Spatial adaptive subspace detection in OTH radar

The work presented here addresses the problem of target detection against spatially structured interference composed of jamming plus noise, where for practical reasons, the received target wavefront may also deviate from the traditional plane wave model. This detection problem arises in over-the-horizon (OTH) radar systems where spatially distributed targets often compete for detection against directional interference that is spread over the entire range-Doppler search space. Conventional detection processing schemes are compared with a recently proposed adaptive subspace detector (ASD) that takes both the spatial structure of the interference and the possibility of target wavefront distortions into account. Experimental array data recorded by the Jindalee sky-wave and Iluka surface-wave OTH radar systems, located in central and northern Australia respectively, is used to evaluate detection performance.     

Stochastic-constraints method in nonstationary hot-cluttercancellation. I. Fundamentals and supervised training applications

This paper considers the use of spatio-temporal adaptive array processing in over-the-horizon radar (OTHR) and airborne radar applications in order to remove nonstationary multipath interference, known as “hot clutter”. Since the spatio-temporal properties of hot clutter cannot be assumed constant over the coherent processing interval (CPI), conventional adaptive techniques fail to provide effective hot-clutter mitigation without simultaneously degrading the properties of the backscattered radar signals, known as “cold clutter”. The approach presented incorporates multiple “stochastic” (data-dependent) constraints to achieve effective hot-clutter suppression, while maintaining distortionless output cold-clutter post-processing stationarity     

Adaptive Radar Detection in Doubly Nonstationary Autoregressive Doppler Spread Clutter

The problem of adaptive radar detection in clutter which is nonstationary both in slow and fast time is addressed. Nonstationarity within a coherent processing interval (CPI) often precludes target detection because of the masking induced by Doppler spreading of the clutter. Across range bins (i.e., fast time), nonstationarity severely limits the amount of training data available to estimate the noise covariance matrix required for adaptive detection. Such difficult clutter conditions are not uncommon in complex multipath propagation conditions where path lengths can change abruptly in dynamic scenarios. To mitigate nonstationary Doppler spread clutter, an approximation to the generalized likelihood ratio test (GLRT) detector is presented wherein the CPI from the hypothesized target range is used for both clutter estimation and target detection. To overcome the lack of training data, a modified time-varying autoregressive (TVAR) model is assumed for the clutter return. In particular, maximum likelihood (ML) estimates of the TVAR parameters, computed from a single snapshot of data, are used in a GLRT for detecting stationary targets in possibly abruptly nonstationary clutter. The GLRT is compared with three alternative methods including a conceptually simpler ad hoc approach based on extrapolation of quasi-stationary data segments. Detection performance is assessed using simulated targets in both synthetically-generated and real radar clutter. Results suggest the proposed GLRT with TVAR clutter modeling can provide between 5–8 dB improvement in signal-to-clutter plus noise ratio (SCNR) when compared with the conventional methods.     

On-board regeneration of uplink signals using a blind multichannelestimator

We propose a restoration method to compensate distortions caused by cross-polarization interference (CPI) and multipath propagation when multichannel transmission is employed in the uplink of an on-board-processing (OBP) satellite. The proposed baseband signal processing architecture is numerically robust and efficient as vector operations are avoided by efficient orthogonal transformations. Since the algorithm is based on higher-than-second-order statistics, the method is very effective in severe distortion conditions where a traditional, perfectly trained Kalman multichannel filter obtains poor performance     

A Systematic Approach to Blind-Speed Elimination

In radars that achieve a high subclutter visibility by coherent processing over several pulses, a serious problem appears in the form of blind Dopplers, or ?speeds,? at which target detection is impossible. Of the possible methods of eliminating these blind speeds, the most basic one that is employed when the performance requirements are high involves the use of several PRF's. These PRF's are chosen so that coverage is obtained at any Doppler with at least one PRF. The problem faced by the radar designer is to select the set of PRF's and the pulse numbers for each PRF so that the search frame time is minimized. This paper evolves a systematic method for the design of the blind-speed elimination scheme. A formalized approach is offered that shows the possible combinations of wavelength, PRF, and pulse number and the tradeoffs involved, without introducing the confusion ordinarily associated with multiparameter choices.     

Stochastic-constraints method in nonstationary hot-cluttercancellation. II. Unsupervised training applications

For pt. I see ibid., vol. 34, pp. 1271-1292 (1998). This paper considers the use of “stochastically constrained” spatial and spatio-temporal adaptive processing in multimode nonstationary interference (“hot clutter”) mitigation for scenarios that do not allow access to a group of range cells that are free from the backscattered sea/terrain signal (“cold clutter”). Since supervised training methods for interference covariance matrix estimation using the cold-clutter-free ranges are inappropriate in this case, we introduce and analyze adaptive routines which can operate on range cells containing a mixture of hot and cold clutter and possible targets (unsupervised training samples). Theoretical and simulation results are complemented by surface-wave over-the-horizon data processing, recently collected during experimental trials in northern Australia     

Stability Measurement Problems and Techniques for Operational Airborne Pulse Doppler Radar

In the deployment of pulse Doppler (PD) radar, determination of phase and amplitude stability is the most difficult measurement problem. Unique requirements are placed on pulse and carrier stability so that the radar can perform in strong clutter. Because of subclutter visibility and sensitivity specifications, coherent noise, which is insignificant for noncoherent pulse radars, becomes extremely important. In solving the measurement problem, special support equipment was developed which is considered to have reached such a degree of refinement that it is probably one of the most technically advanced pieces of field test equipment supporting any operational radar. This paper discusses stability requirements, sources of instability, and the combination of techniques selected for verification of compliance of the PD radar with the stability requirements. The results of a program to develop special field support equipment to satisfy the measurement requirements are emphasized. Results of field experience and the special training required of military field personnel to enable them to effectively use this relatively complex support equipment are discussed.     

FM/AM Noise Test Set for a Pulsed Doppler Radar

A new test method to measure the amplitude noise and phase noise in both CW and pulsed CW signals of a Ku-band pulsed Doppler radar is described. These noises are measured in a simulated environment of radar operation; thus the test results may give direct information to determine radar subclutter visibility. In comparison with the conventional noise test method, this new method not only gives more meaningful results but also can obtain results much faster in testing. Actual test system design is described by block diagrams and theoretical analysis. A method to determine approximate frequency jitter in a transmitter signal is also described.     

基于空时自适应处理的GPS宽带干扰抑制技术

分析研究了GPS宽带干扰抑制的各种空时自适应处理算法,提出了期望信号协方差矩阵的计算方法,该方法使得最大信干噪比方法和最小均方误差方法可以实现。仿真结果表明：和无干扰的GPS接收机相比,基于空时自适应的干扰抑制技术不会引起很大的定位误差。     

空时自适应处理张量波束成形器的外积合成法

为了解决空时自适应处理（Space-Time Adaptive Processing，STAP）对足量平稳训练快拍的要求，给出了一种设计STAP张量波束成形器的新算法——空时自适应处理张量子波束合成（TSS-STAP）法。分析表明：STAP中所需要的张量波束成形器，可首先在张量的各个子维度上分别进行子波束成形器的设计，然后再由张量的外积运算合成各子波束成形器而得到。进一步分析表明：由于本文算法可在较低自由度（DoF）的子维度上对张量波束成形器进行设计，因此降低了设计所需要的训练快拍数和计算复杂度，同时也实现了有效的去相关处理，使得其在非均匀杂波环境下有更好的目标检测性能。在仿真实验中，所提算法有效提升了目标检测结果，同时降低了目标检测所消耗的时间。     

An algorithm for detection of moving optical targets

An algorithm is described for detecting moving optical targets against spatially nonstationary Poisson background and noise. The algorithm has applications in optical detection of objects such as meteors, asteroids, and satellites against a stellar background. A maximum-likelihood approach is used which results in reducing interference from stars. It is shown that by choosing a detection threshold to provide a constant false alarm rate, the resulting algorithm is independent of the signal strength of the target. An analysis of this algorithm is presented, showing the probability of detection for several false-alarm rates     

A persymmetric multiband GLR algorithm

By exploring the covariance structure information to reduce the uncertainty in adaptive processing, a persymmetric generalized likelihood ratio algorithm (PGLR) is developed together with the closed-form expressions of probabilities of detection and false alarm. This multiband algorithm, which requires less computation, can significantly outperform the corresponding unstructured multiband GLR algorithm, especially in a severely nonstationary and/or nonhomogeneous interference environment. Simulation shows that the constant false alarm rate (CFAR) performance of the new algorithm is as insensitive as that of the unstructured multiband GLR to the departure of interference distribution from Gaussian     

机载相控阵雷达中抑制杂波的线性预测法

从一个多通道自回归过程拟合杂波信号的概念出发 ,提出了用线性预测法实现机载相控阵雷达的时空二维自适应信号处理。研究表明 ,杂波过程可以用一个低阶的多通道自回归过程很好地拟合 ,从而使用一个低阶的线性预测处理器以较低的代价实现准最优的处理。同时 ,这种低阶的线性预测处理器还具备冗余的自由度以对付除杂波外的其他有色噪声和干扰     

A new CFAR sidelobe canceler algorithm for radar

Signal or target detection is sometimes complicated by the presence of strong interference. When this interference occurs mainly in the sidelobes of the antenna pattern, a solution to this problem is realized through a sidelobe canceler (SLC) implementation. Since the false-alarm probability is a system parameter of special importance in radar, an interference-canceling technique for radar application should maintain the false-alarm probability constant over a wide range of incident interference power. With the requirements of sidelobe interference cancellation and constant false alarm rate (CFAR), a new algorithm for radar detection in the presence of sidelobe interference is developed from the generalized likelihood ratio test of Neyman-Pearson. In this development, the received interference is modeled as a nonstationary but slowly varying Gaussian random process. Cancellation of the sidelobe interference is based upon a `synchronous' estimate of the spatial covariance of the interference for the range gate being tested. This algorithm provides a fixed false-alarm rate and a fixed threshold which depend only upon the parameters of the algorithm     

Array shape estimation tracking using active sonar reverberation

This paper concerns the problem of array shape estimation and tracking for towed active sonar arrays, using received reverberation returns from a single transmitted CW pulse. Uniform linear arrays (ULAs) deviate from their nominal geometry while being towed due to ship maneuvers as well as ocean currents. In such scenarios, conventional beamforming performed under the assumption of a ULA can sometimes lead to unacceptably high spatial sidelobes. The reverberation leaking through the sidelobes can potentially mask weak targets in Doppler, especially when the target Doppler is close to that of the mainlobe reverberation and the reverberation-to-target ratio (RTR) is very high. Although heading sensors located along the array can be used to provide shape estimates, they may not be sufficiently available or accurate to provide the required sidelobe levels. We propose an array shape calibration algorithm using multipath reverberation returns from each ping as a distributed source of opportunity. More specifically, a maximum likelihood (ML) array shape calibration algorithm is developed, which exploits a deterministic relationship between the reverberation spatial and Doppler frequencies causing it to be low rank in the space-time vector space formed across a single coherent processing interval (CPI). In this application, a sequence of overlapped CPI length snapshots of duration less than the CW pulse is used. The ML estimates obtained for each snapshot are tracked using a Kalman filter with a state equation corresponding to the water pulley model for array dynamics. Simulations performed using real heading sensor data in conjunction with simulated reverberation suggest that 8-10 dB improvement in sidelobe level may be possible using the proposed array shape tracking algorithm versus an algorithm that uses only the available heading information.     

Dimension reduction for array processing with robust interference cancellation

We present a robust solution for data reduction in array processing. The purpose is to reduce the computation and improve the performance of applied signal processing algorithms by mapping the data into a lower dimension beamspace (BS) through a transformation. Nulls steering to interference are incorporated into a transformation using the subspace projection technique, and the BS spatial spectrum estimation accuracy is evaluated and maximized with a measure. The derived transformation tries to preserve the full-dimension Cramer-Rao bounds (CRBs) for the parameters of interest while rejecting undesired signals effectively. When compared with an optimal method and an adaptive approach, simulation results show that significant improvements are obtained in terms of BS direction-of-arrival (DOA) estimation root-mean-squared error (RMSE), bias, and resolution probability.     

Space-time autoregressive filtering for matched subspace STAP

Practical space-time adaptive processing (STAP) implementations rely on reduced-dimension processing, using techniques such as principle components or partially adaptive filters. The dimension reduction not only decreases the computational load, it also reduces the sample support required for estimating the interference statistics. This results because the clutter covariance is implicitly assumed to possess a certain (nonparametric) structure. We demonstrate how imposing a parametric structure on the clutter and jamming can lead to a further reduction in both computation and secondary sample support. Our approach, referred to as space-time autoregressive (STAR) filtering, is applied in two steps: first, a structured subspace orthogonal to that in which the clutter and interference reside is found, and second, a detector matched to this subspace is used to determine whether or not a target is present. Using a realistic simulated data set for circular array STAP, we demonstrate that this approach achieves significantly lower signal-to-interference plus noise ratio (SINR) loss with a computational load that is less than that required by other popular approaches. The STAR algorithm also yields excellent performance with very small secondary sample support, a feature that is particularly attractive for applications involving nonstationary clutter.     

A geographical and operational deep graph convolutional approach for flight delay prediction

Flight delay prediction has attracted great interest in civil aviation community due to its significant role in airline planning, flight scheduling, airport operation, and passenger service. Flight delay is affected by numerous factors and irregularly propagates in air transportation networks owing to flight connectivity, which brings critical challenges to accurate flight delay prediction. In recent years, Graph Convolutional Networks (GCNs) have become popular in flight delay prediction due to the advantage in extracting complicated relationships. However, most of the existing GCN-based methods have failed to effectively capture the spatial–temporal information in flight delay prediction. In this paper, a Geographical and Operational Graph Convolutional Network (GOGCN) is proposed for multi-airport flight delay prediction. The GOGCN is a GCN-based spatial–temporal model that improves node feature representation ability with geographical and operational spatial–temporal interactions in a graph. Specifically, an operational aggregator is designed to extract global operational information based on the graph structure, while a geographical aggregator is developed to capture the similar nature among spatially close airports. Extensive experiments on a real-world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods with a satisfying accuracy improvement.     

Nostradamus: An OTH Radar

ONERA, funded by the French Ministry of Defence has conducted the realization and experimentations of the Doppler Skywave OTH radar called NOSTRADAMUS. One of the main characteristics of Skywave OTH radar is the dependence to the ionosphere for successful operation. The use of the HF band allows Skywave OTH radar to bounce radio waves from the ionosphere, receiving tiny signals back from reflecting surfaces as the sea, islands, ships and aircraft. The knowledge of the behavior of the ionosphere in a real time configuration is of primary importance because it influences on the choice of frequencies. Radars systems require developing a real-time frequency management system (FMS) using prediction program or measurements supplied by vertical or oblique sounders. The French OTH radar concept has been developed and implemented so that the radar could be completely autonomous with respect to others "ionospheric information providers." This paper presents the NOSTRADAMUS system, the frequency management system, and shows some results obtained during the past years