Optimization of multiframe target detection schemes

We optimize the performance of multiframe target detection (MFTD) schemes under extended Neyman-Pearson (NP) criteria. Beyond the per-track detection performance for a specific target path in conventional MFTD studies, we optimize the overall detection performance which is averaged over all the potential target paths. It is shown that the overall MFTD performance is limited by the mobility of a target and also that optimality of MFTD performance depends on how fully one ran exploit the information about the target dynamics. We assume a single target situation and then present systematic optimization by formulating the MFTD problems as binary composite hypotheses testing problems. The resulting optimal solutions suggest computationally efficient implementation algorithms which are similar to the Viterbi algorithm for trellis search. The optimal performances for some typical types of target dynamics are evaluated via Monte-Carlo simulation     

Emulating random process target statistics (using MSF)

The topics of matrix spectral factorization (MSF) in conjunction with results from realization theory are applied here in simulating a stationary multi-input/multi-output (MIMO) linear system from a specified power spectral density matrix. MSF provides the appropriate transfer function matrix and realization theory specifies the corresponding parameters of a linear system having this transfer function This approach can be used to correctly capture the cross correlations that exist in a multichannel vector random process (representing a particular radar target signature). Aspects of the solution to this problem are illustrated using an original representative example problem with a closed-form answer. Existing software programs for accomplishing MSF are identified and one has been successfully validated using the known closed-form solution mentioned above. A streamlined realization algorithm (offered here as the primary theoretical contribution) can be used along with the MSF computer program and can now be applied to actual radar data. Besides multichannel spectral estimation, several other important applications of this same MSF solution methodology are summarized in Appendix B including extending applicability to the complex case (to handle radar polarization issues related to coherent phase processing), and finally in reexpressing second order statistics of a multichannel autoregressive moving average (ARMA) process as those of a simpler but mathematically equivalent autoregressive (AR) process of slightly higher dimensions (as another original application of the same major result offered here)     

Automatic target recognition for hyperspectral imagery using high-order statistics

Due to recent advances in hyperspectral imaging sensors many subtle unknown signal sources that cannot be resolved by multispectral sensors can be now uncovered for target detection, discrimination, and identification. Because the information about such sources is generally not available, automatic target recognition (ATR) presents a great challenge to hyperspectral image analysts. Many approaches developed for ATR are based on second-order statistics in the past years. This paper investigates ATR techniques using high order statistics. For ATR in hyperspectral imagery, most interesting targets usually occur with low probabilities and small population and they generally cannot be described by second-order statistics. Under such circumstances, using high-order statistics to perform target detection have been shown by experiments in this paper to be more effective than using second order statistics. In order to further address a challenging issue in determining the number of signal sources needed to be detected, a recently developed concept of virtual dimensionality (VD) is used to estimate this number. The experiments demonstrate that using high-order statistics-based techniques in conjunction with the VD to perform ATR are indeed very effective     

Enhanced target detection using stereoscopic imaging radar

An earlier correspondence reported experiments which suggested that the visibility of a target in clutter could be improved through stereoscopic viewing of high resolution radar images. Here we provide a more thorough discussion on the application of stereo for improving radar detection and recognition. Experiments are reported which confirm and extend the earlier reported results. An example of the use of stereo in a practical system is provided which demonstrates the potential for acquisition of high quality radar stereograms     

On detection using filter banks and higher order statistics

We suggest a method, based on the use of filter bank and higher order statistics (cumulants), for detection of transient signals. The method first uses a bandpass filter bank, which separates the spectrum of the observed signal into narrow frequency bands. Each subfilter of the filter bank is then followed by a cumulant estimator, and thereby suppressing colored noise. By selecting those subfilters that have large output energies, the filter bank can approximate the behavior of a matched filter. Moreover, no a priori information about the waveform of the signal is needed. The performance of the detector is evaluated by using a simulated signal as well as a measured signal. The presented detector is compared with the optimal matched filter detector.     

Multiple target detection using modified high order correlations

This work is concerned with the problem of multiple target track detection in heavy clutter. Using the “modified high order correlation” (HOC) process and a track scoring mechanism a new method is developed to perform data association and track identification in the presence of heavy clutter. Using this new scheme any number of very close, crossing or splitting target tracks can be resolved without increasing the computational complexity of the algorithm. The applicability of the method for continuous detection of target tracks that can originate and terminate at any scan is also demonstrated, In addition, the operating characteristics as a function of the clutter density are also provided. Simulation results on all the cases are presented     

Dim target detection using high order correlation method

This work presents a method for clutter rejection and dim target track detection from infrared (IR) satellite data using neural networks. A high-order correlation method which recursively computes the spatio-temporal cross-correlations between data of several consecutive scans is developed. The implementation of this scheme using a connectionist network is presented. Several important properties of the high-order correlation method which indicate that the resultant filtered images capture all the target information are established. The simulation results obtained with this approach show at least 93% clutter rejection. Further improvement in the clutter rejection rate is achieved by modifying the high-order correlation method to incorporate the target motion dynamics. The implementation of this modified high-order correlation using a high-order neural network architecture is demonstrated. The simulation results indicate at least 97% clutter rejection rate for this method. A comparison is also made between the methods developed here and the conventional frequency domain three-dimensional (3-D) filtering scheme, and the simulation results are provided     

Rate-constrained target detection

Given little or no a priori information, a real world detection system has the task of allocating limited resources. Often these resources are themselves detection systems that operate at a slower rate, for example a tracker following a radar. Detection systems that are described by serial multiple decision points with each decision device assumed to be more reliable but slower than the device preceding it are considered. It is shown that because of the rate constraint the Neyman-Pearson criterion is suboptimum. An optimum rate-constraint test is developed which has the same likelihood ratio form as the Neyman-Pearson test. A strategy for controlling multiple-point detection sequences is developed that depends only on local information and is shown to be optimum under fairly broad conditions. The strategy can be implemented in practical systems, since it depends on the hit rate which is both controllable and observable. This approach to decision-making has applications in many fields and shows a promise as both an analysis and design tool     

Novel radar target detection

Herein, a novel method of radar target detection based on 2-dimensional (2-D) fractal dimension is proposed. The proposed approach exploits both range information and azimuth information to estimate fractal dimension. Moreover, the approach can increase the number of data points. The above two merits result in the fractal dimension estimated by this method are more accurate and robust than the previous method. The detection performance is also better than the previous, which only makes use of 1-dimensional (1-D) information to estimate fractal dimension. Theoretical analysis and experimental results show that the proposed method performs well in a strong clutter background. The proposed method is also validated by real-life radar data, and the better result has been achieved.     

Universal equations for radar target detection

Two equations express detection probability and detectability factor for all Swerling targets and for partially correlated intermediate cases.     

Motion estimation for moving target detection

This paper describes a suite of techniques for the autonomous detection of moving targets by processing electro-optical sensor imagery (such as visible or infrared imagery). Specific application scenarios that require moving target detection capability are described, and solutions are developed under the constraints imposed by the scenarios. Performance evaluation results are presented using a test data set of over 300 images, consisting of real imagery (visible and infrared) representative of the application scenarios     

Radar target novel characteristic detection method

In this paper, a method of radar target detection based on 2-dimensional (2D) fractal dimension is proposed. The proposed approach exploits both range information and azimuth information to estimate fractal dimension. Moreover, the approach can increase the number of the data points. The above two merits result in the fractal dimension estimated by this method is more accurate and robust than the previous method. The detection performance is also better than the previous one, which only makes use of 1-dimensional (1-D) information to estimate fractal dimension. Theoretical analysis and experimental result show that the proposed method performs well in strong clutter background. The proposed method is also validated by real-life radar data, and the better result has been achieved.     

Optimal polarimetric processing for enhanced target detection

The results of a study of several polarimetric target detection algorithms are summarized. The algorithms were tested using real target-in-clutter data collected by the Lincoln Laboratory 35 GHz synthetic aperture radar (SAR) sensor. Fully polarimetric measurements (HH, HV, VV) are processed into intensity imagery using adaptive and nonadaptive polarimetric whitening filters (PWFs). Then a two-parameter constant false alarm rate (CFAR) detector is run over the imagery to detect the targets. Nonadaptive PWF processed imagery is shown to provide better protection performance than either adaptive PWF processed imagery or single-polarimetric-channel HH imagery. In addition, nonadaptive PWF processed imagery is shown to be visually clearer than adaptive processed imagery     

Full-polarization matched-illumination for target detection and identification

This paper investigates the optimization of the full-polarization radar transmission waveform and the receiver response to maximize either target detection or identification performance. Application of such full-polarization matched-illumination techniques to simulated VHF-band frequency response data of mobile surface targets yields a significant performance improvement over that corresponding to chirped full-polarization transmission waveforms.     

Beam overlap impact on phased-array target detection

The impact of beam overlap on the probability of detection during a single scan of a phased-array volumetric scan radar is examined. Rectangular and triangular beam packing arrangements are considered. Beam positions near the beam most centered on the target are allowed to contribute to the detection process. The treatment of the impact of beam overlap on target detection for a phased array is consistent with the results that would be achieved by proper use of the search radar range equation     

HF OTHR target detection and estimation subsystem

This paper describes the principle of the target detection and estimation with a high frequency ground wave over-the-horizon radar, introduces the structure and the implementation of the detection and estimation subsystem that can process signals in parallel. The accomplished experimental results demonstrate that this system can successfully detect and estimate the over-the-horizon ship and aircraft     

On the performance of polarimetric target detection algorithms

The performance of six polarimetric target detection algorithms is analyzed. The detection performance of the optimal polarimetric detector (OPD), the identity-likelihood-ratio-test (ILRT), the polarimetric whitening filter (PWF), the single-polarimetric channel detector, the span detector, and the power maximization synthesis (PMS) detector is compared. Results are presented for both probabilistic and deterministic targets in the presence of complex Gaussian clutter. The results indicate that the PWF and the ILRT typically achieve near optimal performance. The remaining detection algorithms typically yield performance that is degraded compared to the performance of the OPD, the PWF, and the ILRT     

Search radar detection and track with the Hough transform. II.detection statistics

For pt.I see ibid., vol.30, no.1, (Jan.1994). This paper describes the calculation of P_F and P_D for the Hough transform technique when the primary threshold crossings are weighted by their power before transforming (i.e., noncoherent integration). After expressions for P_F and P_D are derived, we examine the question of optimal granularity of the Hough accumulator space. We also investigate the relationship between primary and secondary thresholds and its effect on detectability     

State estimation using an approximate reduced statistics algorithm

The problem of state estimation using nonlinear additive Gaussian noise measurements is addressed. A geometric model for the posterior state density is assumed based on a multidimensional Haar basis representation. An approximate reduced statistics (ARS) algorithm, suggested by the parameter estimator of Kulhavy is then developed, using successive minimization of relative entropy between model densities and an approximate posterior density. The state estimator thus derived is applied to a bearings-only target tracking problem in a multiple sensor scenario     

A model for target detection with over-the-horizon radar

The effects of including Faraday rotation and multipath on the probability of detecting low-flying, distant, fluctuating and nonfluctuating targets immersed in Rayleigh noise plus clutter are studied. The effect of ionospheric fluctuations is also considered. It is found that both multipath and Faraday rotation strongly influence the detection statistics, with the effect being greatest for linearly polarized targets and less marked for symmetric targets