Narrowband Interference Suppression Using Undecimated Wavelet Packets in Direct- Sequence Spread-Spectrum Receivers

Narrow-band interference (NBI) is a common jamming signal against synthetic aperture radar (SAR), in which the imaging quality can be degraded severely. To suppress NBI effectively, a novel interference suppression algorithm using robust principal component analysis (RPCA) based time-frequency signal separation is proposed. The RPCA algorithm is introduced for time-frequency signal separation for the first time. The experimental results of simulated and measured data demonstrate that the proposed algorithm not only can suppress interference effectively, but also preserve the useful information as much as possible.

In this paper, an improved algorithm for narrow-band interference suppression is described for global navigation satellite system software receiver based on frequency-domain algorithm. The threshold of the interference in navigation signal is determined though N-sigma algorithm. This paper introduces the principle and flowchart of the algorithm. Performance comparison between this algorithm and other algorithms is also given based on simulation results. The results show that the improved algorithm has better anti-interference ability than other methods. This algorithm is suitable to eliminate the narrowband interference in global navigation satellite system software receiver.

This paper presents a novel nonlinear estimation algorithm based on hidden Markov models for narrowband interference suppression in CDMA spread spectrum systems. The proposed algorithm combines a recursive hidden Markov model estimator, a Kalman filter and a recursive prediction error parameter estimation algorithm. It is shown that the proposed algorithm not only outperforms the nonlinear filtering techniques for narrowband interference suppression presented in Rusch and Poor (1994), but that it outperforms and has faster, more robust convergence properties than the cross-coupled expectation maximization based algorithm presented in Logothetis and Krishnamurthy.

In this paper we discussed the realization of narrow-band interferences(NBI) suppression in direct sequence spread spectrum using frequency-domain algorithm, given the block diagram of the algorithm used in the system and carried out computer simulation. The analysis and simulation result indicate that the frequency-domain algorithm is more efficient than that based on commonly used LMS algorithm for the purpose of cancellation of NBI. The implementation of the system used for narrow-band interferences suppression is also specified in detail. This system is based on TMS320C6201.

A novel method of multipath diversity signal combination is proposed for mobile direct sequence spread spectrum (DS-SS) communications. Multiple transmissions are made for each spread signal. Each transmission is independently delayed and amplified in the complex domain according to the delay profile and the estimated path strength of the channel. This is done to facilitate reception of a signal at the mobile unit which is already a RAKE combination of the multipath signals. This method is called a pre-RAKE combination system because the RAKE combination function is performed pre-transmission. Using this method the size and complexity of the mobile unit can be kept to a minimum. The pre-RAKE method retains the advantages of diversity reception in a multi-path fading environment. An examination of the SNR for the traditional RAKE and the pre-RAKE systems and computer simulations show that the performance of the pre-RAKE system is equivalent to that of the RAKE system. >

The time-frequency (TF) filter design of non-stationary signals especially chirp signals are studied in the fractional domain. The design of TF/ linear time variant (LTV) filter is implemented as a non-overlapping short time fractional Fourier transform (STFRFT). The first step of our method involves detecting the presence of a multi component linear FM interference and correctly estimating its orientation in the time-frequency plane using cross spectral estimation of signal parameter via rotational invariant technique. This paper introduces a novel algorithm to excise single and multicomponent chirp-like interferences in direct sequence spread spectrum (DSSS) communication. The proposed method excises the interference with high interference power condition, and technique could be easily extended to any kind of chirp interference with little modification

Remote labs have become popular learning aids due to their versatility and considerable ease of utilisation as compared to their physical counterparts. At Makerere University, the remote labs are based on the standard Massachusetts Institute of Technology (MIT) iLabs Shared Architecture (ISA) - a scalable and generic platform. Presented in this paper is such a lab, addressing the key practical aspects of Direct Sequence Spread Spectrum (DSSS) communication. The lab is built on the National Instruments Educational Laboratory Virtual Instrumentation Suite (NI ELVIS) with the Emona Digital and Analog Telecommunications Experimenter (DATEx) add-on board. It also incorporates switching hardware. The lab facilitates real-time control of the equipment, with users able to set, manipulate and observe signal parameters in both the frequency and the time domains. Simulation and data Acquisition modes of the experiment are supported to provide a richer learning experience.

Clock and carrier recovery are very critical in high speed digital wireless communication for achieving high data rate with low packet error rate. In this paper, we propose a linear estimator for both clock and carrier recovery in a direct sequence spread spectrum (DSSS) communication system and show that this is also the maximum likelihood (ML) estimator. We present simulation results to illustrate the convergence of the estimator under different multipath channel conditions.

Direct sequence spread spectrum (DSSS) communication has been extensively adopted in military and civilian fields because of its high processing gain, anti-jamming capability and low probability of interception (LPI). In this paper, non-cooperative interception of DSSS signal in multiple signals environment is presented. The proposed algorithm is based on the fluctuations of autocorrelation, and also banks upon proper thresholding criteria. This methodology comes out to be a robust practical method for blind detection of DSSS signals in a dense multiple signals environment. In the proposed approach, firstly DSSS signals are blindly detected by leveraging the variations in autocorrelation of the received signal. Then, the presence of desired DSSS signal is confirmed when correlation peaks cross the threshold at multiple positions after a fixed period. In a real-time environment, signals other than spread spectrum with much better signal-to-noise ratio (SNR) can also co-exist. Among these multiple signals, the high SNR signals disturb the threshold selection criteria for detection. Consequently, this leads to erroneous or miss detection of the DSSS signal. The proposed scheme aims to reduce the effect of better SNR signals on the blind detection of DSSS signal. In order to validate the proposed algorithm, QPSK and 16-QAM signals are considered to co-exist with the desired DSSS signal. Analytical and numerical analysis signify the efficiency of the proposed algorithm with acceptable computational complexity, making it suitable for real-time practical systems.

This paper addresses blind adaptive suppression of a digital narrowband interferer in direct sequence spread spectrum (DSSS) communications. We apply an adaptive suppression method for wideband interference proposed by Honig, Madhow and Verdu (see IEEE Trans. on Info. Theory, vol.41, no.7, p.944-60, 1995) to a narrowband interferer. We identify the eigenspaces of the system dynamics to: (1) significantly decrease the convergence times via a new constraint on step size, (2) introduce a simple parameterization of the mean output energy and signal-to-interference ratio, and (3) identify modes of operation where the algorithm ceases to effectively cancel interference. We propose a new adaptive receiver that avoids the convergence anomalies, while capitalizing on the new step size for faster convergence.

Due to the broad bandwidth nature of the direct sequence spread spectrum (DSSS) signal, the application of narrowband interference (NBI) cancelation algorithms based on the Nyquist‐Shannon sampling theory are limited by the high sampling rate. To solve the sampling and processing difficulty in the traditional DSSS communication interference cancelation method, we used the compressive sensing (CS) to reduce the sampling rate of the DSSS communication system, and further proposed a compressed domain NBI elimination method in this study. We proved that NBI with a certain bandwidth and DSSS signal are both sparse in specific dictionaries and proposed the corresponding sparse dictionary construction method. Detailed analysis of the compressed domain features of NBI and the DSSS signal demonstrated that it is possible to extract the strong NBI components from the compressed measurements. Two compressed domain NBI cancelation algorithms were proposed based on this idea. The extracted NBI components were filtered out from the compressed measurements directly in the first algorithm, and then we achieved the DSSS signal demodulation in compressed domain using the separated DSSS signal components with the orthogonal matching pursuit (OMP) algorithm. A proposed extraction filter block sparse Bayesian learning (EFBSBL) framework algorithm was utilized to estimate the NBI from the compressed measurements in the second algorithm. The estimated NBI was eliminated from the received signal, and then we recompressed the DSSS signal after NBI cancelation and realized the DSSS signal demodulation in compressed domain with the OMP algorithm. Simulation results support our theoretical analysis results and reveal that the two proposed algorithms are both effective for NBI cancelation and significant interference cancelation performance are achieved.

In this paper, we propose a new algorithm for occlusion handling for object tracking in the crowded video scenes. The algorithm exploits the properties of undecimated wavelet packet transform (UWPT) coefficients and texture analysis to track arbitrary objects. The algorithm is initialized by the user through specifying a region around the object of interest at the reference frame. Then, coefficients of the UWPT of the region construct a Feature Vector (FV) for every pixel in that region. Optimal search for the best match is then performed by using the generated FVs inside an adaptive search window. Adaptation of the search window is achieved by inter- frame texture analysis to find the direction and speed of the object motion. This temporal texture analysis also assists in tracking of the object under partial or short-term full occlusion. Experimental results show a good performance for occlusion handling for object tracking in crowded scenes, in particular crowds on stairs in airports or train stations.

Traditional time/frequency domain filter-based methods would fail due to the serious overlapping of the communication signal and jamming in time/frequency domain. In order to solve this problem, an anti-jamming algorithm based on single channel source separation by exploiting cyclostationary difference is proposed for direct sequence spread spectrum (DSSS) communication. Firstly, the cyclostationary difference among source signals is used to establish the single channel source separation model based on partial priori information of the DSSS signal. Secondly, cost function of the algorithm is constructed by the training sequences and the minimum mean square error criterion; after that, the optimal separation matrix of the pilot mixed signal can be calculated based on this cost function. Thirdly, the purpose of anti-jamming for DSSS communication can be achieved by separating the communication signal from the service mixed signal based on the optimal separation matrix calculated before. Simulation results show that, compared with the unseparated signal of high bit error rate (BER) close to 0.5, the proposed antijamming algorithm is more effective in anti-jamming with the BER as low as 10–3, which is not only applicable to narrowband jamming such as the single-tone/multi-tone jamming, but also applicable to broadband jamming such as the sweep-spot jamming.

Interference suppression (IS) algorithms have been extensively discussed for it can reveal the target signals among the interference. Numerous IS algorithms have been proposed for passive sonar. To appropriately apply them in different scenarios, a novel category is proposed in this paper to analyze them and classify them into three methods (the adaptive array processing technology based nulling method, the subspace separation based IS method and the joint time-space-frequency based IS method). In the meantime, the complexity, applicable occasions, strengths and weaknesses of three types of IS algorithms are analyzed.

Narrow-band interference (NBI) is a critical issue for synthetic aperture radar (SAR), in which the imaging quality can be degraded severely. To suppress NBI effectively, a novel interference suppression algorithm using robust principal component analysis (RPCA) based signal separation in time-frequency domain is proposed. The RPCA algorithm is introduced for signal separation in the time-frequency domain for the first time. The fundamental assumption of RPCA is that a matrix can be modeled as a combination of a low-rank matrix and a sparse counterpart. In terms of the SAR echo, the short time Fourier transformation (STFT) matrix of mixed signals (i.e., useful SAR signals and NBIs) well fits the assumption of RPCA. Based on this property, radar echoes are first transformed into the time-frequency domain by STFT to form an STFT matrix. Then, the RPCA algorithm is used to decompose the STFT matrix into a low-rank matrix (i.e., NBIs) and a sparse matrix (i.e., useful signals). Finally, the NBIs can be reconstructed and subtracted from the echoes to realize the interference suppression. The experimental results of simulated and measured data demonstrate that the proposed algorithm not only can suppress interference effectively, but also preserve the useful information as much as possible.