Hongtao Su

Nonparametric Detection of FM Signals Using Time-Frequency Ridge Energy

In many practical applications, signals to be detected are unknown nonlinear frequency modulated (FM) and are corrupted by strong noise. The phase histories of signals are assumed to be unknown smooth functions of time and these functions are poorly modeled or unmodeled by a small number of parameters. Thus, the conventional parametric-based detection methods are invalid in these cases. This paper proposes a nonparametric detection method using the ridge energy of observations. The detection process consists of three steps, TF ridge detection, ridge energy extraction, and decision. First, the directionally smoothed-pseudo-Wigner-Ville distribution (DSPWVD) is introduced to highlight the instantaneous frequency (IF) points along a special direction on the IF curve of a signal from noise. Further, an angular maximal distribution (AMAD) is constructed from a set of DSPWVDs to highlight the entire IF curve. As a result, the TF ridge of an observation can be estimated well from its AMAD by the maxima position detector. Second, the ridge energy, the total energy along the TF ridge on the pseudo-Wigner-Ville distribution (PWVD), is extracted. A noisy signal has larger ridge energy than a pure noise does, with a large probability, because pure noise energy is randomly distributed throughout the TF plane while the signal energy in a noisy signal is concentrated along the estimated TF ridge. Third, the ridge energy of an observation is used as the test statistic to decide whether or not a signal of interest is present in the observation, where the decision threshold is determined by a large number of Monte Carlo simulations using pure noise. Finally, the simulation experiments to two test signals are made to verify the effectiveness of the proposed method.

Detection performance of spatial-frequency diversity MIMO radar

For spatial-frequency diversity radar, diversity channels may receive partially correlated target returns and channel signal-to-noise ratios (SNRs) may be different. For six typical scenarios of diversity radar, we design detection algorithms and analyze their detection performances in theory and via numerical results. It is shown that the detectors considering target correlation and channel SNR distribution can improve the detection performance of diversity radar. Whether certain channel SNRs can achieve a higher optimal detection probability than another depends on total channel SNR and false alarm rate.

Multipath signal resolving and time delay estimation for high range resolution radar

High range resolution radar target signature is a promising signature for radar automatic target recognition (ATR), and the multipath time-delay is used for height finding in high range resolution radar recently. We address multipath time-delay estimation under the scenario of closely spaced multipath for high range resolution radar, the target scattered signature can be resolved via the estimated channel parameters. By using the prior information of the typical multipath channel structure in radar application, a channel constrained MODE-RELAX algorithm is proposed to estimate the channel parameters. The resolved target signatures are used to evaluate the classification performance. Numerical results show that the proposed algorithm can estimate the time-delay very well for many cases, and the classification performance can be improved via the resolved signal.

Adaptive Beamforming for Nonstationary HF Interference Cancellation in Skywave Over-the-Horizon Radar

An adaptive degrees of freedom (DOFs) selection principle for notch-widening a partially adaptive beamformer is proposed. This principle indicates the relationship between the desired width of the notch and the required number of adaptive DOFs. The principle implies that a sufficient number of adaptive DOFs are required to produce a notch with the desired width at the direction of a nonstationary interference. That is to say, the number of adaptive DOFs should match the nonstationarity of the interference. The principle is validated by computer simulations that use a wide- notch beam space adaptive multiple sidelobe canceller (MSLC). According to the principle the nonstationary high-frequency (HF) interference cancellation performance is mainly affected by factors such as the number of adaptive DOFs and the nonstationarity of the interference. The effects of the number of adaptive DOFs and adaptive weights update interval on the nonstationary HF interference suppression performance are investigated by using beam space adaptive MSLC to process the experimental data collected by a trial HF over-the-horizon radar (OTHR). Experimental data processing results suggest that the principle will be very helpful in designing nonstationary interference cancellation schemes for practical implementation.

Track-before-detect method based on cost-reference particle filter in non-linear dynamic systems with unknown statistics

Detection of manoeuvring weak targets in radars often encounters circumstance where target movement is modelled by non-linear dynamic systems and received returns are corrupted by background noise of unknown statistics. It is known that the cost-reference particle filter (CRPF) is an efficient algorithm for state estimation of non-linear dynamic systems of unknown statistics. By combining an approximate logarithm likelihood ratio under the piecewise parametric model of signals with the CRPF algorithm, this study proposes a new track-before-detect detector, named CRPF-based detector, for manoeuvring weak target detection from received returns corrupted by background noise of unknown statistics. Experiments using simulated noise and real background noise of over-the-horizon radar are made to verify the CRPF-based detector. The results show that the CRPF-based detector has comparable performance with the two PF-based detectors for background noise of known statistics. For background noise of unknown statistics, the CRPF-based detector attains better detection performance than the two PF-based detectors where an assumptive probabilistic model is imposed on the background noise.

Radio Frequency Interference Cancelation for Skywave Over-the-Horizon Radar

In the user-congested high-frequency band, skywave over-the-horizon radar (OTHR) is easily interfered by radio frequency interference (RFI). Among the existing methods, transient and nontransient RFIs are suppressed by time-domain methods and beamforming methods, respectively. However, the existing beamforming methods cannot work in transient cases, whereas the time-domain methods are usually time-consuming and suffer performance loss in mass interference cases. Therefore, a unified method for two kinds of RFI cancelation is preferred. In this letter, based on the frequency characteristics of the RFI and the regular processing procedure of the skywave OTHR, an adaptive RFI cancelation scheme is proposed. In this scheme, the RFI is localized in the frequency domain, after which snapshots are chosen to complete the frequency-domain adaptive beamforming. The proposed scheme makes it possible to completely cancel both kinds of RFI in the beamforming stage, which is timesaving and proper for practical application. The performance of the proposed scheme is evaluated by experimental data.

Target detection in distributed MIMO radar with registration errors

In this paper, we consider a target detection problem in a distributed multiple-input multiple-output radar with registration errors. Registration errors mean the alignment errors when observation data from different distributed radars are transformed in a common coordinate system. Our study is motivated by the fact that the perfect registration condition is unavailable in practice, even after a registration process. A model for the imperfect registration is introduced where an uncertainty window centered on the cell being tested along range and azimuth in each spatial diversity channel is employed. A generalized likelihood ratio test (GLRT) detector and a Bayesian likelihood ratio test detector are developed based on the uncertainty windows. The probability of false alarm of the proposed GLRT detector is derived. For convenience of comparison, we also analyze the detection performance of the conventional likelihood ratio test (LRT) detector with registration errors. Simulation results demonstrate that the detection performance of the two proposed detectors outperform the conventional LRT detector in the imperfect registration case.

Spectrum reconstruction in the sky-wave OTHR using GAPES

The performance of a sky-wave over-the-horizon radar (OTHR) is severely degraded by the transient interference such as lighting and radio-frequency interference (RFI). After interference excision, the target detection performance is mainly determined by the performance of Doppler spectrum reconstruction. In this paper, instead of the regular linear prediction interpolation method, the gapped-data amplitude and phase estimation (GAPES) method is used for the Doppler spectrum reconstruction. The experimental results show that it is an effective way to achieve spectrum reconstruction.

Visual Attention-Based Target Detection and Discrimination for High-Resolution SAR Images in Complex Scenes

The conventional methods for target detection and discrimination in high-resolution synthetic aperture radar (SAR) images usually have low accuracy and slow speed, especially for large complex scenes. To overcome these drawbacks, in this paper, we propose a target detection and discrimination method based on visual attention model. In the detection stage, to pop out the targets and suppress the background clutter in the saliency map, we select the task-dependent scales from the Gaussian pyramid of the original SAR image. Moreover, we adopt the clustering algorithm to remerge several isolated focus of attention areas, which are obtained from the saliency map, into a complete target region. The candidate target SAR image chips are extracted with relative high accuracy and low time cost in this stage. Since there may be single target, multiple targets, or partial targets with complex clutter in each SAR image chip, it is hard to acquire accurate target-shaped blob via segmentation. Some classical discrimination features which are extracted based on target segmentation may lose effectiveness. In the discrimination stage of our method, to solve the above problem, based on the saliency and gist (SG) features for optical satellite images, we propose the modified SG (MSG) features for SAR target discrimination. The MSG features are complementary to each other and can provide a more complete description of the extracted SAR image chips without segmentation, which also reduces the computation burden. The experimental results on the synthetic images and miniSAR real SAR image data set demonstrate that the proposed target detection and discrimination method can detect and discriminate the targets from the complex background clutter with high accuracy and fast speed in high-resolution SAR images.

Multi-scale target detection in SAR image based on visual attention model

This paper proposes a novel method for synthetic aperture radar (SAR) target detection by using multi-scale SAR images based on visual attention model, which can automatically find the vehicle targets from the complicated background with clutters such as trees and buildings. In our method, firstly, a saliency map is obtained from a Gaussian pyramid of the original SAR image, where the image scales are selected based on the prior size information of the targets to be detected in the image. Secondly, we use the method based on shifts of the focus of attention (FOA) in the saliency map to get a binary image. Finally, the clustering algorithm based on the prior length of targets is employed to extract the target candidate chips in the binary image. In the experiment based on the real SAR image, we compare the proposed method with the classical constant false alarm rate (CFAR) target detection method, which indicates that our method can detect vehicle targets in the image more quickly and with fewer false alarms.