In this paper, we propose an efficient extended Kalman filter based simultaneous localization and mapping (EKF-SLAM) algorithm based on measurement clustering. When we apply conventional EKF-SLAM to the real data, because there are lots of landmarks, the amount of computation increases quadratically as the size of the state vector grows, resulting in the inability to guarantee real-time performance. To reduce the computation amount, we propose the measurement clustering technique before augmenting the landmark position variable to the state vector to prevent indiscriminate increase of length of the state vector. The real data was obtained by driving a vehicle with five radars on the same route twice. Our result shows the estimated path via EKF-SLAM is more accurate than estimated path using odometer only.

Many synthetic aperture radar (SAR) autofocus techniques use range bins containing a single dominant point scatterer to estimate the phase error by maximizing or minimizing an objective function. We analytically show that some widely used objective functions do not give accurate phase error estimates if the objective function is constructed using a range bin containing multiple strong point scatterers (SPSs). Multiple SPSs are often observed in the range bins extracted from SAR images of urban areas containing many bright man-made objects. Such multiple SPSs do not allow us to obtain accurate estimates due to the interference between SPSs. To overcome this multiple scatterer problem, we propose the use of a combined entropy objective function with the local magnitudes of SPSs, along with the multiple-signal classification algorithm. Our experiment with actual SAR data confirms the superiority of the proposed approach.

Many of modern radar systems employ antenna arrays with thousands of anetnnas. These antennas are usually grouped into a few subarrays or beams through the process of beamspace transformation, to estimate the angle of a target. In this letter, we consider a simple but pragmatic beamspace transformation that only allows beam steering, and present a technique of finding optimal steering angles that minimize the Cramér-Rao bound (CRB) for angle estimation. The CRB and mean squared error (MSE) of the angle estimate using the resulting optimal steering angles are compared with those using the widely used DFT-based beam steering.

This paper studies a network information flow problem for a multiple-input multiple-output (MIMO) Gaussian wireless network with K users and a single intermediate relay having M antennas. In this network, each user sends a multicast message to all other users while receiving K-1 independent messages from the other users via an intermediate relay. This network information flow is termed a MIMO Gaussian K-way relay channel. For this channel, it is shown that the optimal sum degrees of freedom (sum-DoF) is KM/K-1, assuming that all nodes have global channel knowledge and operate in full-duplex. A converse argument is derived by cut-set bounds. The achievability is shown by a repetition coding scheme with random beamforming in encoding and a zero-forcing method combined with self-interference cancelation in decoding. Furthermore, under the premise that all nodes have local channel state information at the receiver only and operate in half-duplex mode, it is shown that a total K/2 DoF is achievable when M=K-1. The key to showing this result is a novel encoding and decoding scheme, which creates a set of network code messages with a chain structure during the multiple access phase and performs successive interference cancelation using side-information for the broadcast phase. One major implication of the derived results is that efficient exploitation of the transmit message as side-information leads to an increase in the sum-DoF gain in a multiway relay channel with multicast messages.

Frequency domain blind channel estimation methods for orthogonal frequency division multiplexing (OFDM) systems with multiple antennas are proposed, in which the cross relation between the channel gains and a single snapshot of the received signal on each subcarrier is utilised. Necessary and sufficient conditions for the identifiability of a single‐input multiple‐output OFDM channel estimator are derived and, furthermore, using a first‐order perturbation analysis, the approximation of mean‐squared‐error (MSE) is analytically obtained, which shows that the proposed estimator is approximately unbiased at high signal‐to‐noise ratio. The proposed estimator exhibits lower MSEs than those of both the existing subspace‐based and deterministic blind channel estimators and is less sensitive to the estimation error of the number of multipaths. Finally, the extension of the proposed channel estimation to multiple‐input–multiple‐output OFDM is presented.

This paper considers amplify-and-forward, multiple-input multiple-output, two-path relay systems in which the inter-relay interference (IRI) between two relay nodes leads to a degradation of performance. To solve this issue, we propose two IRI cancellation schemes that exploit the orthogonal projection methods using multiple antennas at the relay nodes; the proposed schemes effectively remove the IRI by projecting it onto the null space of the effective inter-relay channel. Simulation results show that the proposed IRI cancellation schemes achieve close-to-optimal performance in average rate.

Lattice-reduction-aided precoding (LRP) provides near-capacity rates with the use of low-complexity linear receivers for coded multiple-input multiple-output (MIMO) systems. However, a large amount of feedback in the feedback for an integer or binary precoding matrix has been a bottleneck in its implementation. In this paper, we propose a codebook-based LRP scheme for limited-feedback coded MIMO systems. The proposed LRP scheme follows the fundamentals of the previous LRP scheme that employed multilevel binary coset coding so that the precoding matrix is binary. In the proposed LRP scheme, the conventional precoding matrix obtained from the Lenstra-Lenstra-Lovasz algorithm is modified to specific forms that are predefined in a codebook set. The new precoding matrix is selected such that the lower bound on the capacity is maximized for a given codebook set, while the codebook set is designed offline such that the upper bound of the average capacity loss induced by the limitation on the codebook size is minimized. The simulation results show that the proposed LRP scheme nearly achieves the achievable rate of the conventional LRP scheme with a greatly reduced amount of feedback.

This paper uses generalized singular value decomposition (GSVD) to reduce decoding complexity in an amplify-andforward, multiple-input multiple-output, two-way relay channel (TWRC). The key is diagonalization of the effective channels at the source nodes; this formulation also simplifies the optimization problem of designing linear transceivers at each node to maximize the achievable sum rate (ASR). For the given diagonalized structure, we first align the entries of the diagonalized channels using a permutation followed by power allocation, provided in closed form. Simulation results demonstrate that the proposed GSVD-based relaying scheme, with the signal alignment and power allocation, significantly improves the ASR while retaining the diagonalized channel structure.

This paper investigates a network information flow problem for a multiple-input multiple-output (MIMO) Gaussian wireless network with K-users and a single intermediate relay having M antennas. In this network, each user intends to convey a multicast message to all other users while receiving K-1 independent messages from the other users via an intermediate relay. This network information flow is termed a MIMO Gaussian K-way relay channel. For this channel, we show that K/2 degrees of freedom is achievable if M=K-1. To demonstrate this, we come up with an encoding and decoding strategy inspired from cryptography theory. The proposed encoding and decoding strategy involves a signal space alignment for an encryption message for the multiple access phase (MAC) and zero forcing with successive network code decoding for the broadcast (BC) phase. The idea of the signal space alignment for an encryption message is that all users cooperatively choose the precoding vectors to transmit the message so that the relay can receive a proper encryption message with a special structure, network code chain structure. During the BC phase, zero forcing combined with successive network code decoding enables all users to decipher the encryption message from the relay despite the fact that they all have different self-information which they use as a key.

A multiple-input multiple-output two-way relay channel consisting of two communication nodes and a full-duplex relay node in which no direct link exists between the two communication nodes is considered. We propose an achievable scheme that employs horizontally encoded lattice codes combined with generalized singular value decomposition-based precoding for the first phase. The second phase of the proposed scheme follows the fundamentals of the previous scheme, which uses vertically encoded structural bining, with the only difference that the added codeword of the two codewords from the communication nodes, instead of those two individual codewords, is decoded and retransmitted in the proposed scheme. We show that the proposed scheme achieves the cut-set bound asymptotically as the signal-to-noise ratios of the channels tend to infinity.

In this paper, we develop an adaptive transmission scheme in a wireless sensor network (WSN). Distributed sensors can cooperate with the neighborhood and transmit their data to the data collector by choosing a proper transmission mode adaptively based on the channel conditions and spatial correlation among the sensors. By investigating the statistical properties of a correlated virtual multiple-input multiple-output (MIMO) channel between the sensors and data collector, we assess the analytic performances of two different MIMO transmission modes -- spatial multiplexing and transmit diversity achieving schemes. We also evaluate energy efficiencies of two MIMO transmission modes. Based on these result, we derive a new energy efficient mode switching criterion between spatial multiplexing and transmit diversity suitable to a WSN.

A two-stage antenna subset selection scheme is proposed to maximize the channel capacity of amplify-and-forward multiple-input multiple-output relay systems. To reduce the computational complexity of the antenna selection process, the receive and transmit antenna selections are separated in a two-stage algorithm. In stage 1, a subset of receive antennas is selected at the relay node. A subset of transmit antennas is then selected at the relay node in stage 2. The simulation results demonstrate that the proposed scheme achieves nearly the same channel capacity with a reduced amount of computational complexity as compared to an exhaustive search.

In this paper, we study a network information flow problem for a multiple-input-multiple-output (MIMO) Gaussian wireless network with three users each equipped with M antennas and a single intermediate relay equipped with N antennas. In this network, each user intends to convey independent messages for two different users via the intermediate relay while receiving two independent messages from the other two users. This is a generalized version of the two-way relay channel for the three-user case. We will call it a "MIMO Y channel." For this MIMO Y channel, we show that the capacity is 3M log(SNR) + o(log(SNR)) if N ≥ ⌈3M/2⌉ by using two novel signaling techniques, which are signal space alignment for network coding, and network-coding-aware interference nulling beamforming.

We present a lattice reduction (LR) aided successive interference cancellation (SIC) scheme combined with linear pre-equalization in the correlated multi-user MIMO system. The LR-aided SIC schemes have been extensively studied to detect multiple users' symbols due to the nearly optimal performance. However, the LR algorithm and the sequential detecting method inherently incur considerable computational complexity overhead although it is of a polynomial order. To reduce their computational complexities, we decouple the entire system into multiple lower dimensional subsystems using linear pre-equalization based on the correlation among the users. We then utilize the LR-aided SIC parallel in each subsystem. Furthermore, to combine the LR-aided SIC with pre-equalization optimally, we modify the LR-aided SIC scheme, handling the residual interferences after pre-equalization.

We present a multi-user multiple-input multiple-output (MU-MIMO) preceding scheme utilizing generalized singular value decomposition (GSVD). Our work is motivated by the precoding scheme developed by Sadek, Tarighat, and Sayed that maximizes the signal to leakage and noise ratio (SLNR), in which the precoding weight is obtained by the generalized eigenvalue decomposition (GEVD). However, the covariance matrix utilized in GEVD becomes close to be singular as the signal to noise ratio goes high. To improve the numerical accuracy, a GSVD based algorithm is exploited in this paper and a novel method is derived to compute the precoding weights for multiple users by removing redundant computational loads rather than repeating the GSVD algorithm for each user. In addition, we propose a technique to speed up the GSVD based precoding algorithm by preprocessing using the QR decomposition. Finally, to improve the system performance, the antenna selection scheme using GSVD is also proposed which does not require the exhaustive search to choose the active antennas.

The sphere decoding (SD) is a category of algorithms finding the closest lattice point and known to have the expected complexity approximately cubic in the dimension of the lattice point to be searched for a medium-to-high range of signal-to-noise ratios (SNRs). But the expected complexity depends highly on SNR and the portion of high-ordered terms (higher ordered terms than cubic) of it increases rapidly as SNR decreases for low SNRs. The number of points visited by SD is influenced by the processing done before starting to search a closest point. In this paper the minimum mean square error (MMSE) sorted QR-decomposition (SQRD) is proposed as a preprocessing for SD algorithm. By mathematical investigation of MMSE extended matrix we find out that MMSE extension can be generalized and suggest variations of MMSE-SQRD as candidates of the preprocessing for SD. MMSE-SQRD and its variations reduce considerably the dependency of the complexity of SD on SNR.

Deploying relay stations improves the signal-to- interference and noise ratio (SINR) by making a Pico cell environment. Nonetheless, a critical problem associated with deploying RS is additional resource consumption, which reduces the actual transmission rate. Two-way relaying schemes that use physical layer network coding (PNC) reduce the additional resource consumption and thereby improve the transmission rate. In this paper we propose a novel two-way relaying scheme for coded multiple-input multiple-output (MIMO) systems. The proposed scheme involves generalized Schur decomposition-based preceding and successive interference cancellation (SIC). By using the generalized Schur decomposition, orthogonal preceding matrices that minimize the transmit power can be applied, resulting in an improved transmission rate. We have evaluated the proposed system by comparing its transmission rate with that of several conventional schemes.

Bi-static andmulti-static radar requires synchronization between transmitter(s) and receiver(s) due to their separated locations. Especially, for the target doppler estimation, the carrier frequency offset occurred by the discord between the transmit and the receive ends should be estimated before the processing. In this paper, the system model for multi-static radar is provided as a similar form of multi-input multi-output (MIMO) communication system. However, unlike the MIMO case the frequency offset for each receiver is not identical to each other which makes the modeling and problem complicated. Based on the system model, the joint maximum likelihood (ML) solution and the suboptimal estimation is presented for frequency offset compensation. In this approximated approach, each receiver frequency offset is decoupled to others and the computational complexity becomes extremely low while it provides reasonable error performance for small frequency offsets and high signal to noise ratio (SNR).

A relay based wireless network system is basically composed of three nodes, two source nodes which are also destination nodes and the relay node which helps reliable communications between two nodes. The amplify-and-forward (AF) relaying scheme is more convenient to implement than the decode-and-forward (DF) relaying scheme which requires a decoding process at the relay node. In this paper, we design an optimal linear precoder and decoder which minimize the sum of mean squared error (SMSE) when the relay and the destination node have perfect knowledge of the channel state information (CSI) for the forward and the backward channels, respectively. The optimal linear precoder and decoder solutions are obtained by using the proposed iterative algorithm which satisfies the KKT optimality conditions. The average BER performance of the proposed scheme is compared with those of a conventional AF-ZF scheme and AF-MMSE scheme.

Previous researches regard the signals from other users as interfereces to be avoided in wireless ad-hoc network systems (e.g, IEEE 802.11/16). However, if the purpose of ad-hoc communications is relaying information from one node to another node through the third node, we can apply the network coding (NC) at the physical layer to increase a capacity by regarding a received signal from different nodes as one added signal. In this papar, we combine NC for MIMO relay systems with a linear pre-equalizer. For examples, zero-forcing (ZF) or lattice reduction-aided ZF pre-equalizers is used at each transmitter. We have evaluated the proposed system by comparing uncoded BER performance with those of several one-way relaying scheme focused on phase 1 mainly, because the overall performance is upperbounded by that of phase 1. The proposed system increases a multiplexing gain twice, maintaining a same diversity order, compared to one-way relaying. Finally, we combine the lattice encoder and the decoder to achieve an optimal capacity of the proposed scheme.

We present a lattice reduction (LR) aided preceding scheme combined with spatial division multiplexing (SDM) for clusters of correlated multiple users. Previously, the LR aided precoding schemes are deeply studied to transmit different symbols, simultaneously, to multiple users since it shows superior performance to linear precoding schemes like zero-forcing (ZF) algorithm. However, it has high complexity compared with linear precoding schemes. To resolve it, we exploit the spatial separability between clusters. That is, we apply the LR aided scheme for highly correlated users in a same cluster and to suppress the interferences from other clusters, we apply the ZF algorithm. Furthermore, we also propose a new SDM scheme to maximize the transmit signal to interference and noise ratio, which is formulated by generalized eigenvalue problem. We verify the performance of the proposed schemes by Monte-Carlo simulations and compare the computation complexities of them with the conventional LR aided precoding scheme.

Enormous gains of using multiple antennas at both the transmitter and the receiver known as multiple-input multiple-output (MIMO) in wireless communication system have been predicted. Especially, significant improvements in spectral efficiency are given through spatial multiplexing which transmits independent data streams over multiple transmit antennas simultaneously. Recently, many researches demonstrate that similar performance gains are obtained in wireless relay networks with multiple antennas at each terminal. In this paper, we propose a relaying protocol named as alternate transmission relaying protocol which enhances the capacity of the given environment and describe the capacity of MIMO wireless relay networks for the proposed relaying protocol. We consider a system model which has K relay terminals between a designated source terminal and a designated destination terminal. The proposed protocol assumes that no interference exists among the relay terminals. The K relay terminals receive or transmit a signal alternately without an interference. Several singular value decomposition (SVD) based relaying schemes are proposed for the proposed relaying protocol to perform a coherent combining at the destination terminal. In addition, a weight matrix for each relay terminal is designed to increase the capacity of the relaying schemes. We show the theoretical derivations and the simulation results of the proposed schemes in the following sections. Those demonstrate an effectiveness of the proposed schemes.

Linear processing for multi-input multi-output (MIMO) antenna systems is preferred to non-linear ones for computational efficiency. Using channel state information (CSI) at the receiver, channel matrix can be decomposed via singular value decomposition (SVD), and if the transmitter can be fed back with the right-unitary-matrix of the SVD from the receiver, the maximum channel-capacity can be achieved with linear processing in point-to-point wireless MIMO communications. However, if the transmitter receives no-feedback, the optimal linear detector at the receiver is the minimum-mean-squareerror- estimator, of which capacity is far below the channelcapacity. In practice, reducing the amount of feedback information to achieve a "reasonably close channel-capacity" is an important issue in point-to-point wireless communications. In this paper, we propose a limited feedback system employing linear processing, which achieves near-channel-capacity. The feedback information is only an integer matrix, which is much less than that of the right-unitary-matrix of the SVD. Key ideas of the proposed scheme are the lattice reduction and modulo operation. Moreover, the amount of feedback information can be further reduced to a binary matrix using multi-level/multi-stage encode and decode. Under the turbo channel code the proposed scheme shows excellent performance at high data rates. We compare our simulation results with Shannon capacity limits for ergodic MIMO channels.

In cellular mobile communication systems, the link performance can be remarkably improved by deploying relays between the base station and the mobile station. In this paper we propose an efficient duplexing scheme so that both spatial and temporal gain by adding relays can be increased. Using the proposed relaying scheme, the conventional system of four-phase relaying can be simplified to a two-phase relaying system and the additional resource consumption due to relays can be minimized. We show the capacity gain for cell edge users with numerical results.

We propose a new closed-loop MIMO (Multiple- Input Multiple-Output) signal processing scheme that combines transmit beamforming and spatial multiplexing in Rayleigh frequency-flat fading channel environment. Diversity gain is achieved by beamforming, and multiplexing gain by spatial multi- plexing. The proposed scheme is particularly useful for downlink communication, where the number of transmit antennas at the base station is greater than the number of receive antennas at the mobile. Beamforming weight vectors are computed at the receiver using the estimated channel matrix and are sent to the transmitter. The computation is based on the mutual information maximization criterion which reduces to a symmetric eigenvalue problem. The BER (Bit Error Rate) performance comparisons with other known schemes are also presented for the cases of the ML (Maximum Likelihood) decoding and the MMSE (Minimum Mean-Squared Error) nulling and ordered-cancellation decod- ing, through extensive Monte-Carlo simulation. The simulation results show that the proposed scheme has lower BER than the other schemes.

We introduce a modified vertical Bell laboratories layered space-time (V-BLAST) detection algorithm to reduce unexpected effects of the channel estimation errors. Assuming that the channel estimation errors are independent and identically distributed Gaussian random variables, we derive a better nulling weight, which minimizes the power of various terms generated by the channel estimation errors. The symbol ordering operation is also modified by choosing the minimum mean-square error symbol at each iteration. The proposed detection algorithm requires the knowledge of channel estimation error's variance, but its performance is robust to the inaccurate estimation of it. The simulation results show that the performance of the proposed algorithm is superior to that of the conventional V-BLAST detection algorithm when channel estimation errors exist

In this letter, we investigated the optimal minimum-mean-squared-error (MMSE) based successive interference cancellation (SIC) strategy designed for lattice-reduction aided multiple-input multiple-output (MIMO) detectors. For the sake of generating the MMSE-based MIMO symbol estimate at each SIC detection stage, we model the so-called effective symbols generated with the aid of lattice-reduction as joint Gaussian distributed random variables. However, after lattice-reduction, the effective symbols become correlated and exhibit a non-zero mean. Hence, we derive the optimal MMSE SIC detector, which updates the mean and variance of the effective symbols at each SIC detection stage. As a result, the proposed detector achieves a better performance compared to its counterpart dispensing with updating the mean and variance, and performs close to the maximum likelihood detector.

We propose a new vector precoding for the down-link of multiuser multiple-input multiple-output (MIMO) systems. The proposed scheme can be thought as the minimum mean square error (MMSE) version of the vector perturbation technique. We generalize the vector perturbation by allowing the data vector to be perturbed by an arbitrary vector. After the modulo operation at receivers, the remaining part of the perturbation is dealt with as the co-channel interference. We derive the total mean square error (MSE) of the received signal, and find the optimum perturbation vector that minimizes the total MSE. Simulation results show that our scheme outperforms the other compared schemes at all signal-to-noise ratios (SNRs), while the computational complexity is not increased.

Presented in this paper is a study of the capacity evaluation of various multiuser MIMO schemes in cellular environments. The throughputs per user of the generalized zero-forcing with rank adaptation and vector perturbation schemes are compared with the capacity bound of the Gaussian MIMO broadcast channel, obtained by dirty paper coding under proportional fairness scheduling. The average cell throughputs of these schemes are also compared. From these comparisons, this study provides vital information for applying multiuser MIMO schemes in multicell environments

The performance of V-BLAST symbol detection can be seriously degraded if channel information is not perfect. We derive a new nulling matrix at the receiver using the modified TLS (Total Least Squares) solution under channel uncertainties. Using the new nulling matrix, we propose a modified vertical Bell laboratories layered space-time(V-BLAST) detection algorithm to reduce unexpected effects due to channel uncertainties. The proposed algorithm requires only one more scalar value, an upper bound on the 2-induced norm of a channel error matrix, which is relatively robust to the statistical changes of channel environment, than the conventional algorithm does, but the performance is better especially at high SNR. We simulate the proposed algorithm in the presence of receiver's mobility.

We demonstrate an efficient inverse scattering algorithm for reconstructing the coupling profiles or reflection coefficients of a discrete layered medium. The method, called the Schur algorithm combined with an extrapolation method, is based on solving the coupled-mode differential equation in a layer-peeling procedure with a simple extrapolation scheme. In order to reduce the estimation errors caused by discretization of an inhomogeneous medium, we analyze the error propagation of inverse scattering with the Schur algorithm (layer-peeling method) and propose the extrapolation scheme. Numerical examples are provided that compare the Schur algorithm combined with an extrapolation method to the general Schur algorithm in a coarse discretization environment. The comparison shows that the proposed method produces a more accurate reconstruction with a lower order of complexity.

A bootstrap filter algorithm, which uses a sequence of the number of stars to estimate the attitude of a spinning object, is presented. This choice of measurement makes the algorithm practicable to apply to the spinning object without any initial acquisition lock. The statistical model of measurement is derived and incorporated to make a star-density map. The conic fitting method is used to obtain measurements in the star sensor images. The simulation result is presented which demonstrates the ability of attitude estimation and the fast convergence property of the proposed algorithm.

This paper introduces a generalized Schur algorithm in the Krein space with an indefinite inner product. Concepts such as Caratheodory classes and Schur classes used in the classical Schur algorithm cannot be applied in the Krein space since the positive-definiteness corresponds merely to the nonsingularity in the Krein space. We note also that these problems appear when fast algorithms for suboptimal H/sup /spl infin// filtering are implemented. We derive the extended Chandrasekhar algorithm which is a fast implementation of H/sup /spl infin// filtering, and explain the connection between the generalized Schur algorithm and the Chandrasekhar algorithm. Using this result it is possible to derive a fast algorithm for suboptimal H/sup /spl infin// filtering.

Analog optical processors are of interest because of their potentially large bandwidth and the ease with which one can implement addition. Processors for sinusoidal signals are a special case due to the advantages provided by representing them as a complex exponentials. The authors address some issues relevant to optical analog signal processors. It is shown that there are fundamental limitations on optical processors due to the quantum mechanical nature of light that links dynamic range, bandwidth and parallelism to the power required to implement the algorithm. Three well-known analog optical representations of signals are analyzed with respect to power requirements and cascadability, and it is concluded that a representation using four light beams provides the most straightforward implementation of complex arithmetic, requires the least power, and has the best S/N ratio for a cascade process.>