Indoor navigation using the existing wireless infrastructure and mobile devices is a very active research area. The major challenge is to leverage the extensive smartphone sensor suite to achieve location tracking with high accuracy. In this paper, we develop a navigation algorithm which fuses the WiFi received signal strength indicator (RSSI) and smartphone inertial sensor measurements. A sequential Monte Carlo filter is developed for inertial sensor based tracking, and a radiolocation algorithm is developed to infer mobile location based on RSSI measurements. The simulation results show that the proposed algorithm significantly outperforms the extended Kalman filter (EKF), and achieves competitive location accuracy compared with the round trip time (RTT) based ultra-wideband (UWB) system.

In this paper, we develop a cooperative IMU/radio-location-based navigation system, where each node tracks the location not only based on its own measurements, but also via collaboration with neighbor nodes. The key problem is to design a nonlinear filter to fuse IMU and radiolocation information. We apply the Rao-Blackwellization method by using a particle filter and parallel Kalman filters for the estimation of orientation and other states (i.e., position, velocity, etc.), respectively. The proposed method significantly outperforms the extended Kalman filter (EKF) in the set of simulations here.

A new precoder that reduces the feedback overhead in an OFDM-based relay network is proposed. A joint Singular Value Decomposition (SVD) and QR Decomposition is employed at the source-destination link and source-relay-destination link. Using the proposed precoder, only one precoding matrix needs to be fed back, independent of the total number of subcarriers. The link achievable rate using the reduced complexity precoder is evaluated via simulations and compared with that of full SVD precoders and for varying codebook size.

QR decomposition (QRD)-based precoded MIMO-OFDM systems with reduced feedback are proposed to convert the MIMO-OFDM channel into layered subchannels. QRD-M is further combined with either singular value (SVD) or geometric mean decomposition (GMD) of the time-domain channel impulse response matrix. As a result, the receiver in the proposed systems only needs to feed back information describing one precoding matrix for all carriers. Simulation results confirm the bit-error-rate (BER) and throughput performance superiority of the proposed systems compared to conventional SVD per-carrier precoding schemes.

Joint carrier frequency offset (CFO) and channel estimation for uplink MIMO-OFDMA systems over time-varying channels is investigated. To cope with the prohibitive computational complexity involved in estimating multiple CFOs and channels, pilot-assisted and semi-blind schemes comprised of parallel Schmidt Extended Kalman filters (SEKFs) and Schmidt-Kalman Approximate Particle Filters (SK-APF) are proposed. In the SK-APF, a Rao-Blackwellized particle filter (RBPF) is developed to first estimate the nonlinear state variable, i.e. the desired user's CFO, through the sampling-importance-resampling (SIRS) technique. The individual user channel responses are then updated via a bank of Kalman filters conditioned on the CFO sample trajectories. Simulation results indicate that the proposed schemes can achieve highly accurate CFO/channel estimates, and that the particle filtering approach in the SK-APF outperforms the more conventional Schmidt Extended Kalman Filter.

A new closed-loop preceding method has been proposed for MIMO-OFDM systems with limited feedback by using the QR decomposition to the time-domain channel impulse response matrix. In contrast to the other methods where the receiver has to feed back full precoder index of each subcarrier or reduced index for a group of subcarriers to the transmitter, the GMD-based scheme can substantially reduce the amount of feedback information by returning only one precoder index to the transmitter, regardless of the number of subcarriers and channels. With this approach, the amount of required feedback for the proposed scheme does not grow with the number of subcarriers. This paper considers imperfect channel state information in the transmitter due to a feedback delay in the system.

A QRD-based preceded MIMO-OFDM system with reduced feedback is proposed. Unlike the conventional preceding schemes in which the receiver has to feed back information about the channel frequency response of each carrier to the transmitter, the proposed system converts the MIMO-OFDM channel into layered channels by effectively exploiting the QR decomposition of the time-domain channel impulse response matrix. As a result, the receiver in the proposed system only needs to feed back information about one preceding matrix, regardless of the total number of carriers. Furthermore, a computationally efficient implementation scheme is devised for the proposed system. Analytical and simulation results show that the proposed scheme can achieve impressive BER performance compared to the conventional schemes, yet with considerably reduced feedback.

This paper presents a practical low-density parity-check (LDPC) coded OFDM system designed for the underwater acoustic channel with its attendant sparse multipath channel and Doppler effects. The carrier frequency offset (CFO) and channel state information (CSI) are assumed unavailable to both to the transmitter and the receiver. Several different receiver structures are considered, all of which perform CFO/channel estimation, detection and decoding in an iterative manner. The convergence behavior of the iterative receivers and their asymptotic performance are evaluated using the extrinsic information transfer (EXIT) chart method. OFDM receiver performance is further evaluated through simulations and field tests in shallow water.

The performance of multi-hop CSMA/CA networks has in most cases been evaluated via simulations, or analytically using a perfect collision channel model. Using such methods, one can neither have access to the capture nature of the terminals nor evaluate the performance of the network in a fading environment. In this paper a new analytical framework that takes into account these capture and fading effects is presented. The network investigated in this paper has an infinite number of nodes which is assumed to be spatially Poisson distributed. The approach is to develop analytical expressions for the transition probabilities between states of nodes in the network by approximating these states as a Markov chain. A fast algorithm to solve these equations is also given. Numerical results show that the model agrees with simulation results for both omni-directional networks and networks employing beamforming.

We propose an iterative soft detection algorithm based on the QR decomposition and M-algorithm (soft-QRD- M) for MIMO-OFDM incorporating error correction coding. The soft-QRD-M step generates approximate a posteriori probabilities (APPs) with significant computational savings over the optimal sum-product algorithm. Simulation results show comparable performance of the soft-QRD-M detector and SPA in a Turbo- coded iterative MIMO-OFDM receiver.

Joint estimation of the carrier frequency offset (CFO) and channel response of each active user in the uplink of an OFDMA system over time-varying channels is investigated in this work. To cope with the enormous computational complexity involved in tracking the time variations of CFOs and channels, we propose to use the parallel Schmidt Kaiman Filter (PSKF) to break down the complicated optimization problem into multiple parallel but smaller optimization problems. This results in an estimation scheme whose complexity only grows linearly with the number of users. Simulations indicate that the proposed scheme can achieve high estimation accuracy.

This paper presents an online 3D modeling and tracking methodology that uses aerial photographs for mobile augmented reality. Instead of relying on models which are created in advance, the system generates a 3D model for a real building on the fly by combining frontal and aerial views with the help of an optical sensor, an inertial sensor, a GPS unit and a few mouse clicks. A user’s initial pose is estimated using an aerial photograph, which is retrieved from a database according to the user’s GPS coordinates, and an inertial sensor which measures pitch. To track the user’s position and orientation in real-time, feature-based tracking is carried out based on salient points on the edges and the sides of a building the user is keeping in view. We implemented camera pose estimators using both a least squares and an unscented Kalman filter (UKF) approach. The UKF approach results in more stable and reliable vision-based tracking. We evaluate the speed and accuracy of both approaches, and we demonstrate the usefulness of our computations as important building blocks for an Anywhere Augmentation scenario.

The authors consider a system architecture whereby orthogonal space time block codes (OSTBCs) are applied to a distributed set of wireless nodes. The utility offered is that by employing diversity we are able to greatly reduce the necessary link margin usually required to combat fast fading. A distributed set of wireless nodes are particularly well suited for this task as they are typically separated with significant inter-node distance thus having highly uncorrelated channels to a collector. In a typical application, a wireless sensor network (WSN) will have nodes that may wish to transmit data to a possibly mobile standoff collection point. In this paper, the authors discuss many practical aspects necessary for a real-world implementation including time and frequency offset estimation as well as channel tracking. As the waveform has been targeted to field programmable gate array (FPGA) hardware, we look at the relative implementation complexity of each signal processing element.

A cooperative MIMO network is considered with Ns sensors and a collector node with Mc antennas. In a practical implementation of this network, the sensor carriers have relative frequency offsets which must be estimated along with the MIMO channel. Generalized successive interference cancellation (GSIC) is proposed for this joint estimation problem. The primary operations in GSIC are correlation, FFT and cancellation. A reconfigurable hardware (FPGA) implementation of these GSIC primitives is described. A hybrid analysis/simulation for bit error rate (BER) is presented with results for GSIC using Alamouti and G4ccodes.

In this paper, we propose a new statistical joint frequency offset and channel estimation for the blind MIMO-OFDM system, where a state and parameters of dynamic systems are unknown. To reduce a channel mismatch problem, we use the fixed structure interacting multiple model (FS-IMM) and the Rao-Blackwellized Gauss-Hermite filter to estimate a model-dependent channel and offset parameter, which enters an observation function in a nonlinear manner. The resulting structure consists of M parallel bank of Kalman filters driven by sampled nonlinear state variable sequences.

In this paper, we evaluate the performance of the receiver employing an iterative RLS-based data detection and channel estimation for the structured irregular LDPC coded MIMO-OFDM system. Using the EXIT chart analysis, the performance of the detector with various approximate decoding algorithms is analyzed.

In this paper, we propose a new statistical joint fre- quency synchronization and channel estimation for the MIMO- OFDM system. To estimate a channel and offset parameter which enters the observation function in a nonlinear manner, we propose a Gauss-Hermite filter which incorporates the parallel Kalman filters (KFs) conditioned on a chosen offset sample. To reduce its prohibitive complexity in the MIMO system, the Rao- Blackwellization is employed. In the proposed MIMO-OFDM system, the frequency synchronization and channel estimation are made iteratively using one training symbol in front of the packet.

An asynchronous unicast ad hoc network is considered, where each node i is equipped with a receive/transmit beam-former pair (W/sub i/, g/sub i/) designed under a quality-of-service (QoS) SNR constraint. It is first shown that the minimum sum-power beamformers for the network satisfy a weak duality condition, in which the pairs ((g/sub i//sup opt/)*, (W/sub i//sup opt/)*) achieve the same sum power as the primal network. However, the optimum receive beamformer w/sub i//sup opt/ is not in general equal to (g/sub i//sup opt/)*, in contrast to the case of cellular and time-division duplexing networks. Iterative minimum mean-square error (IMMSE) beamforming algorithms are then proposed in which w/sub i/ = g/sub i/* is enforced. These algorithms are shown to be instances of the Power Algorithm in which gi is the maximizing eigenvector of an SNR-related objective matrix. The IMMSE algorithm can also be viewed as a noncooperative beamforming game, in which the payoff includes normalized SNR, and the tax is related to interference caused at other nodes. The existence of fixed points (Nash equilibria) is proved for IMMSE. Furthermore, fixed points of IMMSE are shown to satisfy the first-order necessary conditions for optimization using a network Lagrangian. The IMMSE game is modified to yield a sequential distortionless-response beamforming algorithm, which is shown to be convergent using a Total Interference Function. Extensive simulation results illustrate that IMMSE yields better power efficiency than a greedy noncooperative SNR-maximizing game.

Channel estimation and multiuser detection are enabling technologies for future generations of wireless applications. However, sophisticated algorithms are required for accurate channel estimation and multiuser detection, and real-time implementation of these algorithms is difficult. This paper presents architectural design methods for wireless channel estimation which can be leveraged to enable real-time multiuser detection. We redesign the matching pursuit (MP) channel estimation algorithm to reduce the complexity while maintaining the estimation accuracy. Furthermore, we develop a parameterized intellectual property (IP) core, which provides a hardware implementation of the MP algorithm. Experimental results demonstrate the effectiveness and ef- ficiency of the new algorithm and IP core for channel estimation. The implementation of our MP core on a modern, high performance reconfigurable system is about 216 times faster than running the algorithm on a state of the art microprocessor. The MP core possesses the speed required for performing true multiuser detection, enabling future generations of wireless communication applications.

In this paper, we focus on the frequency offset estimation for a MIMO system with OFDM transmission technique. In our system, we directly approximate the a posteriori distribution employing the Gauss-Hermite integration in the preamble interval. Using this proposed approach, a better frequency offset estimation can be achieved in relatively large frequency offsets compared to the extended Kalman filter based approach over a quasi-static channel environment.

In MIMO systems, where multiple antennas are used at both transmitter and receiver to achieve high spectral efficiency, channel impulse responses are often assumed to be constant over a block or packet. This assumption of block stationarity on channels is valid for most fixed wireless scenarios. However, for communications in a high mobility environment, the assumption will result in considerable performance degradation. In this paper, we focus on channel estimation for a MIMO system with OFDM transmission technique. In our system, pilots are placed on subcarriers for a novel channel estimation at the receiver with Kalman filters. With the channels estimated by a Kalman filter, we apply the OFDM MIMO soft data detector with a reasonable computational cost. The soft outputs of soft data detection are fed back to another soft Kalman filter for an improved channel estimation. By alternatively and iteratively using these two Kalman filters, a better overall performance can be obtained.

Efficient distributed algorithms are an important enabling technology for large-scale ad-hoc wireless sensor and communications networks. In this paper, optimal Bayesian data fusion under the assumption of linear Gaussian state and measurement models is presented. Within this framework, an efficient algorithm for distributed state estimation in ad-hoc networks is developed. Approximate algorithms are then developed for further improvements in network resource efficiency. These include a parameterizable tradeoff of improved communications efficiency for increased latency in the rate at which information propagates through the network. It is also shown that the algorithms are well-suited for use with non-linear measurements. Finally, for distributed node position estimation in a mobile ad-hoc network, simulation results show that accurate, efficient node localization is achieved

Channel estimation and distributed positioning algorithms are presented for geolocation in a wireless ad hoc network. The network uses a direct-sequence code-division multiple-access-based handshaking protocol, in which nodes receive multiple acknowledgment packets in response to a request-to-send waveform. Round-trip travel time (RTT) and angle-of-arrival (AOA) measurements are obtained using the generalized successive interference cancellation/matching pursuits (GSIC/MP) algorithm. The performance of GSIC/MP is evaluated via simulation and comparison to the Crame/spl acute/r-Rao bound. Position estimates are initialized using linearized least-squares and updated by an extended Kalman filter-based algorithm that includes measurement validation for nonline-of-sight error mitigation. The method is generalized for distributed estimation in sparsely connected networks: at each node, position estimates from connected nodes are incorporated via a fusion algorithm and updated using locally processed RTT/AOA measurements. Finally, comprehensive ad hoc network simulations are presented including channel ray tracing, RTT/AOA estimation and validation, and distributed positioning.

The use of multiple transmit/receive antennas forming a multiple-input multiple-output (MIMO) system can significantly enhance channel capacity. This paper considers a V-BLAST-type combination of orthogonal frequency-division multiplexing (OFDM) with MIMO (MIMO-OFDM) for enhanced spectral efficiency and multiuser downlink throughput. A new joint data detection and channel estimation algorithm for MIMO-OFDM is proposed which combines the QRD-M algorithm and Kalman filter. The individual channels between antenna elements are tracked using a Kalman filter, and the QRD-M algorithm uses a limited tree search to approximate the maximum-likelihood detector. A closed-form symbol-error rate, conditioned on a static channel realization, is presented for the M=1 case with QPSK modulation. An adaptive complexity QRD-M algorithm (AC-QRD-M) is also considered which assigns different values of M to each subcarrier according to its estimated received power. A rule for choosing M using subcarrier powers is obtained using a kernel density estimate combined with the Lloyd-Max algorithm.

A sparse-channel estimation algorithm for multiuser environments is developed with application to time-of-arrival (TOA)-based radiolocation. To eliminate multiple-access interference (MAI), the generalized successive interference cancellation (GSIC) algorithm is used. At each GSIC stage, the matching-pursuit (MP) or least squares (LS) methods are used to estimate the sparse channel. The GSIC/MP and GSIC/LS are compared via simulations, and the GSIC/MP performs better than the GSIC/LS.

The use of multiple antennas at both the transmitter and receiver can significantly increase the channel capacity. These systems are called the multiple-input multiple-output (MIMO) systems. By using orthogonal frequency division multiplexing (OFDM) transmission techniques, the MIMO-OFDM system can achieve high spectral efficiency, which makes it an attractive candidate for high-data-rate wireless applications. In this paper, we propose a convolutionally coded MIMO-OFDM system with EM-based channel estimation and a QRD-M data detection algorithm. In our systems, one training symbol is transmitted from each transmit antenna for the MIMO channel estimation at the receiver. With the channel estimates available, we apply the QRD-M algorithm on the estimated channel matrix for suboptimal data detection with reasonable computational cost. The bit error rate (BER) and packet error rate (PER) performance of the MIMO-OFDM systems are compared. In the simulations, the bit error rate performance of our systems is 9 (or 5) dB better than that of uncoded (or coded) BLAST systems.

An advantage of CDMA-based protocols is that packets can be transmitted immediately without prior channel access or resource reservation. In this paper, however, we propose to incorporate a reservation mechanism into CDMA-based protocols for next-generation wireless networks with data, voice, and multimedia applications. The resultant paradigm is called code division multiple access with adaptable reservation (CDMA/AR). The motivations for this change include that QoS guarantees can continue to be provisioned in CDMA/AR-based systems even when many handoff mobile stations enter an already heavily utilized cell. Our simulation results and mathematical analysis show that the adaptable reservation and differentiated admission control mechanisms of CDMA/AR can considerably reduce the handoff-rejection and call-blocking rates for higher-priority traffic to very small values, while achieving throughput close to 100% at the same time.

We consider a quasi-synchronous code-division multiple access (QS-CDMA) cellular system, where the code delay uncertainty at the base station is limited to a small number of chips. For such QS-CDMA systems, the need for code acquisition is eliminated, however, the residual code tracking and channel estimation problems still have to be solved. An extended Kalman filter (EKF) is employed to track the user delays and channel coefficients. By separating data detection, based on the QR decomposition combined with the M-algorithm (QRD-M) from the delay/channel estimation process, the computational complexity can be significantly reduced as the number of users increases. Simulations show that the EKF channel estimator performance is improved when the QRD-M algorithm is used instead of the MMSE detector or decorrelator for data decisions.

A handshaking protocol for radiolocation is considered using DS-CDMA packet transmission. The expectation-maximization (EM) algorithm is employed for code delay acquisition. It is shown that the systematic application of EM on the flat-fading channel yields a parallel interference cancellation (PIC)-based technique for acquisition. On the frequency-selective channel, it is necessary to jointly estimate both the code delays and multipath coefficients. The EM approach in this latter case yields an algorithm that combines PIC with an eigendecomposition/eigenvalue maximization step for channel identification.

A tracking mode receiver for asynchronous direct-sequence CDMA is presented based on the extended Kalman filter (EKF). The EKF jointly estimates the delays and multipath coefficients of the received CDMA waveform, and provides a modified minimum mean-square error (MMSE) estimate of the user data (MMSE-EKF). In order to obtain a practical algorithm, each user signal is tracked individually, with the remaining users modeled as colored Gaussian noise. However, the EKFs are coupled through the multiple access interference (MAI) covariance estimates. In order to obtain meaningful performance measures, approximate worst-case undesired user delays that minimize the desired user SNR and delay estimation Cramer-Rao bound are obtained. It is shown that such worst-case delays can be efficiently computed using the alternating maximization (A-M) algorithm. The resulting bit error rate (BER) performance of the MMSE-EKF tracking receiver is evaluated through a combination of simulation and analysis. The mean-time to lose lock (MTTLL) for a genie-aided EKF delay estimator is also obtained using the A-M computed delays.

We propose a new media-access and connection-establishment protocol for an ad-hoc quasi-synchronous packet radio network (QSPNET). In the QSPNET, the bandwidth is partitioned into a data channel, used to transmit packets, and a control channel, used to make reservations. The transmitted waveforms in the QSPNET are made quasi-synchronous by using a local GPS clock. The QSPNET uses a novel linear decorrelator receiver for multiuser detection, which permits the reception of quasi-synchronous code division multiple access (QS-CDMA) waveforms. We initially describe the QSPNET and its connection and flow control protocols, giving the rules of transmission and reception followed by all mobiles. We also provide performance results for the case where connection requests are generated at each node of the QSPNET according to a random process over an infinite time horizon. In particular, we obtain results on the achievable throughput and the average delay as a function of the transmission radius, the quasi-synchronous uncertainty interval, the duration of the connections, and the buffer size per node.

In order to construct the optimal decorrelator for a quasi-synchronous (QS) code-division multiple-access receiver, it is necessary to estimate the user delays. However, in a QS system, it is shown that delay estimation is equivalent to estimating a parameter vector which is related linearly to the received signal. An exact maximum-likelihood (ML) solution using alternating maximization is first developed for joint estimation of the user delay vectors and amplitudes. Suboptimal recursive least squares (RLS) and least mean square algorithms are then presented which approximate the true ML solution. It is shown that the optimal decorrelator and demodulated symbols are also generated by the RLS algorithm. Simulation and analytical results are presented for the bit-error rate performance of the adaptive receiver, and the average squared error of the estimated delay vectors is compared with the Cramer-Rao bound.

We present simulated error rate and code delay/amplitude tracking performance for two recently developed detectors: the adaptive whitened matched filter, and the quasi-synchronous decorrelator. Rapid convergence (within 50 symbols) of the estimated amplitude and delay of the desired user is demonstrated based on an approximate joint maximum likelihood approach. The adaptive whitened matched filter exhibits better bit error rate performance than the decorrelator, but requires a complex eigenanalysis stage.

It has previously been shown that multiuser interference in a quasi-synchronous code-division multiple-access (QS-CDMA) system can be effectively rejected using a nonadaptive decorrelator. However, the QS decorrelator is orthogonal to the inflated subspace spanned by the undesired users with arbitrary delays in the QS uncertainty interval and, hence, does not perform as well as a decorrelator with a priori knowledge of the delays. Here, we consider a constrained adaptive linear suppression filter which implicitly estimates the delays of the QS undesired signals, and thus approaches the ideal decorrelator of Lupas and Verdu (1990). For QS-CDMA, it is shown through analysis and simulations that the constrained recursive least-squares (C-RLS) algorithm converges faster than an unconstrained RLS adaptive filter, and generally outperforms the nonadaptive QS decorrelator. A cellular quasi-synchronous system is discussed in the present paper.

We study the performance and capacity of a quasi-synchronous code-division multiple-access (QS-CDMA) cellular system employing the decorrelating detector. It is assumed that each of N users has a GPS generated local clock, and attempts to transmit in synchrony with the other users in its cell. The detector makes use of the small timing uncertainties to build the interference signal space and perfectly cancel the interference. Results are compared with those of an adaptive MSE receiver. It is shown that when the LMS adaptive algorithm is employed, the mean-squared-error misadjustment significantly affects the receiver performance. Finally, system capacities in terms of users/cell are evaluated.

Dual-mode adaptive algorithms with rapid convergence properties are presented for the equalization of frequency selective fading channels and the recovery of time-division multiple access (TDMA) mobile radio signals. The dual-mode structure consists of an auxiliary adaptive filter that estimates the channel during the training cycle. The converged filter weights are used to initialize a parallel bank of filters that are adapted blindly during the data cycle. When the symbol timing is known, this filter bank generates error residuals that are used to perform approximate maximum a posteriori symbol detection (MAPSD) and provide reliable decisions of the transmitted signal. For channels with timing jitter, joint estimation of the channel parameters and the symbol timing using an extended Kalman filter algorithm is proposed. Various methods are described to reduce the computational complexity of the MAP detector, usually at the cost of some performance degradation. Also, a blind MAPSD algorithm for combining signals from spatially diverse receivers is derived. This diversity MAPSD (DMAPSD) algorithm, which can be easily modified for the dual-mode TDMA application, maintains a global set of MAP metrics even while blindly tracking the individual spatial channels using local error estimates. The performance of these single-channel and diversity MAPSD dual-mode algorithms are studied via computer simulations for various channel models, including a mobile radio channel simulator for the IS-54 digital cellular TDMA standard.

A linear detector for a quasisynchronous code-division multiple-access (QS-CDMA) cellular system is presented, which is designed according to a minimum mean-square error (MMSE) criterion. By using a time-averaged version of the interfering signal covariance matrix, it is shown that multiuser interference can be rejected without the need to estimate signal time-of-arrival. Furthermore, unlike previous MMSE receiver designs, estimation of the received signal covariance matrix is not required. An asymptotic form of the MMSE detector, corresponding to a decorrelator implemented using a projection operator, is also obtained. Bit-error rate (BER) results are presented which demonstrate the superiority of the MMSE receiver over the conventional matched-filter detector under strong near-far conditions. An analysis of the expected acquisition time T~/sub ACQ/ is given for the decorrelator detector using a serial search scheme. The results obtained show that T~/sub ACQ/ is far less for a code acquisition method using the decorrelator as opposed to a conventional matched filter.

A linear decorrelator detector is proposed for a quasisynchronous code-division multiple-access (QS-CDMA) cellular system. It is assumed that each of N users has a Global Positioning System (GPS) generated local clock and attempts to transmit synchronously with the other users in its cell. The base station thus receives N signature waveforms which are approximately synchronized, but with a small timing error due to local oscillator drift, Doppler shifts, and variable distances from the mobiles to the base. The residual timing offset is discretized, and a decorrelator is constructed which attempts to reject the multiuser interference vectors corresponding to the set of possible offsets. A noncoherent version of the decorrelator detector is presented, along with a maximum-likelihood scheme for synchronizing to the desired signal. Bounds and analytical results for the bit-error rate (BER) are obtained for various interferer conditions.

An importance sampling (IS) simulation technique is presented for Bayesian equalizers, based on the large deviations theory approach developed by Sadowsky and Bucklew (see ibid., vol.36, no.5, p.579, 1990). The resulting simulation density consists of a sum of exponentially twisted distributions. For the additive Gaussian channel, this simulation density is equivalent to the conventional (mean-shift) noise biasing IS method, but with the bias vector chosen from a fixed set in a random manner. In order to properly select the bias vectors, the asymptotic decision boundary of the Bayesian equalizer is first determined. It is shown that the boundary is formed by multiple hyperplanes, and that the appropriate bias vectors are orthogonal to the hyperplanes. The simulation technique is then extended to the recursive symbol-by-symbol detector of Abend and Fritchman (A-F algorithm) proposed in 1970, and simulation results are presented for both recursive and non-recursive equalizers.>

A multiuser detector is developed in which the delays and amplitudes of the incoming waveforms are estimated recursively. The algorithm is an extension of the symbol-by-symbol detector of Abend and Fritchman, originally derived for intersymbol interference (ISI) channels, to the multiuser application. In order to make the multiuser detector adaptive, the likelihoods in the symbol-by-symbol metric update are approximated using a set of extended Kalman filter (EKF) innovations. The EKF's provide, in addition to the likelihoods, joint estimates of the signal delays and amplitudes. The resulting algorithm is quite complex, due to the large number of possible composite symbols corresponding to the multiple users. However, it is shown that the likelihood computations and EKF updates can be expressed it terms of a set of cross-correlation functions, which need only be computed for a subset of the possible symbols. The cross-correlator outputs are updated at the bit rate, and thus the EKF and metric update computations need be performed only at a function of the spread-spectrum chip rate. A metric pruning technique is proposed that further reduces the number of EKF delay/amplitude estimators required. Finally, an important sampling simulation strategy is used to obtain bit-error rate estimates for the adaptive multiuser detector.>

We present a new digital direct-sequence (DS) receiver with joint estimation of code delay, multipath gains and Doppler shift. A parameter estimator consisting of a parallel bank of extended Kalman filters (EKF’s) extracts estimates of the timing, T and the multipath coefficients, fl distorting the received signal. A "detected" estimate of the Doppler shift, Vr distorting the received signal is also provided by the estimator. We compute the bit error rate that results when a RAKE matched filter uses the estimated parameters to detect the DPSK encoded binary data in the received signal. The bit-error rate (BER) is evaluated, and successful performance of the proposed receiver in the presence of Doppler shift distortion is observed in many cases. We demonstrate that the receiver can operate when the multipath coefficients vary in time (Doppler spread).

The paper presents a model for acquisition of a spread-spectrum signal distorted by Doppler shifts. The authors attempt a joint acquisition of both the time delay and the distorting Doppler velocity. They consider the case where the ratio of platform to propagation velocity, v/sub r//c is large enough to generate time compression of the pseudo-noise (PN) sequence itself. A Doppler effect of this type may be encountered in underwater acoustic or intersatellite communications. The simultaneous presence of the unknown Doppler shift and timing necessitates a substantial modification of conventional acquisition strategies. A two-dwell system, using a short duration correlation to acquire the timing information, with a subsequent longer correlation to acquire the Doppler velocity is proposed. The joint acquisition process is modeled by a finite state Markov chain, and the performance of the system is characterized by the mean and variance of the acquisition time. Two systems of linear equations that can be solved for the mean and variance, respectively, are developed. The matrices specifying the systems can be solved efficiently. As a particular example, the authors analyze the performance of systems with spread-spectrum signals having sequence lengths of 15 and 63 chips. It is shown that the two-dwell system can acquire timing and Doppler shifts in a reasonable time period. Although the system studied is not well-suited to multiple-access or antijam applications, the analysis techniques developed may prove useful in evaluating the performance of more complex acquisition techniques.>

In direct-sequence (DS) spread-spectrum communications, it is often necessary to obtain pseudo-noise (PN) code synchronization in the presence of both narrowband interference and frequency-selective multipath. However, conventional delay-lock code tracking loops are not optimized for such applications, nor are they well-suited to digital implementations. A code tracking algorithm based on the extended Kalman filter (EKF) is described that provides both code synchronization and joint estimates of interferer and channel parameters. It is first assumed that the interference can be modeled by an N-th order autoregressive (AR) process, and the multipath by a finite impulse response filter. A composite channel, equivalent to the convolution of the prewhitening filter and multipath coefficients is then constructed. The received waveform is shown to be a linear function of the composite channel parameters, that can therefore be directly estimated by an extended Kalman filter. The code delay /spl tau/ is viewed as a nonlinear parameter, that can likewise by estimated, after an appropriate linearization, using the EKF. The performance of the algorithm is first evaluated by computing the average bit-error rate (BER) of a digital RAKE receiver, that employs the joint delay, channel and interferer estimates derived from the EKF. In addition, a nonlinear analysis of the EKF is performed in which the code delay error and error covariance are modeled as a two-dimensional Markov process. The joint probability density function of these parameters, and nonlinear delay error variance, are obtained via numerical integration of a two-dimensional Chapman-Kolmogorov equation.>

A new blind equalization algorithm based on a suboptimum Bayesian symbol-by-symbol detector is presented. It is first shown that the maximum a posteriori (MAP) sequence probabilities can be approximated using the innovations likelihoods generated by a parallel bank of Kalman filters. These filters generate a set of channel estimates conditioned on the possible symbol subsequences contributing to the intersymbol interference. The conditional estimates and MAP symbol metrics are then combined using a suboptimum Bayesian formula. Two methods are considered to reduce the computational complexity of the algorithm. First, the technique of reduced-state sequence estimation is adopted to reduce the number of symbol subsequences considered in the channel estimation process and hence the number of parallel filters required. Second, it is shown that the Kalman filters can be replaced by simpler least-mean-square (LMS) adaptive filters. A computational complexity analysis of the LMS Bayesian equalizer demonstrates that its implementation in parallel programmable digital signal processing devices is feasible at 16 kbps. The performance of the resulting algorithms is evaluated through bit-error-rate simulations, which are compared to the performance bounds of the maximum-likelihood sequence estimator. It is shown that the Kalman filter and LMS-based algorithms achieve blind start-up and rapid convergence (typically within 200 iterations) for both BPSK and QPSK modulation formats.>

In this paper, analog circuit designs for implementations of Gibbs samplers are presented, which offer fully parallel computation. The Gibbs sampler for a discrete solution space (or Boltzmann machine) can be used to solve both deterministic and probabilistic assignment (association) problems. The primary drawback to the use of a Boltzmann machine for optimization is its computational complexity, since updating of the neurons is typically performed sequentially. We first consider the diffusion equation emulation of a Boltzmann machine introduced by Roysam and Miller (1991), which employs a parallel network of nonlinear amplifiers. It is shown that an analog circuit implementation of the diffusion equation requires a complex neural structure incorporating matched nonlinear feedback amplifiers and current multipliers. We introduce a simpler implementation of the Boltzmann machine, using a "constant gradient" diffusion equation, which eliminates the need for a matched feedback amplifier. The performance of the Roysam and Miller network and the new constant gradient (CG) network is compared using simulations for the multiple-neuron case, and integration of the Chapman-Kolmogorov equation for a single neuron. Based on the simulation results, heuristic criteria for establishing the diffusion equation boundaries, and neuron sigmoidal gain are obtained. The final CG analog circuit is suitable for VLSI implementation, and hence may offer rapid convergence.

A new computational method is presented for solving the data association problem using parallel Boltzmann machines. It is shown that the association probabilities can be computed with arbitrarily small errors if a sufficient number of parallel Boltzmann machines are available. The probability beta(i)(j) that the i th measurement emanated from the jth target can be obtained simply by observing the relative frequency with which neuron v(i,j) in a two-dimensional network is on throughout the layers. Some simple tracking examples comparing the performance of the Boltzmann algorithm to the exact data association solution and with the performance of an alternative parallel method using the Hopfield neural network are also presented.

It is shown that the optimum demodulator for the case of an a priori unknown channel and symbol timing can be approximated using a modified Viterbi algorithm (VA), in which the branch metrics are obtained from the conditional innovations of a bank of extended Kalman filters (EKFs). Each EKF computes channel and timing estimates conditioned on one of the survivor sequences in the trellis. It is also shown that the minimum-variance channel and timing estimates can be approximated by a sum of conditional EKF estimates, weighted by the VA metrics. Simulated bit error rate (BER) results and averaged-squared channel/timing error trajectories are presented, with estimation errors compared to the Cramer-Rao lower bound. The BER performance of the modified VA is also shown to be superior to that obtained using a decision-directed channel/timing estimation algorithm.>

A digital spread-spectrum receiver design is presented for communication over multipath channels with severe Doppler shifts. The characteristics of the underwater channel relevant to spread-spectrum system design are discussed, and a channel model for short-range communications (less than 10 km) is defined. The receiver considered uses a digital coherent RAKE combiner, coupled with an extended Kalman filter (EKF)-based estimator for channel parameters and pseudonoise code delay. Receiver performance is evaluated by computing average bit-error rate (BER) versus iterations of the EKF joint estimator, using both fixed and time-varying channels. It is shown that the BER obtained using the EKF joint estimator closely tracks the optimum BER obtained when the channel, delay, and Doppler parameters are known exactly. Finally, the Cramer-Rao lower bound for time-invariant joint channel, delay, and Doppler estimation is derived, and compared with the ensemble averaged mean-squared error of the EKF estimator.>

The data association problem in multiple target bearings-only motion analysis is considered. The measurements are target bearings obtained from passive sonars in a distributed sensor network. These measurements, after being transmitted to a processing center, are used to estimate the initial states of targets. The overall problem is formulated using the maximum likelihood principle. A two-step iterative method is proposed to maximize the joint likelihood, a nonlinear least-squares method is used to fit the trajectory estimates to the measurements for given data associations, and the linear assignment algorithm is used to solve the data associations for given trajectory estimates. A stochastic relaxation method based on simulated annealing is proposed to augment the second step so that the global maximum of the likelihood function can be located. Monte Carlo simulations are included to evaluate the joint estimator. The Cramer-Rao lower bound of the initial state estimate is derived in the presence of data association uncertainties.>

The problem of delay estimation in the presence of multipath is considered. It is shown that the extended Kalman filter (EKF) can be used to obtain joint estimates of time-of-arrival and multipath coefficients for deterministic signals when the channel can be modeled as a tapped-delay line. Simulation results are presented for the EKF joint estimator used for synchronization in a direct-sequence spread-spectrum system operating over a frequency-selective fading channel. A simplified model of the EKF joint estimator is considered for analysis purposes. The evolution in time of the tracking error probability density function and the nonlinear tracking error variance are examined through numerical solution of the Chapman-Kolmogorov equation. The nonlinear tracking error variance is compared to both the linear error variance estimate directly provided by the EKF and the Cramer-Rao lower bound.>

A fast frequency-hopped (FFH) receiver which uses a prewhitening filter to reject narrowband interference is described. By using an appropriate fractional tap spacing, it is shown that interference can be estimated independently of the desired signal. Bit error rate results are presented for the receiver for linear square-law combining. The results compare favorably to those obtained by a near-optimal automatic gain-control (AGC) combining technique. The performance of the prewhitening filter interference rejection method is shown to be superior to that of a nonparametric self-normalizing receiver. Finally, simulation results for an FFH receiver using the complex least mean-square (LMS) algorithm to update the prewhitening filter coefficients are presented.>

The author considers the problem of demodulating a direct-sequence (DS) spread-spectrum signal in the presence of narrowband interference and multipath. A receiver is considered that is based on the generalized likelihood-ratio test (GLRT), in which the interferer is modeled as an Nth-order circular Gaussian autoregressive (AR) process and the multipath channel is represented by a tapped-delay line. The maximum-likelihood joint estimator for the channel coefficients and interferer AR parameters is then derived. Analytical expressions for bit-error rate are presented for GLRT receiver, under the assumption of perfect estimates of the channel and interferer parameters. The performance of the GLRT receiver is compared to that of a DS receiver using a transversal equalizer. It is shown that the GLRT receiver consistently outperforms the equalizer-based receiver by 2-3 dB. The performance of an adaptive GLRT receiver is evaluated where the recursive least-squares algorithm is used to jointly estimate the interferer and channel parameters.>

The joint probabilistic data association (JPDA) algorithm previously reported by T.E. Fortmann et al. (1983) for tracking multiple targets in the presence of clutter has the drawback that its complexity increases rapidly with the number of targets and returns. An approximation of the JPDA is suggested that solves this problem by using an analog computational network to solve the data association problem. The problem is viewed as that of optimizing a suitably chosen objective function. Simple neural-network structures for the approximate minimization of such functions have been proposed by other researchers. The analog network used here offers a significant degree of parallelism and thus can compute the association probabilities more rapidly. Computer simulations indicate the ability of the algorithm to track many targets simultaneously in the presence of moderate density clutter.>

The impact of imperfect synchronization on the performance of prediction-error interference rejection filters in noncoherent direct-sequence (DS) spread-spectrum communications is considered. Bit-error-rate analysis (BER) analysis (BER) of binary DPSK (differential phase-shift keying) data modulation used in conjunction with direct-sequence spread-spectrum is used as a performance measure. A first-order noncoherent delay-lock loop is used for the pseudonoise (PN) code tracking. Conditional BER results for the DS/DPSK systems for fixed values of the code tracking error are obtained. The average BER of the system is then evaluated by averaging the conditional BER expressions over the probability density function of the code tracking error. Results include the effects of both a single fading tone and a narrowband Gaussian interferer on the overall system performance. Interferer offset frequency is considered in determining the code tracking loop noise as well as the receiver BER.>

It is well known that a prewhitening filter, or interference canceller can significantly enhance the detectability of a wideband signal received in the presence of narrowband interference. For example, an adaptive prewhitening filter using the Widrow LMS algorithm has been shown to provide greatly improved performance in wideband direct-sequence (DS) spread-spectrum communications systems. Frequency-hopping is another useful spread-spectrum signaling format that is, however, very sensitive to narrowband jamming. Unlike DS signals, frequency-hopped (FH) signals are instantaneously narrowband, and thus would not seem to be good candidates for the use of prewhitening techniques for interference suppression. However, when observed over a time span encompassing multiple hops, the FH signal becomes wideband. In this paper, we show how prewhitening filters designed using linear least-squares estimation techniques can be applied to improve the detection performance of FH signals. Two specific interference suppression filters are presented, the first, with taps spaced at the hop duration Th, can reject interference with a bandwidth of up to π/Thradians/sec. The second filter uses "fractionally-spaced" taps at intervals of Th/L. This filter can reject interference with a bandwidth of up to Lπ/Thand provides improved detection performance when the FH signal is linearly combined over L hops. We present analytical results for signal-to-noise ratio improvement factors for the two types of FH interference suppression filters and discuss their implementation using the LMS algorithm.

The multisensor estimation problem in which measurements from N sensors are acquired sequentially is considered. The Block Kalman Filter is shown to be a natural way of fusing sequential data in a variety of situations involving multisensor estimation. This technique is then extended to the case of multisensor tracking applications where false measurements are often present, by applying the probabilistic data association filter (PDAF) technique to the blocked state model.

The Widrow LMS algorithm is considered for the implementation of an adaptive prewhitening filter in a direct-sequence (DS) spread-spectrum receiver. Exact expressions for the steady-state tapweight covariance matrix and resulting average excess mean square error are developed for the real LMS algorithm when the input contains a random binary sequence (used to model a pseudonoise spreading sequence). It is shown here that the output samples of the adaptive filter possess approximately Gaussian statistics under the conditions of slow convergence and a large number of filter taps. Using this approximation, expressions for the resulting bit error rate (BER) when the adaptive algorithm is used to suppress a fading gone jammer are developed, and numerical results obtained from these expressions are compared to simulation results for the DS receiver.

A very small, high quality RELP Vocoder has been implemented with commercially available Integrated Circuits. This full-duplex, single-card design uses two TI TMS320 Digital Signal Processors. One processor implements the RELP Vocoder Analysis algorithm and the second implements the Synthesis algorithm. The Vocoder operates with a software-controlled, flexible data rate between 9.6 and 32 KBPS. It occupies less than 35 square inches with a power consumption of less than 5 W. A real-time demonstration is available.

Linear least squares estimation (LLSE) techniques can provide an effective means of suppressing narrow-band interference in direct sequence (DS) spread-spectrum systems. In the results presented here, analytical expressions for bit error rate are derived for two DS spread-spectrum systems under the conditions of either tone or narrowband Gaussian interference. It is shown that the most common LLSE filter design can lead to performance inferior to that of various other filter designs. However, results are also presented demonstrating that an LLSE filter design motivated by the structure of the maximum-likelihood receiver leads to consistently superior performance. The performance of a system using this new design criterion is compared with that of an approximation to the maximum-likelihood (ML) receiver for the tone interference model and with that of the exact ML receiver for the Gaussian interference. Finally, it is shown that the bit error rate estimate obtained from application of a Gaussian approximation for the test statistic is overly pessimistic for the systems studied here.