Presence Of Strong Interference Research Articles

Eigenanalysis-Based Adaptive Interference Suppression and Its Application in Acoustic Source Range Estimation

Passive localization of weak sources in the presence of strong interferences is generally a difficult task. In this paper, an eigenanalysis-based adaptive interference suppression (EAAIS) method is proposed for estimating the bearing and subsequently the weak source range. Assuming the target of interest (TOI) is in a known bearing sector, first we define a contribution ratio for each eigenvector of the cross-spectral density matrix (CSDM). The eigenvectors not dominated by the TOI are adaptively identified and removed for interference suppression. The remaining eigenvectors are then used to reconstruct the CSDM for TOI bearing estimation. Finally, the acoustic source range estimation is achieved with the reconstructed CSDM, by matching the beam intensity striation pattern, where it is not required to estimate the waveguide-invariant parameter. The effectiveness of the proposed TOI localization is demonstrated using several experimental data sets.

Random matrix theory model for mean notch depth of the diagonally loaded minimum variance distortionless response beamformer for a single interferer case

Adaptive beamformers (ABFs) suppress interferers by placing notches in the beampattern at interferer directions. This suppression improves the detection of weaker signals of interest even in the presence of strong interferers. The magnitude of the notch depth (ND) is an important parameter governing the adaptive gain obtained from using ABFs over conventional beamforming in the presence of interferers. This research derives models for the mean ND of a diagonally loaded minimum variance distortionless response (MVDR) beamformer for a single interferer case. The model describes the mean ND as a function of the number of snapshots, the number of sensors in the array, the interferer to noise ratio (INR) level, the interferer direction, and the diagonal loading level. The derivation exploits random matrix theory (RMT) results on the behavior of the eigenvectors of the spiked covariance matrix. The RMT based ND model predictions are in close agreement with simulation results over a range of INR values and number of snapshots.

Encrypted CDMA Audio Network

We present a secure LAN using sound as the physical layer for low speed applications. In particular, we show a real implementation of a point-to-point or point-to-multipoint secure acoustic network, having a short range, consuming a negligible amount of power, and requiring no specific hardware on mobile clients. The present acoustic network provides VPN-like private channels to multiple users sharing the same medium. It is based on Time-hopping CDMA, and makes use of an encrypted Bloom filter. An asymmetrical error-correction is used to supply data integrity, even in the presence of strong interference. Simulations and real experiments show its feasibility. We also provide some theoretical analysis on the principle of operation.

Spectrum Sensing With High Sensitivity and Interferer Robustness Using Cross-Correlation Energy Detection

Dynamic spectrum access relying on spectrum sensing requires reliable detection of signals in negative signal-to-noise ratio (SNR) conditions to prevent harmful interference to licensed users. Energy detection (ED) is a quite general solution, which does not require any knowledge of the signals to be detected. Unfortunately, it suffers from noise uncertainty in the receiver, which results in an SNR-wall below which signals cannot be reliably detected. Furthermore, distortion components originating from nonlinearity in the sensing receiver cannot be distinguished from true input signals, and is thus another effect that may obscure weak signals and cause false alarms or missed detections. Cross-correlation was recently proposed to reduce the SNR-wall and, at the same time, allow the receiver to be designed for high linearity. This allows for high-fidelity spectrum sensing, both in the presence of strong interference as well as for signals with a negative SNR. In this work, an integrated complementary metal-oxide-semiconductor prototype exploiting cross correlation is presented and tested in practice. The prototype achieves a high linearity of +25 dBm IIP3 at a sensitivity of -184 dBm/Hz, 10 dB below the kT noise floor. The measured results agree well with theory, and, compared to the traditional ED-approach, show both a significant improvement in sensing time, as well as a reduction of 12 dB in the SNR-wall itself. Overall, cross-correlation makes ED faster, more sensitive, more resilient to strong interferers, and more energy-efficient.

Detection of T-Wave Alternans in Fetal Magnetocardiography Using the Generalized Likelihood Ratio Test

T-wave alternans (TWA) is an indicator of cardiac instability and is associated with life-threatening ventricular arrhythmias. Detection of TWA in the adult has been widely investigated and is used routinely for cardiac risk assessment. Detection of TWA in the fetus, however, is much more difficult due to the low amplitude and variable configuration of the signal, the presence of strong interferences, and the brevity of fetal TWA episodes. In this paper, we present a statistical detector based on the generalized likelihood ratio test that is designed for detection of TWA in the fetus. The performance of the detector is evaluated by constructing receiver-operator characteristic curves, using simulated data and real data from subjects with macroscopic TWA. The detector is capable of detecting TWA episodes as brief as 20 beats. The detection performance is improved significantly by modeling the fetal T-wave as a low-rank, low bandwidth signal, and using maximum likelihood estimation to estimate the model parameters. This approach enables all of the data to be used to estimate the noise statistics, providing highly effective suppression of interference, including maternal interference. The method is suitable for routine use because it can be applied to raw, unprocessed recordings, allowing automated analysis of extended fetal magnetocardiography recordings.

A Rao–Blackwellized Particle Filter for Adaptive Beamforming With Strong Interference

A particle filter approach is proposed for adaptive narrowband beamforming in the presence of strong interference and uncertain steering vector of interest signal. From the viewpoint of subspace decomposition, we first describe the known subspace projection beamformer as an orthogonal component of the steering vector that is perpendicular to the interference space. Then a Rao-Blackwellized particle filter algorithm is designed to estimate the subspace projection beamforming weights. All steps including the important sampling, weight updating, resampling and analytical computation have been discussed in detail. Finally, the overall adaptive beamforming algorithm is summarized and the computational complexity analysis is presented. The main contribution of this paper is to apply and formulate Rao-Blackwellized particle filtering to estimate the distribution over the subspace projection beamforming weights. Different from other two particle-filter-based beamformers proposed by Li and Chandrasekar, it is a STI-based method with unknown source steering vector. The sampled state variables are the signal power, noise power and a model parameter for beamformer weights transition; the marginalized analytical state variables are the subspace projection beamforming weights; and the measurements are a series of constructed signal samples by using the estimated projection operator and random noise loading. Numerical simulations show that the proposed beamformer outperforms linearly constrained minimum variance, subspace projection, Bayesian and other two particle-filter-based beamformers. After convergence, it has similar performance to the optimal max-SINR beamformer which uses the true steering vector and ideal interference-plus-noise covariance matrix.

Successive Interference Cancellation for Underwater Acoustic Communications

In this paper, we introduce the addition of an iterative, successive interference cancellation (SIC) process to improve on a multiuser, single-input-multiple-output (SIMO) communications receiver using passive time reversal as a space-time preprocessor. Time reversal has been shown to apply the spatial degrees of freedom to enhance the signal-to-noise ratio (SNR) and suppress interference for a target user. With the introduction of SIC, the receiver can remove the residual interference experienced by each user while preserving the SNR gain achieved by time-reversal preprocessing. The SIC process is a decision-directed approach for removing multiuser interference at the receiver and is similar to the decision-feedback equalizer (DFE) for intersymbol interference (ISI) channels. The interference experienced by each user is estimated at the receiver using previously decoded symbols from interfering users. This estimate is scaled and synchronized before subtraction from the target user's signal after time-reversal combining. Since SIC is applied before symbol decoding, symbol estimates are improved as the process is allowed to iterate until a stationary point is reached. Following time-reversal combining and SIC, a DFE can mitigate any remaining ISI before symbol decisions are made. Data collected from two Focused Acoustic Fields experiments (FAF-05 and FAF-06) are used to demonstrate the performance of the proposed interference cancellation scheme. During the FAF-05 experiment, three users transmitted 16-quadrature amplitude modulation (QAM) symbols simultaneously over the 3-4-kHz frequency band to a 20-element receiving array deployed in 120-m-deep water at a range of 20 km. The FAF-06 experiment included the simultaneous transmissions of 8-QAM symbols from two users over the 9-21-kHz band to a 16-element receiving array in 92-m-deep water at a range of 2.2 km. For both of the examples, SIC is shown to improve the output SNR in the presence of strong interference over time-reversal processing alone. This translates to a significant bit error rate (BER) reduction from 1.53 × 10-2 to 8.80 × 10-4 for the FAF-05 data and from 1.77 × 10-3 to error-free decoding for the FAF-06 data.

Performance analysis of narrowband interference mitigation and near-far resistance scheme for GNSS receivers

This paper presents a method to mitigate the interferences against pseudolite-type and continuous-wave interferences in a global navigation satellite system (GNSS) environment. Ground-based pseudolites can transmit GPS-liked signals and augment GNSS constellations to enhance the accuracy, integrity, and availability. Unfortunately, a nearby pseudolite may induce a strong interference to GPS receivers. As a result, the receiver may fail to acquire and track signals from satellites. In order to account for such a problem, a mitigation scheme that combines the use of frequency domain excision method and direct reconstruction method is proposed to effectively nullify continuous-wave and pseudolite-type interferences, so that the GPS signals can still be tracked in the presence of strong interferences. Experiment results show that with the removal of interferences, the measured narrow-to-wide power ratio for all satellites can be enhanced. A sensitivity analysis is then conducted to assess the performance of the proposed algorithm in the presence of errors in the estimation of the signal power, Doppler frequency and code delay. Finally, the problem of combined interferences of pseudolites and continuous-wave sources is investigated both theoretically and experimentally. It is shown that the method is applicable to mitigate narrowband continuous-wave interferences and pseudolite-type interferences.

Adaptive beamforming for MC-CDMA systems

A novel beamforming algorithm is proposed for MC-CDMA systems under time varying channel conditions to suppress the interference in the presence of strong interferers. An iterative algorithm which computes the beamforming weight vectors is presented in this paper. Thus, the desired data enriched signal improves the performance of MC-CDMA systems under various channel conditions including narrow and wide angle spreading, besides frequency selective fading and are investigated through the frequency domain beamforming technique. The simulation results show a significant reduction in bit error rate (BER) in the presence of interference.

Adaptive Blocker Rejection Continuous-Time $\Sigma \Delta $ ADC for Mobile WiMAX Applications

An adaptive blocker-rejection wideband continuous-time (CT) sigma-delta (SigmaDelta) analog-to-digital converter (ADC) is presented. An integrated blocker detector reconfigures the ADC loop architecture to avoid overloading in the presence of strong interferers, improving receiver channel selectivity and sensitivity without increasing its dynamic range (DR) requirements. The adaptive operation relaxes receiver baseband channel filtering requirements for a worldwide inter-operability for microwave access (WiMAX, IEEE 802.16e) receiver. The ADC achieves 71 dB of dynamic range (DR), 65 dB of peak SNDR and 68 dB of peak SNR over a 10 MHz signal bandwidth, consuming 18 mW from a 1.2 V supply. The ADC system reconfigures the loop filter topology within 51 mus, improving receiver selectivity without any transient impact on BER. In the blocker suppression mode, the ADC can withstand 30 dBc blocker at the adjacent channel, achieving - 22 dB error vector magnitude (EVM) with a 24 Mb/s 16-QAM signal. The IC is fabricated on a 130 nm 8-level metal, metal-insulator-metal (MIM) capacitor, CMOS technology, occupying 1.5 times 0.9 mm2 silicon area.

Performance Analysis of MIMO Based CDMA Code Acquisition Over Rayleigh Fading Channels

This paper proposes a new MIMO based CDMA code acquisition scheme. The pilot codes consist of a number of short Gold code sequences which are transmitted in parallel using a group of transmit antennas. Reception diversity is performed by multiple receive antennas at the receiver. Three different acquisition detection techniques are proposed and compared. Corresponding threshold optimizations are investigated as well. Detection and false alarm probabilities are derived in closed form based on the outputs of non-coherent matched filters. The acquisition performance is evaluated in terms of mean acquisition time (MAT) in Rayleigh fading environment. It is shown that the proposed MIMO acquisition scheme exhibits a much better MAT performance than the conventional single-antenna acquisition scheme. The results reveal that multiple receive antennas can be utilized to significantly reduce the MAT at the expense of receiver complexity increase. On the other hand, increasing the number of transmit antennas makes the MAT performance more robust in the presence of strong interference.

One- and Two-Stage Tunable Receivers

In this paper, we propose and assess a CFAR detector that can adjust its ldquodirectivityrdquo through a real scalar parameter. It relies on the usual assumption that a set of homogeneous training data is available and encompasses as special cases the well-known Kelly's GLRT and the recently introduced W-ABORT detector. More important, it can be tuned in order to control the level to which sidelobe signals are rejected. Such functionality is particularly important to contain the number of false alarms in presence of mismatched signals. We also consider a parametric detector which resorts to a diagonally loaded sample covariance matrix commonly adopted to take advantage of the presence of strong interferers. The performance assessment of such detector has shown that it can significantly outperform Kelly's GLRT in terms of prediction probabilities for matched signals and in terms of selectivity, but it is not strictly CFAR. We also propose to use the CFAR parametric detector as second stage of a two-stage tunable detector and show that such a two-stage detector can outperform already proposed tunable receivers in terms of selectivity. The analysis of the detectors is conducted assuming a homogeneous Gaussian environment; with reference to this scenario and to the CFAR detectors we derive analytical expressions for the probability of false alarm and the probability of detection for both matched and mismatched signals.

Generalized eigen-combining algorithm for adaptive array systems in a co-channel interference environment

This paper presents a generalized eigen-combining algorithm for an adaptive array system that provides a diversity gain in angular spread, and a maximum signal-to-interference ratio (SIR) in the presence of strong interference. The proposed technique generates the maximum ratio of signal components distributed in the spatial channel subspace using channel bases that span the signal subspace and nullify the interference. Through extensive computer simulations, it is shown that the proposed algorithm represents a breakthrough in preventing performance saturation caused by strong co-channel interference.

Advanced Radar Detection Schemes Under Mismatched Signal Models

Adaptive detection of signals embedded in correlated Gaussian noise has been an active field of research in the last decades. This topic is important in many areas of signal processing such as, just to give some examples, radar, sonar, communications, and hyperspectral imaging. Most of the existing adaptive algorithms have been designed following the lead of the derivation of Kelly's detector which assumes perfect knowledge of the target steering vector. However, in realistic scenarios, mismatches are likely to occur due to both environmental and instrumental factors. When a mismatched signal is present in the data under test, conventional algorithms may suffer severe performance degradation. The presence of strong interferers in the cell under test makes the detection task even more challenging. An effective way to cope with this scenario relies on the use of detectors, i.e., detectors capable of changing their directivity through the tuning of proper parameters. The aim of this book is to present some recent advances in the design of tunable detectors and the focus is on the so-called two-stage detectors, i.e., adaptive algorithms obtained cascading two detectors with opposite behaviors. We derive exact closed-form expressions for the resulting probability of false alarm and the probability of detection for both matched and mismatched signals embedded in homogeneous Gaussian noise. It turns out that such solutions guarantee a wide operational range in terms of tunability while retaining, at the same time, an overall performance in presence of matched signals commensurate with Kelly's detector. Table of Contents: Introduction / Adaptive Radar Detection of Targets / Adaptive Detection Schemes for Mismatched Signals / Enhanced Adaptive Sidelobe Blanking Algorithms / Conclusions

Dynamic range improvements and measurements in radar systems

The extension of the dynamic range performance of existing radar systems is integral to the requirement for current sensors to see deeper into clutter, that is, to detect weak signals (e.g. small targets) in the presence of strong interference (e.g. urban clutter signals). The rationale behind this requirement is that this additional capability greatly enhances the utility of military and commercial systems. Two approaches to improving the dynamic range of modern radar and surveillance receivers are described: (1) linearisation of intermodulation distortion (IMD) using digital post-distortion algorithms and (2) linearisation of IMD using frequency retranslation mixer (FRM). The experimental results presented illustrate that the digital post-distortion and FRM approaches are effective in providing IMD improvements for the hardware and test scenarios measured. The improvements measured ranged from 15 to 35 dB for narrowband signals to 2–6 dB for frequency-modulated continuous wave (FMCW) wideband signals. The digital post-distortion approach provided the greatest performance improvement when the input signals were between the analogue-to-digital converter full range and 20 dB below that level. The improvements measured for the FRM linearisation approach ranged from 20 to 25 dB for the narrowband two-tone tests and 16–18 dB for the wideband FMCW signal chirps.

Multiple source localization using a maximum a posteriori Gibbs sampling approach

Multiple source localization in underwater environments is approached within a matched-field processing framework. A maximum a posteriori estimation method is proposed that estimates source location and spectral characteristics of multiple sources via Gibbs sampling. The method facilitates localization of weak sources which are typically masked by the presence of strong interferers. A performance evaluation study based on Monte Carlo simulations shows that the proposed maximum a posteriori estimation approach is superior to simple coherent matched-field interference cancellation. The proposed method is also tested on the estimation of the number of sources present, providing probability distributions in addition to point estimates for the number of sources.

Search of exoplanetary radio signals in the presence of strong interference: enhancing sensitivity by data accumulation

We develop a statistical approach aimed at the detection of weak sporadic pulses on the noise background. The results are applied to modeling the observational time series where pulsed radio emissions have to be recognized against the sky background fluctuations. The proposed methodology demonstrates the efficiency of using the statistics of peak values (integrated tail of probability distribution function of the intensity) for the purpose of signal detection. It is established that the highest sensitivity is reached with this method at low values of the filling factor (duty cycle) of the pulsed signals. If in addition the pulses have sufficiently high intensity, the discussed approach performs better than simple integration over the observational time. Then we discuss the possibility of detecting radio pulses from exoplanetary magnetospheres, especially from known “hot Jupiters” found by radial velocity measurements in the visible and we report our results from extensive observations of several candidate exoplanets with the world largest decameter telescope UTR-2. Although no detection of pulsed emission from exoplanets has been found to date, the analysis demonstrates the feasibility of detection with more stable receivers and longer observational time.

Blind reception of multicarrier ds-cdma using antenna arrays

An asynchronous, multicarrier direct-sequence code-division multiple-access (DS-CDMA) array receiver is proposed based upon blind, composite channel vector estimation. Due to the fact that the combined effect of all received paths is estimated, the total number of paths can be greater than the number of antennas in the receiver array. Furthermore, the proposed approach is not limited by multipath coherency. The receiver is classified as an interference cancelling zero-forcing receiver, and multicarrier operation in a frequency-selective channel means that its performance can exceed that of a single-carrier array DS-CDMA. The effectiveness of the proposed approach, even in the presence of strong interference, is demonstrated by computer simulation studies.

Robust direction of arrival estimation for CDMA signals in impulsive noise

Direction of arrival estimation schemes based on multiuser detection for CDMA signals are investigated. The paper reports the performance improvements achieved by the linear decorrelator and the nonlinear M-regression decorrelator over the conventional matched filter scheme combined with the subspace-based MUSIC algorithm. The near-far resistant properties of the decorrelators show improved bearing estimation performance in the presence of strong interference. In particular, the minimax M-regression decorrelator combined with the MUSIC algorithm achieves an excellent performance trade-off by performing satisfactorily over the entire range of noise distributions, which, on the other hand, is unattainable by the ROC-MUSIC scheme.

Detection of a weak target in the presence of strong interferers

The detection of a weak target in a hostile environment consisting of ambient noise, distant shipping, and ‘‘known’’ interferers, such as passing ships, is treated by applying a parametrically adaptive signal-processing technique. In this approach, the oceanic environment and noise sources are represented by mathematical models and directly incorporated into the processor, thereby allowing their effects to be removed from the decision function prior to target detection. It is then shown that the weak target dominates the resulting decision function. The approach recognizes the variability of the ocean environment and allows the processor to adaptively adjust its inherent parameters and underlying statistics to the changing ocean environment.