccati difference equations from which the filter gains are derived. In the case where an infinite penalty is imposed on the energy of the exogenous disturbance, the celebrated discrete-time Kalman filter is recovered. A local iterations scheme which is based on linear matrix inequalities is proposed to solve these equations. An illustrative example is given where the velocity of a maneuvering target has to be estimated utilizing noisy measurements of the target position.     

Evaluating a multiple-hypothesis multitarget tracking algorithm

The performance evaluation of multiple-hypothesis, multitarget tracking algorithm is presented. We are primarily interested in target-detection/track-initiation capabilities as measures of performance. Through Monte Carlo simulations, a multiple-hypothesis tracking algorithm was evaluated in terms of 1) probability of establishing a track from target returns and 2) false track density. A radar was chosen as the sensor, and a general multiple-hypothesis, multitarget tracking algorithm was used in the Monte Carlo simulations. The simulation results predict the probability of establishing a track from returns of a target as well as the false track density per scan volume per unit time. The effects of the target radar cross section and the radar power, measured through the mean signal-to-noise ratio (SNR) were studied, as were the effects of detection threshold and track quality threshold. Computational requirements were also investigated     

Calculating the performance of linear and square-law detectors

A method is presented for calculating the performance of linear and square-law detectors in detection schemes that employ noncoherent integration. The method consists of transforming the coherent characteristic function, which is usually easy to obtain to a noncoherent moment generating function describing the test statistic of a linear or square-law detector. The method provides a single mathematical framework for many signal models (both classical and new) and can be implemented using standard numerical routines. Although the method is not always optimum in terms of computing speed for specific classical models, its common approach for all signal models makes it very efficient in term of learning and implementation times. Classical results as well as results for an extended set of target models consisting of an arbitrary number of constant amplitude random phase returns are presented to demonstrate the technique. It is shown for the signal parameters considered that the performance difference between the linear and square-law detectors is relatively insignificant     

Interacting multiple model tracking with target amplitude feature

A recursive tracking algorithm is presented which uses the strength of target returns to improve track formation performance and track maintenance through target maneuvers in a cluttered environment. This technique combines the interacting multiple model (IMM) approach with a generalized probabilistic data association (PDA), which uses the measured return amplitude in conjunction with probabilistic models for the target and clutter returns. Key tracking decisions can be made automatically by assessing the probabilities of target models to provide rapid and accurate decisions for both true track acceptance and false track dismissal in track formation. It also provides the ability to accurately continue tracking through coordinated turn target maneuvers     

Fade Probabilities: Measures for Predicting Search-Radar Performance

By itself, the cumulative probability of detection has been inadequate for accurate performance prediction of the AN/APQ-159, airborne, F-5 radar. For this reason, quantitative measures of fading of target echoes were formulated. Probabilities of 10-percent fade and of triple miss are defined here, and algorithms, as well as some important general properties, are derived for their efficient computation by machine in terms of the single-scan probabilities of detection. Both probabilities are cumulative quantities, dependent on the total number of independent looks at the target.     

Comparison of Two Target Classification Techniques

It has been shown that radar returns in the resonance region carry information regarding the overall dimensions and shape of targets. Two radar target classification techniques developed to utilize such returns are discussed. Both of these techniques utilize resonance region backscatter measurements of the radar cross section (RCS) and the intrinsic target backscattered phase. A target catalog used for testing the techniques was generated from measurements of the RCS of scale models of modern aircraft and naval ships using a radar range at The Ohio State University. To test the classification technique, targets had their RCS and phase taken from the data base and corrupted by errors to simulate full-scale propagation path and processing distortion. Several classification methods were then used to determine how well the corrupted measurements fit the measurement target signatures in the catalog. The first technique uses nearest neighbor (NN) algorithms on the RCS magnitude and (range corrected) phase at a number (e.g., 2, 4, or 8) of operating frequencies. The second technique uses an inverse Fourier transformation of the complex multifrequency radar returns to the time domain followed by cross correlation. Comparisons are made of the performance of the two techniques as a function of signal-to-error noise power ratio for various processing options.     

Explicit formulas for two state Kalman, H/sub 2/ and H/sub /spl infin// target tracking filters

The continuous time, two state, target tracking problem is considered from the Kalman, H/sub 2/, and H/sub /spl infin// filter viewpoint. While previous treatments were numerical in nature, analytic transient responses and infinite horizon solutions with analytic performance expressions are presented here. Tracking indices, involving the maneuver and measurement uncertainties, are shown to have a role for both the steady state and transient responses. In addition, the H/sub /spl infin// tracker has a sensor index involving the performance bound and measurement uncertainty, which, along with the tracking index, plays a significant role in the H/sub /spl infin// tracker expressions. Analytical expressions for the probability of target escape, the probability that the target position will be outside the radar beamwidth (BW), are developed not only to compare the performance of various trackers, but also as a design tool to meet tracking specifications. Examples illustrate the performance of the target trackers as a function of the error gain upper bound.     

A Sequential Approach to Target Discrimination

The Wald sequential probability ratio test is applied to the discrimination of targets observed by a radar or other sensor and a form for the classifier involving linear predictive filtering is developed. In this sequential approach, a target is illuminated with consecutive pulses until a classification of the target can be made to within a prescribed probability of error. Because of the linear-predictive formulation, the computational and storage requirements for the classifier are related only to the number of returns necessary to predict the target signature and not to the length of signature observed; a classifier with modest storage and computational requirements can be employed to process signatures consisting of an arbitrarily large number of returns. The classifier is based on some well-known results in mean-square filtering theory and has a simple intuitive interpretation. The classifier structure can also be related to autoregressive time series analysis and innovations process concepts and has an interpretation in the frequency domain in terms of the maximum entropy and maximum likelihood spectral estimates for the target signatures.     

Signal-to-Noise Ratio Calculations in Pulse and Pulse Doppler Radars

In low pulse-repetition frequency (PRF) pulse radars, signal-to-noise ratio (SNR) is usually calculated on a per pulse basis and this value is then multiplied by the number of pulses integrated to obtain the SNR for a given duration of target illumination. In high PRF pulse Doppler radars, SNR is usually calculated by using the centerline power of the transmitted signal spectrum as the target return power because the centerline is kept in the receiver and returns of the PRF lines are notched out [1]. We show here that both methods of SNR calculations are entirely equivalent for matched transmit-receive radar systems.     

Threshold Control for Automatic Detection in Radar Systems

In automatic detection in radar systems an estimate of background clutter power is used to set the detection threshold. Usually detection cells surrounding the cell under test for the presence of a target are used to estimate the clutter power. In the research reported herein, the target location is taken to be uncertain and thus returns from a target could corrupt this clutter power estimate. It is shown how the threshold should be varied to compensate for the resulting degradation in detection performance. The threshold control procedure is based on a priori information about target location that could be supplied by the radar's tracking system. In addition, a simple procedure for calculating detection and false alarm probabilities for Swerling II target models is presented.     

Correlated Clutter and Resultant Properties of Binary Signals

A simple Markov process model of binary, digitized radar clutter returns is assumed. Probability distributions for the number of hits in n observations are developed for small n with a binary parameter describing the process derived for Rayleigh distributed clutter. Tables of distributions are included, along with an example to show the effects of correlation on the false-alarm probabilities of a sliding-window detector.     

A Fast Beamforming Algorithm for Large Arrays

This beamforming algorithm is written specifically for array radars in which the number of array elements K is very large compared with the number of jammers L the radar is designed to suppress. It uses a set of M noise vectors to construct a basis for the jammer component of the antenna output vectors. The component of the quiescent weight vector orthogonal to each basis vector is calculated, renormalized to unit length, and identified as the adapted weight vector. This algorithm is effective in the suppression of many types of jammers. The number of noise samples M required in the construction of the adapted weight vector is approximately equal to L. In the special case of L narrowband noise jammers, for example, a choice of M = L usually reduces the receiver output jammer power to a few dBs above the white noise background. It is permissible to have M相似文献