1 / 18

Mobile Agent Based Progressive Multiple Target Detection in Sensor Networks

Mobile Agent Based Progressive Multiple Target Detection in Sensor Networks. Xiaoling Wang ECE691, Fall 2003. Multiple Target Detection. Similar to BSS (blind source separation) problem It is normally assumed in BSS that the number of targets equals the number of sensors

erma
Download Presentation

Mobile Agent Based Progressive Multiple Target Detection in Sensor Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Mobile Agent Based Progressive Multiple Target Detection in Sensor Networks Xiaoling Wang ECE691, Fall 2003

  2. Multiple Target Detection • Similar to BSS (blind source separation) problem • It is normally assumed in BSS that the number of targets equals the number of sensors • In sensor networks, the number of sensors usually exceeds the number of targets • The first step is to estimate the number of sources in real time

  3. Environment Sensor i Sensor n Sensor 1 x1 x2 x3 Fusion Center Source Number Estimation • Bayesian estimation approach • Traditional centralized scheme(Roberts,98) …… ……

  4. Centralized Bayesian SNE • Suppose denotes the hypothesis that there is m targets in the field, then the Bayesian objective function is: Bayes theorem Eq.1

  5. Centralized Bayesian SNE where : sensor observation matrix : mixing matrix : unmixing matrix, and and : latent variable, : non-linear transformation function : noise, with variance : marginal distribution of

  6. Progressive SNE • Motivation • Reduce data transmission • Conserve energy • Basic idea of progressive scheme • At sensor i, process data locally • Upon receiving a partial result from its previous sensor, update the estimation of the log-likelihood and the mixing matrices • Transmit the updated result to its next sensor

  7. Progressive Scheme Environment x3 xn x2 x1 I(2) I(n-1) I(1) …… Sensor 2 Sensor 3 Sensor n Sensor 1 m

  8. Progressive Estimation of the Log-likelihood Function Denote each term in Eq.1 as T, then T3 depends on the updating rule of mixing matrix A

  9. The updating rule of T7 depends on the updating rule of matrix A Progressive estimation of mixing matrix A: BFGS method

  10. Algorithm of Progressive SNE /*Initialization*/ At sensor 1, for each possible m: Set A (1 by m) to random numbers; Compute W and a; Compute estimation error; Compute each term in Eq.1; Compute L(m); /*Progressive Updating*/ While (max L(m)<threshold) Send A, latent variables a, estimation error and the seven terms in Eq.1 to next sensor i; At sensor i, for each possible m: Add one row to A with random numbers; while (!converge) Update A using BFGS method; Update the accumulated estimation error; Update each term in Eq.1; Compute L(m); /*Generate the final estimation*/ Decide m=arg max L(m); Output m;

  11. Mobile Agent Implementation Step 2 Step 1 Step 3 Step 4

  12. Data transmitted Raw data Processing unit Fusion center Scheme Centralized Data transmitted Mixing matrix A Latent variables a Estimation error Each term in Eq.1 Processing unit Each sensor Scheme Serial Mobile agent implementation Comparison Progressive SNE Centralized SNE

  13. Experiment 1 Targets Signals: 1-second acoustic, 500 samples each

  14. Experiment 2 & 3 Targets: diesel truck and pick- up truck

  15. Progressively Estimated Log-likelihood (Exp.1)

  16. Progressively Estimated Log-likelihood (Exp.2)

  17. Progressively Estimated Log-likelihood (Exp.3)

  18. Discussion • Progressive SNE provides the greatest support to the true number of sources • Progressive SNE can achieve progressive accuracy with the migration of MA • By using MAF, the progressive SNE can stop at any time if the required accuracy is achieved • Progressive SNE can reduce the amount of transmitted data • Experiment 1: 10 sensors, 1-second segment, 500 samples each • Centralized: 144,000 bits • Progressive: 12,096 bits • 8.4%

More Related