In this paper, we propose a direct multiobject tracking (MOT) approach for MIMO-radar signals that operates on raw sensor data via variational message passing (VMP). Unlike classical track-before-detect (TBD) methods, which often rely on simplified likelihood models and exclude nuisance parameters (e.g., object amplitudes, noise variance), our method adopts a superimposed signal model and employs a mean-field approximation to jointly estimate both object existence and object states. By considering correlations within in the radar signal due to closely spaced objects and jointly estimating nuisance parameters, the proposed method achieves robust performance for closeby objects and in low-signal-to-noise ratio (SNR) regimes. Our numerical evaluation based on MIMO-radar signals demonstrate that our VMP-based direct-MOT method outperforms a detect-then-track (DTT) pipeline comprising a super-resolution sparse Bayesian learning (SBL)-based estimation stage followed by classical MOT using global nearest neighbor data association and a Kalman filter.

We propose an analytic model for the second-order characteristics of the radar return signal from a swarm of rotor drones. We consider the case of a swarm of identical drones, with each a number of rotors comprised of a number of rotor blades. By considering the orientation and speed of each rotor as stochastic variables, we derive expressions for the autocorrelation function (ACF) and power spectral density (PSD). The ACF and PSD are in the form of an infinite series with coefficients that drop to zero at a predictable limit. Thus in practical applications, the series may be truncated. As a special case, we show that for deterministic rotor speed, the ACF can be expressed in closed form. We further investigate how system parameters (Blade length, Rotor speed, number of blades, and number of drones) influence the derived expressions for the ACF and PSD.

Recently, there has been a proliferation of wireless communication technologies in unlicensed bands for the Internet of Things. A key question is whether these networks can coexist given that they have different power levels, symbol periods, and access protocols. The main challenge is to characterize the impact of mutual interference arising from distinct uncoordinated networks. It is known that when interferers form a homogeneous Poisson point process and transmit only on a single subband, the interference is often well-modeled by the heavy-tailed α-stable distribution. In this paper, we focus on the scenario where interferers transmit on multiple subbands. Under a policy where each interferer independently accesses each band with probability p, we provide an exact characterization of the interference random vector. Exploiting this characterization, we derive optimal linear combining weights and an analytical approximation for the bit error rate (BER), accurate for large transmit power. A key observation is that the expression for the BER admits an interpretation in terms of an array gain and a fractional diversity gain.

This paper presents the results of wireless channel measurement campaign in the 3 GHz to 8 GHz frequency range. The measurements were performed with focus on the short-range with a transmitter-receiver separation distance less than 9 m in two typical industrial environments: a low clutter density manufacturing space, and a high clutter density one. We analyzed the statistical properties of the most important temporal and large-scale propagation characteristics including total received energy, path loss exponent, maximum excess delay (MED) and root mean square (RMS) delay spread based on the measurements. Statistical models for the RMS delay spread and MED are also presented using the log-normal and Gamma distributions.

This paper presents a low complexity method for computation of the transfer matrix of wireless channels in complex indoor environments using propagation graphs. Multi-room indoor environments can be represented in a vector signal flow graph with with rooms in the complex structure as nodes and propagation between rooms as branches. The transfer matrix can be computed using Masons theorem which lead to a much-reduced computational complexity.

Simple and accurate channel models are required to evaluate the performance of multi-antenna systems with polarized antenna arrays. This paper presents a graph based model for polarized multi-antenna wireless channels by incorporating polarization diversity into the classical propagation graph based model. In addition to classical propagation effects such as attenuation, delay and phase shifts in non-polarized channel models, the proposed model incorporated antenna and scatterer based depolarization effects including polarization coupling and polarimetric responses. An illustration of the procedure for generating the transfer function and impulse response from the model is given for dual polarized channels. Simulation results show that the power delay profile exhibit exponential decay with almost equal decay rate across the co-and cross-polar channels. Our results also show that antenna orientation, relative antenna height and depolarization affect the power-delay properties of the channel.

We propose a hybrid message passing method for distributed cooperative localization and tracking of mobile agents. Belief propagation and mean field message passing are employed for, respectively, the motion-related and measurement-related parts of the factor graph. Using a Gaussian belief approximation, only three real values per message passing iteration have to be broadcast to neighboring agents. Despite these very low communication requirements, the estimation accuracy can be comparable to that of particle-based belief propagation.

Binary message passing decoders for low-density parity-check codes are studied by using extrinsic information transfer charts. The channel delivers hard or soft decisions and the variable node decoder performs all computations in the log-likelihood ratio (L-value) domain. A hard decision results in the Gallager B algorithm and examples show that increasing the channel output alphabet to two bits gains more than 1.0 dB in signal to noise ratio when using optimized codes. Finally, it is shown that errors on cycles consisting only of degree two and three variable nodes cannot be corrected and a necessary and sufficient condition for the existence of a cycle-free subgraph is derived.

We propose a novel algorithm for sensor self-localization in cooperative wireless networks where observations of relative sensor distances are available. The variational message passing (VMP) algorithm is used to implement a mean field solution to the estimation of the posterior probabilities of the sensor positions in an R2scenario. Extension to R3is straight-forward. Compared to non-parametric methods based on belief propagation, the VMP algorithm features significantly lower communication overhead between sensors. This is supported by performance simulations which show that the estimated mean localization error of the algorithm stabilizes after approximately 30 iterations.

A general path loss model for in-room radio channels is proposed. The model is based on experimental observations of the behavior of the delay-power spectrum in closed rooms. In such a room, the early part of the spectrum observed at different positions typically consists of a dominant component (peak) that vanishes as the transmitter-receiver distance increases; the late part decays versus distance according to the same exponential law in delay regardless of the distance. These observations motivate the proposed model of the delay-power spectrum with an early dominant component and a reverberant component. The dominant component is modeled as a Dirac delta function weighted with a factor decaying according to an inverse distance power law (d-n). The reverberant component is an exponentially decaying function versus delay with distance-dependent onset. Its power decays exponentially with distance. The proposed model allows for the prediction of path loss, mean delay, and rms delay spread versus distance. We use measurements to validate the model. We observe good agreement of the model prediction for mean delay and rms delay spread.

Positioning solutions in infrastructure-based wireless networks generally operate by exploiting the channel information of the links between the Wireless Devices and fixed networking Access Points. The major challenge of such solutions is the modeling of both the noise properties of the channel measurements and the user mobility patterns. One class of typical human being movement patterns is the segment-wise linear approach, which is studied in this paper. Current tracking solutions, such as the Constant Velocity model, hardly handle such segment-wise linear patterns. In this paper we propose a segment-wise linear model, called the Drifting Points model. The model results in an increased performance when compared with traditional solutions.

Due to the significantly reduced cost and effort for system calibration time-division multiplexing (TDM) is a commonly used technique to switch between the transmit and receive antennas in multiple-input multiple-output (MIMO) radio channel sounding. Nonetheless, Baum et al. have shown that phase noise of the transmitter and receiver local oscillators, when it is assumed to be a white Gaussian random process, can cause large errors of the estimated channel capacity of a low-rank MIMO channel when the standard channel matrix estimator is used. Experimental evidence shows that consecutive phase noise samples affecting measurement samples collected with real TDM-MIMO channel sounders are correlated. In this contribution a capacity estimator that accounts for the phase noise correlation is proposed. The estimator is based on a linear minimum mean square error estimate of the MIMO channel matrix. It is shown by means of Monte Carlo simulations assuming a measurement- based phase noise model, that the MIMO channel capacity can be estimated accurately for signal-to-noise ratios up to about 35 dB.

In this contribution a low-complexity particle filtering algorithm is proposed to track the parameters of time-variant propagation paths in multiple-input multiple-output (MIMO) radio channels. A state-space model is used to describe the path evolution in delay, azimuth of arrival, azimuth of departure, Doppler frequency and complex amplitude dimensions. The proposed particle filter (PF) has an additional resampling step specifically designed for wideband MIMO channel sounding, where the posterior probability density functions of the path states is usually highly concentrated in the multi-dimensional state space. Preliminary investigations using measurement data show that the proposed PF can track paths stably with a small number of particles, e.g. 5 per path, even in the case where the paths are undetected by the conventional SAGE algorithm.

In this contribution, the Fisher-Bingham-5 (FB5) probability density function (pdf) is used to model the shape of the direction power spectral density function (psdf) of individual path components in the radio channel. The FB5 distribution is selected because, among all direction distributions, it maximizes the entropy under the constraints that the first and second distribution moments are specified. A SAGE (space-alternating generalized expectation-maximization) algorithm is derived based on this model for estimation of the parameters characterizing the direction psdf of each path component in a multi-path scenario. The performance of the SAGE algorithm is evaluated using measurement data. Preliminary results show that the estimated direction psdfs of individual path components exhibit different ovalnesses and tilt angles. These density functions are noticeably more concentrated than the corresponding footprints in the Bartlett spectrum.

In this contribution the radio channel model proposed in [1] is extended to include multiple transmitters and receivers. The propagation environment is modelled using random graphs where vertices of a graph represent scatterers and edges model the wave propagation between scatterers. Furthermore, we develop a closed form analytical expression for the transfer matrix of the propagation graph. It is shown by simulation that impulse response and the delay-power spectrum of the graph exhibit exponentially decaying power as a result of the recursive scattering structure of the graph. The impulse response exhibits a transition from specular to diffuse signal contributions as observed in measurements.

Binary message-passing decoders for LDPC codes are analyzed using EXIT charts. For the analysis, the variable node decoder performs all computations in the L-value domain. For the special case of a hard decision channel, this leads to the well-know Gallager B algorithm, while the analysis can be extended to channels with larger output alphabets. By increasing the output alphabet from hard decisions to four symbols, a gain of more than 1.0 dB is achieved using optimized codes. For this code optimization, the mixing property of EXIT functions has to be modified to the case of binary message-passing decoders.

In this paper, an azimuth spread estimator (ASE) for slightly distributed scatters (SDSs) is derived based on the generalized-array-manifold (GAM) approximation model [1]. To improve the performance of this estimator we propose an array size adaptation (ASA) technique that adjusts the array aperture selectively for each SDS by modifying the number of antennas. This technique is applied to uniform linear arrays but can be extended to two or three dimensional arrays. Simulation results demonstrate the improvement achieved with the combined ASA-ASE scheme. In particular, it is shown that this scheme outperforms the conventional Spread-ESPRIT technique.

To save hardware and reduce the effort to calibrate the system, channel sounding with Tx and Rx antenna arrays is commonly performed in TDM mode where the array elements are successively switched. We refer to this technique as TDM-MIMO channel sounding. The ISI-SAGE algorithm (Fleury, B.H. et al., 2003; Yin, X. et al., 2003), applied in combination with TDM-MIMO channel sounding, makes it possible to extend the Doppler frequency estimation range (DFER) (Yin, X. et al., 2003). The extension is significant when arrays with large element numbers are employed. We derive the signal model for TDM-MIMO channel sounding and report analytical investigations showing that this DFER extension requires selection of switching modes (SMs) tailored to the array characteristics. The SM of a switched array is the temporal order in which the array elements are switched. In fact, the traditionally used SMs of uniform linear and planar arrays, where the elements are switched according to their natural spatial ordering, prove to be inappropriate as they lead to an ambiguity in the joint estimation of DF and directions. We also introduce the concept of normalized sidelobe level (NSL) associated to the SM of a switched array. We show that minimizing the NSL is a sensible criterion for the identification of SM, leading to DF and direction estimates with nearly optimum performance in terms of root mean square estimation error. Finally experimental investigations illustrate the impact of the SM of a uniform planar array on the behaviour of the DF and direction of arrival estimates computed with the ISI-SAGE algorithm.