Doreen M. Sasaki

Improved tracking capability and efficient radar allocation through the fusion of radar and infrared search-and-track observations

The benchmark problem addresses the efficient allocation of an agile-beam radar in the presence of highly maneuverable targets and radar ECM. The multisensor benchmark tracking solution is aided by the presence of a scanning infrared search-and-track (IRST) system. This paper presents methods for applying a tracker with interacting multiple- model (IMM) filtering and multiple-hypothesis tracking (MHT) data asso- ciation to this multiple-sensor tracking and resource allocation problem. It presents a hybrid multisensor tracking architecture in which an IMM- MHT tracker operating on IRST data provides the global IMM-MHT tracker with selected observations. Simulation results quantify the poten- tial improvement from the use of advanced tracking methods and IRST data to enhance agile-beam radar tracker capability.

IMM/MHT solution to radar multisensor benchmark tracking problems

The benchmark problem addresses the efficient allocation of an agile beam radar in the presence of highly maneuverable targets and radar ECM. The multisensor benchmark tracking solution s aided by the presence of a scanning IRST. This paper presents methods for applying an IMM/MHT tracker to this multiple sensor tracking and resource allocation problem. The paper discusses the manner is which IMM/MHT tracking and data association methods lead to efficient agile beam radar allocation and presents results showing that this approach is significantly more efficient than previously proposed methods when only radar data are used. It presents a hybrid multisensor tracking architecture in which an IMM/MHT tracker operating on IRST data provides the global IMM/MHT tracker with selected observations. Simulation results quantify the potential improvement from the use of advanced tracking methods and IRST data to enhance agile beam radar tracker capability.

Multispectral detection of dim slightly extended targets in heavy clutter

The spectral signature of a target is typically unknown apriori because of its dependence upon environmental conditions (e.g., sun angle, atmospheric attenuation and scattering), factors effecting the reflectivity and emissivity of the targets surface (dirt, dust, water, paint, etc) and recent operating history (hot or cold engine, exhaust parts, wheels or tracks, etc.). Because of the high variability of the spectral signature of a target, multispectral detection typically detects spectral anomalies. For example, the canopy of a helicopter hovering in front of tree clutter may glint in the midwave infrared band while the reststrahlen spectral feature of the fuselage paint occurs in the longwave infrared band. Both of these are spectral anomalies relative to the tree clutter. If the target is slightly extended so that it subtends more than one pixel, the spectral anomalies by which the target may be detected will not be spatially collocated. This effectively lowers the ROC (receiver operating characteristic) curve of the detection process. This paper derives the ROC curves for several alternative solutions to this problem. One solution considers all possible spectral n-tuples within a small region. One of these n-tuples would likely contain all of the spectral anomalies of the target. Another solution is to apply a spatial maximum operator to each spectral band prior to the anomaly detector. This also combines all the spectral anomalies form the target into a single n-tuple. These methods have the potential to increase PD but an increase in PFA will also occur. The ROC curves of these solutions to the problem of detecting slightly extended targets are derived and compared to establish relative levels of performance.

Application of IMM/MHT tracking with spectral features to ground targets

This paper considers the problem of tracking dim unresolved ground targets and helicopters in heavy clutter with a ground based sensor. To detect dim targets the threshold must be set low which result in a large number of false alarms. The tracker typically uses the target dynamics to prevent the false tracks. The interesting aspect of this problem is that the targets may be or may become stationary. The tracks of stationary targets are difficult to discriminate from tracks formed by persistent false alarms.

The heavy-tailed distribution of a common CFAR detector

A CFAR detector commonly used for the detection of unresolved targets normalizes the background variance by dividing the detection filter output by the local sample standard deviation. A number of researchers have measured the experimental false alarm probability of this detector and found it to be higher than the probability predicted by a Gaussian density function. This is the case even when the filter output statistics are known to be Gaussian distributed. A number of attempts have been made to heuristically construct distributions which exhibit the heavy tails associated with the measured false alarm probability (e.g. sum of two Gaussian densities or the modified gamma density). This paper presents a first principle derivation of the detector false alarm density function based upon the assumption that the filter output is Gaussian distributed. The resulting false alarm density function is very nearly Gaussian out to about 3.5 standard deviations. Past 3.5 standard deviations the tails of the derived density function are markedly heavier than the corresponding Gaussian tails. The parameters of this new density function are easily estimated from the filter outputs. The analytic results are validated using a Chi-Square goodness-of-fit test and experimental measurements of the false alarm density.

Analysis of the effects of fixed pattern noise on a fully adaptive matched filter

Fully adaptive matched filters typically can suppress clutter to the level of the sensor fixed pattern noise. A fully adaptive filter assumes that the clutter is a wide-sense stationary process which can be modeled by a constant means and unknown covariance function. Fixed pattern noise within a data sequence is unknown and tends to be a non-stationary process. As a result fixed pattern noise is minimally affected by fully adaptive filters. The signal processing philosophy for detecting unresolved targets is to enhance the target signal based on the sensor point spread function. When sensor fixed pattern noise exists, the signal from a point target can be significantly different from the sensor point spread function and can result in a loss in SCR. This SCR loss can make weak targets undetectable. This paper describes the effect of a fully adaptive filter on fixed pattern noise manifested as channel dependent bias and gain errors. Spectral analysis which quantifies the impact of these errors is presented. Experimental results on synthetic data and on real data from an infrared scanning sensor with channel dependent fixed pattern noise are given.

Fully adaptive space-spectral detection of small targets in the absence of a-priori knowledge of the spectral signature of the target

The ability to detect and track dim unresolved targets in heavy clutter can be improved by the inclusion of the spectral dimension. Because of the great variation in targets, operating conditions and environments factors the spectral signature of the target is typically unknown. This paper present a fully adaptive matched filter and tracking paradigm which assumes no a priori information about the spectral signature of the target. It is shown that the full SCR gain can be realized in the absence of the spectral signature of the target. The ROC curve of the detector is used to show that performance loss due to the absence of spectral information is entirely due to an increase in the false alarm probability. This increase in PFA adversely effects tracker performance. The SCR track feature is developed to mitigate these effects. Track features provide an information shunt around the detection threshold nonlinearity that would otherwise block the flow of useful information to the tracker.

Analysis of the cascade of track-before-detect and track-after-detect tracking algorithms

Advanced track-after-detect (TAD) trackers are able to operate with detection thresholds as low as 9.5dB with the use of track features. At lower threshold the increased number of false alarms inhibits track confirmation. In order to track weaker targets, the target SNR must be increased prior to detection. Assuming that the SNR has been increased as much as possible through signal processing, further increase in SNR can be obtained by preceding the detection threshold with a track-before-detect algorithms. This paper analyzes the performance of the cascade of a TBD and a TAD tracking algorithm.

Software systems testing of a closed loop tracking system using a SIMULINK based simulation

This paper discuses a simulation approach that has streamlined the real-time software development process for a closed loop image-based tracking system. The MATLAB/SIMULINK simulation consists of elements constructed from common source modules shared with the deliverable system. The simulation has provided a tool to support algorithm development for the fundamental system components, including a system controller, a servo controller, and an image processor. In addition, the simulation has provided a testbed for verification of system performance. The context for this application is the low rate initial production phase of a tactical airborne avionics system that includes an image-based tracking system.

Performance model for unresolved target detection using multispectral infrared data

The detection of dim targets in heavy clutter requires large gains in the SCR. Gains of the required magnitude have been obtained with space-temporal processing. However, in many cases these gains are either difficult or expensive to realize. If the range to the clutter is small relative to the clutter velocity, the temporal processing will need to include scene registration and optical flow correction. Scene registration is computationally expensive especially for large search volumes. The correction of optical flow is both expensive and typically less than satisfactory. The spectral dimension provides an alternative to the temporal dimension. Since the data in each of the spectral bands is collected simultaneously or nearly so, the problems of registration and optical flow are eliminated. This paper considers the performance of the multi-spectral IR bands. Dual band performance results comparing space spectral processing with space temporal will be shown. An analytic model of the probability of false alarm as a function of the number of spectral bands is presented. A comparison of this model to experimental result using multi-spectral IRST data is given.