In modern systems for air surveillance, it is important to have a high quality situationassessment. SAAB has a system for air surveillance, and in this thesis possibleimprovements of the tracking performance of this system are explored. The focushas been on improving the tracking of highly maneuverable targets observed withlow sampling rate. To evaluate improvements of the tracking performance, a componentthat is similar to the one used in SAAB’s present tracker was implementedin an Interacting Multiple Model (IMM) structure. The use of an Auxiliary ParticleFilter for improving the tracking performance is explored, and a way to fita particle filter into SAAB’s existing IMM framework is proposed. The differentfilters were implemented in Matlab, and evaluation was done by the meansof Monte Carlo simulations. The results from Monte Carlo simulations show significantimprovement when tracking in two dimensions. However, the results inthree dimensions do not display any substantial overall improvement when usingthe particle filter compared to using SAAB’s present filter. It is therefore notworthwhile to switch the filter used in SAAB’s present tracker for a particle filter,at least not under the high SNR circumstances presented in this thesis. However,further studies within this area are recommended before any final decisions aremade.

Yes / A rigorous model for automatic modulation classification (AMC) in cognitive radio (CR) systems is proposed in this paper. This is achieved by exploiting the Kalman filter (KF) integrated with an adaptive interacting multiple model (IMM) for resilient estimation of the channel state information (CSI). A novel approach is proposed, in adding up the squareroot singular values (SRSV) of the decomposed channel using the singular value decompositions (SVD) algorithm. This new scheme, termed Frobenius eigenmode transmission (FET), is chiefly intended to maintain the total power of all individual effective eigenmodes, as opposed to keeping only the dominant one. The analysis is applied over multiple-input multiple-output (MIMO) antennas in combination with a Rayleigh fading channel using a quasi likelihood ratio test (QLRT) algorithm for AMC. The expectation-maximization (EM) is employed for recursive computation of the underlying estimation and classification algorithms. Novel simulations demonstrate the advantages of the combined IMM-KF structure when compared to the perfectly known channel and maximum likelihood estimate (MLE), in terms of achieving the targeted optimal performance with the desirable benefit of less computational complexity loads.

In solving target tracking problems, the Kalman filter (KF) is a systematic estimation algorithm. Whether the state of a moving target adapts to the changes in the observations depends on the model assumptions. The interacting multiple model (IMM) algorithm uses interaction of a bank of parallel KFs by updating associated model probabilities. Every parallel KF has its model probability adjusted by the dynamic system. For moving targets of different dynamic linear models, an IMM with two KFs generally performs well. In this thesis, in order to improve the performance of target tracking and state estimation, multi-sensor data fusion technique will be used. Same types of IMMs can be incorporated in the cooperative IMM-based algorithm. The IMM-based estimators exchange with each other the estimates, model robabilities and model transition probabilities. A distributed algorithm for multi-sensor tracking usually needs a fusion center that integrates decisions or estimates, but the proposed cooperative IMM-based algorithm does not use the architecture. Cooperative IMM estimator structures exchange weights and estimates on the platforms to avoid accumulation of errors. Performance of data fusion may degrade due to different kinds of undesirable environmental effects. The simulations show that an IMM estimator with smaller measurement noise level can be used to compensate the other IMM, which is affected by larger measurement noise. In addition, failure of a sensor will cause the problem that model probabilities can not be updated in the corresponding estimator. Kalman filters will not be able to perform state correction for the moving target. To tackle the problem, we can use the estimates from other IMM estimators by adjusting the corresponding weights and model probabilities. The simulations show that the proposed cooperative IMM structure effectively improve the tracking performance.

As part of the processing of tracking targets, surveillance radars detect the presence of targets and estimate their locations. This dissertation re-examines some of the often ignored practical considerations of radar tracking. With the advent of digital computers, modern radars now use sampled versions of received signals for processing. Sampling rates used in practice result in the bin-straddling phenomenon, which is often treated as an undesired loss in signal power. Here, a signal model that explicitly models the sampling process is used in the derivation of the average loglikelihood ratio test (ALLRT), and its detection performance is shown to defeat the bin-straddling losses seen in traditional radar detectors. In monopulse systems, data samples are taken from the sum and difference channels, by which a target direction-of-arrival (DOA) estimate can be formed. Using the same signal model, we derive new estimators for target range, strength, and DOA and show performance benefits over traditional monopulse techniques that are predominant in practice. Since tracking algorithms require an error variance report on target parameter estimates, we propose using the generalized Cramer-Rao lower bound (GCRLB), which is the CRLB evaluated at estimates rather than true values, as an error variance report. We demonstrate the statistical efficiency and variance consistency of the new estimators. With several parameter estimates collected over time, tracking algorithms are used to compute track state estimates and predict future locations. Using agile- beam surveillance radars with programmable energy waveforms, optimal scheduling of radar resources is a topic of interest. In this dissertation, we focus on the energy management considerations of tracking highly maneuverable aircraft. A comparison between two competing interacting multiple model (IMM) filter configurations is made, and a recently proposed unbiased mixing procedure is extended to the case of three modes. Finally, we introduce the radar management operating curve (RMOC), which shows the fundamental tradeoff in radar time and energy, to aid radar designers in the selection of an overall operating signal-to-noise level.

This thesis evaluates different solutions to the target tracking problem with the use of airborne radar measurements. The purpose of this report is to present and compare options that can improve the tracking performance when the target is performing various manoeuvres while the radar measurements are noisy. A simulation study is done to evaluate and compare the presented solutions, where the evaluating criteria are the estimation errors and the computational complexity. The algorithms investigated are the general pseudo Bayesian of order one (GPB(1)) filter and the interacting multiple model (IMM) filter, each using three motion models, along with several single model Kalman filters. Additionally, the impact on the tracking performance by different choices of radar parameters is also examined. The results show that filters using multiple models are best suited for tracking targets performing different manoeuvres. The tracking performance is improved with both the GPB(1) and IMM algorithms compared to the filters using a single model. Looking at the estimation errors, IMM outperforms the other algorithms and achieves a better general performance for different kinds of manoeuvres. However, IMM have a much higher computational complexity than the filters with a single model. GPB(1) could therefore be more suited for applications where computational power poses a problem, since it is less computationally demanding than IMM. Furthermore, it is shown that different radar parameters have an impact on the tracking performance. The choice of pulse repetition frequency (PRF) and duty cycle used by the radar affects the accuracy of the measurements. The estimation errors of the tracking filters become larger with poor measurements, which also makes it more difficult for the multiple model algorithms to make good use of the different motion models. In most cases, IMM is however less sensitive to the choice of PRF, in relation to how the models are used in the algorithm, compared to GPB(1). Nevertheless, the study shows that there are cases where some combinations of radar parameters drastically reduces the tracking performance and no clear improvement can be seen, not even for IMM.

The current and future trends of modern wireless communication systems place heavy demands on fast data transmissions in order to satisfy end users’ requirements anytime, anywhere. Such demands are obvious in recent applications such as smart phones, long term evolution (LTE), 4 & 5 Generations (4G & 5G), and worldwide interoperability for microwave access (WiMAX) platforms, where robust coding and modulations are essential especially in streaming on-line video material, social media and gaming. This eventually resulted in extreme exhaustion imposed on the frequency spectrum as a rare natural resource due to stagnation in current spectrum management policies. Since its advent in the late 1990s, cognitive radio (CR) has been conceived as an enabling technology aiming at the efficient utilisation of frequency spectrum that can lead to potential direct spectrum access (DSA) management. This is mainly attributed to its internal capabilities inherited from the concept of software defined radio (SDR) to sniff its surroundings, learn and adapt its operational parameters accordingly. CR systems (CRs) may commonly comprise one or all of the following core engines that characterise their architectures; namely, adaptive coded modulation (ACM), automatic modulation classification (AMC) and spectrum sensing (SS). Motivated by the above challenges, this programme of research is primarily aimed at the design and development of new paradigms to help improve the adaptability of CRs and thereby achieve the desirable signal processing tasks at the physical layer of the above core engines. Approximate modelling of Rayleigh and finite state Markov channels (FSMC) with a new concept borrowed from econometric studies have been approached. Then insightful channel estimation by using Kalman filter (KF) augmented with interacting multiple model (IMM) has been examined for the purpose of robust adaptability, which is applied for the first time in wireless communication systems. Such new IMM-KF combination has been facilitated in the feedback channel between wireless transmitter and receiver to adjust the transmitted power, by using a water-filling (WF) technique, and constellation pattern and rate in the ACM algorithm. The AMC has also benefited from such IMM-KF integration to boost the performance against conventional parametric estimation methods such as maximum likelihood estimate (MLE) for channel interrogation and the estimated parameters of both inserted into the ML classification algorithm. Expectation-maximisation (EM) has been applied to examine unknown transmitted modulation sequences and channel parameters in tandem. Finally, the non-parametric multitaper method (MTM) has been thoroughly examined for spectrum estimation (SE) and SS, by relying on Neyman-Pearson (NP) detection principle for hypothesis test, to allow licensed primary users (PUs) to coexist with opportunistic unlicensed secondary users (SUs) in the same frequency bands of interest without harmful effects. The performance of the above newly suggested paradigms have been simulated and assessed under various transmission settings and revealed substantial improvements.

No description available.

With the expected growth of Unmanned Aerial Vehicle (UAV) traffic in the coming years, the demand for UAV tracking solutions in the Air Traffic Control (ATC) industry has been incentivized. To ensure the safe integration of UAVs into airspace, Air Traffic Management (ATM) systems will need to provide a number of services such as UAV tracking. The Interacting Multiple Model Extended Kalman Filter (IMM-EKF) is an industry standard for aircraft tracking, but no such algorithm has been tried and tested for UAV tracking. This thesis aims to determine a suitable tracking algorithm for the specific case of non-cooperative tracking of UAVs from radar data. In non-cooperative tracking scenarios, we do not have any information regarding the UAV other than radar measurements indicating the target’s position. We investigate an Adaptive Interacting Multiple Model Unscented Kalman Filter (IMM-UKF) method with three different motion model combinations in addition to comparing a Cartesian vs. Spherical measurement model. A comparison of motion models shows that using a Constant Jerk (CJ) model to model target maneuvers in the IMM structure reduces the risk of filter divergence as compared to using a turn model, such as Constant Turn (CT) or Constant Angular Velocity (CAV). The CJ model is thus a suitable choice to have as one of the motion models in an IMM structure and works well in conjunction with two Constant Velocity (CV) models. We were not able to determine if the Spherical measurement model is better than the Cartesian measurement model in general. However, the Spherical measurement model improves the accuracy of the state estimate in some cases. Adaptive tuning of the system noise covariance Q and measurement noise covariance R does not improve the accuracy of the state estimate but it improves the filter robustness and consistency when the filter is incorrectly tuned. Based on our results, we believe that the adaptive IMM-UKF shows promise but that there is still room for improvement with regards to both the accuracy and consistency. However, we will need to perform extensive tests with real UAV radar data to draw concrete conclusions. / Med den förväntade tillväxten av trafik med obemannade flygfordon (UAV) under de kommande åren kommer efterfrågan för spårningslösningar för UAV inom flygövervakning. För att säkerställa en säker integration av UAV:er i luftrummet, kommer Air Traffic Management (ATM)-system att behöva tillhandahålla tjänster för UAV-spårning. Det så kallade Interacting Multiple Model Extended Kalman Filter (IMM-EKF) filtret är en industristandard spårning av flygplan, men ingen sådan algoritm har prövats och testats för UAV-spårning. Denna avhandling syftar till att fastställa en lämplig spårningsalgoritm för det specifika fallet med icke samarbetande spårning av UAV från radardata. I icke samarbetande spårningsscenarier har vi ingen information om UAV:n utöver radarmätningar. Vi presenterar en adaptiv metod baserad på IMM-UKF, där vi ersätter EKF i industristandarden IMM-EKF med ett filter av typen UKF. Vi undersöker tre olika kombinationer av rörelsemodeller och jämför också en kartesisk med en sfärisk mätmodell. Vår jämförelse av rörelsemodeller visar om man använder en Constant Jerk (CJ) modell för manövrar i IMM-strukturen minskar risken för divergens jämfört med att använda en svängmodell, såsom Constant Turn (CT) eller Constant Angular Velocity (CAV). CJ-modellen är alltså ett lämpligt val att ha som en av rörelsemodellerna i en IMM-struktur och fungerar bra i kombination med två Constant Velocity (CV) modeller. Vi kunde inte avgöra om den sfäriska modellen var bättre än den kartesiska modellen. Adaptiv inställning av systembrusets kovarians Q och mätbrus kovarians R förbättrar inte tillståndsuppskattningens noggrannhet men den förbättrar filtrets robusthet och konsistens när filtret är felaktigt inställt. Baserat på våra resultat tror vi att den adaptiva IMM-UKF metoden är lovande men att det fortfarande finns utrymme för förbättringar när det gäller både noggrannhet och konsistens i spårningen. Vi kommer dock att behöva utföra omfattande tester med riktiga UAV-radardata för att dra konkreta slutsatser.

Modern aircraft are getting more and better sensors. As a result of this, the pilots are getting moreinformation than they can handle. To solve this problem one can automate the information processingand instead provide the pilots with conclusions drawn from the sensor information. An aircraft’smovement can be used to determine which class (e.g. commercial aircraft, large military aircraftor fighter) it belongs to. This thesis focuses on comparing three classification schemes; a Bayesianclassification scheme with uniform priors, Transferable Belief Model and a Bayesian classificationscheme with entropic priors.The target is modeled by a jump Markov linear system that switches between different modes (flystraight, turn left, etc.) over time. A marginalized particle filter that spreads its particles over thepossible mode sequences is used for state estimation. Simulations show that the results from Bayesianclassification scheme with uniform priors and the Bayesian classification scheme with entropic priorsare almost identical. The results also show that the Transferable Belief Model is less decisive thanthe Bayesian classification schemes. This effect is argued to come from the least committed principlewithin the Transferable Belief Model. A fixed-lag smoothing algorithm is introduced to the filter andit is shown that the classification results are improved. The advantage of having a filter that remembersthe full mode sequence (such as the marginalized particle filter) and not just determines the currentmode (such as an interacting multiple model filter) is also discussed.

Tracking of a single ground target using GMTI radar detections is considered. A Variable Structure- Autonomous- Interactive Multiple Model (VS-A-IMM) structure is developed to address challenges of ground target tracking, while maintaining an acceptable level computational complexity at the same time. The following approach is used in this thesis: Use simple tracker structures / incorporate a priori information such as topographic constraints, road maps as much as possible / use enhanced gating techniques to minimize the eect of clutter / develop methods against stop-move motion and hide motion of the target / tackle on-road/o-road transitions and junction crossings / establish measures against non-detections caused by environment. The tracker structure is derived using a composite state estimation set-up that incorporate multi models and MAP and MMSE estimations. The root mean square position and velocity error performances of the VS-A-IMM algorithm are compared with respect to the baseline IMM and the VS-IMM methods found in the literature. It is observed that the newly developed VS-A-IMM algorithm performs better than the baseline methods in realistic conditions such as on-road/o-road transitions, tunnels, stops, junction crossings, non-detections.