Abstracts:The increasing demand for a better quality of service in radar and wireless communications has attracted research on interference suppression. Nonlinear filtering methods based on spline architecture have been widely utilized in signal processing due to their effectiveness against alpha-stable interference. However, existing nonlinear spline adaptive filter algorithms suffer from high steady-state misalignment. To achieve lower steady-state misalignment along with having comparable computational complexity, we propose a nonlinear spline Versoria prioritization optimization adaptive filter (SPOAF-MVC) for alpha-stable clutter in this article. Furthermore, we study the bound on learning rate and computational complexity for the proposed algorithm. Numerical simulations confirm the effectiveness and efficiency of the proposed SPOAF-MVC algorithm for alpha-stable clutter under the Wiener system identification.

Abstracts:This article proposes a cross-band (CB) correlator and detector for the multifrequency (MF) global navigation satellite system (GNSS) receiver to facilitate the development of robust acquisition in challenging environments. The CB correlator is designed with the projection and weighting operations to combine the multiple signals on different bands in an artificial reference domain. Based on the generalized likelihood ratio test (GLRT), the corresponding decision variable is derived to construct the CB detector considering the combination coefficients and detection threshold. The effectiveness of the proposed design is verified by Monte Carlo simulation and field test. The theoretical results show the improved performance of CB combinations when the signals suffer attenuations or frequency selective fading. The realistic results show the enhanced robustness of GPS and BeiDou Navigation Satellite System (BDS) CB detections in the presence of possible signal blockages and multipath interferences in the urban canyon.

Abstracts:This article studies the intercell interference (ICI) effects in cellular networks for navigation applications. This is achieved through the derivation of an analytical expression for a cellular navigation receiver postcorrelation signal-to-noise power ratio (SNR) in the presence of multiple asynchronous cells. It reveals that in addition to the channel power, the cell loading rate and data modulation order for the interfering cells also play important roles in affecting the received signal quality of the desired cell. Furthermore, the time-of-arrival (TOA) estimation and positioning accuracy degradation due to the ICI is characterized by the Cramer–Rao and Ziv–Zakai lower bounds based on the derived postcorrelation SNR. Simulations are performed to verify the theoretical expressions and the results indicate that the ICI term can be treated as an additional Gaussian disturbance for characterizing the TOA estimation accuracy in cellular navigation receivers.

Abstracts:Low grazing angle S-band radar sea clutter data, collected with the NetRAD multistatic radar system, are analyzed. The Suzuki distribution, a compound Gaussian model with log normal texture, is found to be a good model for both monostatic and bistatic clutter. Clutter textures from simultaneously recorded monostatic and bistatic data are interpreted using hydrodynamics, providing estimates of the water depth and wave direction.

Abstracts:Random frequency and pulse repetition interval agile (RFPA) signals have excellent anti-jamming ability and achieve low probability of intercept (LPI), making them promising for applications in radar systems. However, their matched filter (MF) outputs suffer from random range-velocity sidelobes. In the range dimension, these sidelobes can be classified into two categories: the distant sidelobe floor spread out beyond the minimum interval between pulses, and the near sidelobe plateau confined within about one pulsewidth. These sidelobes seriously degrade the target detection capability of RFPA signals. To address this issue, we propose the concept of a multi-timeslot wide-gap frequency-hopping sequence (multi-timeslot WGFHS) and use it in the design of RFPA signals. In doing so, the distant sidelobes are easily suppressed with a simple low-pass filter (LPF) in the receiver if the parameters of the multi-timeslot WGFHS are chosen properly, while the remaining near sidelobes are suppressed by the iterative adaptive approach based on matched filter outputs (MF-IAA). Simulation results show that the proposed method can effectively suppress the sidelobes of RFPA signals, accurately recover the range-velocity images, and successfully detect weak targets in the presence of strong targets or clutter.

Abstracts:The derivation of estimation lower bounds is paramount to designing and assessing the performance of new estimators. A lot of effort has been devoted to the range-velocity estimation problem, a fundamental stage on several applications, but very few works deal with acceleration, being a key aspect in high dynamics applications. Considering a generic band-limited signal formulation, we derive a new general compact form Cramér–Rao lower bound (CRB) expression for joint time-delay, Doppler stretch, and acceleration estimation. This generalizes and expands upon known delay/Doppler estimation CRB results for both wideband and narrowband signals. This new formulation, especially easy to use, is created based on baseband signal samples, making it valid for a variety of remote sensors. The new CRB expressions are illustrated and validated with representative GPS L1 C/A and linear frequency modulated chirp band-limited signals. The mean-square error of a misspecified estimator (conventional delay/Doppler) is compared with the derived bound. The comparison indicates that for some acceleration ranges the misspecified estimator outperforms a well-specified estimator that accounts for acceleration.

Abstracts:This article proposes a receding horizon-based information-theoretic source search and estimation strategy for a mobile sensor in an urban environment in which an invisible harmful substance is released into the atmosphere. The mobile sensor estimates the source term including its location and release rate by using sensor observations based on Bayesian inference. The sampling-based sequential Monte Carlo method, particle filter, is employed to estimate the source term state in a highly nonlinear and stochastic system. Infotaxis, the information-theoretic gradient-free search strategy is modified to find the optimal search path that maximizes the reduction of the entropy of the source term distribution. In particular, receding horizon Infotaxis (RHI) is introduced to avoid falling into the local optima and to find more successful information gathering paths in obstacle-rich urban environments. Besides, a random sampling method is introduced to reduce the computational load of the RHI for real-time computation. The random sampling method samples the predicted future measurements based on current estimation of the source term and computes the optimal search path using sampled measurements rather than considering all possible future measurements. To demonstrate the benefit of the proposed approach, comprehensive numerical simulations are performed for various conditions. The proposed algorithm increases the success rate by about 30% and reduces the mean search time by about 40% compared with the existing information-theoretic search strategy.

Abstracts:One-bit quantization technique is able to lower power consumption, save storage space, and reduce system cost, which, when tailored for radar sensing, is applicable to small-size platforms, such as unmanned aerial vehicle, small satellite and missile. Prosperous research works on one-bit signal processing have been performed in radar applications. Among them, beamforming with one-bit measurements has not been thoroughly discussed yet. This article addresses the issue of one-bit digital beamforming on receive. First, we revisit the theorem on one-bit quantization generating harmonics. This article extends to analyze the quantization impact on array signals, that is, the dispersive effect, by establishing array signal model and constructing corresponding correlation matrix. The dispersion incurs spatial filter mismatch when forming beams, which poses a big challenge for narrowband digital array systems. This article proceeds to propose a strategy of subband processing for digital receiving and one-bit beamforming. The fundamental and harmonic beams are formed separately within subbands and characterized, respectively. Eventually, two advanced applications are presented for utilizing the fine angular-resolution harmonic beams instead of suppression. One is wide area search for high-speed targets and the other is jamming cancellation. Fruitful simulation results are provided to confirm our theoretical findings.

Abstracts:Passive systems, such as electronic intelligence and electronic support measures systems, aim to extract necessary information from the received radar signals for situational awareness. To achieve this, the system must first deinterleave the radar signals simultaneously coming from different emitters, so that the pulse repetition interval (PRI) patterns will be revealed for further analysis and identification purposes. PRI transform is a well-known deinterleaving method that utilizes the complex autocorrelation function. There are two main versions of the method. The initial version detects only constant PRI schemes, while the second modified version is capable of detecting varying PRI schemes as well. Miss detection of varying PRI patterns is the drawback for the first version, while producing harmonics, especially at high PRI levels, is the disadvantage of the second one. To alleviate these problems, we propose an image segmentation method based on deep learning. The developed preprocessing step uses both versions of the PRI transform outputs to generate 2-D time–PRI images of the collected radar emissions, so that constant and varying PRI patterns are revealed. The images are concatenated and fed to the proposed network, which uses a practicable U-Net structure. The output of the network directly estimates the PRI levels of the existing radars and the time duration of the transmission jointly. In addition to qualitative and quantitative experiments on the synthetic datasets, qualitative experiments are conducted on real measurements, in which we demonstrate that the proposed method effectively utilizes PRI transform in the preprocessing step and outperforms both versions of the PRI transform in terms of accuracy, Jaccard index, structural similarity, and PRI estimation error metrics.

Abstracts:In this article, an adaptive multiple-model-based fault-tolerant controller is developed for nonminimum phase air-breathing hypersonic vehicles (HSVs) in the presence of parametric uncertainties, elevator faults, input saturations, and time-varying error constraints. Compared with the existing works, the elevator-to-lift couplings are taken into account, which makes the HSV longitudinal models exhibit unstable internal-dynamics that impedes the applicability of common nonlinear control methods for control design. In order to solve this problem, a two-layer cascade control strategy is proposed in which the external inputs control the external states and the external state deviations control the internal states: 1) A multiple-model linear quadratic control strategy, based on gap metric, is proposed to guarantee the stability of the internal dynamics; 2) a fault-tolerant control scheme, based on tan-barrier Lyapunov functions, is developed by using a low-pass filter and an auxiliary system in conjunction with adaptive backstepping design. In the control law, the uncertain parameters are replaced by their estimates updated by adaptive laws. Additionally, the stability of the whole system is rigidly proved through standard Lyapunov approach, while the other states and signals in the closed-loop system are guaranteed to be bounded. Simulation results are provided to illustrate the effectiveness of the proposed adaptive multiple-model-based fault-tolerant controller.