SPECIFICATION OF THE INVENTION

1. Title of the invention

A novel system and method for computing the accurate position and velocities of a ground moving object using Radar and IRST sensor measurements for efficient object tracking.

2. Field of Invention

The present invention relates to object tracking from aerial vehicles. In particular, it relates to a methodology of fusing the object data, measured simultaneously by Radar and IRST (Infra-Red Search and Track) sensors with inherent inaccuracies, to accurately compute range, azimuth and elevation of a particular ground moving object.

3. Prior art and Draw backs of prior art

Military aircraft are equipped with variety of sensors. Generally sensor measurement is not accurate and is corrupted fromnoise. Moreover a single sensor is not sufficient to cater the needs of avionics domain. Ground moving object tracking is one such field where multi sensor comes in picture. Prior to this invention, for tracking a target the primary sensor employed is radar. Radar provides range, azimuth and elevation of the target. The range accuracy of radar is good but at the same time its angular accuracies are poor.

4. Aim of Invention

The main objective of this invention is to provide improved accuracies for ground moving object range, azimuth and elevation which in turn will help the pilot to track the object more precisely.

5. Summary of the Invention

This invention presents a system employing a novel method for fusing target data, obtained from Radar and IRST. The system consists of an IRST module and Radar sensor module. Each of the sensor modules receives the respective target measurements in terms of azimuth, elevation and range from radar and IRST sensor. The respective sensor module consists of time update module (002,006), measurement matrix update module (003,007) and measurement update module (004,008).The target model has nine states (position, velocity and acceleration in x,y,z) is constructed. Each of the nine states of the target are predicted based on the last corrected states in the time update module (002,006).

The predicted states and the observations as received from each sensors are used to form the measurement and gain matrix in the measurement matrix update module (003,007).

The predicted states of the target are updated using the received observation from the sensors and the computed gain matrix in the measurement update module(004,008).

Finally the corrected states as obtained from each of the sensor module viz. IRST and radar sensor module is fused in the state vector fusion module(009) using unique vector fusion methodology for improved range and angular accuracies.

6. Brief Description of the Drawings

Figure 1 is a Schematic of fusing radar sensor data with IRST sensor data.

Figure 2, 3, 4is the simulated target profile from target model.

Figure 5 is the simulated range output of radar.

Figure 6is the simulated theta output of radar.

Figure 7is the simulated pshi output of radar.

Figure 8 is the simulated theta output of IRST.

Figure 9 is the simulated pshi output of IRST.

Figure 10 is the simulated Fused range output.

Figure 11 is the simulated Fused theta output.

Figure 12 is the simulated Fused pshi output.

7. Detailed Description of the Invention

The present invention performs fusion of the target data received from radar and IRST sensors. This fusion is done to achieve the best possible estimates for moving ground object range, azimuth and elevation.

At step 001, IRST sensor modulereceives the angular position of ground moving object from IRST sensor. The ground moving object data received from IRST sensor is related to target azimuth and elevation which is corrupted from white noise. The IRST measurements are Similarly, at step 005,Radar sensor module receivesthe ground moving object data from Radar Sensor. The ground moving object data received from radar sensor is related to target azimuth, elevation and range which is corrupted from white noise. The measured data for the ground moving objects as obtained from the radar sensorare.

The entire system consisting of IRST and radar sensor is considered to have 9 states which are the position, velocity and acceleration of the target in x,y,z direction. The formed state matrix for the ground object profile is

The module 002 for IRST and module 006 for radar are the time update modules. At the starting, these modules are fed with initial state matrix and initial state error covariance matrix. The initial state matrix for ground moving object is formed by considering its initial position, initial velocity and initial acceleration. This module predicts the next state of the ground moving object based on the current state and the state transition matrix. The object model which considers position, velocity and acceleration in each of the three Cartesian coordinates system has a. state transition matrix which takes in account the target dynamics. As the system sampling time is small, a linearized state transition matrix is formed. The state transition matrix denoted by 0is


Where T is the sampling time

This module 002 and 006 also performs the task of predicting next state error covariance matrix. Error covariance matrix estimates the amount of error in prediction of states using covariance between states. This matrix consists of variances in position, velocity and acceleration in x,y,z axis of the Cartesian coordinate system. The error covariance matrix denoted by P is

Also the state matrix and the error covariance matrix at t=k-1 is propagated (predicted) for the next kth state using state transition matrix as below in module 002 and 006.
The measurement matrix for relating the estimated state of the ground moving object to estimated measurement for IRST sensoris formed in module 003 IRST sensor measures target Azimuth (8) and Elevation (0).The relation between states of ground moving object and the observed measurement by IRST is as below:

The measurement matrix for the IRST sensoris below

Radar Sensor along with target Azimuth (0) and Elevation (0) measures its Range(r) also.

The relation between target range (r) and its states is as below

The measurement matrix for the radar sensor is

Also in module 003, the errorbetweenactual and estimated measurement for IRST sensor is computed. This computation takes in account theabove formed measurement matrix for IRST and the predicted state of the ground moving object.
Estimated measurement for IRST:

Error in measurement (eir) =

actual IRST sensor measurement — estimated IRST sensor measurement

Error in measurement (ejr) = fir(fe) - zir(k\k - i)

Similarly in module 007, the error betweenactual and estimated measurement for radar sensor is computed. This computation takes in account the above formed measurement matrix for radar and the predicted state of the ground moving object.

Estimated measurement for radar:

Error in measurement (er) = actual radar sensor output — estimated radar sensor measurement

Error in measurement

The gain matrix (G) for IRST and radar is computed in module 003 and module 007
which decides the weightage to be given between estimated states and error in measurement. This matrix computation involves the error covariance matrix P, measurement matrix H and the measurement noise R. The measurement noise is considered in Azimuth and elevation observation coming from IRST sensor. Similarly for radar the measurement noise the measurement noise is considered in azimuth, elevationand range. Each of the sensor output is modeled with different measurement noise.

The gain matrix for IRST computed in module 003 is below as

The gain matrix for radar computed in module 007 is below as

Where, inv. denotes inverse of matrix

The module004 and 008 are the measurement update module. The estimated state of ground moving object is updated with the observation error matrix and the gain matrix G as

The error covariance matrix is also updated using the corrected state in module 003 and 008as

From the module 004 and module 008, the corrected states are fed to the module 009 where the fusion of the 2 state vectors are performed as below

The Fused state contains the best possible estimate for the ground moving objects which helps the user to track it more precisely.

FIG-1

We Claim

1. A methodology for fusing the states of ground moving object as observed from Radar and IRST sensor. This method is characterized by the time update module, measurement matrix module and measurement update module for Radar and IRST.

2. Time update module, as claimed in Claim 1, predicts the next state of the ground moving object based on the previous states. It also predicts the next error covariance matrix based on the last updated error covariance matrix.

3. Measurement matrix module, as claimed in 1, computes the measurement matrix and builds the gain matrix.

4. Measurement update module, as claimed in 1, updates the states of ground moving object based on each sensor observations.

5. State vector fusion module, as claimed in 1, computes the best possible estimates of the states of the ground moving object based on the updated error covariance matrices for each sensor.