We propose to use the power spectral density (PSD) matrices of the received signals of a multi-sensor system as the feature of processing. PSD matrices have structural constraints and they form a manifold in signal sp...
详细信息
In satellite-based systems, such as global navigation satellite systems (GNSS), the use of antenna arrays is becoming increasingly common, as it allows various applications, such as jammer/spoofer suppression or impro...
详细信息
ISBN:
(纸本)9781479914814
In satellite-based systems, such as global navigation satellite systems (GNSS), the use of antenna arrays is becoming increasingly common, as it allows various applications, such as jammer/spoofer suppression or improving the positioning accuracy in multipath-environments. Testing such equipment under realistic and reproducible conditions is often not feasible, for example in the case of a not fully deployed system (e.g., Galileo satellites) or testing of classified transmission technologies (e.g., Galileo public regulated service (PRS) receivers). For tests under reproducible and realistic conditions, we propose 3D wave-field synthesis (WFS) in an over-the-air testbed. This allows a realistic emulation of the radio environment under laboratory conditions. In this paper, a feasibility study for such a testbed is presented. Possible antenna setups for a 3D WFS laboratory environment are proposed. For these setups, the 3D WFS accuracy is investigated for emulating different polarization types and multipath reflections with arbitrary source directions. The results show that with presently available hardware for channel emulation and 3D WFS, a virtual electromagnetic environment can be created for testing receivers with (beamforming) antenna arrays of typical size (120 mm-300mm diameter).
In this paper, a reduced-rank direction-of-arrival (DOA) estimation algorithm for incoherently distributed (ID) noncircular sources based on a uniform linear array (ULA) is proposed. First the noncircularity property ...
详细信息
ISBN:
(数字)9781728119465
ISBN:
(纸本)9781728119465
In this paper, a reduced-rank direction-of-arrival (DOA) estimation algorithm for incoherently distributed (ID) noncircular sources based on a uniform linear array (ULA) is proposed. First the noncircularity property of the signal is utilized to establish an extended generalized array manifold (GAM) model based on the first-order Taylor series approximation. Then, the central DOA of source signals is obtained based on the generalized shift invariance property of the array manifold and the reduced-rank principle. Compared with the algorithm without exploiting the noncircularity information, the proposed algorithm can achieve a higher accuracy and handle more sources. Simulation results are provided to demonstrate the performance of the proposed algorithm.
The multiplatform spatial-coherence restoral (MP-SCOPE) algorithm, introduced in 1988 to blindly separate spatially-coherent, temporally featureless signals based on their differing TDOA and FDOA at spatially separate...
详细信息
ISBN:
(纸本)1424403081
The multiplatform spatial-coherence restoral (MP-SCOPE) algorithm, introduced in 1988 to blindly separate spatially-coherent, temporally featureless signals based on their differing TDOA and FDOA at spatially separated antenna arrays, is extended to blind detection, separation, and location of dense co-channel emitters using wide-baseline collection networks. The original MP-SCORE algorithm is reviewed and related to the ML estimator of the location of a Gaussian-distributed source (or of the location and content of an unknown source) received in the presence of independent Gaussian-distributed interference with arbitrary spatial covariance at each array in the network. False-alarm and miss-rates for the corresponding MP-SCORE detector are also derived, and are shown to be a function of only the time-bandwidth product of the detector and the maximum source SINR attainable at each array in the network. Practical methods for detecting, separating, and locating multiple co-channel sources using W-SCORE are then presented, and the end-to-end algorithm is demonstrated for a GPS C/A code jamming scenario in which two 8-sensor airborne receivers attempt to detect and locate 100 narrowband (2 MHz) noise-loaded emitters continuously transmitting over a wide (150x150 nmi) geographical region at the center of the GPS L1 frequency band. The simulated algorithm detects and geolocates 27 emitters to within 1/2 nmi over a single 26 ms snapshot, and detects and geolocates, 38 emitters to within 445 feet over eleven 26 ms snapshots collected at 15 nmi intervals along the edge of the emission region.
Because of the near-field nature of radio propagation, spherical wave-front and multipath effect are prominent in indoor scenarios, making localization even more difficult. In this paper, we propose a three-dimensiona...
详细信息
ISBN:
(纸本)9781665406338
Because of the near-field nature of radio propagation, spherical wave-front and multipath effect are prominent in indoor scenarios, making localization even more difficult. In this paper, we propose a three-dimensional (3D) indoor localization algorithm that takes these issues into account. Specifically, we first adopted a high-resolution channel parameter estimation method for path delays based on the Space-Alternating Generalized Expectation-maximization (SAGE), and then these path delays are adopted in the 3D localization principles based on the target-antenna geometry. The proposed algorithm is validated by numerical simulations, where the channel data is generated by the propagation graph (PG) to model the true wireless propagation closely in the testing scenarios. The results demonstrate that the proposed approach can deal with both point and non-point targets with the 3D localization errors of less than 30 cm for 97% of the testing trails in a 10 x 20 x 3 m(3) indoor space.
Space Time Adaptive processing. STAP) is a two-dimensional adaptive filtering technique which uses jointly temporal and spatial dimensions to suppress disturbance and to improve target detection. Disturbance contains ...
详细信息
ISBN:
(纸本)9781467310710
Space Time Adaptive processing. STAP) is a two-dimensional adaptive filtering technique which uses jointly temporal and spatial dimensions to suppress disturbance and to improve target detection. Disturbance contains both the clutter arriving from signal backscattering of the ground and the thermal noise resulting from the sensors noise. In practical cases, the STAP clutter can be considered to have a low rank structure. Using this assumption, a low rank vector STAP filter is derived based on the projector onto the clutter subspace. With new STAP applications like MIMO STAP or polarimetric STAP, the generalization of the classic filters to multidimensional configurations arises. A possible solution consists in keeping the multidimensional structure and in extending the classic filters with multilinear algebra. Using the low-rank structure of the clutter, we propose in this paper a new low-rank tensor STAP filter based on a generalization of the Higher Order Singular Value Decomposition. HOSVD) in order to use at the same time the simple. for example time, spatial, polarimetric, ...) and the combined information. for example spatio-temporal). Results are shown for two cases : classic 2D STAP and 3D polarimetric STAP. In the classic case, vector and tensor filters are equivalent. In the polarimetric case, we show the enhancement of the tensor filter.
In many statistical signalprocessing applications, the quality of the estimation of parameters of interest plays an important role. We focus in this paper, on the estimation of the covariance matrix. In the classical...
详细信息
ISBN:
(纸本)9781467310710
In many statistical signalprocessing applications, the quality of the estimation of parameters of interest plays an important role. We focus in this paper, on the estimation of the covariance matrix. In the classical Gaussian context, the Sample Covariance Matrix (SCM) is the most often used, since it is the Maximum Likelihood estimate. It is easy to manage and has a lot of well-known statistical properties. However it may exhibit poor performance in context of non-Gaussian signals, contaminated or missing data. In that case M-estimators provide a good alternative. In this paper, we extend to the complex data case, a theoretical result already proposed by Tyler in the real data case, deriving the asymptotical distribution of any homogeneous functional of degree 0 of the M-estimates. Then, applying this result to the Adaptive Normalized Matched Filter (ANMF), we obtain a robust ANMF and give the relationship between its Probability of False Alarm (P-fa) and the detection threshold.
Close-in sensing is needed for urban warfare operations, where Ground Moving Target Indication (GMTI) could be provided via forward or rear-facing multi-function array radars mounted on small highly-maneuverable airbo...
详细信息
ISBN:
(纸本)1424403081
Close-in sensing is needed for urban warfare operations, where Ground Moving Target Indication (GMTI) could be provided via forward or rear-facing multi-function array radars mounted on small highly-maneuverable airborne platforms. However, airborne radar arrays oriented any direction other than side-looking cause an elevation dependent angle-Doppler relationship in the clutter returns. This non-stationarity is acute in close-in sensing geometries where elevation diversity exists over the scene of interest. However, planar arrays have an inherent advantage over linear arrays due to their ability to observe clutter statistics as a function of elevation. This paper demonstrates the utility of elevation diversity by synthesizing a single 3D-STAP filter that exhibits an elevation dependent azimuth-Doppler response which is tailored to null the clutter "bowl" which characterizes the forward-looking clutter spectrum. Such a capability is particularly exploitable on transmit, where all elevation angles are simultaneously illuminated. To demonstrate potential benefits, this paper proposes the use of recently developed Space Time Illumination Patterns (STIP) from a planar AESA to invoke elevation diverse space-time illumination in a forward-looking clutter scenario. It is shown that 3D-STIP (azimuth-elevation-Doppler) facilitates elevation specific space-time beamforming which removes the clutter energy from a given Doppler frequency across all ranges, potentially simplifying processing on receive. Simulations using synthesized training data and clairvoyant covariance knowledge are conducted to demonstrate proof-of-concept.
Deterministic broadband time-varying pulses such as chirp signals are widely used in seismic applications to determine the layering structure of the earth. Various broadband matched field processors (MFP) which have b...
详细信息
ISBN:
(纸本)0780363396
Deterministic broadband time-varying pulses such as chirp signals are widely used in seismic applications to determine the layering structure of the earth. Various broadband matched field processors (MFP) which have been already developed for use in inversion [1,2] rely on only their frequency distribution so their performance fail in the presence of similar powerful broadband correlated noises. Because chirp and other deterministic time-varying signals have distinct signatures in time-frequency space, we have introduced a cross relation based MFP in time-frequency space that incorporates these signatures in the processing to counter the effect of broadband correlated noises in the received data. Additionally, white noise can be removed by using a Cross-cross relation (CCR) time-frequency (TF)-MFP that contains only cross components of the received signals in its structure. We demonstrate the advantages of this process over the conventional MFPs to date.
This work addresses the issue of undersampled phase retrieval using the gradient framework and proximal regularization theorem. It is formulated as an optimization problem in terms of least absolute shrinkage and sele...
详细信息
ISBN:
(纸本)9781538647523
This work addresses the issue of undersampled phase retrieval using the gradient framework and proximal regularization theorem. It is formulated as an optimization problem in terms of least absolute shrinkage and selection operator (LASSO) form with (l(2)+l(1)) norms minimization in the case of sparse incident signals. Then, inspired by the compressive phase retrieval via majorization-minimization technique (C-PRIME) algorithm, a gradient-based PRIME algorithm is proposed to solve a quadratic approximation of the original problem. Moreover, we also proved that the C-PRIME method can be regarded as a special case of the proposed algorithm. As demonstrated by simulation results, both the magnitude and phase recovery abilities of the proposed algorithm are excellent. Furthermore, the experimental results also show the mean square error (MSE) performance of the proposed algorithm versus iterative step.
暂无评论