Dagens svenska korruptionsbrottslagstiftning står nu i begrepp att förändras efter tryck från såväl nationellt som internationellt håll efter att stommen i lagstiftningen har sett i princip likadan ut under de senaste fyrtiofem åren. Under åren som gått har kritiken mot den befintliga lagstiftningen vuxit, vilket kulminerade i att regeringen tillsatte en utredning som 2010 kunde överlämna betänkandet Mutbrott. Enligt direktivet skulle utredningen se över hur lagen kunde moderniseras samt ta fram ett förslag till en kod för näringslivets självreglering. Efter att det sedvanliga remissförfarandet genomförts kunde propositionen 2011/12:79 - En reformerad mutbrottslagstiftning antas av riksdagen och den kommer att träda ikraft den 1 juli 2012. Propositionen innehåller ett flertal reformer där bl.a. korruptionsbrotten konsolideras till 10 kap. i brottsbalken, benämningarna på mutbrott och bestickning byts till givande respektive tagande av muta, nykriminalisering av handel med inflytande och vårdslös finansiering. Den här uppsatsen har genom användning av en klassisk rättsdogmatisk metod bearbetat den historiska utvecklingen, gällande rätt samt utvecklingen som lett fram till den av riksdagen antagna propositionen. Författaren riktar kritik mot att korruptionsbrottslagstiftningen behandlar den offentliga och privata sektorn gemensamt samt anser att lagen behöver bli mer konkret. Regeringens ambition att skapa en kod för näringslivets självreglering är blott en följd av en otydlig lagstiftning och ett osäkert rättsläge. Uppsatsen avslutas med ett förslag om att lagstiftningen för offentlig och privat sektor särskiljs med anledning av att de har så pass olika skyddsintressen.

In the thesis, we present the structures of interacting multiple model (IMM) algorithm-based filter design for real-time motion orientation estimation and tracking by using inertial sensor measurements in three-dimensional space. The major sensor such as gyroscope, though has high-sensitivity characteristics, suffers from bias build-up and error drift over time. The complementary sensors such as accelerometer and magnetometer, on the other hand, have low sensitivity, but do not suffer from bias problems. By using individual inertial and magnetic sensors, measurements of multiple modes can be interactively computed. The IMM based designs show the advantages of weighting individual sensors in different motion states. We propose a signal processing architecture based on the IMM algorithm. It is composed of three parallel Kalman filters (KFs), each deals with measured signals from accelerometer, magnetometer and gyroscope, respectively. The accelerometer cannot effectively sense the rotation around the vertical axis; while the magnetometer can only sense the rotation around vertical axis. Therefore, estimation accuracy with the parallel filtering arrangement of the IMM algorithm-based structure may be affected. A scheme using the residual signal, which is computed in the IMM, provides the information of gyroscope-based KF to the other two filters for feasible calculation of update weights. Related research also usually combined the information of major and complementary sensors in estimator designs. In the literature, existing ¡§Triad¡¨ methods with quaternion-based extended Kalman filter (EKF), process the measurements from major and complementary sensors. To compensate the functions, we propose to use a gyroscope-based EKF and a Triad EKF in forming a parallel multiple model-based structure. The analysis and performance evaluation shows advantages and disadvantages of using EKFs and KFs in IMM-based filtering approachs. Simulation results validate the proposed estimator design concept, and show that the scheme is capable of reducing the overall estimation errors by flexible computation of model weights.

The major problems encountered in wireless location are the effects caused by non-line of sight (NLOS) propagation and multipath interference. In the thesis, we propose an approach to mitigate NLOS error. First of all, we use improved biased Kalman filter (IBKF) based on time of arrival (TOA) measurement to identify and mitigate NLOS error. Applying the statistic characteristic that the standard deviation of the NLOS propagation errors is generally much larger than that of measurement noises in the LOS condition, we combine hypothesis test and sliding window to identify NLOS error. According to the feedback identification and the calculated standard deviation, IBKF switches biased or unbiased to process TOA measurement. Nevertheless, the performance of IBKF-TOA is still affected slightly by NLOS error. Since extended Kalman filter (EKF) based on received signal strength (RSS) measurement is designed for prespecified environments, the effect of NLOS mitigation is more obvious. Moreover, EKF-RSS not only exists higher error probability in NLOS identification, but also needs longer time to converge in the cases that start with NLOS. Comparing IBKF-TOA with EKF-RSS, we adopt interacting multiple model (IMM) in the proposed data fusion structure for processing TOA and RSS measurements. In the proposed scheme, we reserve the basic IMM structure and add the step of NLOS identification into basic IMM structure. By accurate NLOS identification results and soft decision of IMM, the proposed scheme will switch to adequate filter mode and obtain better estimation. With simulation in UWB channel, the analysis and performance evaluation show advantages and disadvantages of using IBKF-TOA, EKF-RSS, and proposed scheme. Simulation results reveal that NLOS error can be mitigated effectively by data fusion of TOA and RSS measurements and can achieve high accuracy in positioning and tracking.

In solving target tracking problems, the Kalman filter (KF) is one of the most widely used estimators. Whether the state of target movement 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 Kalman filters to solve the hypothetical model of tracking maneuvering target. Based on the function of soft switching, the IMM algorithm, with parallel Kalman filters of different dimensions, can perform well by adjusting the model weights. Nonetheless, the uncertainty in measured data and the types of sensing systems used for target tracking may still hinder the signal processing in the IMM. In order to improve the performance of target tracking and signal estimation, the concept of data fusion can be adapted in the IMM-based structures. Multiple IMM based estimators can be used in the structure of multi-sensor data fusion. In this thesis, we propose a dual-IMM estimator structure, in which data fusion of the two IMM estimators is achieved by updating associated model probabilities. Suppose that two sensors for measuring the moving target is affected by the different degrees of noise, the measured data can be processed first through two separate IMM estimators. Then, the IMM-based estimators exchange with each other the estimates, model probabilities and model transition probabilities. The dual-IMM estimator will integrate the shared data based on the proposed dual-IMM algorithm. The dual-IMM estimator can be used to avoid degraded performance of single IMM with insufficient data or undesirable environmental effects. The simulation results show that a single IMM estimator with smaller measurement noise level can be used to compensate the other IMM, which is affected by larger measurement noise. Improved overall performance from the dual-IMM estimator is obtained. Generally speaking, the two IMM estimators in the proposed structure achieve better performance when same level of measurement noise is assumed. The proposed dual-IMM estimator structure can be easily extended to multiple-IMM structure for estimation and data fusion.

<p>In angle-only target tracking the aim is to estimate the state of a target with use of measurement of elevation and azimuth. The state consists of relative position and velocity between the target and the platform. The platform is an Unmanned Aerial Vehicle (UAV) and the tracking system is meant to be a part of the platform’s anti-collision system. In the case where both the target and the platform travel with constant velocity the angle measurements do not provide any information of the range between the target and the platform. The platform has to maneuver to be able to estimate the range to the target.</p><p>Two filters are implemented and tested on simulated data. The first filter is based on a Extended Kalman Filter (EKF) and is designed for tracking nonmaneuvering targets. Different platform maneuvers are studied and the influence of initial errors and the geometry of the simulation scenario is investigated. The filter is able to estimate the position of the target if the platform maneuvers and the target travels with constant velocity. Maneuvering targets on the other hand can not be tracked by the filter.</p><p>The second filter is an interacting multiple model (IMM) filter, designed for tracking maneuvering targets. The filter performance is highly dependent of the geometry of the scenario. The filter has been tuned for a scenario where the target approaches the platform from the front. In this scenario the filter is able to track both maneuvering and non-maneuvering targets. If the target approaches the platform from the side on the other hand, the filter has problems with distinguish target maneuvers from measurement noise.</p>

In angle-only target tracking the aim is to estimate the state of a target with use of measurement of elevation and azimuth. The state consists of relative position and velocity between the target and the platform. The platform is an Unmanned Aerial Vehicle (UAV) and the tracking system is meant to be a part of the platform’s anti-collision system. In the case where both the target and the platform travel with constant velocity the angle measurements do not provide any information of the range between the target and the platform. The platform has to maneuver to be able to estimate the range to the target. Two filters are implemented and tested on simulated data. The first filter is based on a Extended Kalman Filter (EKF) and is designed for tracking nonmaneuvering targets. Different platform maneuvers are studied and the influence of initial errors and the geometry of the simulation scenario is investigated. The filter is able to estimate the position of the target if the platform maneuvers and the target travels with constant velocity. Maneuvering targets on the other hand can not be tracked by the filter. The second filter is an interacting multiple model (IMM) filter, designed for tracking maneuvering targets. The filter performance is highly dependent of the geometry of the scenario. The filter has been tuned for a scenario where the target approaches the platform from the front. In this scenario the filter is able to track both maneuvering and non-maneuvering targets. If the target approaches the platform from the side on the other hand, the filter has problems with distinguish target maneuvers from measurement noise.

For target location and tracking in wireless communication systems, mobile target positioning and tracking play an important role. Since multi-sensor system can be used as an efficient solution to target positioning process, more accurate target location estimation and tracking results can be obtained. However, both the deployment of designed multi-sensor and location algorithm may affect the overall performance of position location. In this thesis, based on the time difference of arrival (TDOA), two closed-form least-square location methods, spherical-interpolation (SI) method and spherical-intersection (SX) method are used to estimate the target location. The two location methods are different from the usual process of iterative and nonlinear minimization. The locations of the target and the designed multiple sensors may yield geometric effects on location performance. The constraints and performance of the two location methods will first be introduced. To achieve real-time target tracking, the Kalman filtering structures are used to combine the SI and SX methods. Because these two positioning and tracking systems have different and complementary performance inside and outside the multi-sensor array, we consider using data fusion to improve location estimation results by using interacting multiple model (IMM) based estimator, in which internal filters running in parallel are designed as the SX-KF1 and the SI-KF2. However, due to the time-varying characteristics of measurement noises, we propose an adjusting scheme for measurement noise variance assignment in the Kalman filters to obtain improved location estimation results. Simulation results are obtained by running Matlab program. In three-dimensional multi-sensor array scenarios, the results of moving target location estimation shows that the IMM-based estimators effectively improve the position performance.

In the thesis, we propose a non-line of sight (NLOS) mitigation approach based on the interacting multiple model (IMM) algorithm. The IMM-based structure, composed of a biased Kalman filter (BKF) and a Kalman filter with NLOS-discarding process (KF-D), is capable of mitigating the ranging error caused by the NLOS effects, and therefore improving the performance and accuracy in wireless location systems. The NLOS effect on signal transmission is one of the major factors that affect the accuracy of the time-based location systems. Effective NLOS identification and mitigation usually count on pre-determined statistic distribution and hypothesis assumption in the signals. Because the variance of the NLOS error is much large than that of measurement noise, hypothesis testing on the LOS/NLOS status can be formulated.The BKF combines the sliding window and decides the status by using hypothesis testing. The calculated variance and the detection result are used in switching between the biased and unbiased modes in the Kalman filter. In the contrast, the KF-D scheme identifies the NLOS status and tries to eliminate the NLOS effects by directly using the estimated results from the LOS stage. The KF-D scheme can achieve reasonably good NLOS mitigation if the estimates in the LOS status are obtained. Due to the discarding process, changes of the state vector within the NLOS stage are possibly ignored, and will cause larger errors in the state estimates. The BKF and KF-D can make up for each other by formulating the filters in an IMM structure, which could tune up the probabilities of BKF and KF-D. In our approach, the measured data are smoothed by sliding window and a BKF. The variance of data and the hypothesis test result are passed to the two filters. The BKF switches between the biased/unbiased modes by using the result. The KF-D may receive the estimated value from BKF based on the results. The probability computation unit changes the weights to get the estimated TOA values. With the simulations in ultra-wideband (UWB) signals, it can be seen that the proposed IMM-based approach can effectively mitigate the NLOS effects and increase the accuracy in wireless position.

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Diplomová práce se zabývá optimalizací systému pro sledování letadel, využívaného pro řízení letového provozu. Je popsána metodika vyhodnocování přesnosti sledovacího systému a přehled relevantních algoritmů pro sledování objektů. Dále jsou navrženy tři přístupy k řešení problému. První se pokouší identifikovat parametry filtrovacích algoritmů pomocí algoritmu Expectation-Maximisation, implementací metody maximální věrohodnosti. Druhý přístup je založen na prostých odhadech parametrů normálního rozložení z naměřených a referenčních dat. Nakonec je zkoumána možnost řešení pomocí optimalizačního algoritmu Evoluční strategie. Závěrečné vyhodnocení ukazuje, že třetí přístup je pro daný problém nejvhodnější.