W. Dale Blair

IMM algorithm for tracking targets that maneuver through coordinated turns

The interacting multiple model (IMM) algorithm uses multiple models that interact through state mixing to track a target maneuvering through an arbitrary trajectory. However, when a target maneuvers through a coordinated turn, the acceleration vector of the target changes magnitude and direction, and the maneuvering target models commonly used in the IMM (e.g., constant acceleration) can exhibit considerable model error. To address this problem an IMM algorithm that includes a constant velocity model, a constant speed model with the kinematic constraint for constant speed targets, and the exponentially increasing acceleration (EIA) model for maneuver response is proposed. The constant speed model utilizes a turning rate in the state transition matrix to achieve constant speed prediction. The turning rate is calculated from the velocity and acceleration estimates of the constant speed model. The kinematic constraint for constant speed targets is utilized as a pseudomeasurement in the filtering process with the constant speed model. Simulation results that demonstrate the benefits of the EIA model and the kinematic constraint to the IMM algorithm are given. The tracking performance of the proposed IMM algorithm is compared with that of an IMM algorithm utilizing constant velocity and constant turn rate models.

A MIMO radar benchmarking environment

With the growing amount of research being devoted to the concept of multiple-input multiple-output (MIMO) radar, there has been a lack of a common simulation and benchmarking environment for determining the viability and cost-effectiveness of MIMO radar architectures and algorithms. To this end, GTRI has developed a MIMO Benchmark environment to serve this purpose, which is to be made publically available to researchers in order to compare the performance of MIMO techniques with those of more conventional phased array radar systems. This paper describes the problem that the MIMO Benchmark is intended to be used to assist in solving, in the form of a new challenge problem for the MIMO community, as well as providing a summary of the architecture of the MIMO Benchmark infrastructure.123

IMM algorithm and aperiodic data

The interacting multiple method (IMM) algorithm is an effective technique for tracking maneuvering targets. The IMM algorithm uses multiple models that interact through state mixing to track a target maneuvering through an arbitrary trajectory. The state estimates are mixed according to their model probabilities and the model switching probabilities that are governed by an underlying Markov chain. In the IMM algorithm, the probability pij of switching from model i to model j is often assumed to be uniform between each measurement update. However, for multiple sensors operating asynchronously or a sensor with a probability of detection less than one, the data will be aperiodic. To overcome this limitation, the model switching probabilities are modeled as time-dependent. IMM algorithms with constant and time-dependent model switching probabilities are evaluated for the cases of a two sensor tracking system and a sensor with a probability of detection of detection less than one.

NNJPDA for tracking closely spaced Rayleigh targets with possibly merged measurements

The measurements of the two closely-spaced targets will be merged when the target echoes are not resolved in angle, range, or radial velocity. The modified Cramer Rao lower bound (CRLB) is given for monopulse direction-of-arrival (DOA) estimation for two unresolved Rayleigh targets and used to give insight into the antenna boresight pointing. A monopulse processing technique is given for DOA estimation of two unresolved Rayleigh targets. The Nearest Neighbor Joint Probabilistic Data Association Algorithm is extended to include the possibility of merged monopulse measurements of Rayleigh targets. The monopulse signals are incorporated into the data association as a feature to discriminate between merged and resolved measurements.

Asynchronous data fusion for target tracking with a multitasking radar and optical sensor

A technique is developed for fusing asynchronous data from two dissimilar sensors, where one sensor provides data at a high rate relative to the other. The idea is to obtain a least-squares estimate of the high data rate sensor data at the time when the other sensor observation is taken. A previously developed synchronous data fusion algorithm is then used to fuse the time aligned data for updating the target state estimates. The case of fusing data from an optical sensor that provides periodic data at a high rate and a radar that provides quasi-periodic data at a low data rate is considered. The performance of a track filter utilizing this data fusion approach is shown via simulation to provide results that are similar to those obtained by the standard sequential data processing approach that requires significantly more computations.

Enhanced electronically scanned array resource management through multisensor integration

The integration of multiple sensors for target tracking and resource management has been intensely investigated and several effective techniques have been developed. These conventional techniques employ decision-directed logic and are very complex but have the potential to improve performance. For most systems, each sensor provides its information to a central location where the integration occurs. The central track is employed for system decisions and it is typically not used by the individual sensors. This low level of integration provides a manageable tracking environment but restricts the potential for system improvement. An electronically scanned array (ESA) is highly controllable and has the ability to greatly enhance tracking performance. Resource allocation for an ESA is critical since it must support multiple functions, and several modern techniques have been developed to enhance its performance as a stand-alone sensor by effectively managing its time-energy budget. The integration of an ESA with other sensors can further enhance the tracking and reduce the resource allocation requirements of the ESA. This paper presents a technique for ESA resource management through the use of multisensor integration. The proposed technique avoids the decision-directed logic associated with conventional techniques by employing the interacting multiple model (IMM) algorithm. Simulation results are provided to demonstrate the effectiveness of this modern integration technique.

Tracking maneuvering targets with multiple sensors using the interacting multiple model algorithm

One of the more difficult targets to track is an aircraft performing high speed maneuvers. The Interacting Multiple Model (IMM) algorithm uses multiple models that interact through state mixing to track a target maneuvering through an arbitrary trajectory. The IMM algorithm provides significantly better tracking results when compared to a single model track filter. To improve tracking performance, multiple sensors can be used to provide more information about the target. Using the measurements from several sensors with a single motion model track filter can provide improved track performance when compared to a single sensor system. Since single sensor track filters use decision-directed approaches for maneuver response, using multiple sensors with a single model track filter would be difficult to implement because periodic measurement updates cannot be expected and the sensors may be dissimilar with different accuracies. Thus a very complex tracking algorithm would be required. While the tracking performance of a single model may improve with additional sensors, it can be erratic for maneuvering targets. Using multiple sensors with the IMM algorithm can improve the IMM algorithm performance without the erratic performance exhibited by single model trackers. Target tracking with multisensor systems is described along with the IMM algorithm. Comparisons of track performance with IMM algorithm and single motion model track filters are presented for several sensor systems.

Least-squares approach to asynchronous data fusion

The fusion of asynchronous data is usually achieved by sequentially processing the data as it arrives at the central processor. However, if the data rate is too high and the data from different sensors are taken at times that are arbitrarily close together, some other technique is necessary. One way of overcoming this problem is to take the data within a specified time interval and compress it. While there are many ways of compressing data, the method chosen must retain all of the datas salient features. This paper discusses methods that utilize least- squares techniques for compressing data from one or more sensors and a central filter for processing the compressed data. Simulated tracking results from a central filter using the compressed data are compared to the results from an optimal central filter that processes the data sequentially. The error covariance associated with the optical filtering approach is compared with that of the central filter processing the compressed data. Also, some generalized theorems concerning the fusion of synchronized state and measurement vectors of different dimensions are given.

EM-based measurement fusion for HRR radar centroid processing

This paper develops a new algorithm for high range resolution (HRR) radar centroid processing for scenarios where there are closely spaced objects. For range distributed targets with multiple discrete scatterers, HRR radars will receive detections across multiple range bins. When the resolution is very high, and the target has significant extent, then it is likely that the detections will not occur in adjacent bins. For target tracking purposes, the multiple detections must be grouped and fused to create a single object report and a range centroid estimate is computed since the detections are range distributed. With discrete scatterer separated by multiple range bins, then when closely spaced objects are present there is uncertainty about which detections should be grouped together for fusion. This paper applies the EM algorithm to form a recursive measurement fusion algorithm that segments the data into object clusters while simultaneously forming a range centroid estimate with refined bearing and elevation estimates.

Update to the Hybrid Conditional Averaging Performance Prediction of the IMM Algorithm

Traditionally the performance evaluation of a target tracking algorithm is accomplished via Monte Carlo simulations for each specific scenario of interest. For some applications, the time and computational resource requirements of performing the necessary simulations for algorithm design is excessive; so the need for performance prediction becomes paramount. One method of performance prediction developed during the early 1990s is the hybrid conditional averaging (HYCA) technique, which can be used to predict the performance of the interacting multiple model (IMM) algorithm. Applying the HYCA technique to the IMM algorithm as originally developed leads to poor performance prediction in certain situations. A new extension used in these circumstances is shown to lead to superior performance prediction without an increase in computational complexity compared with the originally developed algorithm for such situations.