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.     

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     

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.     

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     

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     

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     

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     

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     

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.     

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     

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     

Low-altitude target detection by coastline operated marine radar

Relevant to a Richian family of fluctuating targets with a composite background of sea-plus-land clutter, the performance prediction of a radar operating in near-coastal regions is elucidated by assuming noncoherent integration of the pulses. Considering the dominance of land clutter, a modified K-distributed statistic is indicated for the overall clutter envelope; and the corresponding probability of false alarm and probability of detection are deduced for fixed threshold detection (s) based on N pulses integrated in the presence of the sea-plus-land clutter and the noise. Even when the target offers a dominant scattered echo, the worst situations of the land clutter affecting the detection performance are indicated     

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     

Superimposed HMM transient detection via target tracking ideas

The quickest detection of superimposed hidden Markov model (HMM) transient signals is addressed. It is assumed that a known HMM is always extant but at an unknown time a second known HMM may also be present, and overlapped with the previous. Two approaches are proposed. The first treats the superimposed HMMs as a unit with an expanded state space, thus converting the problem of detecting superimposed HMMs into detection of a change in HMM, this being readily solved using a previously proposed procedure. Such an approach, though excellent in terms of performance, is not suitable for the superposition of multiple HMMs with large state dimensions due to computational complexity. A second detection scheme (based on multiple target tracking ideas) with much lower computational needs but little loss in terms of performance, is therefore developed     

Improved SAR target detection via extended fractal features

The utility of the extended fractal (EF) feature is evaluated for the enhancement of the focus of attention (FOA) stage of a synthetic aperture radar (SAR) automatic target recognition (ATR) system. Unlike more traditional SAR detection features that distinguish target pixels from the background only on the basis of contrast, the EF feature is sensitive to both the contrast and size of objects. Furthermore, the structure for the EF feature computational algorithm lends itself to very fast implementation, and it can be shown that the new feature has a CFAR-like (constant false alarm rate) property. We demonstrate the improved performance using the new feature by testing a number of different detection approaches over two databases of SAR imagery     

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     

Difference beam aided target detection in monopulse radar

The classical detection step in a monopulse radar system is based on the sum beam only,the performance of which is not optimal when target is not at the beam center. Target detection aided by the difference beam can improve the performance at this case. However, the existing difference beam aided target detectors have the problem of performance deterioration at the beam center, which has limited their application in real systems. To solve this problem, two detectors are proposed in this paper. Assuming the monopulse ratio is known, a generalized likelihood ratio test(GLRT) detector is derived, which can be used when targeting information on target direction is available. A practical dual-stage detector is proposed for the case that the monopulse ratio is unknown. Simulation results show that performances of the proposed detectors are superior to that of the classical detector.     

Analysis of multiframe target detection using pixel statistics

Current track-before-detect (TBD) algorithms are developed and analyzed using a path statistic for each potential object trajectory. However this path statistic does not characterize overall performance gain. We propose a pixel-based statistic. This allows the TBD approach to be characterized as an image enhancement algorithm with detection gains compared with single frame detections. It is shown that for the TBD approach to have superior detection over single frame detection the target signal-to-noise ratio (SNR) must be greater than a threshold SNR in order to overcome the uncertainty in the target path. Tradeoffs are made for a class of velocity constrained target paths in terms of the detection gain with respect to the maximum target velocity and number of frames integrated     

Moving target detection via airborne HRR phased array radar

We study moving target detection in the presence of temporally and spatially correlated ground clutter for airborne high range resolution (HRR) phased array radar. We divide the HRR range profiles into large range segments to avoid the range migration problems that occur in the HRR radar data. Since each range segment contains a sequence of HRR range bins, no information is lost due to the division and hence no loss of resolution occurs. We show how to use a vector autoregressive (VAR) filtering technique to suppress the ground clutter. Then a moving target detector based on a generalized likelihood ratio test (GLRT) detection strategy is derived. The detection threshold is determined according to the desired false alarm rate, which is made possible via an asymptotic statistical analysis. After the target Doppler frequency and spatial signature vectors are estimated from the VAR-filtered data as if a target were present, a simple detection variable is computed and compared with the detection threshold to render a decision on the presence of a target. Numerical results are provided to demonstrate the performance of the proposed moving target detection algorithm