A new adaptive multiple modelling approach for non-linear and non-stationary systems

This paper proposes a novel adaptive multiple modelling algorithm for non-linear and non-stationary systems. This simple modelling paradigm comprises K candidate sub-models which are all linear. With data available in an online fashion, the performance of all candidate sub-models are monitored based on the most recent data window, and M best sub-models are selected from the K candidates. The weight coefficients of the selected sub-model are adapted via the recursive least square (RLS) algorithm, while the coefficients of the remaining sub-models are unchanged. These M model predictions are then optimally combined to produce the multi-model output. We propose to minimise the mean square error based on a recent data window, and apply the sum to one constraint to the combination parameters, leading to a closed-form solution, so that maximal computational efficiency can be achieved. In addition, at each time step, the model prediction is chosen from either the resultant multiple model or the best sub-model, whichever is the best. Simulation results are given in comparison with some typical alternatives, including the linear RLS algorithm and a number of online non-linear approaches, in terms of modelling performance and time consumption

Two-path succussive relaying with hybrid demodulate and forward

This paper proposes a novel demodulation-and-forward (DMF) scheme for the two-path succussive relay system. While the two-path relaying avoids the data rate loss that occurs in many one-relay cooperative systems, its performance is severely limited by interrelay interference. In this paper, we propose a hybrid DMF scheme for the two-path relay system so that the relays can switch between direct and differential demodulation modes according to channel conditions. The hybrid DMF scheme not only performs better than existing two-path approaches but is easy to achieve synchronization at the relays as well, which is particularly important as a relay receives signals from both the source and the other relay. The proposed hybrid DMF scheme provides an innovative way to implement the two-path relaying scheme

An improved resampling approach for particle filters in tracking

Resampling is an essential step in particle filtering (PF) methods in order to avoid degeneracy. Systematic resampling is one of a number of resampling techniques commonly used due to some of its desirable properties such as ease of implementation and low computational complexity. However, it has a tendency of resampling very low weight particles especially when a large number of resampled particles are required which may affect state estimation. In this paper, we propose an improved version of the systematic resampling technique which addresses this problem and demonstrate performance improvement

Game theoretic data association for multi-target tracking with varying number of targets

We investigate a game theoretic data association technique for multi-target tracking (MTT) with varying number of targets. The problem of target state-estimate-to-track data association has been considered. We use the SMC-PHD filter to handle the MTT aspect and obtain target state estimates. We model the interaction between target tracks as a game by considering them as players and the set of target state estimates as strategies. Utility functions for the players are defined and a regret-based learning algorithm with a forgetting factor is used to find the equilibrium of the game. Simulation results are presented to demonstrate the performance of the proposed technique

Study of relay selection in a multi-cell cognitive network

This paper studies best relay selection in a multi-cell cognitive network with amplify-and-forward (AF) relays. We derive the analytical integral-form expression of the cumulative distribution function (CDF) for the received signal-to-noise-plus-interference-ratio (SINR) at the destination node, based on which the closed-form of the outage probability is obtained. Analysis shows that the proposed relay selection scheme achieves the best SINR at the destination node with interference to the primary user being limited by a pre-defined level. Simulation results are also presented to verify the analysis. The proposed relay selection approach is an attractive way to obtain diversity gain in a multi-cell cognitive network

Decode-and-forward buffer-aided relay selection in cognitive relay networks

This paper investigates decode-and-forward (DF) buffer-aided relay selection for underlay cognitive relay networks (CRNs) in the presence of both primary transmitter and receiver. We propose a novel buffer-aided relay selection scheme for the CRN, where the best relay is selected with the highest signal-to-interference ratio (SIR) among all available source-to-relay and relay-to-destination links while keeping the interference to the primary destination within a certain level. A new closed-form expression for the outage probability of the proposed relay selection scheme is obtained. Both simulation and theoretical results are shown to confirm performance advantage over the conventional max-min relay selection scheme, making the proposed scheme attractive for CRNs

Data association using game theory for multi-target tracking in passive bistatic radar

We investigate a game theoretic data association technique for multi-target tracking (MTT) with varying number of targets in a real passive bi-static radar (PBR) environment. The radar measurements were obtained through a PBR developed using National Instrument (NI) Universal Software Radio Peripheral (USRP). We considered the problem of associating target state-estimates-to-tracks for varying number of targets. We use the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter to perform the multi-target tracking in order to obtain the target state estimates and model the interaction between target tracks as a game. Experimental results using this real radar data demonstrate effectiveness of the game theoretic data association for multiple target tracking

Dual antenna selection in secure cognitive radio networks

This paper investigates data transmission and physical layer secrecy in cognitive radio network. We propose to apply full duplex transmission and dual antenna selection at secondary destination node. With the full duplex transmission, the secondary destination node can simultaneously apply the receiving and jamming antenna selection to improve the secondary data transmission and primary secrecy performance respectively. This describes an attractive scheme in practice: unlike that in most existing approaches, the secrecy performance improvement in the CR network is no longer at the price of the data transmission loss. The outage probabilities for both the data transmission and physical layer secrecy are analyzed. Numerical simulations are also included to verify the performance of the proposed scheme

Buffer-aided 5G cooperative networks: Considering the source delay

© 2019 Association for Computing Machinery. Applying relays employed with data buffers drastically enhance the performance of cooperative networks. However, lengthening the packet delay is still a serious challenge for cooperative networks. This paper thoroughly studies a new factor affecting the packet delay which is the source delay. This factor plays a key role in calculating the total delay that messages encounter before reaching their destination. This delay is crucial especially in applications that require their messages to get transmitted as fast as possible. Markov chain is employed to model the system and analyze the source delay. Numerical simulations verify the analytical model, the results show that buffer-aided relays can beat non-buffer relays in terms of average packet delay, especially at low signal to noise ratio (SNR) range. This makes adding buffers to relays an attractive solution for the packet delay in 5G applications

Kalman-gain aided particle PHD filter for multi-target tracking

We propose an efficient SMC-PHD filter which employs the Kalman-gain approach during weight update to correct predicted particle states by minimizing the mean square error (MSE) between the estimated measurement and the actual measurement received at a given time in order to arrive at a more accurate posterior. This technique identifies and selects those particles belonging to a particular target from a given PHD for state correction during weight computation. Besides the improved tracking accuracy, fewer particles are required in the proposed approach. Simulation results confirm the improved tracking performance when evaluated with different measures