Adaptive Interference Suppression and Target Detection with Enhanced Environment Learning Capabilities

In this letter, we address adaptive radar detection of point-like targets in Gaussian clutter with an unknown covariance matrix. To this end, we first exploit the symmetrically structured power spectral density of the clutter to transfer data from the complex to the real domain. Then, the spillover of target energy is incorporated into the design criteria to come up with two architectures capable of guaranteeing improved detection performances and range estimation. The performance assessments, conducted on both simulated data and real recorded datasets, demonstrate the effectiveness of the newly proposed detectors compared with the state-of-the-art counterparts, which ignore either the clutter spectral symmetry or the energy spillover.

We address adaptive detection of extended radar targets embedded in homogeneous Gaussian noise plus subspace interference. We assume that the actual steering vector lies in a fixed and unknown direction of a preassigned and known subspace, while interfering signals are supposed to belong to an unknown subspace, with directions possibly varying from one resolution cell to another. The resulting detection problem is formulated in the framework of statistical hypothesis testing and solved using an ad hoc algorithm strongly related to the GLRT. The performance analysis, carried out also in comparison to natural benchmarks, shows that the proposed detector is an effective solution to the problem of detecting a one-dimensional signal embedded in noise and unknown subspace interference.

The paper addresses adaptive detection of extended targets in Gaussian noise with unknown statistics. It is assumed that the radar can change the transmitted signal in azimuth. More precisely, it makes use of N N-dimensional signatures; the possible useful signal from each radar cell is a coherent pulse train while the disturbance is a Gaussian process, independent from cell to cell, but with the same (unknown) covariance matrix regardless of the illuminated one. Based on the above model, we propose an adaptive detector designed according to a two-step procedure. Its performance assessment and the comparison with a previously-proposed detector show that the proposed one can be a viable means to cope with uncertain scenarios.

In this paper an adaptive narrow-band interference (NBI) suppression scheme based on a code-aided technique in the global navigation satellite system inter-satellite links (ISLs) is proposed. Unlike the commonly used infinte impulse response (IIR) notch filter and frequency domain filtering, the code-aided technique does not require interference detection or fast Fourier transforms, and the adaptive performance is better. We introduce the principle and implementation of code-aided technique, and the concept of interference influence coefficient is proposed to evaluate the influence of residual interference on the carrier-to-noise ratio (CNR) at the receiving end. Through simulation and by combining the tracking CNR after antijamming, the optimal antijamming parameters and scheme are obtained. Meanwhile, we analyze the relationship between the loss of CNR caused by residual interference and the interference influence coefficient. The final results of the bit error rate (BER) and ranging error in ISLs indicate that the adaptive interference suppression scheme based on the code-aided technique can effectively suppress NBI and ensure the reliability of ISL communications. In the case where the pseudo code rate is 10.23 MHz and the BER does not exceed $1 \times {10^{ - 5}}$ , for the NBI with a bandwidth of 2 MHz, the maximum interference-to-signal ratio (ISR) that can be suppressed by the proposed scheme is 40 dB. For the NBI with a bandwidth of 1 MHz, the maximum ISR that can be suppressed is 55 dB, and for single frequency interference, the maximum ISR that can be suppressed is 75 dB.

The UWB through-wall radar plays an unique role in disaster rescue operation, by providing the identification and location of the on-site survivors under fire, earthquake, collapse and other disaster conditions, as well as anti-terrorism and explosive ordnance disposal scenarios. However, the scenario of the disaster site may be complex, and the internal structure and furnishings inside the building could not be predicted in advance. A special scene with metal obstacles in the detection space is investigated in this paper. The UWB through wall radar test system and its composition is presented, and then the algorithms for human target adaptive motion filter detection and obstacle interference suppression are discussed. Finally, a tracking trace aggregation algorithm for strong offset targets is proposed. By analyzing the test data., the results show that the adaptive target detection method proposed in this paper could effectively eliminate the interference of metal obstacles, achieving enough signal-to-noise ratio and signal-to-clutter ratio. The improved point trace aggregation algorithm is also verified in target tracking test, achieving ideal tracking and detection results are obtained.

Many radar detection algorithms that assume a stationary environment (clutter) have been proposed and analyzed over the years. However, in practice, changes in the nonstationary environment can perturb the parameters of the clutter distribution, or even alter the clutter distribution family, which can greatly deteriorate the target detection capability. To avoid such potential performance degradation, cognitive radar systems are envisioned which are required to rapidly realize the nonstationarity, accurately learn the new characteristics of the environments, and adaptively update the detector. In this paper, aiming to develop a fully cognitive radar for target detection in nonstationary environments, we propose a unifying framework that integrates (i) change-point detection of clutter distributions by using a data-driven cumulative sum (CUSUM) algorithm and its extended version, (ii) learning/identification of clutter distribution by applying sparse theory and kernel density estimation methods, and (iii) adaptive target detection by automatically modifying the likelihood-ratio test and corresponding detection threshold. Further, with extensive numerical examples, we demonstrate the achieved improvements in detection performance due to the proposed framework in comparison with a nonadaptive case, an adaptive matched filter method, and the clairvoyant case. We also use Wilcoxon rank-sum tests to evaluate the statistical significance of the performance improvements.

Adaptive Detection of Radar Range-Doppler Dual-Spread Targets in Lognormal-Texture Clutter

In this paper the problem of target detection using coherent pulse terrain was considered for MIMO radars. We have formulated this problem as a hypothesis test. Using the generalized likelihood ratio test (GLRT) two adaptive decision schemes have been developed for clutter with unknown statistics. The performance of the proposed detectors have been evaluated using Monte-Carlo simulations. The results show the superiority of the MIMO radaars with temporal coherent processing over the conventional phased arrays.

A method for target detection that achieves clutter rejection by the use of multiple observations of the same target scene is developed. Multiple scene observations can be obtained by processing separate frequency bands of the same target scene or by recursively processing sequential observations in time. Optimal detection algorithms are developed, based on the assumption that the image intensity can be modeled as a variable mean spatial Gaussian process. Several fast detection algorithms are derived which make use of the fact that the covariance matrices of many optical and infrared (IR) images can be accurately approximated by diagonal matrices. These algorithms provide efficient solutions to the problem of processing multiple correlated scenes or multiple sequential imaging. Computer simulations based on actual optical and IR image data were used for checking the theoretical results. The new detection algorithms achieved performance improvement in detection signal-to-noise ratio of up to 10 dB over conventional target correlation methods.

A common method for moving target detection in image sequences involves a self-adaptive threshold for segmentation. This paper discusses each pixel modeling in the image as a mixture of Gaussian distributions and gives an improved method to update the model. First, the characteristics of the different frames method and a mixture of Gaussian distributions will be discussed. And then, the two methods, different frames method and a mixture of Gaussian distributions are combined to decide which learning rate is suitable for the updating method at this moment. If there is no moving target in the image sequences, the background model should be updated quickly to get the real background with low noise. The better foreground images will be gotten by this method with low computational complexity. The improved algorithm performs more robustly and powerfully than the classical Gaussian Mixture Model in moving target detecting.

This paper addresses polarimetric adaptive coherent detection of radar targets embedded in sea clutter. Initially, radar clutter data across multiple polarimetric channels is modeled using a compound Gaussian framework featuring an unspecified speckle covariance matrix and lognormal texture distribution. Subsequently, three adaptive polarimetric coherent detectors are derived, employing parameter estimation and two-step versions of the generalized likelihood ratio test (GLRT): the complex parameter Rao and Wald tests. These detectors utilize both clutter texture distribution information and radar data’s polarimetric aspects to enhance detection performance. Simulation experiments demonstrate the superiority of three proposed detectors over the competitors, and that they are sensitive to polarimetric channel parameters such as secondary data quantity, target or clutter speckle correlation, and signal-to-clutter ratio disparity. Additionally, the proposed detectors exhibit a near-constant false alarm rate relative to average clutter power and speckle covariance matrix.

Adaptive detection of wideband radar targets embedded in homogeneous Gaussian clutter is discussed, by utilizing the persymmetric clutter covariance matrix. Relying on the decision schemes of the one-step and two-step generalized likelihood ratio tests, two adaptive persymmetric detectors are introduced to mitigate the demanding requirement of secondary data. The performance assessment conducted by Monte Carlo simulation confirms their constant false alarm rate properties. And the simulation results furthermore shows that, both aforementioned persymmetric detectors can effectively avoid the collapsing loss resulted from no target scattering in some of the range cells under test.

This paper studies adaptive radar detection of pointlike targets in the presence of Gaussian interference and possible diffuse multipath. For this problem, a Tunable Adaptive Matched Filter (T-AMF) detector which contains a tuning parameter ruled by the strength of multipath effects was proposed. This paper develops a two-stage strategy for the selection of the tuning parameter by jointly using a two-step energy detector and the expected likelihood (EL)-based principle. Once plugged into the T-AMF, the selected parameter yields a fully adaptive detector free of any user parameter. Remarkably, the new architecture possesses the desired constant false alarm rate (CFAR) property with respect to the disturbance covariance. Finally, its detection performance is assessed and validated via numerical examples.

We address the problem of adaptive radar detection of point-like targets in presence of Gaussian noise with unknown spectral properties, adopting a “second-order approach” to target modeling. In fact, the presence of a useful signal is modeled in terms of a rank-1 modification of the noise covariance matrix, similarly for the possible presence of a fictitious signal under the null hypothesis, aimed at increasing the selectivity of the detector. A set of homogeneous training data is assumed to be available. Results show that the proposed detectors can outperform natural competitors, especially assuming a limited number of training data.

The ability to detect and track dim unresolved targets in heavy clutter can be improved by the inclusion of the spectral dimension. Because of the great variation in targets, operating conditions and environments factors the spectral signature of the target is typically unknown. This paper present a fully adaptive matched filter and tracking paradigm which assumes no a priori information about the spectral signature of the target. It is shown that the full SCR gain can be realized in the absence of the spectral signature of the target. The ROC curve of the detector is used to show that performance loss due to the absence of spectral information is entirely due to an increase in the false alarm probability. This increase in PFA adversely effects tracker performance. The SCR track feature is developed to mitigate these effects. Track features provide an information shunt around the detection threshold nonlinearity that would otherwise block the flow of useful information to the tracker.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.