Ahmed O. Nasif

Estimation of maximum interference-free power level for opportunistic spectrum access

We consider a scenario in which frequency agile radios opportunistically share a fixed spectrum resource with a set of primary nodes. We develop a collaborative scheme for a group of frequency agile radios to estimate the maximum power at which they can transmit on a given frequency channel, without causing harmful interference to the primary receivers. The proposed scheme relies on signal strength measurements taken by a group of frequency agile radios, which are then used by a target node to characterize the spatial size of its perceived spectrum hole in terms of the maximum permissible transmit power. We derive an approximation to the maximum interference-free transmit power using the Cramer-Rao bound on localization accuracy. We present numerical results to demonstrate the effectiveness of the proposed scheme under a variety of scenarios.

Estimation of Interference-Free Transmit Power for Opportunistic Spectrum Access

We consider a scenario in which frequency agile radios opportunistically share a fixed spectrum resource with a set of primary nodes. We develop a collaborative scheme for frequency agile radios to estimate the maximum power at which they can transmit, without causing harmful interference to the primary receivers. The proposed scheme relies on signal strength measurements, which are used to localize primary transmitters. An approximation to the maximum interference-free transmit power is derived using the Cramer-Rao lower bound on localization accuracy. We present numerical results to demonstrate the effectiveness of the proposed scheme under a variety of scenarios.

Opportunistic spectrum sharing with multiple cochannel primary transmitters

We present a distributed, collaborative algorithm to enable opportunistic spectrum access for cognitive radios in the presence of multiple cochannel transmitters. A spectrum hole detection and estimation technique based on received signal strength observations is developed, which allows the coexistence of both licensed and unlicensed transmitters. We address the issues of how to perform collaborative spectrum sensing in the presence of multiple cochannel transmitters and how to determine the maximum transmit power that can be used for a given frequency channel by a cognitive radio while avoiding harmful interference to the licensed network. Simulation results are provided to validate the feasibility and performance of the proposed scheme.

Measurement clustering criteria for localization of multiple transmitters

We consider the problem of localizing multiple cochannel transmitters belonging to a licensed or primary network using signal strength measurements taken by a group of unlicensed or secondary nodes. Traditional localization techniques can be applied to multiple transmitter localization, provided that: (1) the total number of cochannel transmitters in the system is known, and (2) an appropriate set of clustered measurements is available. In this paper, we present two criteria to determine the total number of cochannel transmitters in the primary system. The first criterion is called the net MMSE criterion, which uses the Cramér-Rao lower bound on localization accuracy. The second criterion is the information theoretic criterion, minimum description length. Both of these criteria lead to measurement clustering algorithms in a natural way. Although we consider only signal strength measurements, the approach can be generalized to include other types of observations (e.g., time and angle information) with independent measurements in additive noise. Our numerical results demonstrate the effectiveness of the proposed approach to measurement clustering.

Collaborative Opportunistic Spectrum Access in the Presence of Multiple Transmitters

We present a collaborative algorithm to enable opportunistic spectrum access for cognitive radios in the presence of multiple co-channel transmitters. A spectrum hole detection and estimation technique based on received signal strength observations is developed, which allows the coexistence of both licensed and unlicensed transmitters. We address the issue of how to perform collaborative spectrum sensing in the presence of multiple co-channel transmitters and how to determine the maximum transmit power that can be used for a given frequency channel by a cognitive radio while avoiding harmful interference to the licensed network. We provide some simulation results to validate the feasibility of our approach.

Upper bound on false alarm rate for landmine detection and classification using syntactic pattern recognition

Recently, there has been considerable interest in the development of robust, cost-effective and high performance non-metallic landmine detection systems using ground penetrating radar (GPR). Many of the available solutions try to discriminate landmines from clutter by extracting some form of statistical or geometrical information from the raw GPR data, and oftentimes, it is difficult to assess the performance of such systems without performing extensive field experiments. In our approach, a landmine is characterized by a binary-valued string corresponding to its impedance discontinuity profile in the depth direction. This profile can be detected very quickly utilizing syntactic pattern recognition. Such an approach is expected to be very robust in terms of probability of detection (Pd) and low false alarm rates (FAR), since it exploits the inner structure of a landmine. In this paper, we develop a method to calculate an upper bound on the FAR, which is the probability of false alarm per unit area. First, we parameterize the number of possible mine patterns in terms of the number of impedance discontinuities, dither and noise. Then, a combinatorial enumeration technique is used to quantify the number of admissible strings. The upper bound on FAR is given as the ratio of an upper bound on the number of possible mine pattern strings to the number of admissible strings per unit area. The numerical results show that the upper bound is smaller than the FAR reported in the literature for a wide range of parameter choices.

Cavity induced modulation of K a -band radar signals

Anechoic chamber measurements were made of sniper rifles and surrogates from 25.0 GHz to 40.0 GHz at 5 MHz steps utilizing a stepped-frequency, horizontally linearly polarized (HH) bistatic radar. It was found that a modulation was induced on the reradiated signal when illuminating the rifles near on-boresight. Further analysis of the data led to the development of a model comprised of a linear decomposition of the targets into 3 coherently summable reradiators: a long cylinder, a perforated disk, and a resonant cavity. The cavity induced modulation (CIM) which was found in the measurements can be used to locate and detect sniper rifles and other cavities. Additional measurements were made in a clutter environment at an Air Force test range at ranges up to 100 meters which confirmed the existence and detectability of the CIM.

Observations on syntactic landmine detection using impulse ground-penetrating radar

We discuss some results and observations on applying syntactic pattern recognition (SPR) methodology for landmine detection using impulse ground-penetrating radar (GPR). In the SPR approach, the GPR A-scans are first converted into binary-valued strings by inverse filtering, followed by concavity detection to identify the peaks and valleys representing the locations of impedance discontinuities in the return signal. During the training phase, the characteristic binary strings for a particular landmine are found by looking at all the exemplars of that mine and selecting the collection of strings that yield the best detection results on these exemplars. These characteristic strings can be detected very efficiently using finite state machines (FSMs). Finally, the FSM detections are clustered to assign confidence to each detection, and discard sparse detections. Provided that the impulse GPR provides enough resolution in range, the SPR method can be a robust and high-speed solution for landmine detection and classification, because it aims to exploit the impedance discontinuity profile of the target, which is a description of the internal material structure of the target and little affected by external clutter. To evaluate the proposed methodology, the SPR scheme is applied to a set of impulse GPR data taken at a government test site. We suggest that coherent frequency-agile radar may be a better option for the SPR approach, since it addresses some of the drawbacks of a non-coherent impulse GPR caused by internally non-coherent within-channel signals which necessitate non-coherent integration and its attendant longer integration times, and non-coherent adjacent channels which severely limit the ability to do spatial, or at a minimum, cross-range processing if the GPR is in a linear array antenna.

Characterization of binary string statistics for syntactic landmine detection

Syntactic landmine detection has been proposed to detect and classify non-metallic landmines using ground penetrating radar (GPR). In this approach, the GPR return is processed to extract characteristic binary strings for landmine and clutter discrimination. In our previous work, we discussed the preprocessing methodology by which the amplitude information of the GPR A-scan signal can be effectively converted into binary strings, which identify the impedance discontinuities in the signal. In this work, we study the statistical properties of the binary string space. In particular, we develop a Markov chain model to characterize the observed bit sequence of the binary strings. The state is defined as the number of consecutive zeros between two ones in the binarized A-scans. Since the strings are highly sparse (the number of zeros is much greater than the number of ones), defining the state this way leads to fewer number of states compared to the case where each bit is defined as a state. The number of total states is further reduced by quantizing the number of consecutive zeros. In order to identify the correct order of the Markov model, the mean square difference (MSD) between the transition matrices of mine strings and non-mine strings is calculated up to order four using training data. The results show that order one or two maximizes this MSD. The specification of the transition probabilities of the chain can be used to compute the likelihood of any given string. Such a model can be used to identify characteristic landmine strings during the training phase. These developments on modeling and characterizing the string statistics can potentially be part of a real-time landmine detection algorithm that identifies landmine and clutter in an adaptive fashion.