Francois Chan

Adaptive Viterbi decoding of convolutional codes over memoryless channels

In this paper, an adaptive decoding algorithm for convolutional codes, which is a modification of the Viterbi algorithm (VA) is presented. For a given code, the proposed algorithm yields nearly the same error performance as the VA while requiring a substantially smaller average number of computations. Unlike most of the other suboptimum algorithms, this algorithm is self-synchronizing. If the transmitted path is discarded, the adaptive Viterbi algorithm (AVA) can recover the state corresponding to the transmitted path after a few trellis depths. Using computer simulations over hard and soft 3-bit quantized additive white Gaussian noise channels, it is shown that codes with a constraint length K up to 11 can be used to improve the bit-error performance over the VA with K=7 while maintaining a similar average number of computations. Although a small variability of the computational effort is present with our algorithm, this variability is exponentially distributed, leading to a modest size of the input buffer and, hence, a small probability of overflow.

Hybrid localization of an emitter by combining angle-of-arrival and received signal strength measurements

Emitter localization, using different types of sensors and integrating their output properly can potentially increase localization accuracy. It is relatively easy to measure the received signal strength (RSS), but RSS localization is generally less accurate than angle-of-arrival (AOA) localization in most practical environments. When both RSS and AOA are available, fusing their measurements can improve localization accuracy. This paper develops a set of linear equations that optimally combines both AOA and RSS measurements, and is suitable for solution by weighted least squares. The simulation results show that at sufficiently low noise, the hybrid scheme is near optimal.

Likelihood-Based Modulation Classification for Multiple-Antenna Receiver

Likelihood-based algorithms for the classification of linear digital modulations are systematically investigated for a multiple receive antennas configuration. Existing modulation classification (MC) algorithms are first extended to the case of multiple receive antennas and then a critical problem is identified that the overall performance of the multiple antenna systems is dominated by the worst channel estimate of a particular antenna. To address the performance degradation issue, we propose a new MC algorithm by optimally combining the log likelihood functions (LLFs). Furthermore, to analyze the upper-bound performance of the existing and the proposed MC algorithms, the exact Cramer-Rao Lower Bound (CRLB) expressions of non-data-aided joint estimates of amplitude, phase, and noise variance are derived for general rectangular quadrature amplitude modulation (QAM). Numerical results demonstrate the accuracy of the CRLB expressions and verify that the results reported in the literature for quadrature phase-shift keying (QPSK) and 16-QAM are special cases of our derived expressions. Also, it is demonstrated that the probability of correct classification of the new algorithm approaches the theoretical bounds and a substantial performance improvement is achieved compared to the existing MC algorithm.

Computer design of super-orthogonal space-time trellis codes

Super-orthogonal space-time trellis codes (SOSTTC) designed by hand can significantly improve the performance of space-time trellis codes. This paper introduces a new representation of SOSTTC based on a generator matrix that allows a systematic and exhaustive search of all possible codes. This verifies that some of the known codes are optimal, and provides a means to easily implement encoders and decoders with a large number of states without relying on a graphic representation.

Design rules for extended super-orthogonal space-time trellis codes

Super-orthogonal space-time trellis codes (SOSTTCs) can be systematically designed by computer by using the generator matrix representation. However, an exhaustive search becomes prohibitively complex as the number of states or the constellation size increases. Even for 8PSK, the complexity would be prohibitive. In this paper, it is shown that the number of matrices that generate unique codes and satisfy the set-partitioning rules is significantly smaller than the total number of possible matrices. Rules for the design of the generator matrices so that set-partitioning rules are satisfied are proposed. These rules lead to significant complexity reductions and are required for MPSK or MQAM SOSTTCs and codes with more than two transmit antennas. New QPSK and 8PSK SOSTTCs with spectral efficiencies of 2 and 3 b/s/Hz, respectively, which outperform previously known codes are presented.

Detection for an AF Cooperative Diversity Network in the Presence of Interference

We consider an amplify-and-forward (AF) cooperative diversity network consisting of one source, multiple relays, one destination, and multiple interferers. It is assumed that only partial channel information of the interferers is known at the system. For this scenario, we derive an optimum/suboptimum detection rule. Furthermore, we propose an optimum relay power allocation scheme to maximize the instantaneous signal-to-interference-plus-noise-ratio (SINR). An exact and closed-form instantaneous BER expression is also presented. Numerical results demonstrate that the proposed scheme considerably outperforms the existing schemes.

Frequency Estimation of Uncooperative Coherent Pulse Radars

RF Frequency estimation is required in many applications, such as Radar Electronic Warfare (REW) and telecommunications. For example, passive location estimation of uncooperative radar sites in a target area is an important military application and can be achieved by measuring the Doppler-shifted frequencies of trains of modulated pulses received by an Electronic Support Measure (ESM) receiver on-board of a moving platform such as an aircraft or Unmanned Aerial Vehicle (UAV). The accuracy of the location technique depends on the accuracy of Doppler frequency measurements. There are several techniques that can be used to estimate accurately the frequency of continuous wave signals. However, estimating the frequency of trains of modulated pulses is more challenging because the pulse durations are very short. Furthermore, the computational complexity required for accurate estimation may become impractical if the Pulse Repetition Frequency (PRF) becomes small. In this paper, an FFT-based approach is considered. Techniques to improve the frequency step size, such as the Zoom FFT technique and the secant method, will be presented. Simulation results show that the frequency estimation approaches presented here can closely approach the Cramer-Rao Lower Bound (CRLB) in most cases.

Doppler Frequency Geolocation of Uncooperative Radars

Passive geolocation of uncooperative radar emitters remains an important problem in radar electronic warfare. Several location estimation techniques have been investigated in the past. In this paper, we present a passive geolocation technique for radar emitters using Doppler frequency measurements. For uncooperative sources, neither the emitter location, nor its transmitted frequency is known a priori. The relationship between these unknowns and the measured Doppler frequencies is non-linear. In the special case where the moving receiver measures frequencies along a straight path at constant speed, the relationship becomes linear in the Cartesian location coordinates. A simple 1-D discrete search for the transmitted frequency is followed by a least squares (LS) estimator to provide a coarse estimate of the emitter coordinates. This is followed by Newtons algorithm to provide a maximum likelihood (ML) estimation. The simulation results demonstrate that the resulting ML estimator approximately meets the Cramer-Rao lower bound (CRLB).

Received signal strength localization with an unknown path loss exponent

Received Signal Strength (RSS) measurements obtained at locations in the vicinity of an emitter can be used to estimate the emitter location given a suitable path loss model. For commonly used propagation models, the RSS has a dependence on the emitter to sensor separation, d, of the form P ∝ d−α, where the path loss exponent, α, is typically between 2 and 4 depending on the terrain. This uncertainty is a problem since a solution for the emitter location obtained using a suboptimal choice of α will suffer from degraded accuracy. To resolve this issue, a near maximum likelihood (ML) estimator for both α and emitter position (x, y) has been developed. By expressing (x, y) in terms of α and substituting the results into the ML-function, the 3-D minimization problem is simplified to 1-D. A simple search in α then gives the (α, x, y) estimates. Simulation experiments corroborate the proposed approach and demonstrate estimation accuracy close to the Cramer-Rao Lower Bound.

Estimation of symbol rate from the autocorrelation function

The estimation of the symbol rate (or symbol duration Ts) of a symbol sequence in noise has important applications in symbol timing recovery and radio surveillance. Since a symbol sequence has spectral peaks at intervals 1/Ts, Ts estimation centers mostly on the establishment of spectral rate lines from the sequence. Examples include the delay and multiply circuit, and the cyclostationarity based estimators. This paper presents an alternative. It first computes the autocorrelation function of a sequence, which has a first discontinuity at lag Ts. Locating this discontinuity then gives an estimate of Ts. Simulation results show that, for the range of signal-to-noise-ratios under consideration, errors of less than 3% of Ts are achievable.