A study is performed of several multiple model tracking filter architectures that do not employ a Markov Switching
Matrix in its weighting mathematics. The Markov Switching Matrix which is common to multiple model tracking
filters does not have an "optimum" rule for defining its constituent probabilities. The only real constraint on
the probabilities is that each row of the matrix must add to unity. The other general rule is that the diagonal
elements should be "close to unity" and the off-diagonal terms should be correspondingly "small". Other than
these constraints, values are typically selected by observing the filter tracking performance over a wide set
of trajectory types and target dynamics. Several architectures are presented and their tracking performance
discussed. Comparisons are made with the performance of a conventional IMM for the same data.
A particular method of detecting unresolved targets using simulated generic monopulse radar data is examined in detail. The system is assumed to be incorrectly calibrated i.e. the decision boundary is calculated based on erroneous values governing the hypothesis that only a single target is present in the range cell. The system performance is analyzed under varying values for target ranges, angles between the beam pointing direction and the actual off-boresight angle of the targets, waveform power and number of pulses. Design strategies are advanced to maintain good detection probabilities under conditions of miscalibrated decision boundaries.
KEYWORDS: Sensors, Filtering (signal processing), Monte Carlo methods, Electronic filtering, Data processing, Sensor fusion, Time metrology, Error analysis, Data fusion, 3D acquisition
Recently the authors developed a new filter that uses data generated by asynchronous sensors to produce a state estimate that is optimal in the minimum mean square sense. The solution accounts for communications delay between sensors platform and fusion center. It also deals with out of sequence data as well as latent data by processing the information in a batch-like manner. This paper compares, using simulated targets and Monte Carlo simulations, the performance of the filter to the optimal sequential processing approach. It was found that the new asynchronous Multisensor track fusion filter (AMSTFF) performance is identical to that of the extended sequential Kalman filter (SEKF), while the new filter updates its track at a much lower rate than the SEKF.
KEYWORDS: Target detection, Radar, Silicon, Calibration, Signal to noise ratio, Detection and tracking algorithms, Sensors, Infrared search and track, Warfare, Signal processing
A particular method of detecting unresolved targets using simulated monopulse radar data is examined in detail. The system is assumed to be correctly calibrated i.e. the decision boundary is calculated based on the true values governing the hypothesis that only a single target is present in the range cell. The system performance is analyzed under varying values for target ranges, angles between the beam pointing direction and the actual off-boresight angle of the targets, waveform power and number of pulses. It is shown that these parameters have a pronounced impact on the Boundary, Metric and Decision Surfaces. The False Alarm probability for a single target as a function of waveform power is considered, as also are the detection probabilities when two targets are present. The important issue of locating the decision point on the Boundary Surface is briefly discussed.
KEYWORDS: Lithium, Detection and tracking algorithms, Filtering (signal processing), Data analysis, Lutetium, Data processing, Network centric warfare, Data fusion, Signal processing, Information operations
What underpins this vision as axiomatic is the mantra information is power. Besides the necessary requirement of information exchange networks with sufficient bandwidth and computational power to treat the data being passed around the network; algorithms are required to make sense of the data. It is estimation algorithms that turn the straw (data) into gold (information). Both proper execution and improvements in estimation algorithms are the enabling technology that facilitates the formation and usage of data across the envisioned warfare networks. We focus on some of the requirements that are driving the formation of these networks from a surface navy perspective in terms of estimation. We also discuss how these requirements focus the design of potentially new algorithms. We also discuss some of the crucial issues that may drive future requirements and algorithms.
Sensor data fusion has long been recognized as a means to improve target tracking. Common practice assumes that the sensors used are synchronous, i.e., they have identical data rate, measurements are taken at the same time, and have no communication delays between sensors platform and central processing center. Such assumptions are invalid in practice. Previous work of the authors dealt designing asynchronous track fusion filter that removes such assumptions when considering the multi-sensor target tracking case. This paper deals with the existence of a solution to the asynchronous track fusion problem for the case of three asynchronous sensors. In addition, the performance deterioration of the filter is analyzed as a function of the track fusion update rate for CV targets.
Sensor data fusion has long been recognized as a means to improve target tracking. Common practice assumes that the sensors used are synchronous, i.e., they have identical data rate, measurements are taken at the same time, and have no communication delays between sensors platform and central processing center. Such assumptions are invalid in practice. Previous work of the authors dealt designing asynchronous track fusion filter that removes such assumptions when considering the multi-sensor target tracking case. This paper deals with the existence of a solution to the asynchronous track fusion problem for the case of two asynchronous sensors. In addition, the performance deterioration of the filter is analyzed as a function of the track fusion update rate.
Sensor data fusion has long been recognized as a means to improve target tracking. Common practice assumes that the sensors used are synchronous (i.e., perform the same operation at the identical time), take measurements at the same time and have no communication delays between sensor platforms and the central processing center. Such assumptions are not valid in practice. This paper removes these assumptions when dealing with multisensor target tracking. In particular, it assumes that the sensors used can have different data rates and communication delays, between local and central platforms. A new tracking algorithm using asynchronous sensors is proposed and derived in this paper.
KEYWORDS: Sensors, Composites, Error analysis, Detection and tracking algorithms, Filtering (signal processing), Radar, Spherical lenses, Data modeling, Monte Carlo methods, Information fusion
The integration of multiple sensors for target tracking is complex but has the potential to provide very accurate state estimates. For most applications, each sensor provides its information to a central location where the integration is performed and the resulting composite track can be very accurate when compared to the individual sensor tracks. This composite track has the potential to provide enhanced system decisions and targeting information not otherwise available. However, sensor bias can severely degrade composite tracking performance when it is not properly considered. This paper presents algorithms and simulation result for the composite tracking of maneuvering targets through the use of multisensor-multisite integration in the presence of sensor residual bias.
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