In tracking, many types of sensor data can be obtained and utilized to distinguish a particular target. Commonly, kinematic information is used for tracking, but this can be combined with identification attributes and parametric information passively collected from the targets emitters. Along with the standard tracking process (predict, associate, score, update, and initiate) that operates in all kinematic trackers, parametric data can also be utilized to perform these steps and provide a means for feature fusion. Feature fusion, utilizing parametrics from multiple sources, yields a rich data set providing many degrees of freedom to separate and correlate data into appropriate tracks. Parametric radar data can take on many dynamics to include: stable, agile, jitter, and others. By utilizing a running sample mean and sample variance a good estimate of radar parametrics is achieved. However, when dynamics are involved, a severe lag can occur and a non-optimal estimate is achieved. This estimate can yield incorrect associations in feature space and cause track fragmentation or miscorrelation.
In this paper we investigate the accuracy of the interacting multiple model (IMM) filter at estimating the first and second moments of radar parametrics. The algorithm is assessed by Monte Carlo simulation and compared against a running sample mean/variance technique. We find that the IMM approach yields a better result due to its ability to quickly adapt to dynamical systems with the proper model and tuning.
Highly maneuvering threats are a major concern for the Navy and the DoD and the technology discussed in this paper is intended to help address this issue. A neural extended Kalman filter algorithm has been embedded in an interacting multiple model architecture for target tracking. The neural extended Kalman filter algorithm is used to improve motion model prediction during maneuvers. With a better target motion mode, noise reduction can be achieved through a maneuver. Unlike the interacting multiple model architecture which uses a high process noise model to hold a target through a maneuver with poor velocity and acceleration estimates, a neural extended Kalman filter is used to predict corrections to the velocity and acceleration states of a target through a maneuver. The neural extended Kalman filter estimates the weights of a neural network, which in turn are used to modify the state estimate predictions of the filter as measurements are processed. The neural network training is performed on-line as data is processed. In this paper, the simulation results of a tracking problem using a neural extended Kalman filter embedded in an interacting multiple model tracking architecture are shown. Preliminary results on the 2nd Benchmark Problem are also given.
An autonomous field of sensor nodes needs to acquire and track targets of interest traversing the field. Small detection ranges limit the detectability of the field. As detections occur in the field, detections are transmitted acoustically to a master node. Both detection processing and acoustic communication drain a node's battery. In order to maximize field life, an approach must be developed to control detector thresholds and acoustic communication routing. To address these problems, an adaptive threshold control scheme has been developed. This technique minimizes the power consumption while still maintaining the field level probability of detection. Acoustic communication routing of the field is also performed to minimize power consumption and therefore, extend the life of the field. The control law developed is based on an evolutionary programming approach. Evolutionary programming is a stochastic optimization algorithm used to solve N-P hard problems. Results are provided which demonstrate the ability to maintain a constant field level probability of detection while extending the life of the sensor field.
Proc. SPIE. 4048, Signal and Data Processing of Small Targets 2000
KEYWORDS: Signal to noise ratio, Detection and tracking algorithms, Digital filtering, Data processing, Signal processing, Electronic filtering, Motion models, Passive sonar, Filtering (signal processing), Process modeling
Passive sonar signal processing generally includes tracking of narrowband and/or broadband signature components observed on a Lofargram or on a Bearing-Time-Record (BTR) display. Fielded line tracking approaches to date have been recursive and single-hypthesis-oriented Kalman- or alpha-beta filters, with no mechanism for considering tracking alternatives beyond the most recent scan of measurements. While adaptivity is often built into the filter to handle changing track dynamics, these approaches are still extensions of single target tracking solutions to multiple target tracking environment. This paper describes an application of multiple-hypothesis, multiple target tracking technology to the sonar line tracking problem. A Multiple Hypothesis Line Tracker (MHLT) is developed which retains the recursive minimum-mean-square-error tracking behavior of a Kalman Filter in a maximum-a-posteriori delayed-decision multiple hypothesis context. Multiple line track filter states are developed and maintained using the interacting multiple model (IMM) state representation. Further, the data association and assignment problem is enhanced by considering line attribute information (line bandwidth and SNR) in addition to beam/bearing and frequency fit. MHLT results on real sonar data are presented to demonstrate the benefits of the multiple hypothesis approach. The utility of the system in cluttered environments and particularly in crossing line situations is shown.
Recent research has demonstrated the benefits of a multiple hypothesis, multiple model sonar line tracking solution, achieved at significant computational cost. We have developed an adaptive architecture that trades computational resources for algorithm complexity based on environmental conditions. A Fuzzy Logic Rule-Based approach is applied to adaptively assign algorithmic resources to meet system requirements. The resources allocated by the Fuzzy Logic algorithm include (1) the number of hypotheses permitted (yielding multi-hypothesis and single-hypothesis modes), (2) the number of signal models to use (yielding an interacting multiple model capability), (3) a new track likelihood for hypothesis generation, (4) track attribute evaluator activation (for signal to noise ratio, frequency bandwidth, and others), and (5) adaptive cluster threshold control. Algorithm allocation is driven by a comparison of current throughput rates to a desired real time rate. The Fuzzy Logic Controlled (FLC) line tracker, a single hypothesis line tracker, and a multiple hypothesis line tracker are compared on real sonar data. System resource usage results demonstrate the utility of the FLC line tracker.
Proc. SPIE. 3720, Signal Processing, Sensor Fusion, and Target Recognition VIII
KEYWORDS: Detection and tracking algorithms, Sensors, Control systems, Distributed computing, Magnetic sensors, Acquisition tracking and pointing, Motion models, Filtering (signal processing), Fuzzy logic, Data fusion
The Deployable Autonomous Distributed System is an ocean surveillance system that contains a field of sensor nodes. Each sensor node provides target detections to a master node in the field for fusion by a Multiple Hypothesis Tracker Correlator (MHTC). The overall performance of a fusion engine depends upon the set of parameters that are used by the Multiple Hypothesis Tracker. Although a static set of parameters may work well over a wide range of scenarios, they may not lead to optimal performance in all cases. This paper addresses Level 4 fusion to improve performance of the data fusion system at the master node by using a fuzzy logic controller to adaptively tune the parameters. By using a set of linguistic rule based fuzzy logic algorithms, the tuning parameters of the MHTC are modified. A set of metrics are used to determine the added worth of the fuzzy logic controller.
In distributed track-level level fusion system, it is a well-known fact that successful fusion of tracks from offboard sources requires that these tracks do not contain underlying biases or offsets. Unfortunately, this lack of bias or offset is often not the case, because the offboard tracking system references its tracks to a coordinate system that is offset and misaligned with respect to truth due to navigational drift and sensor misalignment. In this paper, we present a technical approach for both detecting and correcting for these biases in a noncooperative target sense. Furthermore, the algorithms are configured to operate in a multiple-hypothesis tracking environment. These algorithms have been implemented in a simulated air threat environment, and performance improvements have been noted of up to an order of magnitude in target/track miss distance.